From 806d20612c80394773ef7ddaf90d34306eee0ec4 Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Wed, 22 Nov 2023 22:11:25 -0500 Subject: [PATCH 01/73] initial commit --- README.md | 2 +- adbpyg_adapter/adapter.py | 487 +++++++++++++++++++++++++++++++------- adbpyg_adapter/tracing.py | 84 +++++++ pyproject.toml | 5 + tests/conftest.py | 15 +- tests/test_adapter.py | 9 +- 6 files changed, 507 insertions(+), 95 deletions(-) create mode 100644 adbpyg_adapter/tracing.py diff --git a/README.md b/README.md index 6690d09..03b56f1 100644 --- a/README.md +++ b/README.md @@ -289,7 +289,7 @@ Prerequisite: `arangorestore` 2. `cd pyg-adapter` 3. (create virtual environment of choice) 4. `pip install torch` -5. `pip install -e .[dev]` +5. `pip install -e .[dev][tracing]` 6. (create an ArangoDB instance with method of choice) 7. `pytest --url <> --dbName <> --username <> --password <>` diff --git a/adbpyg_adapter/adapter.py b/adbpyg_adapter/adapter.py index 1c86515..ee1c208 100644 --- a/adbpyg_adapter/adapter.py +++ b/adbpyg_adapter/adapter.py @@ -12,7 +12,7 @@ from pandas import DataFrame, Series from rich.console import Group from rich.live import Live -from rich.progress import Progress +from rich.progress import Progress, TaskID from torch import Tensor, cat, tensor from torch_geometric.data import Data, HeteroData from torch_geometric.data.storage import EdgeStorage, NodeStorage @@ -21,6 +21,7 @@ from .abc import Abstract_ADBPyG_Adapter from .controller import ADBPyG_Controller from .exceptions import ADBMetagraphError, InvalidADBEdgesError, PyGMetagraphError +from .tracing import TRACING_ENABLED, TracingManager, create_tracer, with_tracing from .typings import ( ADBMap, ADBMetagraph, @@ -39,6 +40,9 @@ validate_pyg_metagraph, ) +if TRACING_ENABLED: + from opentelemetry import trace + class ADBPyG_Adapter(Abstract_ADBPyG_Adapter): """ArangoDB-PyG adapter. @@ -52,6 +56,11 @@ class ADBPyG_Adapter(Abstract_ADBPyG_Adapter): :param logging_lvl: Defaults to logging.INFO. Other useful options are logging.DEBUG (more verbose), and logging.WARNING (less verbose). :type logging_lvl: str | int + :param tracer: The OpenTelemetry tracer instance. Requires the `tracing` + extra to be installed (i.e `pip install adbpyg-adapter[tracing]`). + See `adbpyg_adapter.tracing.create_tracer` for details on how to + create a tracer instance. + :type tracer: opentelemetry.trace.Tracer :raise TypeError: If invalid parameter types """ @@ -60,8 +69,10 @@ def __init__( db: StandardDatabase, controller: ADBPyG_Controller = ADBPyG_Controller(), logging_lvl: Union[str, int] = logging.INFO, + tracer: Optional["trace.Tracer"] = None, ): self.set_logging(logging_lvl) + self.set_tracer(tracer) if not isinstance(db, StandardDatabase): msg = "**db** parameter must inherit from arango.database.StandardDatabase" @@ -86,12 +97,43 @@ def cntrl(self) -> ADBPyG_Controller: return self.__cntrl # pragma: no cover def set_logging(self, level: Union[int, str]) -> None: + """Set the logging level for the adapter instance. + + :param level: The logging level (i.e logging.INFO, logging.DEBUG, etc.) + :type level: str | int + """ logger.setLevel(level) + def set_tracer(self, tracer: Optional["trace.Tracer"]) -> None: + """Set the OpenTelemetry tracer for the adapter instance. Requires + the `tracing` extra to be installed (i.e `pip install adbpyg-adapter[tracing]`). + + :param tracer: The OpenTelemetry tracer instance. + :type tracer: opentelemetry.trace.Tracer + :raise ImportError: If OpenTelemetry is not installed. + """ + if TRACING_ENABLED: + self.__tracer = tracer or create_tracer("adbpyg-adapter") + TracingManager.set_tracer(self.__tracer) + else: + self.__tracer = None + + def __set_tracer_attributes(self, **attributes: Any) -> None: + """Set the OpenTelemetry tracer attributes for the adapter instance. + + :param attributes: The OpenTelemetry tracer attributes. + :type attributes: Any + """ + if TRACING_ENABLED: + current_span = trace.get_current_span() + for k, v in attributes.items(): + current_span.set_attribute(k, v) + ########################### # Public: ArangoDB -> PyG # ########################### + @with_tracing def arangodb_to_pyg( self, name: str, @@ -259,6 +301,7 @@ def udf_v1_x(v1_df): build a PyG-ready Tensor from a DataFrame equivalent to the associated ArangoDB collection. """ + self.__set_tracer_attributes(name=name) logger.debug(f"--arangodb_to_pyg('{name}')--") validate_adb_metagraph(metagraph) @@ -292,22 +335,13 @@ def udf_v1_x(v1_df): if preserve_key is not None: node_data[preserve_key] = [] - # 1. Fetch ArangoDB vertices - v_col_cursor, v_col_size = self.__fetch_adb_docs( - v_col, meta, **adb_export_kwargs - ) - - # 2. Process ArangoDB vertices - self.__process_adb_cursor( - "#8929C2", - v_col_cursor, - v_col_size, - self.__process_adb_vertex_df, + self.__process_adb_v_col( v_col, - adb_map, meta, + adb_export_kwargs, + adb_map, preserve_key, - node_data=node_data, + node_data, ) #################### @@ -321,30 +355,22 @@ def udf_v1_x(v1_df): for e_col, meta in metagraph.get("edgeCollections", {}).items(): logger.debug(f"Preparing '{e_col}' edges") - # 1. Fetch ArangoDB edges - e_col_cursor, e_col_size = self.__fetch_adb_docs( - e_col, meta, **adb_export_kwargs - ) - - # 2. Process ArangoDB edges - self.__process_adb_cursor( - "#40A6F5", - e_col_cursor, - e_col_size, - self.__process_adb_edge_df, + self.__process_adb_e_col( e_col, - adb_map, meta, + adb_export_kwargs, + adb_map, preserve_key, - data=data, - v_cols=v_cols, - strict=strict, - is_homogeneous=is_homogeneous, + data, + v_cols, + strict, + is_homogeneous, ) logger.info(f"Created PyG '{name}' Graph") return data + @with_tracing def arangodb_collections_to_pyg( self, name: str, @@ -398,6 +424,7 @@ def arangodb_collections_to_pyg( name, metagraph, preserve_adb_keys, strict, **adb_export_kwargs ) + @with_tracing def arangodb_graph_to_pyg( self, name: str, @@ -449,6 +476,7 @@ def arangodb_graph_to_pyg( # Public: PyG -> ArangoDB # ########################### + @with_tracing def pyg_to_arangodb( self, name: str, @@ -548,6 +576,7 @@ def y_tensor_to_2_column_dataframe(pyg_tensor, adb_df): 4) Dissasembles the 2-feature Tensor into two ArangoDB attributes, where each attribute holds one feature value. """ + self.__set_tracer_attributes(name=name) logger.debug(f"--pyg_to_arangodb('{name}')--") validate_pyg_metagraph(metagraph) @@ -583,6 +612,10 @@ def y_tensor_to_2_column_dataframe(pyg_tensor, adb_df): node_data: NodeStorage edge_data: EdgeStorage + # Define PyG Batch Processing Functions + process_node_batch_fn: Callable[[int, int], DataFrame] + process_edge_batch_fn: Callable[[int, int], DataFrame] + spinner_progress = get_import_spinner_progress(" ") ############# @@ -594,41 +627,35 @@ def y_tensor_to_2_column_dataframe(pyg_tensor, adb_df): meta = n_meta.get(n_type, {}) node_data = pyg_g if is_homogeneous else pyg_g[n_type] - node_data_batch_size = batch_size or node_data.num_nodes - - start_index = 0 - end_index = min(node_data_batch_size, node_data.num_nodes) - batches = ceil(node_data.num_nodes / node_data_batch_size) bar_progress = get_bar_progress(f"(PyG → ADB): '{n_type}'", "#97C423") bar_progress_task = bar_progress.add_task(n_type, total=node_data.num_nodes) - with Live(Group(bar_progress, spinner_progress)): - for _ in range(batches): - # 1. Process the Node batch - df = self.__process_pyg_node_batch( - n_type, - node_data, - meta, - pyg_map, - is_explicit_metagraph, - is_custom_controller, - start_index, - end_index, - ) - - bar_progress.advance(bar_progress_task, advance=len(df)) - - # 2. Insert the ArangoDB Node Documents - self.__insert_adb_docs( - spinner_progress, df, n_type, use_async, **adb_import_kwargs - ) + process_node_batch_fn = ( + lambda start_index, end_index: self.__process_pyg_node_batch( + n_type, + node_data, + meta, + pyg_map, + is_explicit_metagraph, + is_custom_controller, + start_index, + end_index, + ) + ) - # 3. Update the batch indices - start_index = end_index - end_index = min( - end_index + node_data_batch_size, node_data.num_nodes - ) + with Live(Group(bar_progress, spinner_progress)): + self.__process_pyg_n_type( + n_type, + node_data.num_nodes, + batch_size or node_data.num_nodes, + process_node_batch_fn, + bar_progress, + bar_progress_task, + spinner_progress, + use_async, + adb_import_kwargs, + ) ############# # PyG Edges # @@ -639,41 +666,35 @@ def y_tensor_to_2_column_dataframe(pyg_tensor, adb_df): meta = e_meta.get(e_type, {}) edge_data = pyg_g if is_homogeneous else pyg_g[e_type] - edge_data_batch_size = batch_size or edge_data.num_edges - - start_index = 0 - end_index = min(edge_data_batch_size, edge_data.num_edges) - batches = ceil(edge_data.num_edges / edge_data_batch_size) bar_progress = get_bar_progress(f"(PyG → ADB): {e_type}", "#994602") bar_progress_task = bar_progress.add_task(e_type, total=edge_data.num_edges) - with Live(Group(bar_progress, spinner_progress)): - for _ in range(batches): - # 1. Process the Edge batch - df = self.__process_pyg_edge_batch( - e_type, - edge_data, - meta, - pyg_map, - is_explicit_metagraph, - is_custom_controller, - start_index, - end_index, - ) - - bar_progress.advance(bar_progress_task, advance=len(df)) - - # 2. Insert the ArangoDB Edge Documents - self.__insert_adb_docs( - spinner_progress, df, e_type[1], use_async, **adb_import_kwargs - ) + process_edge_batch_fn = ( + lambda start_index, end_index: self.__process_pyg_edge_batch( + e_type, + edge_data, + meta, + pyg_map, + is_explicit_metagraph, + is_custom_controller, + start_index, + end_index, + ) + ) - # 3. Update the batch indices - start_index = end_index - end_index = min( - end_index + edge_data_batch_size, edge_data.num_edges - ) + with Live(Group(bar_progress, spinner_progress)): + self.__process_pyg_e_type( + e_type, + edge_data.num_edges, + batch_size or edge_data.num_edges, + process_edge_batch_fn, + bar_progress, + bar_progress_task, + spinner_progress, + use_async, + adb_import_kwargs, + ) logger.info(f"Created ArangoDB '{name}' Graph") return adb_graph @@ -682,6 +703,113 @@ def y_tensor_to_2_column_dataframe(pyg_tensor, adb_df): # Private: ArangoDB -> PyG # ############################ + @with_tracing + def __process_adb_v_col( + self, + v_col: str, + meta: Union[Set[str], Dict[str, ADBMetagraphValues]], + adb_export_kwargs: Dict[str, Any], + adb_map: ADBMap, + preserve_key: Optional[str], + node_data: NodeStorage, + ) -> None: + """ArangoDB -> PyG: Processes the ArangoDB Vertex Collection. + + :param v_col: The ArangoDB vertex collection. + :type v_col: str + :param meta: The metagraph for the current **v_col**. + :type meta: Set[str] | Dict[str, ADBMetagraphValues] + :param adb_export_kwargs: Keyword arguments to specify AQL query options + when fetching documents from the ArangoDB instance. + :type adb_export_kwargs: Dict[str, Any] + :param adb_map: The ArangoDB -> PyG map. + :type adb_map: adbpyg_adapter.typings.ADBMap + :param preserve_key: The PyG key to preserve the ArangoDB _key values. + :type preserve_key: Optional[str] + :param node_data: The PyG NodeStorage object. + :type node_data: torch_geometric.data.storage.NodeStorage + """ + + # 1. Fetch ArangoDB vertices + v_col_cursor, v_col_size = self.__fetch_adb_docs( + v_col, meta, **adb_export_kwargs + ) + + # 2. Process ArangoDB vertices + self.__process_adb_cursor( + "#8929C2", + v_col_cursor, + v_col_size, + self.__process_adb_vertex_df, + v_col, + adb_map, + meta, + preserve_key, + node_data=node_data, + ) + + self.__set_tracer_attributes(v_col=v_col, v_col_size=v_col_size) + + @with_tracing + def __process_adb_e_col( + self, + e_col: str, + meta: Union[Set[str], Dict[str, ADBMetagraphValues]], + adb_export_kwargs: Dict[str, Any], + adb_map: ADBMap, + preserve_key: Optional[str], + data: Union[Data, HeteroData], + v_cols: List[str], + strict: bool, + is_homogeneous: bool, + ) -> None: + """ArangoDB -> PyG: Processes the ArangoDB Edge Collection. + + :param e_col: The ArangoDB edge collection. + :type e_col: str + :param meta: The metagraph for the current **e_col**. + :type meta: Set[str] | Dict[str, ADBMetagraphValues] + :param adb_export_kwargs: Keyword arguments to specify AQL query options + when fetching documents from the ArangoDB instance. + :type adb_export_kwargs: Dict[str, Any] + :param adb_map: The ArangoDB -> PyG map. + :type adb_map: adbpyg_adapter.typings.ADBMap + :param preserve_key: The PyG key to preserve the ArangoDB _key values. + :type preserve_key: Optional[str] + :param data: The PyG Data or HeteroData object. + :type data: torch_geometric.data.Data | torch_geometric.data.HeteroData + :param v_cols: The list of ArangoDB vertex collections. + :type v_cols: List[str] + :param strict: Set fault tolerance when loading a graph from ArangoDB. If set + to false, this will ignore invalid edges (e.g. dangling/half edges). + :type strict: bool + :param is_homogeneous: Whether the ArangoDB graph is homogeneous or not. + :type is_homogeneous: bool + """ + # 1. Fetch ArangoDB edges + e_col_cursor, e_col_size = self.__fetch_adb_docs( + e_col, meta, **adb_export_kwargs + ) + + # 2. Process ArangoDB edges + self.__process_adb_cursor( + "#40A6F5", + e_col_cursor, + e_col_size, + self.__process_adb_edge_df, + e_col, + adb_map, + meta, + preserve_key, + data=data, + v_cols=v_cols, + strict=strict, + is_homogeneous=is_homogeneous, + ) + + self.__set_tracer_attributes(e_col=e_col, e_col_size=e_col_size) + + @with_tracing def __fetch_adb_docs( self, col: str, @@ -744,6 +872,7 @@ def get_aql_return_value( return cursor, col_size + @with_tracing def __process_adb_cursor( self, progress_color: str, @@ -796,6 +925,7 @@ def __process_adb_cursor( if cursor.has_more(): cursor.fetch() + @with_tracing def __process_adb_vertex_df( self, i: int, @@ -840,6 +970,7 @@ def __process_adb_vertex_df( return i + @with_tracing def __process_adb_edge_df( self, _: int, @@ -936,6 +1067,7 @@ def __process_adb_edge_df( return 1 # Useless return value, but needed for type hinting + @with_tracing def __split_adb_ids(self, s: Series) -> Series: """AranogDB -> PyG: Helper method to split the ArangoDB IDs within a Series into two columns @@ -948,6 +1080,7 @@ def __split_adb_ids(self, s: Series) -> Series: """ return s.str.split(pat="/", n=1, expand=True) + @with_tracing def __set_pyg_data( self, meta: Union[Set[str], Dict[str, ADBMetagraphValues]], @@ -982,6 +1115,7 @@ def __set_pyg_data( m = f"'{k}' key in PyG Data must point to a Tensor" raise TypeError(m) + @with_tracing def __build_tensor_from_dataframe( self, adb_df: DataFrame, @@ -1038,6 +1172,7 @@ def __build_tensor_from_dataframe( # Private: PyG -> ArangoDB # ############################ + @with_tracing def __get_node_and_edge_types( self, name: str, @@ -1081,6 +1216,7 @@ def __get_node_and_edge_types( return node_types, edge_types + @with_tracing def __etypes_to_edefinitions(self, edge_types: List[EdgeType]) -> List[Json]: """PyG -> ArangoDB: Converts PyG edge_types to ArangoDB edge_definitions @@ -1125,6 +1261,7 @@ def __etypes_to_edefinitions(self, edge_types: List[EdgeType]) -> List[Json]: return edge_definitions + @with_tracing def __ntypes_to_ocollections( self, node_types: List[str], edge_types: List[EdgeType] ) -> List[str]: @@ -1148,6 +1285,7 @@ def __ntypes_to_ocollections( orphan_collections = set(node_types) ^ non_orphan_collections return list(orphan_collections) + @with_tracing def __create_adb_graph( self, name: str, @@ -1186,6 +1324,7 @@ def __create_adb_graph( orphan_collections, ) + @with_tracing def __process_pyg_node_batch( self, n_type: str, @@ -1219,6 +1358,8 @@ def __process_pyg_node_batch( :return: The ArangoDB DataFrame representing the PyG Node batch. :rtype: pandas.DataFrame """ + self.__set_tracer_attributes(start_index=start_index, end_index=end_index) + # 1. Set the ArangoDB Node Data df = self.__set_adb_data( DataFrame(index=range(start_index, end_index)), @@ -1243,6 +1384,7 @@ def __process_pyg_node_batch( return df + @with_tracing def __process_pyg_edge_batch( self, e_type: EdgeType, @@ -1276,6 +1418,8 @@ def __process_pyg_edge_batch( :return: The ArangoDB DataFrame representing the PyG Edge batch. :rtype: pandas.DataFrame """ + self.__set_tracer_attributes(start_index=start_index, end_index=end_index) + src_n_type, _, dst_n_type = e_type # 1. Fetch the Edge Index of the current batch @@ -1316,6 +1460,165 @@ def __process_pyg_edge_batch( return df + @with_tracing + def __process_pyg_n_type( + self, + n_type: str, + node_data_total_size: int, + node_data_batch_size: int, + process_node_batch_fn: Callable[..., DataFrame], + bar_progress: Progress, + bar_progress_task: TaskID, + spinner_progress: Progress, + use_async: bool, + adb_import_kwargs: Dict[str, Any], + ) -> None: + """PyG -> ArangoDB: Processes a PyG Node type. A simple wrapper + around the __process_batches method in order to set the tracer + attributes accordingly, and to provide a more descriptive + tracer span name. + + :param n_type: The PyG node type. + :type n_type: str + :param node_data_total_size: The total size of the PyG NodeStorage object. + :type node_data_total_size: int + :param node_data_batch_size: The batch size of the PyG NodeStorage object. + :type node_data_batch_size: int + :param process_node_batch_fn: The function to process the PyG Node batch. + :type process_node_batch_fn: Callable + :param bar_progress: The progress bar. + :type bar_progress: Progress + :param bar_progress_task: The progress bar task ID. + :type bar_progress_task: TaskID + :param spinner_progress: The spinner progress bar. + :type spinner_progress: Progress + :param use_async: Whether to use asynchronous insertion. + :type use_async: bool + :param adb_import_kwargs: Keyword arguments to specify import options + when inserting documents into the ArangoDB instance. + :type adb_import_kwargs: Dict[str, Any] + """ + self.__set_tracer_attributes(n_type=n_type, n_type_size=node_data_total_size) + + self.__process_batches( + n_type, + node_data_total_size, + node_data_batch_size, + process_node_batch_fn, + bar_progress, + bar_progress_task, + spinner_progress, + use_async, + adb_import_kwargs, + ) + + @with_tracing + def __process_pyg_e_type( + self, + e_type: EdgeType, + edge_data_total_size: int, + edge_data_batch_size: int, + process_edge_batch_fn: Callable[..., DataFrame], + bar_progress: Progress, + bar_progress_task: TaskID, + spinner_progress: Progress, + use_async: bool, + adb_import_kwargs: Dict[str, Any], + ) -> None: + """PyG -> ArangoDB: Processes a PyG Edge type. A simple wrapper + around the __process_batches method in order to set the tracer + attributes accordingly, and to provide a more descriptive + tracer span name. + + :param e_type: The PyG edge type. + :type e_type: torch_geometric.typing.EdgeType + :param edge_data_total_size: The total size of the PyG EdgeStorage object. + :type edge_data_total_size: int + :param edge_data_batch_size: The batch size of the PyG EdgeStorage object. + :type edge_data_batch_size: int + :param process_edge_batch_fn: The function to process the PyG Edge batch. + :type process_edge_batch_fn: Callable + :param bar_progress: The progress bar. + :type bar_progress: Progress + :param bar_progress_task: The progress bar task ID. + :type bar_progress_task: TaskID + :param spinner_progress: The spinner progress bar. + :type spinner_progress: Progress + :param use_async: Whether to use asynchronous insertion. + :type use_async: bool + :param adb_import_kwargs: Keyword arguments to specify import options + when inserting documents into the ArangoDB instance. + :type adb_import_kwargs: Dict[str, Any] + """ + self.__set_tracer_attributes(e_type=e_type, e_type_size=edge_data_total_size) + + self.__process_batches( + e_type[1], + edge_data_total_size, + edge_data_batch_size, + process_edge_batch_fn, + bar_progress, + bar_progress_task, + spinner_progress, + use_async, + adb_import_kwargs, + ) + + def __process_batches( + self, + col: str, + total_size: int, + batch_size: int, + process_batch_fn: Callable[..., DataFrame], + bar_progress: Progress, + bar_progress_task: TaskID, + spinner_progress: Progress, + use_async: bool, + adb_import_kwargs: Dict[str, Any], + ) -> None: + """PyG -> ArangoDB: Processes the PyG Node or Edge batches. No tracing + decorator required here. + + :param col: The ArangoDB collection name. + :type col: str + :param total_size: The total size of the PyG NodeStorage or EdgeStorage object. + :type total_size: int + :param batch_size: The batch size of the PyG NodeStorage or EdgeStorage object. + :type batch_size: int + :param process_batch_fn: The function to process the PyG Node or Edge batch. + :type process_batch_fn: Callable + :param bar_progress: The progress bar. + :type bar_progress: Progress + :param bar_progress_task: The progress bar task ID. + :type bar_progress_task: TaskID + :param spinner_progress: The spinner progress bar. + :type spinner_progress: Progress + :param use_async: Whether to use asynchronous insertion. + :type use_async: bool + :param adb_import_kwargs: Keyword arguments to specify import options + when inserting documents into the ArangoDB instance. + :type adb_import_kwargs: Dict[str, Any] + """ + start_index = 0 + end_index = min(batch_size, total_size) + batches = ceil(total_size / batch_size) + + for _ in range(batches): + # 1. Process the batch + df = process_batch_fn(start_index, end_index) + + bar_progress.advance(bar_progress_task, advance=len(df)) + + # 2. Insert the ArangoDB Documents + self.__insert_adb_docs( + spinner_progress, df, col, use_async, **adb_import_kwargs + ) + + # 3. Update the batch indices + start_index = end_index + end_index = min(end_index + batch_size, total_size) + + @with_tracing def __set_adb_data( self, df: DataFrame, @@ -1394,6 +1697,7 @@ def __set_adb_data( return df + @with_tracing def __build_dataframe_from_tensor( self, pyg_tensor: Tensor, @@ -1471,6 +1775,7 @@ def __build_dataframe_from_tensor( raise PyGMetagraphError(f"Invalid {meta_val} type") # pragma: no cover + @with_tracing def __insert_adb_docs( self, spinner_progress: Progress, @@ -1494,6 +1799,8 @@ def __insert_adb_docs( https://docs.python-arango.com/en/main/specs.html#arango.collection.Collection.import_bulk :param adb_import_kwargs: Any """ + self.__set_tracer_attributes(col=col, size=len(df)) + action = f"ADB Import: '{col}' ({len(df)})" spinner_progress_task = spinner_progress.add_task("", action=action) diff --git a/adbpyg_adapter/tracing.py b/adbpyg_adapter/tracing.py new file mode 100644 index 0000000..04c7ab9 --- /dev/null +++ b/adbpyg_adapter/tracing.py @@ -0,0 +1,84 @@ +from functools import wraps +from typing import Any, Callable, Optional, TypeVar, cast + +try: + from opentelemetry import trace + from opentelemetry.sdk.resources import SERVICE_NAME, Resource + from opentelemetry.sdk.trace import TracerProvider + from opentelemetry.sdk.trace.export import ( + BatchSpanProcessor, + ConsoleSpanExporter, + SpanExporter, + ) + from opentelemetry.trace import Tracer + + TRACING_ENABLED = True +except ImportError: + TRACING_ENABLED = False + + +class TracingManager: + __tracer: Optional["Tracer"] = None + + @classmethod + def get_tracer(cls) -> Optional["Tracer"]: + return cls.__tracer + + @classmethod + def set_tracer(cls, tracer: "Tracer") -> None: + cls.__tracer = tracer + + +T = TypeVar("T", bound=Callable[..., Any]) + + +def with_tracing(method: T) -> T: + @wraps(method) + def decorator(*args: Any, **kwargs: Any) -> Any: + tracer = TracingManager.get_tracer() + + if tracer is None: + return method(*args, **kwargs) + + with tracer.start_as_current_span(method.__name__): + return method(*args, **kwargs) + + return cast(T, decorator) + + +def create_tracer( + name: str, + enable_console_tracing: bool = False, + span_exporters: list["SpanExporter"] = [], +) -> "Tracer": + """ + Create a tracer instance. + + :param name: The name of the tracer. + :type name: str + :param enable_console_tracing: Whether to enable console tracing. Default is False. + :type enable_console_tracing: bool + :param span_exporters: A list of SpanExporter instances to use for tracing. + For example, to export to a local Jaeger instance running via docker, you + could use `[OTLPSpanExporter(endpoint="http://localhost:4317", insecure=True)]`. + :type oltp_span_exporters: list[OTLPSpanExporter] + :return: A configured tracer instance. + :rtype: opentelemetry.trace.Tracer + """ + if not TRACING_ENABLED: + m = "OpenTelemetry is not installed. Cannot create tracer. Use `pip install adbpyg-adapter[tracing]`" # noqa: E501 + raise RuntimeError(m) + + resource = Resource(attributes={SERVICE_NAME: name}) + provider = TracerProvider(resource=resource) + + if enable_console_tracing: + console_processor = BatchSpanProcessor(ConsoleSpanExporter()) + provider.add_span_processor(console_processor) + + for span_exporter in span_exporters: + provider.add_span_processor(BatchSpanProcessor(span_exporter)) + + trace.set_tracer_provider(provider) + + return trace.get_tracer(__name__) diff --git a/pyproject.toml b/pyproject.toml index 9552e8a..f39c6c2 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -56,6 +56,11 @@ dev = [ "types-requests>=2.27.11", "networkx>=2.5.1", ] +tracing = [ + "opentelemetry-api==1.21.0", + "opentelemetry-sdk==1.21.0", + "opentelemetry-exporter-otlp-proto-grpc==1.21.0" +] [project.urls] "Homepage" = "https://github.com/arangoml/pyg-adapter" diff --git a/tests/conftest.py b/tests/conftest.py index 1ca6adb..d4c3a18 100755 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -6,6 +6,8 @@ from arango import ArangoClient from arango.database import StandardDatabase +from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter +from opentelemetry.trace import Tracer from pandas import DataFrame from torch import Tensor, tensor from torch_geometric.data import Data, HeteroData @@ -13,10 +15,12 @@ from torch_geometric.typing import EdgeType from adbpyg_adapter import ADBPyG_Adapter, ADBPyG_Controller +from adbpyg_adapter.tracing import create_tracer from adbpyg_adapter.typings import Json con: Json db: StandardDatabase +tracer: Tracer adbpyg_adapter: ADBPyG_Adapter PROJECT_DIR = Path(__file__).parent.parent @@ -49,8 +53,17 @@ def pytest_configure(config: Any) -> None: con["dbName"], con["username"], con["password"], verify=True ) + global tracer + tracer = create_tracer( + "adbpyg-adapter-test", + enable_console_tracing=False, + span_exporters=[ + OTLPSpanExporter(endpoint="http://localhost:4317", insecure=True) + ], + ) + global adbpyg_adapter - adbpyg_adapter = ADBPyG_Adapter(db, logging_lvl=logging.INFO) + adbpyg_adapter = ADBPyG_Adapter(db, logging_lvl=logging.INFO, tracer=tracer) def pytest_exception_interact(node: Any, call: Any, report: Any) -> None: diff --git a/tests/test_adapter.py b/tests/test_adapter.py index 3042a8d..10cb124 100644 --- a/tests/test_adapter.py +++ b/tests/test_adapter.py @@ -36,6 +36,7 @@ get_fake_homo_graph, get_karate_graph, get_social_graph, + tracer, udf_key_df_to_tensor, udf_users_x_tensor_to_df, udf_v2_x_tensor_to_df, @@ -393,7 +394,9 @@ def test_pyg_to_arangodb_with_controller() -> None: data = get_karate_graph() db.delete_graph(name, drop_collections=True, ignore_missing=True) - ADBPyG_Adapter(db, Custom_ADBPyG_Controller()).pyg_to_arangodb(name, data) + ADBPyG_Adapter(db, Custom_ADBPyG_Controller(), tracer=tracer).pyg_to_arangodb( + name, data + ) for doc in db.collection(f"{name}_N"): assert "foo" in doc @@ -680,7 +683,7 @@ def test_adb_graph_to_pyg_to_arangodb_with_missing_document_and_strict( data = get_karate_graph() db.delete_graph(name, drop_collections=True, ignore_missing=True) - ADBPyG_Adapter(db).pyg_to_arangodb(name, data) + ADBPyG_Adapter(db, tracer=tracer).pyg_to_arangodb(name, data) graph = db.graph(name) v_cols: Set[str] = graph.vertex_collections() @@ -710,7 +713,7 @@ def test_adb_graph_to_pyg_to_arangodb_with_missing_document_and_permissive( data = get_karate_graph() db.delete_graph(name, drop_collections=True, ignore_missing=True) - ADBPyG_Adapter(db).pyg_to_arangodb(name, data) + ADBPyG_Adapter(db, tracer=tracer).pyg_to_arangodb(name, data) graph = db.graph(name) v_cols: Set[str] = graph.vertex_collections() From d0d66b736925caa01934ae5da205a828d28f6520 Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Wed, 22 Nov 2023 22:19:48 -0500 Subject: [PATCH 02/73] fix `pip install` --- .github/workflows/build.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 8b2dbce..b758b16 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -34,7 +34,7 @@ jobs: run: | pip install torch==2.1.0 pip install torch-scatter torch-sparse -f https://data.pyg.org/whl/torch-$(python -c 'import torch; print(torch.__version__.split("+")[0])')+cpu.html - pip install -e .[dev] + pip install -e .[dev][tracing] - name: Run black run: black --check --verbose --diff --color ${{env.PACKAGE_DIR}} ${{env.TESTS_DIR}} From b2100a5f68b6236b0bf3d8b1491fd0ca7ace21fa Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Wed, 22 Nov 2023 22:23:37 -0500 Subject: [PATCH 03/73] fix pip install for real this time --- .github/workflows/build.yml | 2 +- README.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index b758b16..99f6a15 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -34,7 +34,7 @@ jobs: run: | pip install torch==2.1.0 pip install torch-scatter torch-sparse -f https://data.pyg.org/whl/torch-$(python -c 'import torch; print(torch.__version__.split("+")[0])')+cpu.html - pip install -e .[dev][tracing] + pip install -e .[dev, tracing] - name: Run black run: black --check --verbose --diff --color ${{env.PACKAGE_DIR}} ${{env.TESTS_DIR}} diff --git a/README.md b/README.md index 03b56f1..221eaaa 100644 --- a/README.md +++ b/README.md @@ -289,7 +289,7 @@ Prerequisite: `arangorestore` 2. `cd pyg-adapter` 3. (create virtual environment of choice) 4. `pip install torch` -5. `pip install -e .[dev][tracing]` +5. `pip install -e .[dev, tracing]` 6. (create an ArangoDB instance with method of choice) 7. `pytest --url <> --dbName <> --username <> --password <>` From f173fc66b4529a6ccca5b7da064dc878679aaec0 Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Wed, 22 Nov 2023 22:30:15 -0500 Subject: [PATCH 04/73] fix pip install again.. --- .github/workflows/build.yml | 2 +- README.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 99f6a15..39f47e1 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -34,7 +34,7 @@ jobs: run: | pip install torch==2.1.0 pip install torch-scatter torch-sparse -f https://data.pyg.org/whl/torch-$(python -c 'import torch; print(torch.__version__.split("+")[0])')+cpu.html - pip install -e .[dev, tracing] + pip install -e '.[dev, tracing]' - name: Run black run: black --check --verbose --diff --color ${{env.PACKAGE_DIR}} ${{env.TESTS_DIR}} diff --git a/README.md b/README.md index 221eaaa..7f8339f 100644 --- a/README.md +++ b/README.md @@ -289,7 +289,7 @@ Prerequisite: `arangorestore` 2. `cd pyg-adapter` 3. (create virtual environment of choice) 4. `pip install torch` -5. `pip install -e .[dev, tracing]` +5. `pip install -e '.[dev, tracing]'` 6. (create an ArangoDB instance with method of choice) 7. `pytest --url <> --dbName <> --username <> --password <>` From 5734076cc3f869bf613a0bec046a2a035d8ecde5 Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Wed, 22 Nov 2023 22:30:35 -0500 Subject: [PATCH 05/73] parameterize otlp endpoint --- tests/conftest.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/tests/conftest.py b/tests/conftest.py index d4c3a18..7c8b80b 100755 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -30,6 +30,7 @@ def pytest_addoption(parser: Any) -> None: parser.addoption("--dbName", action="store", default="_system") parser.addoption("--username", action="store", default="root") parser.addoption("--password", action="store", default="") + parser.addoption("--otlp-endpoint", action="store", default="http://localhost:4317") def pytest_configure(config: Any) -> None: @@ -58,7 +59,7 @@ def pytest_configure(config: Any) -> None: "adbpyg-adapter-test", enable_console_tracing=False, span_exporters=[ - OTLPSpanExporter(endpoint="http://localhost:4317", insecure=True) + OTLPSpanExporter(endpoint=config.getoption("otlp-endpoint"), insecure=True) ], ) From e778392a72438763816dd6819b85597bd96ad149 Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Wed, 22 Nov 2023 22:40:51 -0500 Subject: [PATCH 06/73] fix: `otlp_endpoint` param --- tests/conftest.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/tests/conftest.py b/tests/conftest.py index 7c8b80b..f1f2a86 100755 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -30,7 +30,7 @@ def pytest_addoption(parser: Any) -> None: parser.addoption("--dbName", action="store", default="_system") parser.addoption("--username", action="store", default="root") parser.addoption("--password", action="store", default="") - parser.addoption("--otlp-endpoint", action="store", default="http://localhost:4317") + parser.addoption("--otlp_endpoint", action="append", default=[]) def pytest_configure(config: Any) -> None: @@ -59,7 +59,8 @@ def pytest_configure(config: Any) -> None: "adbpyg-adapter-test", enable_console_tracing=False, span_exporters=[ - OTLPSpanExporter(endpoint=config.getoption("otlp-endpoint"), insecure=True) + OTLPSpanExporter(endpoint=endpoint, insecure=True) + for endpoint in config.getoption("otlp_endpoint") ], ) From 82e0e3d83b64f943822b78eead6f6f8234b57ec8 Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Wed, 22 Nov 2023 22:46:12 -0500 Subject: [PATCH 07/73] fix: `span_exporters` param --- adbpyg_adapter/tracing.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/adbpyg_adapter/tracing.py b/adbpyg_adapter/tracing.py index 04c7ab9..2cda904 100644 --- a/adbpyg_adapter/tracing.py +++ b/adbpyg_adapter/tracing.py @@ -1,5 +1,5 @@ from functools import wraps -from typing import Any, Callable, Optional, TypeVar, cast +from typing import Any, Callable, List, Optional, TypeVar, cast try: from opentelemetry import trace @@ -49,7 +49,7 @@ def decorator(*args: Any, **kwargs: Any) -> Any: def create_tracer( name: str, enable_console_tracing: bool = False, - span_exporters: list["SpanExporter"] = [], + span_exporters: List["SpanExporter"] = [], ) -> "Tracer": """ Create a tracer instance. @@ -61,7 +61,7 @@ def create_tracer( :param span_exporters: A list of SpanExporter instances to use for tracing. For example, to export to a local Jaeger instance running via docker, you could use `[OTLPSpanExporter(endpoint="http://localhost:4317", insecure=True)]`. - :type oltp_span_exporters: list[OTLPSpanExporter] + :type span_exporters: List[opentelemetry.sdk.trace.export.SpanExporter] :return: A configured tracer instance. :rtype: opentelemetry.trace.Tracer """ From 123e6d61fb41e332edb81e1cb0cb673696a8fee8 Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Wed, 22 Nov 2023 22:58:18 -0500 Subject: [PATCH 08/73] cleanup --- adbpyg_adapter/adapter.py | 7 ++----- adbpyg_adapter/tracing.py | 18 ++++++++---------- tests/test_adapter.py | 4 +--- 3 files changed, 11 insertions(+), 18 deletions(-) diff --git a/adbpyg_adapter/adapter.py b/adbpyg_adapter/adapter.py index ee1c208..2bbf839 100644 --- a/adbpyg_adapter/adapter.py +++ b/adbpyg_adapter/adapter.py @@ -21,7 +21,7 @@ from .abc import Abstract_ADBPyG_Adapter from .controller import ADBPyG_Controller from .exceptions import ADBMetagraphError, InvalidADBEdgesError, PyGMetagraphError -from .tracing import TRACING_ENABLED, TracingManager, create_tracer, with_tracing +from .tracing import TRACING_ENABLED, TracingManager, with_tracing from .typings import ( ADBMap, ADBMetagraph, @@ -113,10 +113,7 @@ def set_tracer(self, tracer: Optional["trace.Tracer"]) -> None: :raise ImportError: If OpenTelemetry is not installed. """ if TRACING_ENABLED: - self.__tracer = tracer or create_tracer("adbpyg-adapter") - TracingManager.set_tracer(self.__tracer) - else: - self.__tracer = None + TracingManager.set_tracer(tracer) def __set_tracer_attributes(self, **attributes: Any) -> None: """Set the OpenTelemetry tracer attributes for the adapter instance. diff --git a/adbpyg_adapter/tracing.py b/adbpyg_adapter/tracing.py index 2cda904..b00a1fb 100644 --- a/adbpyg_adapter/tracing.py +++ b/adbpyg_adapter/tracing.py @@ -13,7 +13,7 @@ from opentelemetry.trace import Tracer TRACING_ENABLED = True -except ImportError: +except ImportError: # pragma: no cover TRACING_ENABLED = False @@ -35,13 +35,11 @@ def set_tracer(cls, tracer: "Tracer") -> None: def with_tracing(method: T) -> T: @wraps(method) def decorator(*args: Any, **kwargs: Any) -> Any: - tracer = TracingManager.get_tracer() + if tracer := TracingManager.get_tracer(): + with tracer.start_as_current_span(method.__name__): + return method(*args, **kwargs) - if tracer is None: - return method(*args, **kwargs) - - with tracer.start_as_current_span(method.__name__): - return method(*args, **kwargs) + return method(*args, **kwargs) return cast(T, decorator) @@ -65,18 +63,18 @@ def create_tracer( :return: A configured tracer instance. :rtype: opentelemetry.trace.Tracer """ - if not TRACING_ENABLED: + if not TRACING_ENABLED: # pragma: no cover m = "OpenTelemetry is not installed. Cannot create tracer. Use `pip install adbpyg-adapter[tracing]`" # noqa: E501 raise RuntimeError(m) resource = Resource(attributes={SERVICE_NAME: name}) provider = TracerProvider(resource=resource) - if enable_console_tracing: + if enable_console_tracing: # pragma: no cover console_processor = BatchSpanProcessor(ConsoleSpanExporter()) provider.add_span_processor(console_processor) - for span_exporter in span_exporters: + for span_exporter in span_exporters: # pragma: no cover provider.add_span_processor(BatchSpanProcessor(span_exporter)) trace.set_tracer_provider(provider) diff --git a/tests/test_adapter.py b/tests/test_adapter.py index 10cb124..f0fe2b0 100644 --- a/tests/test_adapter.py +++ b/tests/test_adapter.py @@ -394,9 +394,7 @@ def test_pyg_to_arangodb_with_controller() -> None: data = get_karate_graph() db.delete_graph(name, drop_collections=True, ignore_missing=True) - ADBPyG_Adapter(db, Custom_ADBPyG_Controller(), tracer=tracer).pyg_to_arangodb( - name, data - ) + ADBPyG_Adapter(db, Custom_ADBPyG_Controller()).pyg_to_arangodb(name, data) for doc in db.collection(f"{name}_N"): assert "foo" in doc From 4e2b3f038a8aba214e08e0bbdfad753be6ed636c Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Wed, 22 Nov 2023 23:08:59 -0500 Subject: [PATCH 09/73] Update README.md --- README.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 7f8339f..d3f8279 100644 --- a/README.md +++ b/README.md @@ -293,11 +293,12 @@ Prerequisite: `arangorestore` 6. (create an ArangoDB instance with method of choice) 7. `pytest --url <> --dbName <> --username <> --password <>` -**Note**: A `pytest` parameter can be omitted if the endpoint is using its default value: +**Note**: `pytest` parameters: ```python def pytest_addoption(parser): parser.addoption("--url", action="store", default="http://localhost:8529") parser.addoption("--dbName", action="store", default="_system") parser.addoption("--username", action="store", default="root") parser.addoption("--password", action="store", default="") + parser.addoption("--otlp_endpoint", action="append", default=[]) # OpenTelemetry Protocol Exporter endpoint ``` From 9ca93bdd93efa3b9d782a13ae1da1d4c4cb9fe7c Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Thu, 30 Nov 2023 08:55:23 -0500 Subject: [PATCH 10/73] new: `TracingManager.set_attributes()` --- adbpyg_adapter/adapter.py | 29 +++++++++-------------------- adbpyg_adapter/tracing.py | 7 +++++++ 2 files changed, 16 insertions(+), 20 deletions(-) diff --git a/adbpyg_adapter/adapter.py b/adbpyg_adapter/adapter.py index 2bbf839..a392521 100644 --- a/adbpyg_adapter/adapter.py +++ b/adbpyg_adapter/adapter.py @@ -115,17 +115,6 @@ def set_tracer(self, tracer: Optional["trace.Tracer"]) -> None: if TRACING_ENABLED: TracingManager.set_tracer(tracer) - def __set_tracer_attributes(self, **attributes: Any) -> None: - """Set the OpenTelemetry tracer attributes for the adapter instance. - - :param attributes: The OpenTelemetry tracer attributes. - :type attributes: Any - """ - if TRACING_ENABLED: - current_span = trace.get_current_span() - for k, v in attributes.items(): - current_span.set_attribute(k, v) - ########################### # Public: ArangoDB -> PyG # ########################### @@ -298,7 +287,7 @@ def udf_v1_x(v1_df): build a PyG-ready Tensor from a DataFrame equivalent to the associated ArangoDB collection. """ - self.__set_tracer_attributes(name=name) + TracingManager.set_attributes(name=name) logger.debug(f"--arangodb_to_pyg('{name}')--") validate_adb_metagraph(metagraph) @@ -573,7 +562,7 @@ def y_tensor_to_2_column_dataframe(pyg_tensor, adb_df): 4) Dissasembles the 2-feature Tensor into two ArangoDB attributes, where each attribute holds one feature value. """ - self.__set_tracer_attributes(name=name) + TracingManager.set_attributes(name=name) logger.debug(f"--pyg_to_arangodb('{name}')--") validate_pyg_metagraph(metagraph) @@ -745,7 +734,7 @@ def __process_adb_v_col( node_data=node_data, ) - self.__set_tracer_attributes(v_col=v_col, v_col_size=v_col_size) + TracingManager.set_attributes(v_col=v_col, v_col_size=v_col_size) @with_tracing def __process_adb_e_col( @@ -804,7 +793,7 @@ def __process_adb_e_col( is_homogeneous=is_homogeneous, ) - self.__set_tracer_attributes(e_col=e_col, e_col_size=e_col_size) + TracingManager.set_attributes(e_col=e_col, e_col_size=e_col_size) @with_tracing def __fetch_adb_docs( @@ -1355,7 +1344,7 @@ def __process_pyg_node_batch( :return: The ArangoDB DataFrame representing the PyG Node batch. :rtype: pandas.DataFrame """ - self.__set_tracer_attributes(start_index=start_index, end_index=end_index) + TracingManager.set_attributes(start_index=start_index, end_index=end_index) # 1. Set the ArangoDB Node Data df = self.__set_adb_data( @@ -1415,7 +1404,7 @@ def __process_pyg_edge_batch( :return: The ArangoDB DataFrame representing the PyG Edge batch. :rtype: pandas.DataFrame """ - self.__set_tracer_attributes(start_index=start_index, end_index=end_index) + TracingManager.set_attributes(start_index=start_index, end_index=end_index) src_n_type, _, dst_n_type = e_type @@ -1495,7 +1484,7 @@ def __process_pyg_n_type( when inserting documents into the ArangoDB instance. :type adb_import_kwargs: Dict[str, Any] """ - self.__set_tracer_attributes(n_type=n_type, n_type_size=node_data_total_size) + TracingManager.set_attributes(n_type=n_type, n_type_size=node_data_total_size) self.__process_batches( n_type, @@ -1547,7 +1536,7 @@ def __process_pyg_e_type( when inserting documents into the ArangoDB instance. :type adb_import_kwargs: Dict[str, Any] """ - self.__set_tracer_attributes(e_type=e_type, e_type_size=edge_data_total_size) + TracingManager.set_attributes(e_type=e_type, e_type_size=edge_data_total_size) self.__process_batches( e_type[1], @@ -1796,7 +1785,7 @@ def __insert_adb_docs( https://docs.python-arango.com/en/main/specs.html#arango.collection.Collection.import_bulk :param adb_import_kwargs: Any """ - self.__set_tracer_attributes(col=col, size=len(df)) + TracingManager.set_attributes(col=col, size=len(df)) action = f"ADB Import: '{col}' ({len(df)})" spinner_progress_task = spinner_progress.add_task("", action=action) diff --git a/adbpyg_adapter/tracing.py b/adbpyg_adapter/tracing.py index b00a1fb..51d2d34 100644 --- a/adbpyg_adapter/tracing.py +++ b/adbpyg_adapter/tracing.py @@ -28,6 +28,13 @@ def get_tracer(cls) -> Optional["Tracer"]: def set_tracer(cls, tracer: "Tracer") -> None: cls.__tracer = tracer + @classmethod + def set_attributes(self, **attributes: Any) -> None: + if TRACING_ENABLED and self.__tracer is not None: + current_span = trace.get_current_span() + for k, v in attributes.items(): + current_span.set_attribute(k, v) + T = TypeVar("T", bound=Callable[..., Any]) From d54925181a8d450d8ed70d84995ed117176fd897 Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Sat, 2 Dec 2023 22:13:45 -0500 Subject: [PATCH 11/73] early exit for `with_tracing` --- adbpyg_adapter/tracing.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/adbpyg_adapter/tracing.py b/adbpyg_adapter/tracing.py index 51d2d34..b2fa2fa 100644 --- a/adbpyg_adapter/tracing.py +++ b/adbpyg_adapter/tracing.py @@ -40,6 +40,9 @@ def set_attributes(self, **attributes: Any) -> None: def with_tracing(method: T) -> T: + if not TRACING_ENABLED: + return method + @wraps(method) def decorator(*args: Any, **kwargs: Any) -> Any: if tracer := TracingManager.get_tracer(): From eeb9a7a809c8d0f3230c5783bea9a8c2c4df1c52 Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Sat, 2 Dec 2023 22:13:59 -0500 Subject: [PATCH 12/73] cleanup tracer import --- adbpyg_adapter/adapter.py | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/adbpyg_adapter/adapter.py b/adbpyg_adapter/adapter.py index a392521..4d0a0f6 100644 --- a/adbpyg_adapter/adapter.py +++ b/adbpyg_adapter/adapter.py @@ -41,7 +41,7 @@ ) if TRACING_ENABLED: - from opentelemetry import trace + from opentelemetry.trace import Tracer class ADBPyG_Adapter(Abstract_ADBPyG_Adapter): @@ -69,7 +69,7 @@ def __init__( db: StandardDatabase, controller: ADBPyG_Controller = ADBPyG_Controller(), logging_lvl: Union[str, int] = logging.INFO, - tracer: Optional["trace.Tracer"] = None, + tracer: Optional["Tracer"] = None, ): self.set_logging(logging_lvl) self.set_tracer(tracer) @@ -104,11 +104,13 @@ def set_logging(self, level: Union[int, str]) -> None: """ logger.setLevel(level) - def set_tracer(self, tracer: Optional["trace.Tracer"]) -> None: + def set_tracer(self, tracer: Optional["Tracer"]) -> None: """Set the OpenTelemetry tracer for the adapter instance. Requires the `tracing` extra to be installed (i.e `pip install adbpyg-adapter[tracing]`). - :param tracer: The OpenTelemetry tracer instance. + :param tracer: The OpenTelemetry tracer instance. See + `adbpyg_adapter.tracing.create_tracer` for details on how to + create a tracer instance. :type tracer: opentelemetry.trace.Tracer :raise ImportError: If OpenTelemetry is not installed. """ From bba46cd13e46f250e9656e4b0cf4a0d17ef28ee6 Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Mon, 4 Dec 2023 22:45:27 -0500 Subject: [PATCH 13/73] new: `benchmark` dir --- .github/workflows/build.yml | 2 +- benchmark/compare.py | 88 + benchmark/diff/README.md | 3 + benchmark/traces/branch/README.md | 3 + benchmark/traces/head/README.md | 3 + benchmark/traces/head/arangodb_to_pyg.json | 2293 ++++++++++++++++++++ benchmark/traces/head/pyg_to_arangodb.json | 469 ++++ benchmark/write.py | 201 ++ 8 files changed, 3061 insertions(+), 1 deletion(-) create mode 100644 benchmark/compare.py create mode 100644 benchmark/diff/README.md create mode 100644 benchmark/traces/branch/README.md create mode 100644 benchmark/traces/head/README.md create mode 100644 benchmark/traces/head/arangodb_to_pyg.json create mode 100644 benchmark/traces/head/pyg_to_arangodb.json create mode 100644 benchmark/write.py diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 39f47e1..61c03f8 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -34,7 +34,7 @@ jobs: run: | pip install torch==2.1.0 pip install torch-scatter torch-sparse -f https://data.pyg.org/whl/torch-$(python -c 'import torch; print(torch.__version__.split("+")[0])')+cpu.html - pip install -e '.[dev, tracing]' + pip install -e '.[dev]' - name: Run black run: black --check --verbose --diff --color ${{env.PACKAGE_DIR}} ${{env.TESTS_DIR}} diff --git a/benchmark/compare.py b/benchmark/compare.py new file mode 100644 index 0000000..7a1c277 --- /dev/null +++ b/benchmark/compare.py @@ -0,0 +1,88 @@ +import json +import pathlib + + +class DiffSpan: + def __init__( + self, + spanID: str, + operationName: str, + duration_head: int, + duration_branch: int, + tags: list[dict[str, str]], + ): + self.spanID = spanID + self.operationName = operationName + self.duration_head = duration_head + self.duration_branch = duration_branch + self.improvement = f"{round((1 - duration_branch / duration_head) * 100)}%" + self.tags = tags + self.children: dict[str, "DiffSpan"] = {} + + def add_child(self, span_id: str, child: "DiffSpan"): + self.children[span_id] = child + + def to_dict(self, include_children: bool = True): + res = { + "spanID": self.spanID, + "operationName": self.operationName, + "duration_head": self.duration_head, + "duration_branch": self.duration_branch, + "improvement": self.improvement, + "tags": self.tags, + } + + if include_children: + res["children"] = [child.to_dict() for child in self.children.values()] + + return res + + +class DiffTree: + def __init__(self, head_trace: dict, branch_trace: dict): + self.root_span = self.__build_diff_tree(head_trace, branch_trace) + + def __build_diff_tree(self, head_trace: dict, branch_trace: dict): + diff_span = DiffSpan( + head_trace["spanID"], + head_trace["operationName"], + head_trace["duration"], + branch_trace["duration"], + head_trace["tags"], + ) + + # Recursively build the tree for child spans + for head_child_span, branch_child_span in zip( + head_trace["children"], branch_trace["children"] + ): + child_span = self.__build_diff_tree(head_child_span, branch_child_span) + diff_span.add_child(head_child_span["spanID"], child_span) + + return diff_span + + def to_dict(self): + return self.root_span.to_dict() + + def to_json_file(self, operation: str): + current_dir = pathlib.Path(__file__).parent.absolute() + with open(f"{current_dir}/diff/{operation}.json", "w") as file: + file.write(json.dumps(self.to_dict(), indent=4)) + + +def main(): + current_dir = pathlib.Path(__file__).parent.absolute() + + for operation in ["pyg_to_arangodb", "arangodb_to_pyg"]: + head_trace = json.load(open(f"{current_dir}/traces/head/{operation}.json")) + branch_trace = json.load(open(f"{current_dir}/traces/branch/{operation}.json")) + + diff_tree = DiffTree(head_trace, branch_trace) + diff_tree.to_json_file(operation) + + print("-" * 50) + print(json.dumps(diff_tree.root_span.to_dict(include_children=False), indent=4)) + print("-" * 50) + + +if __name__ == "__main__": + main() diff --git a/benchmark/diff/README.md b/benchmark/diff/README.md new file mode 100644 index 0000000..0886721 --- /dev/null +++ b/benchmark/diff/README.md @@ -0,0 +1,3 @@ +Empty directory to store the diff files during Github Actions. + +See .github/workflows/benchmark.yml and benchmark/compare.py for more details. \ No newline at end of file diff --git a/benchmark/traces/branch/README.md b/benchmark/traces/branch/README.md new file mode 100644 index 0000000..4e9f9cd --- /dev/null +++ b/benchmark/traces/branch/README.md @@ -0,0 +1,3 @@ +Empty directory to store the branche's trace files during Github Actions. + +See .github/workflows/benchmark.yml and benchmark/write.py for more details. \ No newline at end of file diff --git a/benchmark/traces/head/README.md b/benchmark/traces/head/README.md new file mode 100644 index 0000000..d9db19d --- /dev/null +++ b/benchmark/traces/head/README.md @@ -0,0 +1,3 @@ +Stores the traces for the current head of the repository. + +See .github/workflows/benchmark.yml and benchmark/write.py for more details. \ No newline at end of file diff --git a/benchmark/traces/head/arangodb_to_pyg.json b/benchmark/traces/head/arangodb_to_pyg.json new file mode 100644 index 0000000..2f94ecf --- /dev/null +++ b/benchmark/traces/head/arangodb_to_pyg.json @@ -0,0 +1,2293 @@ +{ + "spanID": "935ddd725129fb7c", + "operationName": "arangodb_to_pyg", + "duration": 812573, + "tags": { + "name": "FakeHeteroGraphBenchmark" + }, + "children": [ + { + "spanID": "4a5308cc3dfabc08", + "operationName": "__process_adb_v_col", + "duration": 46005, + "tags": { + "v_col": "v0", + "v_col_size": 1008 + }, + "children": [ + { + "spanID": "307bf3262f120554", + "operationName": "__fetch_adb_docs", + "duration": 30747, + "tags": {}, + "children": [] + }, + { + "spanID": "2fcd81b5d24bace4", + "operationName": "__process_adb_cursor", + 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"__process_pyg_edge_batch", + "duration": 5611, + "tags": { + "start_index": 0, + "end_index": 10034 + }, + "children": [ + { + "spanID": "9a6a5f92cca74147", + "operationName": "__set_adb_data", + "duration": 1833, + "tags": {}, + "children": [ + { + "spanID": "966e12778c1745a7", + "operationName": "__build_dataframe_from_tensor", + "duration": 1442, + "tags": {}, + "children": [] + } + ] + } + ] + }, + { + "spanID": "71eacd0549a3e80e", + "operationName": "__insert_adb_docs", + "duration": 141286, + "tags": { + "col": "e0", + "size": 10034 + }, + "children": [] + } + ] + } + ] +} \ No newline at end of file diff --git a/benchmark/write.py b/benchmark/write.py new file mode 100644 index 0000000..b271cf7 --- /dev/null +++ b/benchmark/write.py @@ -0,0 +1,201 @@ +import argparse +import json +import pathlib +import random +import time +from typing import Any, Dict, List + +import numpy as np +import requests +import torch +from arango import ArangoClient +from torch_geometric.datasets import FakeHeteroDataset + +try: + from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter +except ImportError: + m = """ + OpenTelemetry is not installed. + Please install it with `pip install adbpyg-adapter[tracing]` + """ + + raise ImportError(m) + +from adbpyg_adapter import ADBPyG_Adapter +from adbpyg_adapter.tracing import create_tracer + +seed = 0 +torch.manual_seed(seed) +random.seed(seed) +np.random.seed(seed) + + +class JaegerSpan: + def __init__( + self, + spanID: str, + operationName: str, + duration: int, + tags: list[dict[str, str]], + ): + self.spanID = spanID + self.operationName = operationName + self.duration = duration + self.tags = { + tag["key"]: tag["value"] + for tag in tags + if tag["key"] not in ["span.kind", "internal.span.format"] + } + self.children: dict[str, "JaegerSpan"] = {} + + def add_child(self, span_id: str, child: "JaegerSpan"): + self.children[span_id] = child + + def to_dict(self): + return { + "spanID": self.spanID, + "operationName": self.operationName, + "duration": self.duration, + "tags": self.tags, + "children": [child.to_dict() for child in self.children.values()], + } + + +class JaegerSpanTree: + def __init__(self, jaeger_json_data: Dict[str, Any]): + self.root_span = self.__build_span_tree(jaeger_json_data) + + def __build_span_tree(self, jaeger_json_data: Dict[str, Any]): + sorted_spans = sorted( + jaeger_json_data["data"][0]["spans"], key=lambda span: span["startTime"] + ) + + root_spans: List[JaegerSpan] = [] + span_dict: Dict[str, JaegerSpan] = {} + span: Dict[str, Any] + for span in sorted_spans: + span_id = span["spanID"] + span_dict[span["spanID"]] = JaegerSpan( + span_id, span["operationName"], span["duration"], span["tags"] + ) + + references = span.get("references", []) + if len(references) == 0: + root_spans.append(span_dict[span_id]) + continue + + for ref in references: + if ref["refType"] == "CHILD_OF": + parent_span = span_dict[ref["spanID"]] + parent_span.add_child(span_id, span_dict[span_id]) + + assert len(root_spans) == 1 + return root_spans[0] + + def to_dict(self): + return self.root_span.to_dict() + + def to_json_file(self, output: str): + current_dir = pathlib.Path(__file__).parent.absolute() + with open(f"{current_dir}/traces/{output}", "w") as file: + file.write(json.dumps(self.to_dict(), indent=4)) + + +def parse_args(): + parser = argparse.ArgumentParser() + + parser.add_argument("--url", type=str, default="http://localhost:8529") + parser.add_argument("--dbName", type=str, default="_system") + parser.add_argument("--username", type=str, default="root") + parser.add_argument("--password", type=str, default="test") + parser.add_argument("--otlp_endpoint", type=str, default="localhost:4317") + parser.add_argument( + "--output_dir", type=str, default="branch", choices=["branch", "head"] + ) + + # Parse the arguments + args = parser.parse_args() + + return args + + +def get_adapter(args) -> ADBPyG_Adapter: + db = ArangoClient(hosts=args.url).db( + args.dbName, username=args.username, password=args.password, verify=True + ) + + tracer = create_tracer( + "adbpyg-adapter-benchmark", + enable_console_tracing=False, + span_exporters=[OTLPSpanExporter(endpoint=args.otlp_endpoint, insecure=True)], + ) + + return ADBPyG_Adapter(db, tracer=tracer) + + +def run_pyg_to_arangodb(adapter: ADBPyG_Adapter, name: str) -> None: + data = FakeHeteroDataset(edge_dim=2)[0] + adapter.db.delete_graph(name, drop_collections=True, ignore_missing=True) + adapter.pyg_to_arangodb(name, data) + + +def run_arangodb_to_pyg(adapter: ADBPyG_Adapter, name: str) -> None: + adapter.arangodb_to_pyg( + name, + { + "vertexCollections": { + "v0": {"x", "y"}, + "v1": {"x"}, + "v2": {"x"}, + }, + "edgeCollections": { + "e0": {"edge_attr": "edge_attr"}, + }, + }, + ) + + +def get_span_tree(operation: str, start_time: str) -> JaegerSpanTree: + url = "http://localhost:16686/api/traces" + params = { + "service": "adbpyg-adapter-benchmark", + "operation": operation, + "tag": "name:FakeHeteroGraphBenchmark", + "start": start_time, + } + + response = requests.get(url, params=params) + assert response.status_code == 200 + + return JaegerSpanTree(response.json()) + + +def main(): + # 1. Parse the arguments + args = parse_args() + + # 2. Get the adapter + adbpyg_adapter = get_adapter(args) + + # 3. Run the benchmark + # TODO: Figure out why Jaeger is reporting the traces + # in the **same** operation... (only a problem for benchmarking) + name = "FakeHeteroGraphBenchmark" + start_time = str(time.time()).replace(".", "") + run_pyg_to_arangodb(adbpyg_adapter, name) + run_arangodb_to_pyg(adbpyg_adapter, name) + + # Wait for OTLP Export + time.sleep(5) + + # 4. Get the span trees + pyg_to_arangodb_span_tree = get_span_tree("pyg_to_arangodb", start_time) + arangodb_to_pyg_span_tree = get_span_tree("arangodb_to_pyg", start_time) + + # 5. Write the span trees to disk + pyg_to_arangodb_span_tree.to_json_file(f"{args.output_dir}/pyg_to_arangodb.json") + arangodb_to_pyg_span_tree.to_json_file(f"{args.output_dir}/arangodb_to_pyg.json") + + +if __name__ == "__main__": + main() From 68e68a353278d62ae7da7c049a3dd022abec7900 Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Mon, 4 Dec 2023 22:45:44 -0500 Subject: [PATCH 14/73] cleanup `tests` --- tests/conftest.py | 51 ++++++++++++++++++++++++++++--------------- tests/test_adapter.py | 4 +--- 2 files changed, 35 insertions(+), 20 deletions(-) diff --git a/tests/conftest.py b/tests/conftest.py index f1f2a86..335c9a3 100755 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -2,12 +2,14 @@ import os import subprocess from pathlib import Path -from typing import Any, Callable, Dict +from typing import Any, Callable, Dict, Optional + +import torch +import random +import numpy as np from arango import ArangoClient from arango.database import StandardDatabase -from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter -from opentelemetry.trace import Tracer from pandas import DataFrame from torch import Tensor, tensor from torch_geometric.data import Data, HeteroData @@ -18,13 +20,25 @@ from adbpyg_adapter.tracing import create_tracer from adbpyg_adapter.typings import Json +try: + from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter + from opentelemetry.trace import Tracer + + TRACING_ENABLED = True +except ImportError: + TRACING_ENABLED = False + +seed = 0 +torch.manual_seed(seed) +random.seed(seed) +np.random.seed(seed) + con: Json db: StandardDatabase -tracer: Tracer +tracer: Optional["Tracer"] adbpyg_adapter: ADBPyG_Adapter PROJECT_DIR = Path(__file__).parent.parent - def pytest_addoption(parser: Any) -> None: parser.addoption("--url", action="store", default="http://localhost:8529") parser.addoption("--dbName", action="store", default="_system") @@ -43,10 +57,11 @@ def pytest_configure(config: Any) -> None: } print("----------------------------------------") - print("URL: " + con["url"]) - print("Username: " + con["username"]) - print("Password: " + con["password"]) - print("Database: " + con["dbName"]) + print(f"URL: {con['url']}") + print(f"Username: {con['username']}") + print(f"Password: {con['password']}") + print(f"Database: {con['dbName']}") + print(f"TRACING_ENABLED: {TRACING_ENABLED}") print("----------------------------------------") global db @@ -55,14 +70,16 @@ def pytest_configure(config: Any) -> None: ) global tracer - tracer = create_tracer( - "adbpyg-adapter-test", - enable_console_tracing=False, - span_exporters=[ - OTLPSpanExporter(endpoint=endpoint, insecure=True) - for endpoint in config.getoption("otlp_endpoint") - ], - ) + tracer = None + if TRACING_ENABLED: + tracer = create_tracer( + "adbpyg-adapter-test", + enable_console_tracing=False, + span_exporters=[ + OTLPSpanExporter(endpoint=endpoint) + for endpoint in config.getoption("otlp_endpoint") + ], + ) global adbpyg_adapter adbpyg_adapter = ADBPyG_Adapter(db, logging_lvl=logging.INFO, tracer=tracer) diff --git a/tests/test_adapter.py b/tests/test_adapter.py index f0fe2b0..fde2215 100644 --- a/tests/test_adapter.py +++ b/tests/test_adapter.py @@ -389,7 +389,7 @@ def test_pyg_to_adb_ambiguity_error() -> None: adbpyg_adapter.pyg_to_arangodb("graph", d) -def test_pyg_to_arangodb_with_controller() -> None: +def test_pyg_to_adb_with_controller() -> None: name = "Karate_3" data = get_karate_graph() db.delete_graph(name, drop_collections=True, ignore_missing=True) @@ -821,7 +821,6 @@ def test_full_cycle_homogeneous_with_preserve_adb_keys() -> None: db.delete_graph(name, drop_collections=True, ignore_missing=True) - def test_full_cycle_imdb_with_preserve_adb_keys() -> None: name = "imdb" db.delete_graph(name, drop_collections=True, ignore_missing=True) @@ -894,7 +893,6 @@ def test_full_cycle_imdb_with_preserve_adb_keys() -> None: db.delete_graph(name, drop_collections=True) - def assert_pyg_to_adb( name: str, pyg_g: Union[Data, HeteroData], From c3745341cf26d373cd10997246495a86707ccce9 Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Mon, 4 Dec 2023 22:47:45 -0500 Subject: [PATCH 15/73] Create benchmark.yml --- .github/workflows/benchmark.yml | 124 ++++++++++++++++++++++++++++++++ 1 file changed, 124 insertions(+) create mode 100644 .github/workflows/benchmark.yml diff --git a/.github/workflows/benchmark.yml b/.github/workflows/benchmark.yml new file mode 100644 index 0000000..d86c9d7 --- /dev/null +++ b/.github/workflows/benchmark.yml @@ -0,0 +1,124 @@ +name: benchmark + +on: + workflow_dispatch: + push: + branches: + - master + paths: + - 'src/**' + - "benchmark/*.py" + pull_request: + branches: + - master + paths: + - 'src/**' + - "benchmark/*.py" + +jobs: + # write_benchmark: + # if: github.event_name == 'push' + # runs-on: ubuntu-latest + # steps: + # - uses: actions/checkout@v4 + # with: + # fetch-depth: 0 + + # - name: Create new branch + # run: git checkout -b actions/benchmark + + # - name: Set branch upstream + # run: git push -u origin actions/benchmark + # env: + # GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + + # - name: Setup Python + # uses: actions/setup-python@v4 + # with: + # cache: 'pip' + # cache-dependency-path: setup.py + + # - name: Set up ArangoDB Instance via Docker + # run: docker run -d --name arangodb -p 8529:8529 -e ARANGO_ROOT_PASSWORD= arangodb/arangodb + + # - name: Start Jaeger Instance + # run: docker run -d --name jaeger --rm \ + # -e COLLECTOR_ZIPKIN_HOST_PORT=:9411 \ + # -p 16686:16686 \ + # -p 4317:4317 \ + # -p 4318:4318 \ + # -p 9411:9411 \ + # jaegertracing/all-in-one:latest + + # - name: Install packages + # run: | + # pip install torch==2.1.0 + # pip install torch-scatter torch-sparse -f https://data.pyg.org/whl/torch-$(python -c 'import torch; print(torch.__version__.split("+")[0])')+cpu.html + # pip install -e '.[dev, tracing]' + + # - name: Run Python Script + # run: python benchmark/write.py --output_dir head + + # - name: Make commit for auto-generated benchmark files + # uses: EndBug/add-and-commit@v9 + # env: + # GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + # with: + # add: "./benchmark/main/*.json" + # new_branch: actions/benchmark + # message: "generate benchmark files for $GITHUB_SHA" + + # - name: Create pull request for the auto generated changelog + # run: | + # echo "PR_URL=$(gh pr create \ + # --title "benchmark: $GITHUB_SHA" \ + # --body "beep boop, i am a robot ($GITHUB_SHA)" \ + # --label documentation)" >> $GITHUB_ENV + # env: + # GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + + # - name: Alert developer of open PR + # run: echo "Benchmark $PR_URL is ready to be merged by developer." + + compare_benchmarks: + if: github.event_name == 'pull_request' + runs-on: ubuntu-latest + steps: + - name: Checkout code + uses: actions/checkout@v4 + + - name: Setup Python + uses: actions/setup-python@v4 + with: + cache: 'pip' + cache-dependency-path: setup.py + + - name: Set up ArangoDB Instance via Docker + run: docker run -d --name arangodb -p 8529:8529 -e ARANGO_ROOT_PASSWORD= arangodb/arangodb + + - name: Start Jaeger Instance + run: docker run -d --name jaeger --rm \ + -e COLLECTOR_ZIPKIN_HOST_PORT=:9411 \ + -p 16686:16686 \ + -p 4317:4317 \ + -p 4318:4318 \ + -p 9411:9411 \ + jaegertracing/all-in-one:latest + + - name: Install packages + run: | + pip install torch==2.1.0 + pip install torch-scatter torch-sparse -f https://data.pyg.org/whl/torch-$(python -c 'import torch; print(torch.__version__.split("+")[0])')+cpu.html + pip install -e '.[dev, tracing]' + + - name: Run Benchmark Write for PR + run: python benchmark/write.py --output_dir branch + + - name: Run Benchmark Comparison against main + run: python benchmark/compare.py + + - name: Echo PyG to ArangoDB Comparison + run: cat benchmark/diff/pyg_to_arangodb.json | jq + + - name: Echo ArangoDB to PyG Comparison + run: cat benchmark/diff/arangodb_to_pyg.json | jq \ No newline at end of file From cde8808ea7d219f06b948af87cb8113eb52c55b6 Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Mon, 4 Dec 2023 22:47:50 -0500 Subject: [PATCH 16/73] Update .gitignore --- .gitignore | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/.gitignore b/.gitignore index b7b415f..6a59526 100644 --- a/.gitignore +++ b/.gitignore @@ -118,4 +118,8 @@ adbpyg_adapter/version.py .vscode # PyG Data -tests/data/pyg \ No newline at end of file +tests/data/pyg + +# "Current" Benchmark Results +benchmark/traces/branch/*.json +benchmark/diff/*.json \ No newline at end of file From 2f5c23a407455be5f9052f24272f33df3cf7fa51 Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Mon, 4 Dec 2023 22:49:47 -0500 Subject: [PATCH 17/73] Update benchmark.yml --- .github/workflows/benchmark.yml | 8 +------- 1 file changed, 1 insertion(+), 7 deletions(-) diff --git a/.github/workflows/benchmark.yml b/.github/workflows/benchmark.yml index d86c9d7..15484e8 100644 --- a/.github/workflows/benchmark.yml +++ b/.github/workflows/benchmark.yml @@ -97,13 +97,7 @@ jobs: run: docker run -d --name arangodb -p 8529:8529 -e ARANGO_ROOT_PASSWORD= arangodb/arangodb - name: Start Jaeger Instance - run: docker run -d --name jaeger --rm \ - -e COLLECTOR_ZIPKIN_HOST_PORT=:9411 \ - -p 16686:16686 \ - -p 4317:4317 \ - -p 4318:4318 \ - -p 9411:9411 \ - jaegertracing/all-in-one:latest + run: docker run -d --name jaeger --rm -e COLLECTOR_ZIPKIN_HOST_PORT=:9411 -p 16686:16686 -p 4317:4317 -p 4318:4318 -p 9411:9411 jaegertracing/all-in-one:latest - name: Install packages run: | From bc8920022de8da70ff0b7e239e7ec8fb21e2e4ec Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Mon, 4 Dec 2023 22:54:06 -0500 Subject: [PATCH 18/73] Update benchmark.yml --- .github/workflows/benchmark.yml | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/.github/workflows/benchmark.yml b/.github/workflows/benchmark.yml index 15484e8..fe7af4f 100644 --- a/.github/workflows/benchmark.yml +++ b/.github/workflows/benchmark.yml @@ -16,7 +16,7 @@ on: - "benchmark/*.py" jobs: - # write_benchmark: + # write_traces: # if: github.event_name == 'push' # runs-on: ubuntu-latest # steps: @@ -80,7 +80,7 @@ jobs: # - name: Alert developer of open PR # run: echo "Benchmark $PR_URL is ready to be merged by developer." - compare_benchmarks: + compare_traces: if: github.event_name == 'pull_request' runs-on: ubuntu-latest steps: @@ -93,17 +93,19 @@ jobs: cache: 'pip' cache-dependency-path: setup.py - - name: Set up ArangoDB Instance via Docker + - name: Start ArangoDB Instance run: docker run -d --name arangodb -p 8529:8529 -e ARANGO_ROOT_PASSWORD= arangodb/arangodb - name: Start Jaeger Instance run: docker run -d --name jaeger --rm -e COLLECTOR_ZIPKIN_HOST_PORT=:9411 -p 16686:16686 -p 4317:4317 -p 4318:4318 -p 9411:9411 jaegertracing/all-in-one:latest + # TODO: revisit why [tracing] is not working - name: Install packages run: | pip install torch==2.1.0 pip install torch-scatter torch-sparse -f https://data.pyg.org/whl/torch-$(python -c 'import torch; print(torch.__version__.split("+")[0])')+cpu.html - pip install -e '.[dev, tracing]' + pip install -e '.[dev]' + pip install opentelemetry-api==1.21.0 opentelemetry-sdk==1.21.0 opentelemetry-exporter-otlp-proto-grpc==1.21.0 - name: Run Benchmark Write for PR run: python benchmark/write.py --output_dir branch From f0352c4612ec91143d3616a69970c59eb0fa85fb Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Mon, 4 Dec 2023 22:55:54 -0500 Subject: [PATCH 19/73] Update benchmark.yml --- .github/workflows/benchmark.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/benchmark.yml b/.github/workflows/benchmark.yml index fe7af4f..21d3ef5 100644 --- a/.github/workflows/benchmark.yml +++ b/.github/workflows/benchmark.yml @@ -90,6 +90,7 @@ jobs: - name: Setup Python uses: actions/setup-python@v4 with: + python-version: "3.10" cache: 'pip' cache-dependency-path: setup.py From 19dd07349e53bc55fc441eabf7018281c2045540 Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Mon, 4 Dec 2023 22:55:59 -0500 Subject: [PATCH 20/73] fix lint --- tests/conftest.py | 6 +++--- tests/test_adapter.py | 2 ++ 2 files changed, 5 insertions(+), 3 deletions(-) diff --git a/tests/conftest.py b/tests/conftest.py index 335c9a3..2a98d5c 100755 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -1,13 +1,12 @@ import logging import os +import random import subprocess from pathlib import Path from typing import Any, Callable, Dict, Optional -import torch -import random import numpy as np - +import torch from arango import ArangoClient from arango.database import StandardDatabase from pandas import DataFrame @@ -39,6 +38,7 @@ adbpyg_adapter: ADBPyG_Adapter PROJECT_DIR = Path(__file__).parent.parent + def pytest_addoption(parser: Any) -> None: parser.addoption("--url", action="store", default="http://localhost:8529") parser.addoption("--dbName", action="store", default="_system") diff --git a/tests/test_adapter.py b/tests/test_adapter.py index fde2215..2998efb 100644 --- a/tests/test_adapter.py +++ b/tests/test_adapter.py @@ -821,6 +821,7 @@ def test_full_cycle_homogeneous_with_preserve_adb_keys() -> None: db.delete_graph(name, drop_collections=True, ignore_missing=True) + def test_full_cycle_imdb_with_preserve_adb_keys() -> None: name = "imdb" db.delete_graph(name, drop_collections=True, ignore_missing=True) @@ -893,6 +894,7 @@ def test_full_cycle_imdb_with_preserve_adb_keys() -> None: db.delete_graph(name, drop_collections=True) + def assert_pyg_to_adb( name: str, pyg_g: Union[Data, HeteroData], From 261d2247d29448bea3b5c2a409c5edcfc368566a Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Mon, 4 Dec 2023 22:59:13 -0500 Subject: [PATCH 21/73] cleanup --- .github/workflows/benchmark.yml | 8 +++----- benchmark/write.py | 2 +- 2 files changed, 4 insertions(+), 6 deletions(-) diff --git a/.github/workflows/benchmark.yml b/.github/workflows/benchmark.yml index 21d3ef5..efe7377 100644 --- a/.github/workflows/benchmark.yml +++ b/.github/workflows/benchmark.yml @@ -100,18 +100,16 @@ jobs: - name: Start Jaeger Instance run: docker run -d --name jaeger --rm -e COLLECTOR_ZIPKIN_HOST_PORT=:9411 -p 16686:16686 -p 4317:4317 -p 4318:4318 -p 9411:9411 jaegertracing/all-in-one:latest - # TODO: revisit why [tracing] is not working - name: Install packages run: | pip install torch==2.1.0 pip install torch-scatter torch-sparse -f https://data.pyg.org/whl/torch-$(python -c 'import torch; print(torch.__version__.split("+")[0])')+cpu.html - pip install -e '.[dev]' - pip install opentelemetry-api==1.21.0 opentelemetry-sdk==1.21.0 opentelemetry-exporter-otlp-proto-grpc==1.21.0 + pip install -e '.[dev, tracing]' - - name: Run Benchmark Write for PR + - name: Write traces for branch run: python benchmark/write.py --output_dir branch - - name: Run Benchmark Comparison against main + - name: Compare PR traces against Master traces run: python benchmark/compare.py - name: Echo PyG to ArangoDB Comparison diff --git a/benchmark/write.py b/benchmark/write.py index b271cf7..6176aa3 100644 --- a/benchmark/write.py +++ b/benchmark/write.py @@ -107,7 +107,7 @@ def parse_args(): parser.add_argument("--url", type=str, default="http://localhost:8529") parser.add_argument("--dbName", type=str, default="_system") parser.add_argument("--username", type=str, default="root") - parser.add_argument("--password", type=str, default="test") + parser.add_argument("--password", type=str, default="") parser.add_argument("--otlp_endpoint", type=str, default="localhost:4317") parser.add_argument( "--output_dir", type=str, default="branch", choices=["branch", "head"] From 10b1d6ca56f51c2ea9320ece7c906060e49ddf61 Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Mon, 4 Dec 2023 23:02:40 -0500 Subject: [PATCH 22/73] Update benchmark.yml --- .github/workflows/benchmark.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/benchmark.yml b/.github/workflows/benchmark.yml index efe7377..aeabc58 100644 --- a/.github/workflows/benchmark.yml +++ b/.github/workflows/benchmark.yml @@ -106,7 +106,7 @@ jobs: pip install torch-scatter torch-sparse -f https://data.pyg.org/whl/torch-$(python -c 'import torch; print(torch.__version__.split("+")[0])')+cpu.html pip install -e '.[dev, tracing]' - - name: Write traces for branch + - name: Write PR traces run: python benchmark/write.py --output_dir branch - name: Compare PR traces against Master traces From e08b11149e244dad42b3750a4e7fcf07ed7f0ab9 Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Mon, 4 Dec 2023 23:06:09 -0500 Subject: [PATCH 23/73] Update benchmark.yml --- .github/workflows/benchmark.yml | 2 -- 1 file changed, 2 deletions(-) diff --git a/.github/workflows/benchmark.yml b/.github/workflows/benchmark.yml index aeabc58..311f316 100644 --- a/.github/workflows/benchmark.yml +++ b/.github/workflows/benchmark.yml @@ -9,8 +9,6 @@ on: - 'src/**' - "benchmark/*.py" pull_request: - branches: - - master paths: - 'src/**' - "benchmark/*.py" From aff682e21a56c9f88351baa4bdbac16bdedbd72a Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Mon, 4 Dec 2023 23:13:30 -0500 Subject: [PATCH 24/73] Update benchmark.yml --- .github/workflows/benchmark.yml | 7 ++----- 1 file changed, 2 insertions(+), 5 deletions(-) diff --git a/.github/workflows/benchmark.yml b/.github/workflows/benchmark.yml index 311f316..add332b 100644 --- a/.github/workflows/benchmark.yml +++ b/.github/workflows/benchmark.yml @@ -2,16 +2,13 @@ name: benchmark on: workflow_dispatch: + pull_request: push: branches: - master paths: - - 'src/**' + - "src/*.py" - "benchmark/*.py" - pull_request: - paths: - - 'src/**' - - "benchmark/*.py" jobs: # write_traces: From 83672c54a5a8c5f0593bfa415e77e0eb12fb105a Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Fri, 15 Dec 2023 20:17:17 -0500 Subject: [PATCH 25/73] cleanup tracing, new spans, new attributes --- adbpyg_adapter/adapter.py | 143 +++++++++++++++++++++----------------- adbpyg_adapter/tracing.py | 12 +++- 2 files changed, 90 insertions(+), 65 deletions(-) diff --git a/adbpyg_adapter/adapter.py b/adbpyg_adapter/adapter.py index 4d0a0f6..27daa2d 100644 --- a/adbpyg_adapter/adapter.py +++ b/adbpyg_adapter/adapter.py @@ -21,7 +21,12 @@ from .abc import Abstract_ADBPyG_Adapter from .controller import ADBPyG_Controller from .exceptions import ADBMetagraphError, InvalidADBEdgesError, PyGMetagraphError -from .tracing import TRACING_ENABLED, TracingManager, with_tracing +from .tracing import ( + TRACING_ENABLED, + TracingManager, + start_as_current_span, + with_tracing, +) from .typings import ( ADBMap, ADBMetagraph, @@ -717,6 +722,7 @@ def __process_adb_v_col( :param node_data: The PyG NodeStorage object. :type node_data: torch_geometric.data.storage.NodeStorage """ + TracingManager.set_attributes(v_col=v_col) # 1. Fetch ArangoDB vertices v_col_cursor, v_col_size = self.__fetch_adb_docs( @@ -736,8 +742,6 @@ def __process_adb_v_col( node_data=node_data, ) - TracingManager.set_attributes(v_col=v_col, v_col_size=v_col_size) - @with_tracing def __process_adb_e_col( self, @@ -774,6 +778,8 @@ def __process_adb_e_col( :param is_homogeneous: Whether the ArangoDB graph is homogeneous or not. :type is_homogeneous: bool """ + TracingManager.set_attributes(e_col=e_col) + # 1. Fetch ArangoDB edges e_col_cursor, e_col_size = self.__fetch_adb_docs( e_col, meta, **adb_export_kwargs @@ -795,8 +801,6 @@ def __process_adb_e_col( is_homogeneous=is_homogeneous, ) - TracingManager.set_attributes(e_col=e_col, e_col_size=e_col_size) - @with_tracing def __fetch_adb_docs( self, @@ -848,6 +852,7 @@ def get_aql_return_value( """ col_size: int = self.__db.collection(col).count() + TracingManager.set_attributes(col=col, col_size=col_size, meta=str(meta)) with get_export_spinner_progress(f"ADB Export: '{col}' ({col_size})") as p: p.add_task(col) @@ -855,7 +860,7 @@ def get_aql_return_value( cursor: Cursor = self.__db.aql.execute( f"FOR doc IN @@col RETURN {get_aql_return_value(meta)}", bind_vars={"@col": col}, - **{**adb_export_kwargs, **{"stream": True}}, + **{**adb_export_kwargs, "stream": True}, ) return cursor, col_size @@ -899,17 +904,15 @@ def __process_adb_cursor( progress = get_bar_progress(f"(ADB → PyG): '{col}'", progress_color) progress_task_id = progress.add_task(col, total=col_size) + i = 0 with Live(Group(progress)): - i = 0 while not cursor.empty(): - cursor_batch = len(cursor.batch()) - df = DataFrame([cursor.pop() for _ in range(cursor_batch)]) + df = DataFrame(cursor.batch()) + cursor.batch().clear() i = process_adb_df(i, df, col, adb_map, meta, preserve_key, **kwargs) progress.advance(progress_task_id, advance=len(df)) - df.drop(df.index, inplace=True) - if cursor.has_more(): cursor.fetch() @@ -944,6 +947,8 @@ def __process_adb_vertex_df( :return: The last PyG Node id value. :rtype: int """ + TracingManager.set_attributes(i=i, vertex_df_size=len(df)) + # 1. Map each ArangoDB _key to a PyG node id for adb_key in df["_key"]: adb_map[v_col][adb_key] = i @@ -999,6 +1004,8 @@ def __process_adb_edge_df( but is needed for type hinting. :rtype: int """ + TracingManager.set_attributes(edge_df_size=len(df)) + # 1. Split the ArangoDB _from & _to IDs into two columns df[["from_col", "from_key"]] = self.__split_adb_ids(df["_from"]) df[["to_col", "to_key"]] = self.__split_adb_ids(df["_to"]) @@ -1008,50 +1015,56 @@ def __process_adb_edge_df( df[["from_col", "to_col"]].value_counts().items() ): edge_type = (from_col, e_col, to_col) - edge_data: EdgeStorage = data if is_homogeneous else data[edge_type] - - # 3. Check for partial Edge Collection import - if from_col not in v_cols or to_col not in v_cols: - logger.debug(f"Skipping {edge_type}") - continue - - logger.debug(f"Preparing {count} {edge_type} edges") - - # 4. Get the edge data corresponding to the current edge type - et_df: DataFrame = df[ - (df["from_col"] == from_col) & (df["to_col"] == to_col) - ] - # 5. Map each ArangoDB from/to _key to the corresponding PyG node id - from_nodes = et_df["from_key"].map(adb_map[from_col]).tolist() - to_nodes = et_df["to_key"].map(adb_map[to_col]).tolist() - - # 6. Set/Update the PyG Edge Index - edge_index = tensor([from_nodes, to_nodes]) - edge_data.edge_index = torch.cat( - (edge_data.get("edge_index", tensor([])), edge_index), dim=1 - ) - - # 7. Deal with invalid edges - if torch.any(torch.isnan(edge_data.edge_index)): - if strict: - m = f"Invalid edges found in Edge Collection {e_col}, {from_col} -> {to_col}." # noqa: E501 - raise InvalidADBEdgesError(m) - else: - # Remove the invalid edges - edge_data.edge_index = edge_data.edge_index[ - :, ~torch.any(edge_data.edge_index.isnan(), dim=0) - ] - - # 8. Set the PyG Edge Data - self.__set_pyg_data(meta, edge_data, et_df) - - # 9. Maintain the ArangoDB _key values - if preserve_key is not None: - if preserve_key not in edge_data: - edge_data[preserve_key] = [] + with start_as_current_span("__process_adb_edge_type_df"): + TracingManager.set_attributes( + edge_type=edge_type, + edge_type_df_size=count, + ) - edge_data[preserve_key].extend(list(et_df["_key"])) + # 3. Check for partial Edge Collection import + if from_col not in v_cols or to_col not in v_cols: + logger.debug(f"Skipping {edge_type}") + TracingManager.set_attributes(skipped=True) + continue + + logger.debug(f"Preparing {count} {edge_type} edges") + + # 4. Get the edge data corresponding to the current edge type + et_df: DataFrame = df[ + (df["from_col"] == from_col) & (df["to_col"] == to_col) + ] + + # 5. Map each ArangoDB from/to _key to the corresponding PyG node id + from_nodes = et_df["from_key"].map(adb_map[from_col]).tolist() + to_nodes = et_df["to_key"].map(adb_map[to_col]).tolist() + + # 6. Set/Update the PyG Edge Index + edge_data: EdgeStorage = data if is_homogeneous else data[edge_type] + existing_ei = edge_data.get("edge_index", tensor([])) + new_ei = tensor([from_nodes, to_nodes]) + edge_data.edge_index = torch.cat((existing_ei, new_ei), dim=1) + + # 7. Deal with invalid edges + if torch.any(torch.isnan(edge_data.edge_index)): + if strict: + m = f"Invalid edges found in Edge Collection {e_col}, {from_col} -> {to_col}." # noqa: E501 + raise InvalidADBEdgesError(m) + else: + # Remove the invalid edges + edge_data.edge_index = edge_data.edge_index[ + :, ~torch.any(edge_data.edge_index.isnan(), dim=0) + ] + + # 8. Set the PyG Edge Data + self.__set_pyg_data(meta, edge_data, et_df) + + # 9. Maintain the ArangoDB _key values + if preserve_key is not None: + if preserve_key not in edge_data: + edge_data[preserve_key] = [] + + edge_data[preserve_key].extend(list(et_df["_key"])) return 1 # Useless return value, but needed for type hinting @@ -1091,6 +1104,7 @@ def __set_pyg_data( """ valid_meta: Dict[str, ADBMetagraphValues] valid_meta = meta if type(meta) is dict else {m: m for m in meta} + TracingManager.set_attributes(meta=str(valid_meta)) for k, v in valid_meta.items(): t = self.__build_tensor_from_dataframe(df, k, v) @@ -1125,8 +1139,8 @@ def __build_tensor_from_dataframe( :rtype: torch.Tensor :raise adbpyg_adapter.exceptions.ADBMetagraphError: If invalid **meta_val**. """ - m = f"__build_tensor_from_dataframe(df, '{meta_key}', {type(meta_val)})" - logger.debug(m) + TracingManager.set_attributes(meta_key=meta_key, meta_val=str(meta_val)) + logger.debug(f"__build_tensor_from_dataframe(df, {meta_key}, {str(meta_val)})") if type(meta_val) is str: return tensor(adb_df[meta_val].to_list()) @@ -1652,6 +1666,7 @@ def __set_adb_data( valid_meta: Dict[Any, PyGMetagraphValues] valid_meta = meta if type(meta) is dict else {m: m for m in meta} + TracingManager.set_attributes(meta=str(valid_meta)) pyg_keys = ( set(valid_meta.keys()) @@ -1664,15 +1679,16 @@ def __set_adb_data( data = pyg_data[meta_key] meta_val = valid_meta.get(meta_key, str(meta_key)) - if ( - type(meta_val) is str - and type(data) is list - and len(data) == pyg_data_size - ): + if len(data) != pyg_data_size: + m = f"Skipping {meta_key} due to invalid length ({len(data)} != {pyg_data_size})" # noqa: E501 + logger.debug(m) + continue + + if type(meta_val) is str and type(data) is list: meta_val = "_key" if meta_val in ["_v_key", "_e_key"] else meta_val df = df.join(DataFrame(data[start_index:end_index], columns=[meta_val])) - if type(data) is Tensor and len(data) == pyg_data_size: + if type(data) is Tensor: df = df.join( self.__build_dataframe_from_tensor( data[start_index:end_index], @@ -1713,9 +1729,8 @@ def __build_dataframe_from_tensor( :rtype: pandas.DataFrame :raise adbpyg_adapter.exceptions.PyGMetagraphError: If invalid **meta_val**. """ - logger.debug( - f"__build_dataframe_from_tensor(df, '{meta_key}', {type(meta_val)})" - ) + TracingManager.set_attributes(meta_key=meta_key, meta_val=str(meta_val)) + logger.debug(f"__build_dataframe_from_tensor(df, {meta_key}, {type(meta_val)})") if type(meta_val) is str: df = DataFrame(index=range(start_index, end_index), columns=[meta_val]) diff --git a/adbpyg_adapter/tracing.py b/adbpyg_adapter/tracing.py index b2fa2fa..c6f39ea 100644 --- a/adbpyg_adapter/tracing.py +++ b/adbpyg_adapter/tracing.py @@ -1,5 +1,6 @@ +from contextlib import contextmanager from functools import wraps -from typing import Any, Callable, List, Optional, TypeVar, cast +from typing import Any, Callable, Iterator, List, Optional, TypeVar, cast try: from opentelemetry import trace @@ -54,6 +55,15 @@ def decorator(*args: Any, **kwargs: Any) -> Any: return cast(T, decorator) +@contextmanager +def start_as_current_span(*args: Any, **kwargs: Any) -> Iterator[None]: + if tracer := TracingManager.get_tracer(): + with tracer.start_as_current_span(*args, **kwargs): + yield + else: + yield + + def create_tracer( name: str, enable_console_tracing: bool = False, From 9e2fa4cdfd902e9b37efa2b973b57381925dc334 Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Fri, 15 Dec 2023 21:04:26 -0500 Subject: [PATCH 26/73] cleanup: `benchmark` --- benchmark/compare.py | 50 +- benchmark/traces/head/README.md | 3 - benchmark/traces/head/arangodb_to_pyg.json | 2293 ----------- benchmark/traces/main/README.md | 3 + benchmark/traces/main/arangodb_to_pyg.json | 3395 +++++++++++++++++ .../{head => main}/pyg_to_arangodb.json | 188 +- benchmark/write.py | 178 +- 7 files changed, 3670 insertions(+), 2440 deletions(-) delete mode 100644 benchmark/traces/head/README.md delete mode 100644 benchmark/traces/head/arangodb_to_pyg.json create mode 100644 benchmark/traces/main/README.md create mode 100644 benchmark/traces/main/arangodb_to_pyg.json rename benchmark/traces/{head => main}/pyg_to_arangodb.json (73%) diff --git a/benchmark/compare.py b/benchmark/compare.py index 7a1c277..d01b868 100644 --- a/benchmark/compare.py +++ b/benchmark/compare.py @@ -5,18 +5,16 @@ class DiffSpan: def __init__( self, - spanID: str, - operationName: str, - duration_head: int, - duration_branch: int, - tags: list[dict[str, str]], + span_id: str, + operation_name: str, + main_duration: int, + branch_duration: int, ): - self.spanID = spanID - self.operationName = operationName - self.duration_head = duration_head - self.duration_branch = duration_branch - self.improvement = f"{round((1 - duration_branch / duration_head) * 100)}%" - self.tags = tags + self.span_id = span_id + self.operation_name = operation_name + self.main_span_duration = main_duration + self.branch_span_duration = branch_duration + self.improvement = f"{round((1 - main_duration / branch_duration) * 100)}%" self.children: dict[str, "DiffSpan"] = {} def add_child(self, span_id: str, child: "DiffSpan"): @@ -24,12 +22,11 @@ def add_child(self, span_id: str, child: "DiffSpan"): def to_dict(self, include_children: bool = True): res = { - "spanID": self.spanID, - "operationName": self.operationName, - "duration_head": self.duration_head, - "duration_branch": self.duration_branch, + "span_id": self.span_id, + "operation_name": self.operation_name, + "main_duration": self.main_span_duration, + "branch_duration": self.branch_span_duration, "improvement": self.improvement, - "tags": self.tags, } if include_children: @@ -39,21 +36,22 @@ def to_dict(self, include_children: bool = True): class DiffTree: - def __init__(self, head_trace: dict, branch_trace: dict): - self.root_span = self.__build_diff_tree(head_trace, branch_trace) + def __init__(self, main_trace: dict, branch_trace: dict): + self.root_span = self.__build_diff_tree(main_trace, branch_trace) + + def __build_diff_tree(self, main_trace: dict, branch_trace: dict): + assert main_trace["operationName"] == branch_trace["operationName"] - def __build_diff_tree(self, head_trace: dict, branch_trace: dict): diff_span = DiffSpan( - head_trace["spanID"], - head_trace["operationName"], - head_trace["duration"], + main_trace["spanID"], + main_trace["operationName"], + main_trace["duration"], branch_trace["duration"], - head_trace["tags"], ) # Recursively build the tree for child spans for head_child_span, branch_child_span in zip( - head_trace["children"], branch_trace["children"] + main_trace["children"], branch_trace["children"] ): child_span = self.__build_diff_tree(head_child_span, branch_child_span) diff_span.add_child(head_child_span["spanID"], child_span) @@ -73,10 +71,10 @@ def main(): current_dir = pathlib.Path(__file__).parent.absolute() for operation in ["pyg_to_arangodb", "arangodb_to_pyg"]: - head_trace = json.load(open(f"{current_dir}/traces/head/{operation}.json")) + main_trace = json.load(open(f"{current_dir}/traces/main/{operation}.json")) branch_trace = json.load(open(f"{current_dir}/traces/branch/{operation}.json")) - diff_tree = DiffTree(head_trace, branch_trace) + diff_tree = DiffTree(main_trace, branch_trace) diff_tree.to_json_file(operation) print("-" * 50) diff --git a/benchmark/traces/head/README.md b/benchmark/traces/head/README.md deleted file mode 100644 index d9db19d..0000000 --- a/benchmark/traces/head/README.md +++ /dev/null @@ -1,3 +0,0 @@ -Stores the traces for the current head of the repository. - -See .github/workflows/benchmark.yml and benchmark/write.py for more details. \ No newline at end of file diff --git a/benchmark/traces/head/arangodb_to_pyg.json b/benchmark/traces/head/arangodb_to_pyg.json deleted file mode 100644 index 2f94ecf..0000000 --- a/benchmark/traces/head/arangodb_to_pyg.json +++ /dev/null @@ -1,2293 +0,0 @@ -{ - "spanID": "935ddd725129fb7c", - "operationName": "arangodb_to_pyg", - "duration": 812573, - "tags": { - "name": "FakeHeteroGraphBenchmark" - }, - "children": [ - { - "spanID": "4a5308cc3dfabc08", - "operationName": "__process_adb_v_col", - "duration": 46005, - "tags": { - "v_col": "v0", - 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"__build_dataframe_from_tensor", - "duration": 8669, - "tags": {}, + "duration": 3497, + "tags": { + "meta_key": "y", + "meta_val": "y" + }, "children": [] }, { "spanID": "78de58575487ce1e", "operationName": "__build_dataframe_from_tensor", - "duration": 533, - "tags": {}, + "duration": 1515, + "tags": { + "meta_key": "x", + "meta_val": "x" + }, "children": [] } ] @@ -80,7 +88,7 @@ { "spanID": "19c78df48f4ff31e", "operationName": "__insert_adb_docs", - "duration": 111367, + "duration": 118460, "tags": { "col": "v0", "size": 1008 @@ -92,7 +100,7 @@ { "spanID": "6f25e2a25a921187", "operationName": "__process_pyg_n_type", - "duration": 78057, + "duration": 77546, "tags": { "n_type": "v1", "n_type_size": 821 @@ -101,7 +109,7 @@ { "spanID": "9c6316b950f24455", "operationName": "__process_pyg_node_batch", - "duration": 2157, + "duration": 2240, "tags": { "start_index": 0, "end_index": 821 @@ -110,14 +118,19 @@ { "spanID": "e9bb17bca3f2c9bf", "operationName": "__set_adb_data", - "duration": 1266, - "tags": {}, + "duration": 1327, + "tags": { + "meta": "{}" + }, "children": [ { "spanID": "f77383c13458a748", "operationName": "__build_dataframe_from_tensor", - "duration": 966, - "tags": {}, + "duration": 1000, + "tags": { + "meta_key": "x", + "meta_val": "x" + }, "children": [] } ] @@ -127,7 +140,7 @@ { "spanID": "7a1d50068d723104", "operationName": "__insert_adb_docs", - "duration": 75805, + "duration": 75209, "tags": { "col": "v1", "size": 821 @@ -139,7 +152,7 @@ { "spanID": "dd84f39e71545a13", "operationName": "__process_pyg_n_type", - "duration": 74717, + "duration": 73123, "tags": { "n_type": "v2", "n_type_size": 894 @@ -148,7 +161,7 @@ { "spanID": "42af9fc385776e9a", "operationName": "__process_pyg_node_batch", - "duration": 2129, + "duration": 2076, "tags": { "start_index": 0, "end_index": 894 @@ -157,14 +170,19 @@ { "spanID": "ce164dba0ff18e02", "operationName": "__set_adb_data", - "duration": 1201, - "tags": {}, + "duration": 1175, + "tags": { + "meta": "{}" + }, "children": [ { "spanID": "8c778ea6eb2083e6", "operationName": "__build_dataframe_from_tensor", - "duration": 892, - "tags": {}, + "duration": 869, + "tags": { + "meta_key": "x", + "meta_val": "x" + }, "children": [] } ] @@ -174,7 +192,7 @@ { "spanID": "03983ca8ea7e9d49", "operationName": "__insert_adb_docs", - "duration": 72499, + "duration": 70960, "tags": { "col": "v2", "size": 894 @@ -186,7 +204,7 @@ { "spanID": "b83e90ec17e0aa3c", "operationName": "__process_pyg_e_type", - "duration": 144376, + "duration": 135382, "tags": { "e_type": "[\"v2\",\"e0\",\"v1\"]", "e_type_size": 8895 @@ -195,7 +213,7 @@ { "spanID": "66194cb1d71037d1", "operationName": "__process_pyg_edge_batch", - "duration": 6604, + "duration": 6889, "tags": { "start_index": 0, "end_index": 8895 @@ -204,14 +222,19 @@ { "spanID": "d3290a4cb5d32b16", "operationName": "__set_adb_data", - "duration": 1962, - "tags": {}, + "duration": 1731, + "tags": { + "meta": "{}" + }, "children": [ { "spanID": "ab0c1681c8f8e3d0", "operationName": "__build_dataframe_from_tensor", - "duration": 1527, - "tags": {}, + "duration": 1314, + "tags": { + "meta_key": "edge_attr", + "meta_val": "edge_attr" + }, "children": [] } ] @@ -221,7 +244,7 @@ { "spanID": "004ae545a0116be5", "operationName": "__insert_adb_docs", - "duration": 137652, + "duration": 128374, "tags": { "col": "e0", "size": 8895 @@ -233,7 +256,7 @@ { "spanID": "7e5b1e7f9ca5499d", "operationName": "__process_pyg_e_type", - "duration": 123071, + "duration": 116710, "tags": { "e_type": "[\"v1\",\"e0\",\"v2\"]", "e_type_size": 8161 @@ -242,7 +265,7 @@ { "spanID": "de1b372ad3fbf47a", "operationName": "__process_pyg_edge_batch", - "duration": 4637, + "duration": 4913, "tags": { "start_index": 0, "end_index": 8161 @@ -251,14 +274,19 @@ { "spanID": "3e70f16a55485822", "operationName": "__set_adb_data", - "duration": 1552, - "tags": {}, + "duration": 1661, + "tags": { + "meta": "{}" + }, "children": [ { "spanID": "534097cabaf3897a", "operationName": "__build_dataframe_from_tensor", - "duration": 1176, - "tags": {}, + "duration": 1252, + "tags": { + "meta_key": "edge_attr", + "meta_val": "edge_attr" + }, "children": [] } ] @@ -268,7 +296,7 @@ { "spanID": "ded733e8b421eaeb", "operationName": "__insert_adb_docs", - "duration": 118335, + "duration": 111671, "tags": { "col": "e0", "size": 8161 @@ -280,7 +308,7 @@ { "spanID": "30e9c5cc101fbccc", "operationName": "__process_pyg_e_type", - "duration": 201707, + "duration": 199804, "tags": { "e_type": "[\"v0\",\"e0\",\"v1\"]", "e_type_size": 10022 @@ -289,7 +317,7 @@ { "spanID": "9148624feac1c14f", "operationName": "__process_pyg_edge_batch", - "duration": 5772, + "duration": 5482, "tags": { "start_index": 0, "end_index": 10022 @@ -298,14 +326,19 @@ { "spanID": "3d15eef738c1962e", "operationName": "__set_adb_data", - "duration": 1806, - "tags": {}, + "duration": 1820, + "tags": { + "meta": "{}" + }, "children": [ { "spanID": "f7b0b7d2cda8056c", "operationName": "__build_dataframe_from_tensor", - "duration": 1388, - "tags": {}, + "duration": 1439, + "tags": { + "meta_key": "edge_attr", + "meta_val": "edge_attr" + }, "children": [] } ] @@ -315,7 +348,7 @@ { "spanID": "cd9d2b7d247a8333", "operationName": "__insert_adb_docs", - "duration": 195821, + "duration": 194210, "tags": { "col": "e0", "size": 10022 @@ -327,7 +360,7 @@ { "spanID": "72ae22448b0163c1", "operationName": "__process_pyg_e_type", - "duration": 135807, + "duration": 124746, "tags": { "e_type": "[\"v1\",\"e0\",\"v0\"]", "e_type_size": 8179 @@ -336,7 +369,7 @@ { "spanID": "149818d11759edc3", "operationName": "__process_pyg_edge_batch", - "duration": 4828, + "duration": 4521, "tags": { "start_index": 0, "end_index": 8179 @@ -345,14 +378,19 @@ { "spanID": "51ef1922fe43c49e", "operationName": "__set_adb_data", - "duration": 1587, - "tags": {}, + "duration": 1521, + "tags": { + "meta": "{}" + }, "children": [ { "spanID": "820865d6e005b860", "operationName": "__build_dataframe_from_tensor", - "duration": 1199, - "tags": {}, + "duration": 1164, + "tags": { + "meta_key": "edge_attr", + "meta_val": "edge_attr" + }, "children": [] } ] @@ -362,7 +400,7 @@ { "spanID": "eece328bff7b118e", "operationName": "__insert_adb_docs", - "duration": 130868, + "duration": 120125, "tags": { "col": "e0", "size": 8179 @@ -374,7 +412,7 @@ { "spanID": "1beb37117d41e602", "operationName": "__process_pyg_e_type", - "duration": 127700, + "duration": 120860, "tags": { "e_type": "[\"v1\",\"e0\",\"v1\"]", "e_type_size": 8159 @@ -383,7 +421,7 @@ { "spanID": "8d1fd9b74d2b9deb", "operationName": "__process_pyg_edge_batch", - "duration": 4847, + "duration": 5168, "tags": { "start_index": 0, "end_index": 8159 @@ -392,14 +430,19 @@ { "spanID": "b4e1357d4a84eb03", "operationName": "__set_adb_data", - "duration": 1672, - "tags": {}, + "duration": 1667, + "tags": { + "meta": "{}" + }, "children": [ { "spanID": "8c25166a1ff39849", "operationName": "__build_dataframe_from_tensor", - "duration": 1251, - "tags": {}, + "duration": 1240, + "tags": { + "meta_key": "edge_attr", + "meta_val": "edge_attr" + }, "children": [] } ] @@ -409,7 +452,7 @@ { "spanID": "d080e66e552f233a", "operationName": "__insert_adb_docs", - "duration": 122735, + "duration": 115575, "tags": { "col": "e0", "size": 8159 @@ -421,7 +464,7 @@ { "spanID": "8a5006c1ec188efb", "operationName": "__process_pyg_e_type", - "duration": 147005, + "duration": 148804, "tags": { "e_type": "[\"v0\",\"e0\",\"v2\"]", "e_type_size": 10034 @@ -430,7 +473,7 @@ { "spanID": "f6be1f723405095c", "operationName": "__process_pyg_edge_batch", - "duration": 5611, + "duration": 5613, "tags": { "start_index": 0, "end_index": 10034 @@ -439,14 +482,19 @@ { "spanID": "9a6a5f92cca74147", "operationName": "__set_adb_data", - "duration": 1833, - "tags": {}, + "duration": 1902, + "tags": { + "meta": "{}" + }, "children": [ { "spanID": "966e12778c1745a7", "operationName": "__build_dataframe_from_tensor", - "duration": 1442, - "tags": {}, + "duration": 1509, + "tags": { + "meta_key": "edge_attr", + "meta_val": "edge_attr" + }, "children": [] } ] @@ -456,7 +504,7 @@ { "spanID": "71eacd0549a3e80e", "operationName": "__insert_adb_docs", - "duration": 141286, + "duration": 143086, "tags": { "col": "e0", "size": 10034 diff --git a/benchmark/write.py b/benchmark/write.py index 6176aa3..60dbaff 100644 --- a/benchmark/write.py +++ b/benchmark/write.py @@ -3,14 +3,19 @@ import pathlib import random import time +from collections import defaultdict from typing import Any, Dict, List import numpy as np import requests import torch from arango import ArangoClient +from retry import retry from torch_geometric.datasets import FakeHeteroDataset +# import uuid + + try: from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter except ImportError: @@ -38,23 +43,28 @@ def __init__( duration: int, tags: list[dict[str, str]], ): - self.spanID = spanID - self.operationName = operationName + self.span_id = spanID + self.operation_name = operationName self.duration = duration self.tags = { tag["key"]: tag["value"] for tag in tags if tag["key"] not in ["span.kind", "internal.span.format"] } + self.children: dict[str, "JaegerSpan"] = {} + self.parent: "JaegerSpan" = None def add_child(self, span_id: str, child: "JaegerSpan"): self.children[span_id] = child - def to_dict(self): + def set_parent(self, parent: "JaegerSpan"): + self.parent = parent + + def to_dict(self) -> dict[str, Any]: return { - "spanID": self.spanID, - "operationName": self.operationName, + "spanID": self.span_id, + "operationName": self.operation_name, "duration": self.duration, "tags": self.tags, "children": [child.to_dict() for child in self.children.values()], @@ -62,37 +72,108 @@ def to_dict(self): class JaegerSpanTree: - def __init__(self, jaeger_json_data: Dict[str, Any]): - self.root_span = self.__build_span_tree(jaeger_json_data) - - def __build_span_tree(self, jaeger_json_data: Dict[str, Any]): - sorted_spans = sorted( - jaeger_json_data["data"][0]["spans"], key=lambda span: span["startTime"] - ) - - root_spans: List[JaegerSpan] = [] - span_dict: Dict[str, JaegerSpan] = {} - span: Dict[str, Any] - for span in sorted_spans: - span_id = span["spanID"] - span_dict[span["spanID"]] = JaegerSpan( - span_id, span["operationName"], span["duration"], span["tags"] + def __init__( + self, + jaeger_endpoint: str, + service_name: str, + operation_name: str, + start_time: str, + tags: Dict[str, Any] = {}, + ) -> None: + self.jaeger_endpoint = jaeger_endpoint + self.service_name = service_name + self.operation_name = operation_name + self.start_time = start_time + self.tags = tags + + self.root_span: JaegerSpan = None + self.span_id_to_span: Dict[str, JaegerSpan] = {} + self.operation_name_to_span: Dict[str, List[JaegerSpan]] = defaultdict(list) + + self.__build_span_tree() + print(f"Built span tree for {self.service_name}-{self.operation_name}") + + def get_spans_by_operation_name(self, operation_name: str) -> List[JaegerSpan]: + return self.operation_name_to_span[operation_name] + + def get_span_by_span_id(self, span_id: str) -> JaegerSpan: + return self.span_id_to_span[span_id] + + def get_span_tag_value(self, span_id: str, tag_key: str) -> str: + return self.span_id_to_span[span_id].tags[tag_key] + + def __build_span_tree(self) -> None: + for span in self.__fetch_sorted_spans(): + span_id: str = span["spanID"] + operation_name: str = span["operationName"] + + span_object = JaegerSpan( + span_id, + operation_name, + span["duration"], + span["tags"], ) + self.span_id_to_span[span_id] = span_object + self.operation_name_to_span[operation_name].append(span_object) + references = span.get("references", []) if len(references) == 0: - root_spans.append(span_dict[span_id]) + if self.root_span is not None: + m = f"Found multiple root spans: {self.root_span.span_id} and {span_id}" + print(m) + raise Exception(m) + + self.root_span = self.span_id_to_span[span_id] continue for ref in references: if ref["refType"] == "CHILD_OF": - parent_span = span_dict[ref["spanID"]] - parent_span.add_child(span_id, span_dict[span_id]) + parent_span_id = ref["spanID"] + parent_span = self.span_id_to_span[parent_span_id] + child_span = self.span_id_to_span[span_id] + + parent_span.add_child(span_id, child_span) + child_span.set_parent(parent_span) + + def __fetch_sorted_spans(self) -> List[Dict[str, Any]]: + params = { + "service": self.service_name, + "operation": self.operation_name, + "tag": [f"{k}:{v}" for k, v in self.tags.items()], + "start": self.start_time, + } + + traces = self.__get_jaeger_traces(f"{self.jaeger_endpoint}/api/traces", params) + + if len(traces) > 1: + m = f"Found multiple traces for {params}" + print(m) + raise Exception(m) + + spans = traces[0]["spans"] + return sorted(spans, key=lambda span: span["startTime"]) - assert len(root_spans) == 1 - return root_spans[0] + @retry(tries=6, delay=2, backoff=2) + def __get_jaeger_traces( + self, url: str, params: dict[str, Any] + ) -> List[dict[str, Any]]: + response = requests.get(url, params=params) - def to_dict(self): + if response.status_code != 200: + m = f"Failed to fetch traces for {params}: {response.status_code}" + print(m) + raise Exception(m) + + traces = response.json()["data"] + if len(traces) == 0: + m = f"No traces found for {params}" + print(m) + raise Exception(m) + + return traces + + def to_dict(self) -> dict[str, Any]: return self.root_span.to_dict() def to_json_file(self, output: str): @@ -108,9 +189,10 @@ def parse_args(): parser.add_argument("--dbName", type=str, default="_system") parser.add_argument("--username", type=str, default="root") parser.add_argument("--password", type=str, default="") - parser.add_argument("--otlp_endpoint", type=str, default="localhost:4317") + parser.add_argument("--jaeger_endpoint", type=str, default="http://localhost:16686") + parser.add_argument("--otlp_endpoint", type=str, default="http://localhost:4317") parser.add_argument( - "--output_dir", type=str, default="branch", choices=["branch", "head"] + "--output_dir", type=str, choices=["branch", "head"], required=True ) # Parse the arguments @@ -119,13 +201,13 @@ def parse_args(): return args -def get_adapter(args) -> ADBPyG_Adapter: +def get_adapter(args, service_name: str) -> ADBPyG_Adapter: db = ArangoClient(hosts=args.url).db( args.dbName, username=args.username, password=args.password, verify=True ) tracer = create_tracer( - "adbpyg-adapter-benchmark", + service_name, enable_console_tracing=False, span_exporters=[OTLPSpanExporter(endpoint=args.otlp_endpoint, insecure=True)], ) @@ -155,27 +237,14 @@ def run_arangodb_to_pyg(adapter: ADBPyG_Adapter, name: str) -> None: ) -def get_span_tree(operation: str, start_time: str) -> JaegerSpanTree: - url = "http://localhost:16686/api/traces" - params = { - "service": "adbpyg-adapter-benchmark", - "operation": operation, - "tag": "name:FakeHeteroGraphBenchmark", - "start": start_time, - } - - response = requests.get(url, params=params) - assert response.status_code == 200 - - return JaegerSpanTree(response.json()) - - def main(): + service_name = f"adbpyg-adapter-benchmark" + # 1. Parse the arguments args = parse_args() # 2. Get the adapter - adbpyg_adapter = get_adapter(args) + adbpyg_adapter = get_adapter(args, service_name) # 3. Run the benchmark # TODO: Figure out why Jaeger is reporting the traces @@ -189,8 +258,21 @@ def main(): time.sleep(5) # 4. Get the span trees - pyg_to_arangodb_span_tree = get_span_tree("pyg_to_arangodb", start_time) - arangodb_to_pyg_span_tree = get_span_tree("arangodb_to_pyg", start_time) + pyg_to_arangodb_span_tree = JaegerSpanTree( + args.jaeger_endpoint, + service_name, + "pyg_to_arangodb", + start_time, + {"name": name}, + ) + + arangodb_to_pyg_span_tree = JaegerSpanTree( + args.jaeger_endpoint, + service_name, + "arangodb_to_pyg", + start_time, + {"name": name}, + ) # 5. Write the span trees to disk pyg_to_arangodb_span_tree.to_json_file(f"{args.output_dir}/pyg_to_arangodb.json") From 2e9151d88d25fde31e39bac8bc8860eeb8be9b3a Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Fri, 15 Dec 2023 21:04:34 -0500 Subject: [PATCH 27/73] add `retry` dev dependency --- pyproject.toml | 1 + 1 file changed, 1 insertion(+) diff --git a/pyproject.toml b/pyproject.toml index f39c6c2..5bfc925 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -55,6 +55,7 @@ dev = [ "types-setuptools>=57.4.9", "types-requests>=2.27.11", "networkx>=2.5.1", + "retry", ] tracing = [ "opentelemetry-api==1.21.0", From 3260723b0ecffa10701f95ef18eb7abf7027c965 Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Fri, 15 Dec 2023 21:11:29 -0500 Subject: [PATCH 28/73] fix: `__set_adb_data` --- adbpyg_adapter/adapter.py | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/adbpyg_adapter/adapter.py b/adbpyg_adapter/adapter.py index 27daa2d..2cfe26b 100644 --- a/adbpyg_adapter/adapter.py +++ b/adbpyg_adapter/adapter.py @@ -1679,16 +1679,21 @@ def __set_adb_data( data = pyg_data[meta_key] meta_val = valid_meta.get(meta_key, str(meta_key)) + if not isinstance(data, (list, Tensor)): + m = f"Skipping {meta_key} due to invalid type ({type(data)})" + logger.debug(m) + continue + if len(data) != pyg_data_size: m = f"Skipping {meta_key} due to invalid length ({len(data)} != {pyg_data_size})" # noqa: E501 logger.debug(m) continue - if type(meta_val) is str and type(data) is list: + if isinstance(data, list): meta_val = "_key" if meta_val in ["_v_key", "_e_key"] else meta_val df = df.join(DataFrame(data[start_index:end_index], columns=[meta_val])) - if type(data) is Tensor: + elif isinstance(data, Tensor): df = df.join( self.__build_dataframe_from_tensor( data[start_index:end_index], From bca1b1d1f82a431ec943035207ee350b1c3e1c22 Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Fri, 15 Dec 2023 21:11:35 -0500 Subject: [PATCH 29/73] fix: `improvement` --- benchmark/compare.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/benchmark/compare.py b/benchmark/compare.py index d01b868..4bcff7b 100644 --- a/benchmark/compare.py +++ b/benchmark/compare.py @@ -14,7 +14,7 @@ def __init__( self.operation_name = operation_name self.main_span_duration = main_duration self.branch_span_duration = branch_duration - self.improvement = f"{round((1 - main_duration / branch_duration) * 100)}%" + self.improvement = f"{round((1 - branch_duration / main_duration) * 100)}%" self.children: dict[str, "DiffSpan"] = {} def add_child(self, span_id: str, child: "DiffSpan"): From c5cb4c0d24b8e739c59a23e026aba5dc5fe14b2f Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Fri, 15 Dec 2023 21:21:57 -0500 Subject: [PATCH 30/73] update benchmark.yml --- .github/workflows/benchmark.yml | 122 ++++++++++++++++---------------- 1 file changed, 61 insertions(+), 61 deletions(-) diff --git a/.github/workflows/benchmark.yml b/.github/workflows/benchmark.yml index add332b..2074df0 100644 --- a/.github/workflows/benchmark.yml +++ b/.github/workflows/benchmark.yml @@ -11,69 +11,69 @@ on: - "benchmark/*.py" jobs: - # write_traces: - # if: github.event_name == 'push' - # runs-on: ubuntu-latest - # steps: - # - uses: actions/checkout@v4 - # with: - # fetch-depth: 0 - - # - name: Create new branch - # run: git checkout -b actions/benchmark - - # - name: Set branch upstream - # run: git push -u origin actions/benchmark - # env: - # GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} - - # - name: Setup Python - # uses: actions/setup-python@v4 - # with: - # cache: 'pip' - # cache-dependency-path: setup.py - - # - name: Set up ArangoDB Instance via Docker - # run: docker run -d --name arangodb -p 8529:8529 -e ARANGO_ROOT_PASSWORD= arangodb/arangodb + generate_master_traces: + if: github.event_name == 'push' + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + with: + fetch-depth: 0 + + - name: Create new branch + run: git checkout -b actions/benchmark + + - name: Set branch upstream + run: git push -u origin actions/benchmark + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + + - name: Setup Python + uses: actions/setup-python@v4 + with: + cache: 'pip' + cache-dependency-path: setup.py + + - name: Start ArangoDB Instance + run: docker run -d --name arangodb -p 8529:8529 -e ARANGO_ROOT_PASSWORD= arangodb/arangodb + + - name: Start Jaeger Instance + run: docker run -d --name jaeger --rm -e COLLECTOR_ZIPKIN_HOST_PORT=:9411 -p 16686:16686 -p 4317:4317 -p 4318:4318 -p 9411:9411 jaegertracing/all-in-one:latest - # - name: Start Jaeger Instance - # run: docker run -d --name jaeger --rm \ - # -e COLLECTOR_ZIPKIN_HOST_PORT=:9411 \ - # -p 16686:16686 \ - # -p 4317:4317 \ - # -p 4318:4318 \ - # -p 9411:9411 \ - # jaegertracing/all-in-one:latest - - # - name: Install packages - # run: | - # pip install torch==2.1.0 - # pip install torch-scatter torch-sparse -f https://data.pyg.org/whl/torch-$(python -c 'import torch; print(torch.__version__.split("+")[0])')+cpu.html - # pip install -e '.[dev, tracing]' - - # - name: Run Python Script - # run: python benchmark/write.py --output_dir head - - # - name: Make commit for auto-generated benchmark files - # uses: EndBug/add-and-commit@v9 - # env: - # GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} - # with: - # add: "./benchmark/main/*.json" - # new_branch: actions/benchmark - # message: "generate benchmark files for $GITHUB_SHA" - - # - name: Create pull request for the auto generated changelog - # run: | - # echo "PR_URL=$(gh pr create \ - # --title "benchmark: $GITHUB_SHA" \ - # --body "beep boop, i am a robot ($GITHUB_SHA)" \ - # --label documentation)" >> $GITHUB_ENV - # env: - # GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + - name: Install packages + run: | + pip install torch==2.1.0 + pip install torch-scatter torch-sparse -f https://data.pyg.org/whl/torch-$(python -c 'import torch; print(torch.__version__.split("+")[0])')+cpu.html + pip install -e '.[dev, tracing]' + + - name: Run Python Script + run: python benchmark/write.py --output_dir master + + - name: Echo PyG to ArangoDB + run: cat benchmark/traces/master/pyg_to_arangodb.json | jq + + - name: Echo ArangoDB to PyG + run: cat benchmark/traces/master/arangodb_to_pyg.json | jq + + # - name: Make commit for auto-generated benchmark files + # uses: EndBug/add-and-commit@v9 + # env: + # GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + # with: + # add: "./benchmark/traces/master/*.json" + # new_branch: actions/benchmark + # message: "generate benchmark files for $GITHUB_SHA" + + # - name: Create pull request for the auto generated benchmark + # run: | + # echo "PR_URL=$(gh pr create \ + # --title "benchmark: $GITHUB_SHA" \ + # --body "beep boop, i am a robot" \ + # --label documentation)" >> $GITHUB_ENV + # env: + # GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} - # - name: Alert developer of open PR - # run: echo "Benchmark $PR_URL is ready to be merged by developer." + # - name: Alert developer of open PR + # run: echo "Benchmark $PR_URL is ready to be merged by developer." compare_traces: if: github.event_name == 'pull_request' From 8382e8baa5eb636299df344658a39c5a94287d2a Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Fri, 15 Dec 2023 21:22:07 -0500 Subject: [PATCH 31/73] cleanup: `benchmark` --- benchmark/compare.py | 32 +++++++++---------- benchmark/traces/main/README.md | 3 -- benchmark/traces/master/README.md | 3 ++ .../{main => master}/arangodb_to_pyg.json | 0 .../{main => master}/pyg_to_arangodb.json | 0 benchmark/write.py | 2 +- 6 files changed, 20 insertions(+), 20 deletions(-) delete mode 100644 benchmark/traces/main/README.md create mode 100644 benchmark/traces/master/README.md rename benchmark/traces/{main => master}/arangodb_to_pyg.json (100%) rename benchmark/traces/{main => master}/pyg_to_arangodb.json (100%) diff --git a/benchmark/compare.py b/benchmark/compare.py index 4bcff7b..9ad89db 100644 --- a/benchmark/compare.py +++ b/benchmark/compare.py @@ -7,14 +7,14 @@ def __init__( self, span_id: str, operation_name: str, - main_duration: int, + master_duration: int, branch_duration: int, ): self.span_id = span_id self.operation_name = operation_name - self.main_span_duration = main_duration - self.branch_span_duration = branch_duration - self.improvement = f"{round((1 - branch_duration / main_duration) * 100)}%" + self.master_duration = master_duration + self.branch_duration = branch_duration + self.improvement = f"{round((1 - branch_duration / master_duration) * 100)}%" self.children: dict[str, "DiffSpan"] = {} def add_child(self, span_id: str, child: "DiffSpan"): @@ -24,8 +24,8 @@ def to_dict(self, include_children: bool = True): res = { "span_id": self.span_id, "operation_name": self.operation_name, - "main_duration": self.main_span_duration, - "branch_duration": self.branch_span_duration, + "master_duration": self.master_duration, + "branch_duration": self.branch_duration, "improvement": self.improvement, } @@ -36,22 +36,22 @@ def to_dict(self, include_children: bool = True): class DiffTree: - def __init__(self, main_trace: dict, branch_trace: dict): - self.root_span = self.__build_diff_tree(main_trace, branch_trace) + def __init__(self, master_trace: dict, branch_trace: dict): + self.root_span = self.__build_diff_tree(master_trace, branch_trace) - def __build_diff_tree(self, main_trace: dict, branch_trace: dict): - assert main_trace["operationName"] == branch_trace["operationName"] + def __build_diff_tree(self, master_trace: dict, branch_trace: dict): + assert master_trace["operationName"] == branch_trace["operationName"] diff_span = DiffSpan( - main_trace["spanID"], - main_trace["operationName"], - main_trace["duration"], + master_trace["spanID"], + master_trace["operationName"], + master_trace["duration"], branch_trace["duration"], ) # Recursively build the tree for child spans for head_child_span, branch_child_span in zip( - main_trace["children"], branch_trace["children"] + master_trace["children"], branch_trace["children"] ): child_span = self.__build_diff_tree(head_child_span, branch_child_span) diff_span.add_child(head_child_span["spanID"], child_span) @@ -71,10 +71,10 @@ def main(): current_dir = pathlib.Path(__file__).parent.absolute() for operation in ["pyg_to_arangodb", "arangodb_to_pyg"]: - main_trace = json.load(open(f"{current_dir}/traces/main/{operation}.json")) + master_trace = json.load(open(f"{current_dir}/traces/master/{operation}.json")) branch_trace = json.load(open(f"{current_dir}/traces/branch/{operation}.json")) - diff_tree = DiffTree(main_trace, branch_trace) + diff_tree = DiffTree(master_trace, branch_trace) diff_tree.to_json_file(operation) print("-" * 50) diff --git a/benchmark/traces/main/README.md b/benchmark/traces/main/README.md deleted file mode 100644 index 6e1044b..0000000 --- a/benchmark/traces/main/README.md +++ /dev/null @@ -1,3 +0,0 @@ -Stores the traces for the current state of pyg-adapter@main. - -See .github/workflows/benchmark.yml and benchmark/write.py for more details. \ No newline at end of file diff --git a/benchmark/traces/master/README.md b/benchmark/traces/master/README.md new file mode 100644 index 0000000..c249cd5 --- /dev/null +++ b/benchmark/traces/master/README.md @@ -0,0 +1,3 @@ +Stores the traces for the current state of pyg-adapter@master. + +See .github/workflows/benchmark.yml and benchmark/write.py for more details. \ No newline at end of file diff --git a/benchmark/traces/main/arangodb_to_pyg.json b/benchmark/traces/master/arangodb_to_pyg.json similarity index 100% rename from benchmark/traces/main/arangodb_to_pyg.json rename to benchmark/traces/master/arangodb_to_pyg.json diff --git a/benchmark/traces/main/pyg_to_arangodb.json b/benchmark/traces/master/pyg_to_arangodb.json similarity index 100% rename from benchmark/traces/main/pyg_to_arangodb.json rename to benchmark/traces/master/pyg_to_arangodb.json diff --git a/benchmark/write.py b/benchmark/write.py index 60dbaff..11b7f95 100644 --- a/benchmark/write.py +++ b/benchmark/write.py @@ -192,7 +192,7 @@ def parse_args(): parser.add_argument("--jaeger_endpoint", type=str, default="http://localhost:16686") parser.add_argument("--otlp_endpoint", type=str, default="http://localhost:4317") parser.add_argument( - "--output_dir", type=str, choices=["branch", "head"], required=True + "--output_dir", type=str, choices=["branch", "master"], required=True ) # Parse the arguments From 0ed2071b1a7bfdcc0b176d69b35cd15729b48c1c Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Fri, 15 Dec 2023 21:28:47 -0500 Subject: [PATCH 32/73] install `tracing` dependencies in `build.yml` this should pass coverage --- .github/workflows/build.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 61c03f8..39f47e1 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -34,7 +34,7 @@ jobs: run: | pip install torch==2.1.0 pip install torch-scatter torch-sparse -f https://data.pyg.org/whl/torch-$(python -c 'import torch; print(torch.__version__.split("+")[0])')+cpu.html - pip install -e '.[dev]' + pip install -e '.[dev, tracing]' - name: Run black run: black --check --verbose --diff --color ${{env.PACKAGE_DIR}} ${{env.TESTS_DIR}} From de4a1a7298db8ade2329cc16e361a0c3b6637451 Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Fri, 15 Dec 2023 21:47:56 -0500 Subject: [PATCH 33/73] pragma no cover --- adbpyg_adapter/tracing.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/adbpyg_adapter/tracing.py b/adbpyg_adapter/tracing.py index c6f39ea..ea0bde5 100644 --- a/adbpyg_adapter/tracing.py +++ b/adbpyg_adapter/tracing.py @@ -42,7 +42,7 @@ def set_attributes(self, **attributes: Any) -> None: def with_tracing(method: T) -> T: if not TRACING_ENABLED: - return method + return method # pragma: no cover @wraps(method) def decorator(*args: Any, **kwargs: Any) -> Any: From 78a88deeb7ab731c4137067742d1484ce66f3568 Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Sat, 23 Dec 2023 13:28:48 -0500 Subject: [PATCH 34/73] update `master` traces --- benchmark/traces/master/arangodb_to_pyg.json | 1120 ++++++++++++------ benchmark/traces/master/pyg_to_arangodb.json | 161 ++- 2 files changed, 849 insertions(+), 432 deletions(-) diff --git a/benchmark/traces/master/arangodb_to_pyg.json b/benchmark/traces/master/arangodb_to_pyg.json index 65d53f9..d734b40 100644 --- a/benchmark/traces/master/arangodb_to_pyg.json +++ b/benchmark/traces/master/arangodb_to_pyg.json @@ -1,7 +1,8 @@ { "spanID": "935ddd725129fb7c", "operationName": "arangodb_to_pyg", - "duration": 744860, + "startTime": 1703354941312582, + "duration": 907787, "tags": { "name": "FakeHeteroGraphBenchmark" }, @@ -9,7 +10,8 @@ { "spanID": "4a5308cc3dfabc08", "operationName": "__process_adb_v_col", - "duration": 41588, + "startTime": 1703354941312652, + "duration": 53370, "tags": { "v_col": "v0" }, @@ -17,24 +19,27 @@ { "spanID": "307bf3262f120554", "operationName": "__fetch_adb_docs", - "duration": 28951, + "startTime": 1703354941312676, + "duration": 37020, "tags": { "col": "v0", "col_size": 1008, - "meta": "{'y', 'x'}" + "meta": "{'x', 'y'}" }, "children": [] }, { "spanID": "2fcd81b5d24bace4", "operationName": "__process_adb_cursor", - "duration": 12535, + "startTime": 1703354941349765, + "duration": 16240, "tags": {}, "children": [ { "spanID": "9cdeb3e60870e15c", "operationName": "__process_adb_vertex_df", - "duration": 6713, + "startTime": 1703354941352153, + "duration": 8414, "tags": { "i": 0, "vertex_df_size": 1000 @@ -43,28 +48,31 @@ { "spanID": "a81ad477fb3675b8", "operationName": "__set_pyg_data", - "duration": 6515, + "startTime": 1703354941352398, + "duration": 8163, "tags": { - "meta": "{'y': 'y', 'x': 'x'}" + "meta": "{'x': 'x', 'y': 'y'}" }, "children": [ { "spanID": "79fdef7c42930b33", "operationName": "__build_tensor_from_dataframe", - "duration": 148, + "startTime": 1703354941352427, + "duration": 7880, "tags": { - "meta_key": "y", - "meta_val": "y" + "meta_key": "x", + "meta_val": "x" }, "children": [] }, { "spanID": "16febaa011af923d", "operationName": "__build_tensor_from_dataframe", - "duration": 6306, + "startTime": 1703354941360374, + "duration": 176, "tags": { - "meta_key": "x", - "meta_val": "x" + "meta_key": "y", + "meta_val": "y" }, "children": [] } @@ -75,7 +83,8 @@ { "spanID": "c1f254b8adc0da7a", "operationName": "__process_adb_vertex_df", - "duration": 401, + "startTime": 1703354941363673, + "duration": 1458, "tags": { "i": 1000, "vertex_df_size": 8 @@ -84,28 +93,31 @@ { "spanID": "e07405eb215663ab", "operationName": "__set_pyg_data", - "duration": 318, + "startTime": 1703354941363771, + "duration": 1353, "tags": { - "meta": "{'y': 'y', 'x': 'x'}" + "meta": "{'x': 'x', 'y': 'y'}" }, "children": [ { "spanID": "ec62b2c82648ee38", "operationName": "__build_tensor_from_dataframe", - "duration": 45, + "startTime": 1703354941363804, + "duration": 120, "tags": { - "meta_key": "y", - "meta_val": "y" + "meta_key": "x", + "meta_val": "x" }, "children": [] }, { "spanID": "d7ab792809e469e6", "operationName": "__build_tensor_from_dataframe", - "duration": 80, + "startTime": 1703354941365013, + "duration": 79, "tags": { - "meta_key": "x", - "meta_val": "x" + "meta_key": "y", + "meta_val": "y" }, "children": [] } @@ -120,7 +132,8 @@ { "spanID": "e5eeac76148b2758", "operationName": "__process_adb_v_col", - "duration": 31267, + "startTime": 1703354941366089, + "duration": 71304, "tags": { "v_col": "v1" }, @@ -128,7 +141,8 @@ { "spanID": "ec4f217bb306d1a8", "operationName": "__fetch_adb_docs", - "duration": 23667, + "startTime": 1703354941366122, + "duration": 62232, "tags": { "col": "v1", "col_size": 821, @@ -139,13 +153,15 @@ { "spanID": "8a64c1b9d450fe4a", "operationName": "__process_adb_cursor", - "duration": 7491, + "startTime": 1703354941428423, + "duration": 8950, "tags": {}, "children": [ { "spanID": "642bfa42aef9c00b", "operationName": "__process_adb_vertex_df", - "duration": 5090, + "startTime": 1703354941429933, + "duration": 6105, "tags": { "i": 0, "vertex_df_size": 821 @@ -154,7 +170,8 @@ { "spanID": "b48d73f1d67e55fd", "operationName": "__set_pyg_data", - "duration": 4886, + "startTime": 1703354941430171, + "duration": 5861, "tags": { "meta": "{'x': 'x'}" }, @@ -162,7 +179,8 @@ { "spanID": "468ff53d864a7a50", "operationName": "__build_tensor_from_dataframe", - "duration": 4845, + "startTime": 1703354941430199, + "duration": 5810, "tags": { "meta_key": "x", "meta_val": "x" @@ -180,7 +198,8 @@ { "spanID": "cfc6e62585940927", "operationName": "__process_adb_v_col", - "duration": 28841, + "startTime": 1703354941437474, + "duration": 38361, "tags": { "v_col": "v2" }, @@ -188,7 +207,8 @@ { "spanID": "d977e9933c49d76f", "operationName": "__fetch_adb_docs", - "duration": 21565, + "startTime": 1703354941437507, + "duration": 29308, "tags": { "col": "v2", "col_size": 894, @@ -199,13 +219,15 @@ { "spanID": "e521460637176e84", "operationName": "__process_adb_cursor", - "duration": 7168, + "startTime": 1703354941466891, + "duration": 8926, "tags": {}, "children": [ { "spanID": "96fd35d0adf20806", "operationName": "__process_adb_vertex_df", - "duration": 4811, + "startTime": 1703354941468418, + "duration": 6024, "tags": { "i": 0, "vertex_df_size": 894 @@ -214,7 +236,8 @@ { "spanID": "f323ca74d3447490", "operationName": "__set_pyg_data", - "duration": 4611, + "startTime": 1703354941468664, + "duration": 5771, "tags": { "meta": "{'x': 'x'}" }, @@ -222,7 +245,8 @@ { "spanID": "9466e4726b5f5241", "operationName": "__build_tensor_from_dataframe", - "duration": 4554, + "startTime": 1703354941468691, + "duration": 5715, "tags": { "meta_key": "x", "meta_val": "x" @@ -240,7 +264,8 @@ { "spanID": "73581a8146743741", "operationName": "__process_adb_e_col", - "duration": 642856, + "startTime": 1703354941475895, + "duration": 744361, "tags": { "e_col": "e0" }, @@ -248,7 +273,8 @@ { "spanID": "a905d7507e1ea9c5", "operationName": "__fetch_adb_docs", - "duration": 27507, + "startTime": 1703354941475931, + "duration": 10377, "tags": { "col": "e0", "col_size": 53450, @@ -259,13 +285,15 @@ { "spanID": "ff0ac0f1a425799a", "operationName": "__process_adb_cursor", - "duration": 615241, + "startTime": 1703354941486371, + "duration": 733870, "tags": {}, "children": [ { "spanID": "eabca8d0b341facd", "operationName": "__process_adb_edge_df", - "duration": 5426, + "startTime": 1703354941487877, + "duration": 9804, "tags": { "edge_df_size": 1000 }, @@ -273,21 +301,24 @@ { "spanID": "cb175a5afb82860d", "operationName": "__split_adb_ids", - "duration": 704, + "startTime": 1703354941487990, + "duration": 814, "tags": {}, "children": [] }, { "spanID": "151665705b7c709a", "operationName": "__split_adb_ids", - "duration": 528, + "startTime": 1703354941489141, + "duration": 654, "tags": {}, "children": [] }, { "spanID": "9cdf5a865306f3f5", "operationName": "__process_adb_edge_type_df", - "duration": 2307, + "startTime": 1703354941492873, + "duration": 4770, "tags": { "edge_type": "[\"v2\",\"e0\",\"v1\"]", "edge_type_df_size": 1000 @@ -296,7 +327,8 @@ { "spanID": "7c879b741d878f9f", "operationName": "__set_pyg_data", - "duration": 416, + "startTime": 1703354941497151, + "duration": 485, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -304,7 +336,8 @@ { "spanID": "a1515607964a870c", "operationName": "__build_tensor_from_dataframe", - "duration": 358, + "startTime": 1703354941497190, + "duration": 430, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -320,7 +353,8 @@ { "spanID": "d857010255d44936", "operationName": "__process_adb_edge_df", - "duration": 4080, + "startTime": 1703354941503809, + "duration": 5345, "tags": { "edge_df_size": 1000 }, @@ -328,21 +362,24 @@ { "spanID": "3e37952d30bcab0e", "operationName": "__split_adb_ids", - "duration": 535, + "startTime": 1703354941503909, + "duration": 710, "tags": {}, "children": [] }, { "spanID": "bb42e0b20426465e", "operationName": "__split_adb_ids", - "duration": 498, + "startTime": 1703354941504946, + "duration": 643, "tags": {}, "children": [] }, { "spanID": "1dfc83524562be7f", "operationName": "__process_adb_edge_type_df", - "duration": 1845, + "startTime": 1703354941506930, + "duration": 2187, "tags": { "edge_type": "[\"v2\",\"e0\",\"v1\"]", "edge_type_df_size": 1000 @@ -351,7 +388,8 @@ { "spanID": "38701a14b490b608", "operationName": "__set_pyg_data", - "duration": 367, + "startTime": 1703354941508640, + "duration": 470, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -359,7 +397,8 @@ { "spanID": "cb69ca385f3f5638", "operationName": "__build_tensor_from_dataframe", - "duration": 317, + "startTime": 1703354941508673, + "duration": 412, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -375,7 +414,8 @@ { "spanID": "552116dd2ba4b180", "operationName": "__process_adb_edge_df", - "duration": 4037, + "startTime": 1703354941515203, + "duration": 5046, "tags": { "edge_df_size": 1000 }, @@ -383,21 +423,24 @@ { "spanID": "d0dfae436d16ee18", "operationName": "__split_adb_ids", - "duration": 601, + "startTime": 1703354941515305, + "duration": 751, "tags": {}, "children": [] }, { "spanID": "19c16a0d0febd845", "operationName": "__split_adb_ids", - "duration": 508, + "startTime": 1703354941516393, + "duration": 655, "tags": {}, "children": [] }, { "spanID": "2577bffac87a7463", "operationName": "__process_adb_edge_type_df", - "duration": 1685, + "startTime": 1703354941518109, + "duration": 2106, "tags": { "edge_type": "[\"v2\",\"e0\",\"v1\"]", "edge_type_df_size": 1000 @@ -406,7 +449,8 @@ { "spanID": "b29a8b06daf66c5f", "operationName": "__set_pyg_data", - "duration": 351, + "startTime": 1703354941519735, + "duration": 473, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -414,7 +458,8 @@ { "spanID": "0b9475b138018b47", "operationName": "__build_tensor_from_dataframe", - "duration": 309, + "startTime": 1703354941519765, + "duration": 420, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -430,7 +475,8 @@ { "spanID": "92e8e269d12ecbc4", "operationName": "__process_adb_edge_df", - "duration": 3974, + "startTime": 1703354941535957, + "duration": 5092, "tags": { "edge_df_size": 1000 }, @@ -438,21 +484,24 @@ { "spanID": "e8f6cf32a25b59fd", "operationName": "__split_adb_ids", - "duration": 597, + "startTime": 1703354941536070, + "duration": 738, "tags": {}, "children": [] }, { "spanID": "88c132adefbfc19e", "operationName": "__split_adb_ids", - "duration": 518, + "startTime": 1703354941537135, + "duration": 655, "tags": {}, "children": [] }, { "spanID": "ae3b16ec9a27d858", "operationName": "__process_adb_edge_type_df", - "duration": 1581, + "startTime": 1703354941538861, + "duration": 2156, "tags": { "edge_type": "[\"v2\",\"e0\",\"v1\"]", "edge_type_df_size": 1000 @@ -461,7 +510,8 @@ { "spanID": "06d599e812f175ff", "operationName": "__set_pyg_data", - "duration": 348, + "startTime": 1703354941540565, + "duration": 447, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -469,7 +519,8 @@ { "spanID": "a28f5ab01fdb8b32", "operationName": "__build_tensor_from_dataframe", - "duration": 307, + "startTime": 1703354941540595, + "duration": 398, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -485,7 +536,8 @@ { "spanID": "9b38fe803042e325", "operationName": "__process_adb_edge_df", - "duration": 3929, + "startTime": 1703354941546965, + "duration": 5033, "tags": { "edge_df_size": 1000 }, @@ -493,21 +545,24 @@ { "spanID": "9371a71fd480865f", "operationName": "__split_adb_ids", - "duration": 575, + "startTime": 1703354941547079, + "duration": 736, "tags": {}, "children": [] }, { "spanID": "64264cd51ea45cd6", "operationName": "__split_adb_ids", - "duration": 494, + "startTime": 1703354941548148, + "duration": 648, "tags": {}, "children": [] }, { "spanID": "5ec17dbe176ea1b1", "operationName": "__process_adb_edge_type_df", - "duration": 1627, + "startTime": 1703354941549891, + "duration": 2075, "tags": { "edge_type": "[\"v2\",\"e0\",\"v1\"]", "edge_type_df_size": 1000 @@ -516,7 +571,8 @@ { "spanID": "fb0323a1d576d415", "operationName": "__set_pyg_data", - "duration": 358, + "startTime": 1703354941551503, + "duration": 457, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -524,7 +580,8 @@ { "spanID": "0950fd131db53334", "operationName": "__build_tensor_from_dataframe", - "duration": 310, + "startTime": 1703354941551536, + "duration": 401, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -540,7 +597,8 @@ { "spanID": "0589f8779b025244", "operationName": "__process_adb_edge_df", - "duration": 3649, + "startTime": 1703354941557204, + "duration": 4655, "tags": { "edge_df_size": 1000 }, @@ -548,21 +606,24 @@ { "spanID": "f606254131d0b664", "operationName": "__split_adb_ids", - "duration": 523, + "startTime": 1703354941557283, + "duration": 699, "tags": {}, "children": [] }, { "spanID": "2f5a522af87f43fd", "operationName": "__split_adb_ids", - "duration": 514, + "startTime": 1703354941558270, + "duration": 638, "tags": {}, "children": [] }, { "spanID": "1fb797fab7d6467b", "operationName": "__process_adb_edge_type_df", - "duration": 1548, + "startTime": 1703354941559854, + "duration": 1971, "tags": { "edge_type": "[\"v2\",\"e0\",\"v1\"]", "edge_type_df_size": 1000 @@ -571,7 +632,8 @@ { "spanID": "35e8579a7aaf0e89", "operationName": "__set_pyg_data", - "duration": 337, + "startTime": 1703354941561364, + "duration": 455, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -579,7 +641,8 @@ { "spanID": "ccfdba9bba26d851", "operationName": "__build_tensor_from_dataframe", - "duration": 300, + "startTime": 1703354941561393, + "duration": 405, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -595,7 +658,8 @@ { "spanID": "efdd35f80fa34266", "operationName": "__process_adb_edge_df", - "duration": 3632, + "startTime": 1703354941567044, + "duration": 4722, "tags": { "edge_df_size": 1000 }, @@ -603,21 +667,24 @@ { "spanID": "05d51433ade9b2b4", "operationName": "__split_adb_ids", - "duration": 542, + "startTime": 1703354941567135, + "duration": 701, "tags": {}, "children": [] }, { "spanID": "6cf55b158b53031d", "operationName": "__split_adb_ids", - "duration": 502, + "startTime": 1703354941568142, + "duration": 656, "tags": {}, "children": [] }, { "spanID": "19fbeb1d9edfa3da", "operationName": "__process_adb_edge_type_df", - "duration": 1525, + "startTime": 1703354941569766, + "duration": 1968, "tags": { "edge_type": "[\"v2\",\"e0\",\"v1\"]", "edge_type_df_size": 1000 @@ -626,7 +693,8 @@ { "spanID": "428a1c22d5fdb76a", "operationName": "__set_pyg_data", - "duration": 341, + "startTime": 1703354941571281, + "duration": 445, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -634,7 +702,8 @@ { "spanID": "3888447911ebcd49", "operationName": "__build_tensor_from_dataframe", - "duration": 305, + "startTime": 1703354941571310, + "duration": 397, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -650,7 +719,8 @@ { "spanID": "a59cec98126cbc8f", "operationName": "__process_adb_edge_df", - "duration": 3657, + "startTime": 1703354941577551, + "duration": 4899, "tags": { "edge_df_size": 1000 }, @@ -658,21 +728,24 @@ { "spanID": "59acdd984d125e7f", "operationName": "__split_adb_ids", - "duration": 525, + "startTime": 1703354941577635, + "duration": 696, "tags": {}, "children": [] }, { "spanID": "2e2950656fa231e9", "operationName": "__split_adb_ids", - "duration": 485, + "startTime": 1703354941578629, + "duration": 648, "tags": {}, "children": [] }, { "spanID": "80ee526e0fa07a3f", "operationName": "__process_adb_edge_type_df", - "duration": 1562, + "startTime": 1703354941580252, + "duration": 2044, "tags": { "edge_type": "[\"v2\",\"e0\",\"v1\"]", "edge_type_df_size": 1000 @@ -681,7 +754,8 @@ { "spanID": "0a14b90a7795e986", "operationName": "__set_pyg_data", - "duration": 354, + "startTime": 1703354941581790, + "duration": 493, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -689,7 +763,8 @@ { "spanID": "19d5f97098b33c6e", "operationName": "__build_tensor_from_dataframe", - "duration": 313, + "startTime": 1703354941581820, + "duration": 444, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -705,7 +780,8 @@ { "spanID": "fcfcfa81b306d700", "operationName": "__process_adb_edge_df", - "duration": 4464, + "startTime": 1703354941587725, + "duration": 7314, "tags": { "edge_df_size": 1000 }, @@ -713,21 +789,24 @@ { "spanID": "3308fb2e642aad48", "operationName": "__split_adb_ids", - "duration": 536, + "startTime": 1703354941587807, + "duration": 705, "tags": {}, "children": [] }, { "spanID": "5bca47be429817c5", "operationName": "__split_adb_ids", - "duration": 492, + "startTime": 1703354941588814, + "duration": 652, "tags": {}, "children": [] }, { "spanID": "bb4a06cbe786ab37", "operationName": "__process_adb_edge_type_df", - "duration": 1424, + "startTime": 1703354941591893, + "duration": 1965, "tags": { "edge_type": "[\"v2\",\"e0\",\"v1\"]", "edge_type_df_size": 895 @@ -736,7 +815,8 @@ { "spanID": "d69c91c278601602", "operationName": "__set_pyg_data", - "duration": 311, + "startTime": 1703354941593442, + "duration": 410, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -744,7 +824,8 @@ { "spanID": "eb21a3f6e6fd68e8", "operationName": "__build_tensor_from_dataframe", - "duration": 273, + "startTime": 1703354941593472, + "duration": 361, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -758,7 +839,8 @@ { "spanID": "2b5f693291dc59ef", "operationName": "__process_adb_edge_type_df", - "duration": 859, + "startTime": 1703354941593882, + "duration": 1138, "tags": { "edge_type": "[\"v1\",\"e0\",\"v2\"]", "edge_type_df_size": 105 @@ -767,7 +849,8 @@ { "spanID": "ac322c12b29c467d", "operationName": "__set_pyg_data", - "duration": 85, + "startTime": 1703354941594899, + "duration": 115, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -775,7 +858,8 @@ { "spanID": "f76fbfb83412fc12", "operationName": "__build_tensor_from_dataframe", - "duration": 57, + "startTime": 1703354941594926, + "duration": 78, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -791,7 +875,8 @@ { "spanID": "0edc6d2bc470f0e7", "operationName": "__process_adb_edge_df", - "duration": 3696, + "startTime": 1703354941600365, + "duration": 4600, "tags": { "edge_df_size": 1000 }, @@ -799,21 +884,24 @@ { "spanID": "ad1b8f60c9e4dab2", "operationName": "__split_adb_ids", - "duration": 547, + "startTime": 1703354941600439, + "duration": 674, "tags": {}, "children": [] }, { "spanID": "d86dbf1128805c5d", "operationName": "__split_adb_ids", - "duration": 504, + "startTime": 1703354941601402, + "duration": 647, "tags": {}, "children": [] }, { "spanID": "57a1cb712975d279", "operationName": "__process_adb_edge_type_df", - "duration": 1528, + "startTime": 1703354941603003, + "duration": 1931, "tags": { "edge_type": "[\"v1\",\"e0\",\"v2\"]", "edge_type_df_size": 1000 @@ -822,7 +910,8 @@ { "spanID": "402d0baf878b9f6b", "operationName": "__set_pyg_data", - "duration": 337, + "startTime": 1703354941604471, + "duration": 457, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -830,7 +919,8 @@ { "spanID": "98c752051e01a934", "operationName": "__build_tensor_from_dataframe", - "duration": 301, + "startTime": 1703354941604500, + "duration": 411, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -846,7 +936,8 @@ { "spanID": "713b7e05ebe21368", "operationName": "__process_adb_edge_df", - "duration": 3826, + "startTime": 1703354941610461, + "duration": 4671, "tags": { "edge_df_size": 1000 }, @@ -854,21 +945,24 @@ { "spanID": "2cc0f859aa6524ab", "operationName": "__split_adb_ids", - "duration": 563, + "startTime": 1703354941610541, + "duration": 680, "tags": {}, "children": [] }, { "spanID": "78bc71750361524c", "operationName": "__split_adb_ids", - "duration": 488, + "startTime": 1703354941611510, + "duration": 639, "tags": {}, "children": [] }, { "spanID": "68ef8f5fae68690a", "operationName": "__process_adb_edge_type_df", - "duration": 1599, + "startTime": 1703354941613106, + "duration": 1997, "tags": { "edge_type": "[\"v1\",\"e0\",\"v2\"]", "edge_type_df_size": 1000 @@ -877,7 +971,8 @@ { "spanID": "91b15f5de66cd36e", "operationName": "__set_pyg_data", - "duration": 349, + "startTime": 1703354941614647, + "duration": 450, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -885,7 +980,8 @@ { "spanID": "82339e23dff3334b", "operationName": "__build_tensor_from_dataframe", - "duration": 308, + "startTime": 1703354941614676, + "duration": 404, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -901,7 +997,8 @@ { "spanID": "4fbaecc0eae2025e", "operationName": "__process_adb_edge_df", - "duration": 3621, + "startTime": 1703354941620324, + "duration": 4625, "tags": { "edge_df_size": 1000 }, @@ -909,21 +1006,24 @@ { "spanID": "5b6e4ae7a6208143", "operationName": "__split_adb_ids", - "duration": 538, + "startTime": 1703354941620406, + "duration": 690, "tags": {}, "children": [] }, { "spanID": "d670f668637e0edc", "operationName": "__split_adb_ids", - "duration": 489, + "startTime": 1703354941621386, + "duration": 649, "tags": {}, "children": [] }, { "spanID": "403d1f83a859890c", "operationName": "__process_adb_edge_type_df", - "duration": 1516, + "startTime": 1703354941622986, + "duration": 1932, "tags": { "edge_type": "[\"v1\",\"e0\",\"v2\"]", "edge_type_df_size": 1000 @@ -932,7 +1032,8 @@ { "spanID": "8f837ef727460f22", "operationName": "__set_pyg_data", - "duration": 343, + "startTime": 1703354941624469, + "duration": 444, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -940,7 +1041,8 @@ { "spanID": "032f06cab0d9c2aa", "operationName": "__build_tensor_from_dataframe", - "duration": 307, + "startTime": 1703354941624498, + "duration": 398, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -956,7 +1058,8 @@ { "spanID": "bdd7d19b753c7c99", "operationName": "__process_adb_edge_df", - "duration": 3710, + "startTime": 1703354941630063, + "duration": 4626, "tags": { "edge_df_size": 1000 }, @@ -964,21 +1067,24 @@ { "spanID": "55fea08e143e2e04", "operationName": "__split_adb_ids", - "duration": 542, + "startTime": 1703354941630139, + "duration": 692, "tags": {}, "children": [] }, { "spanID": "0bb2c3f0bd30291a", "operationName": "__split_adb_ids", - "duration": 495, + "startTime": 1703354941631120, + "duration": 644, "tags": {}, "children": [] }, { "spanID": "47e7f5938b5885ca", "operationName": "__process_adb_edge_type_df", - "duration": 1587, + "startTime": 1703354941632699, + "duration": 1958, "tags": { "edge_type": "[\"v1\",\"e0\",\"v2\"]", "edge_type_df_size": 1000 @@ -987,7 +1093,8 @@ { "spanID": "3d792fa12284b7a4", "operationName": "__set_pyg_data", - "duration": 343, + "startTime": 1703354941634195, + "duration": 456, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -995,7 +1102,8 @@ { "spanID": "f40048d7c31d5a97", "operationName": "__build_tensor_from_dataframe", - "duration": 305, + "startTime": 1703354941634224, + "duration": 410, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -1011,7 +1119,8 @@ { "spanID": "5a2b745b7b59051b", "operationName": "__process_adb_edge_df", - "duration": 3550, + "startTime": 1703354941640359, + "duration": 4824, "tags": { "edge_df_size": 1000 }, @@ -1019,21 +1128,24 @@ { "spanID": "49b25ded9c31d9b2", "operationName": "__split_adb_ids", - "duration": 533, + "startTime": 1703354941640450, + "duration": 717, "tags": {}, "children": [] }, { "spanID": "5bf49c04ac642b4c", "operationName": "__split_adb_ids", - "duration": 486, + "startTime": 1703354941641487, + "duration": 650, "tags": {}, "children": [] }, { "spanID": "f2686baa971c702d", "operationName": "__process_adb_edge_type_df", - "duration": 1498, + "startTime": 1703354941643137, + "duration": 2014, "tags": { "edge_type": "[\"v1\",\"e0\",\"v2\"]", "edge_type_df_size": 1000 @@ -1042,7 +1154,8 @@ { "spanID": "a23d4c9de456697c", "operationName": "__set_pyg_data", - "duration": 342, + "startTime": 1703354941644698, + "duration": 448, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -1050,7 +1163,8 @@ { "spanID": "9efee464da90f534", "operationName": "__build_tensor_from_dataframe", - "duration": 306, + "startTime": 1703354941644728, + "duration": 400, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -1066,7 +1180,8 @@ { "spanID": "b732d46f21e15094", "operationName": "__process_adb_edge_df", - "duration": 3589, + "startTime": 1703354941650394, + "duration": 5282, "tags": { "edge_df_size": 1000 }, @@ -1074,21 +1189,24 @@ { "spanID": "635518f74f6fa985", "operationName": "__split_adb_ids", - "duration": 527, + "startTime": 1703354941650483, + "duration": 857, "tags": {}, "children": [] }, { "spanID": "6a174c1cbf9cc545", "operationName": "__split_adb_ids", - "duration": 489, + "startTime": 1703354941651708, + "duration": 736, "tags": {}, "children": [] }, { "spanID": "a69cfb85d432f8db", "operationName": "__process_adb_edge_type_df", - "duration": 1507, + "startTime": 1703354941653491, + "duration": 2091, "tags": { "edge_type": "[\"v1\",\"e0\",\"v2\"]", "edge_type_df_size": 1000 @@ -1097,7 +1215,8 @@ { "spanID": "0063e42f14aa451c", "operationName": "__set_pyg_data", - "duration": 341, + "startTime": 1703354941655111, + "duration": 464, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -1105,7 +1224,8 @@ { "spanID": "313b32b798363189", "operationName": "__build_tensor_from_dataframe", - "duration": 304, + "startTime": 1703354941655142, + "duration": 409, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -1121,7 +1241,8 @@ { "spanID": "559b5975b2d650af", "operationName": "__process_adb_edge_df", - "duration": 3600, + "startTime": 1703354941660950, + "duration": 4707, "tags": { "edge_df_size": 1000 }, @@ -1129,21 +1250,24 @@ { "spanID": "3d4a5d5128fafd04", "operationName": "__split_adb_ids", - "duration": 536, + "startTime": 1703354941661033, + "duration": 692, "tags": {}, "children": [] }, { "spanID": "a32c9b6f391cf046", "operationName": "__split_adb_ids", - "duration": 492, + "startTime": 1703354941662022, + "duration": 648, "tags": {}, "children": [] }, { "spanID": "60ef147172b8ff39", "operationName": "__process_adb_edge_type_df", - "duration": 1522, + "startTime": 1703354941663645, + "duration": 1979, "tags": { "edge_type": "[\"v1\",\"e0\",\"v2\"]", "edge_type_df_size": 1000 @@ -1152,7 +1276,8 @@ { "spanID": "e01bbf50b5d97ef7", "operationName": "__set_pyg_data", - "duration": 342, + "startTime": 1703354941665172, + "duration": 446, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -1160,7 +1285,8 @@ { "spanID": "91725f0aac7c8803", "operationName": "__build_tensor_from_dataframe", - "duration": 305, + "startTime": 1703354941665202, + "duration": 398, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -1176,7 +1302,8 @@ { "spanID": "6a1689addfe1b307", "operationName": "__process_adb_edge_df", - "duration": 3636, + "startTime": 1703354941716880, + "duration": 4931, "tags": { "edge_df_size": 1000 }, @@ -1184,21 +1311,24 @@ { "spanID": "66faf98908135d58", "operationName": "__split_adb_ids", - "duration": 539, + "startTime": 1703354941716978, + "duration": 710, "tags": {}, "children": [] }, { "spanID": "b3ab1b2cdf26f517", "operationName": "__split_adb_ids", - "duration": 491, + "startTime": 1703354941718007, + "duration": 643, "tags": {}, "children": [] }, { "spanID": "6b10e53a9145de05", "operationName": "__process_adb_edge_type_df", - "duration": 1557, + "startTime": 1703354941719718, + "duration": 2060, "tags": { "edge_type": "[\"v1\",\"e0\",\"v2\"]", "edge_type_df_size": 1000 @@ -1207,7 +1337,8 @@ { "spanID": "a985ab61c5adf681", "operationName": "__set_pyg_data", - "duration": 343, + "startTime": 1703354941721315, + "duration": 457, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -1215,7 +1346,8 @@ { "spanID": "0bf9c0efb5816b74", "operationName": "__build_tensor_from_dataframe", - "duration": 303, + "startTime": 1703354941721344, + "duration": 403, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -1231,7 +1363,8 @@ { "spanID": "720299e32a69acc7", "operationName": "__process_adb_edge_df", - "duration": 4553, + "startTime": 1703354941727395, + "duration": 5699, "tags": { "edge_df_size": 1000 }, @@ -1239,21 +1372,24 @@ { "spanID": "425cb200105ada6b", "operationName": "__split_adb_ids", - "duration": 549, + "startTime": 1703354941727494, + "duration": 732, "tags": {}, "children": [] }, { "spanID": "285e25b4b3969057", "operationName": "__split_adb_ids", - "duration": 500, + "startTime": 1703354941728515, + "duration": 640, "tags": {}, "children": [] }, { "spanID": "870f084c7244f536", "operationName": "__process_adb_edge_type_df", - "duration": 1480, + "startTime": 1703354941730093, + "duration": 1859, "tags": { "edge_type": "[\"v0\",\"e0\",\"v1\"]", "edge_type_df_size": 944 @@ -1262,7 +1398,8 @@ { "spanID": "7cbd7025e28bc9ff", "operationName": "__set_pyg_data", - "duration": 318, + "startTime": 1703354941731526, + "duration": 420, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -1270,7 +1407,8 @@ { "spanID": "8fb83babe8754cd3", "operationName": "__build_tensor_from_dataframe", - "duration": 287, + "startTime": 1703354941731556, + "duration": 379, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -1284,7 +1422,8 @@ { "spanID": "c167733f9a9e4310", "operationName": "__process_adb_edge_type_df", - "duration": 875, + "startTime": 1703354941731976, + "duration": 1104, "tags": { "edge_type": "[\"v1\",\"e0\",\"v2\"]", "edge_type_df_size": 56 @@ -1293,7 +1432,8 @@ { "spanID": "e245a4600004884c", "operationName": "__set_pyg_data", - "duration": 80, + "startTime": 1703354941732974, + "duration": 100, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -1301,7 +1441,8 @@ { "spanID": "7e9cf84f09f6048f", "operationName": "__build_tensor_from_dataframe", - "duration": 45, + "startTime": 1703354941733001, + "duration": 56, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -1317,7 +1458,8 @@ { "spanID": "4fe30c9a53710f57", "operationName": "__process_adb_edge_df", - "duration": 4071, + "startTime": 1703354941738338, + "duration": 5418, "tags": { "edge_df_size": 1000 }, @@ -1325,21 +1467,24 @@ { "spanID": "77863fe5d675ebf7", "operationName": "__split_adb_ids", - "duration": 552, + "startTime": 1703354941738413, + "duration": 678, "tags": {}, "children": [] }, { "spanID": "cf1da1100cc36d8c", "operationName": "__split_adb_ids", - "duration": 752, + "startTime": 1703354941739379, + "duration": 647, "tags": {}, "children": [] }, { "spanID": "e00111e5d29dc5df", "operationName": "__process_adb_edge_type_df", - "duration": 1627, + "startTime": 1703354941740969, + "duration": 2755, "tags": { "edge_type": "[\"v0\",\"e0\",\"v1\"]", "edge_type_df_size": 1000 @@ -1348,7 +1493,8 @@ { "spanID": "cffa6cddf963a7ef", "operationName": "__set_pyg_data", - "duration": 363, + "startTime": 1703354941743226, + "duration": 492, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -1356,7 +1502,8 @@ { "spanID": "3020da5c6a46721a", "operationName": "__build_tensor_from_dataframe", - "duration": 321, + "startTime": 1703354941743255, + "duration": 444, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -1372,7 +1519,8 @@ { "spanID": "ffda03368c6e9037", "operationName": "__process_adb_edge_df", - "duration": 3644, + "startTime": 1703354941749202, + "duration": 4930, "tags": { "edge_df_size": 1000 }, @@ -1380,21 +1528,24 @@ { "spanID": "a2121ac5f689a4a5", "operationName": "__split_adb_ids", - "duration": 543, + "startTime": 1703354941749302, + "duration": 760, "tags": {}, "children": [] }, { "spanID": "155e18b1fa83ada4", "operationName": "__split_adb_ids", - "duration": 493, + "startTime": 1703354941750412, + "duration": 663, "tags": {}, "children": [] }, { "spanID": "b9bdee2dd663049d", "operationName": "__process_adb_edge_type_df", - "duration": 1520, + "startTime": 1703354941752116, + "duration": 1985, "tags": { "edge_type": "[\"v0\",\"e0\",\"v1\"]", "edge_type_df_size": 1000 @@ -1403,7 +1554,8 @@ { "spanID": "fca055362169df82", "operationName": "__set_pyg_data", - "duration": 339, + "startTime": 1703354941753648, + "duration": 447, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -1411,7 +1563,8 @@ { "spanID": "66dd779403c54c71", "operationName": "__build_tensor_from_dataframe", - "duration": 301, + "startTime": 1703354941753677, + "duration": 400, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -1427,7 +1580,8 @@ { "spanID": "adb328cbf3158c0c", "operationName": "__process_adb_edge_df", - "duration": 3703, + "startTime": 1703354941759461, + "duration": 5212, "tags": { "edge_df_size": 1000 }, @@ -1435,21 +1589,24 @@ { "spanID": "50f0fc2b6ae04d52", "operationName": "__split_adb_ids", - "duration": 547, + "startTime": 1703354941759552, + "duration": 736, "tags": {}, "children": [] }, { "spanID": "36a98d7400de59f5", "operationName": "__split_adb_ids", - "duration": 521, + "startTime": 1703354941760633, + "duration": 700, "tags": {}, "children": [] }, { "spanID": "b7a28e0a03a89879", "operationName": "__process_adb_edge_type_df", - "duration": 1535, + "startTime": 1703354941762509, + "duration": 2129, "tags": { "edge_type": "[\"v0\",\"e0\",\"v1\"]", "edge_type_df_size": 1000 @@ -1458,7 +1615,8 @@ { "spanID": "009a815bc1378be5", "operationName": "__set_pyg_data", - "duration": 343, + "startTime": 1703354941764163, + "duration": 468, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -1466,7 +1624,8 @@ { "spanID": "d29e8693faf1501b", "operationName": "__build_tensor_from_dataframe", - "duration": 307, + "startTime": 1703354941764195, + "duration": 415, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -1482,7 +1641,8 @@ { "spanID": "8741ae91acfebb4b", "operationName": "__process_adb_edge_df", - "duration": 4394, + "startTime": 1703354941770352, + "duration": 5490, "tags": { "edge_df_size": 1000 }, @@ -1490,21 +1650,24 @@ { "spanID": "190865159cb017c1", "operationName": "__split_adb_ids", - "duration": 553, + "startTime": 1703354941770438, + "duration": 720, "tags": {}, "children": [] }, { "spanID": "1e707c5230c1fb6a", "operationName": "__split_adb_ids", - "duration": 823, + "startTime": 1703354941771690, + "duration": 792, "tags": {}, "children": [] }, { "spanID": "a636425c9bbd750d", "operationName": "__process_adb_edge_type_df", - "duration": 1718, + "startTime": 1703354941773583, + "duration": 2223, "tags": { "edge_type": "[\"v0\",\"e0\",\"v1\"]", "edge_type_df_size": 1000 @@ -1513,7 +1676,8 @@ { "spanID": "dfa7c6ed32d1f81b", "operationName": "__set_pyg_data", - "duration": 379, + "startTime": 1703354941775337, + "duration": 463, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -1521,7 +1685,8 @@ { "spanID": "47acf2f64d6b234f", "operationName": "__build_tensor_from_dataframe", - "duration": 335, + "startTime": 1703354941775369, + "duration": 407, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -1537,7 +1702,8 @@ { "spanID": "fa7ff8bfb044284a", "operationName": "__process_adb_edge_df", - "duration": 4060, + "startTime": 1703354941782451, + "duration": 4845, "tags": { "edge_df_size": 1000 }, @@ -1545,21 +1711,24 @@ { "spanID": "19a5711b2ea60b99", "operationName": "__split_adb_ids", - "duration": 623, + "startTime": 1703354941782536, + "duration": 736, "tags": {}, "children": [] }, { "spanID": "da9bb01779c147c7", "operationName": "__split_adb_ids", - "duration": 503, + "startTime": 1703354941783563, + "duration": 673, "tags": {}, "children": [] }, { "spanID": "658de17eec3aa314", "operationName": "__process_adb_edge_type_df", - "duration": 1647, + "startTime": 1703354941785253, + "duration": 2009, "tags": { "edge_type": "[\"v0\",\"e0\",\"v1\"]", "edge_type_df_size": 1000 @@ -1568,7 +1737,8 @@ { "spanID": "14d30dbca0acf4c9", "operationName": "__set_pyg_data", - "duration": 359, + "startTime": 1703354941786803, + "duration": 453, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -1576,7 +1746,8 @@ { "spanID": "4653a5600597aab6", "operationName": "__build_tensor_from_dataframe", - "duration": 318, + "startTime": 1703354941786831, + "duration": 404, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -1592,7 +1763,8 @@ { "spanID": "73f660d8e9f41cc0", "operationName": "__process_adb_edge_df", - "duration": 3805, + "startTime": 1703354941793198, + "duration": 4837, "tags": { "edge_df_size": 1000 }, @@ -1600,21 +1772,24 @@ { "spanID": "cad6e514ccc14d51", "operationName": "__split_adb_ids", - "duration": 559, + "startTime": 1703354941793292, + "duration": 714, "tags": {}, "children": [] }, { "spanID": "dc8215271da3b7e2", "operationName": "__split_adb_ids", - "duration": 507, + "startTime": 1703354941794317, + "duration": 648, "tags": {}, "children": [] }, { "spanID": "2227d96d41a93f90", "operationName": "__process_adb_edge_type_df", - "duration": 1594, + "startTime": 1703354941795965, + "duration": 2038, "tags": { "edge_type": "[\"v0\",\"e0\",\"v1\"]", "edge_type_df_size": 1000 @@ -1623,7 +1798,8 @@ { "spanID": "8557716aa7502a81", "operationName": "__set_pyg_data", - "duration": 351, + "startTime": 1703354941797542, + "duration": 455, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -1631,7 +1807,8 @@ { "spanID": "a699bae0d138d150", "operationName": "__build_tensor_from_dataframe", - "duration": 309, + "startTime": 1703354941797571, + "duration": 401, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -1647,7 +1824,8 @@ { "spanID": "58d87776a51ad4f3", "operationName": "__process_adb_edge_df", - "duration": 3888, + "startTime": 1703354941803833, + "duration": 5048, "tags": { "edge_df_size": 1000 }, @@ -1655,21 +1833,24 @@ { "spanID": "df3277fd1d77ce40", "operationName": "__split_adb_ids", - "duration": 566, + "startTime": 1703354941803924, + "duration": 736, "tags": {}, "children": [] }, { "spanID": "4745dd9e27896389", "operationName": "__split_adb_ids", - "duration": 518, + "startTime": 1703354941804975, + "duration": 666, "tags": {}, "children": [] }, { "spanID": "04c14982d9ead926", "operationName": "__process_adb_edge_type_df", - "duration": 1701, + "startTime": 1703354941806652, + "duration": 2196, "tags": { "edge_type": "[\"v0\",\"e0\",\"v1\"]", "edge_type_df_size": 1000 @@ -1678,7 +1859,8 @@ { "spanID": "0a68e88e0ad40415", "operationName": "__set_pyg_data", - "duration": 362, + "startTime": 1703354941808386, + "duration": 456, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -1686,7 +1868,8 @@ { "spanID": "ae55cdff34ab18fd", "operationName": "__build_tensor_from_dataframe", - "duration": 314, + "startTime": 1703354941808415, + "duration": 404, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -1702,7 +1885,8 @@ { "spanID": "8ef066d44279b14d", "operationName": "__process_adb_edge_df", - "duration": 3877, + "startTime": 1703354941815068, + "duration": 5524, "tags": { "edge_df_size": 1000 }, @@ -1710,21 +1894,24 @@ { "spanID": "f24dfdd850910bdc", "operationName": "__split_adb_ids", - "duration": 567, + "startTime": 1703354941815198, + "duration": 792, "tags": {}, "children": [] }, { "spanID": "f03d866a5decc06a", "operationName": "__split_adb_ids", - "duration": 502, + "startTime": 1703354941816331, + "duration": 856, "tags": {}, "children": [] }, { "spanID": "e8ec01b3914591ae", "operationName": "__process_adb_edge_type_df", - "duration": 1636, + "startTime": 1703354941818389, + "duration": 2163, "tags": { "edge_type": "[\"v0\",\"e0\",\"v1\"]", "edge_type_df_size": 1000 @@ -1733,7 +1920,8 @@ { "spanID": "0ac0cf0dd974c146", "operationName": "__set_pyg_data", - "duration": 347, + "startTime": 1703354941820051, + "duration": 494, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -1741,7 +1929,8 @@ { "spanID": "bfc74ca9d8ab0b30", "operationName": "__build_tensor_from_dataframe", - "duration": 307, + "startTime": 1703354941820086, + "duration": 420, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -1757,7 +1946,8 @@ { "spanID": "b38a05fbf61164ce", "operationName": "__process_adb_edge_df", - "duration": 3963, + "startTime": 1703354941826433, + "duration": 4991, "tags": { "edge_df_size": 1000 }, @@ -1765,21 +1955,24 @@ { "spanID": "a7c5cb879b8b71a1", "operationName": "__split_adb_ids", - "duration": 583, + "startTime": 1703354941826524, + "duration": 709, "tags": {}, "children": [] }, { "spanID": "b65d12267e969cf3", "operationName": "__split_adb_ids", - "duration": 509, + "startTime": 1703354941827545, + "duration": 650, "tags": {}, "children": [] }, { "spanID": "e7180322a4e695c9", "operationName": "__process_adb_edge_type_df", - "duration": 1701, + "startTime": 1703354941829200, + "duration": 2191, "tags": { "edge_type": "[\"v0\",\"e0\",\"v1\"]", "edge_type_df_size": 1000 @@ -1788,7 +1981,8 @@ { "spanID": "a3e04b3b756b0715", "operationName": "__set_pyg_data", - "duration": 359, + "startTime": 1703354941830882, + "duration": 502, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -1796,7 +1990,8 @@ { "spanID": "5f58d5b56f790959", "operationName": "__build_tensor_from_dataframe", - "duration": 313, + "startTime": 1703354941830912, + "duration": 447, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -1812,7 +2007,8 @@ { "spanID": "89b5b368df14c612", "operationName": "__process_adb_edge_df", - "duration": 4600, + "startTime": 1703354941837118, + "duration": 6324, "tags": { "edge_df_size": 1000 }, @@ -1820,21 +2016,24 @@ { "spanID": "353545792da44da1", "operationName": "__split_adb_ids", - "duration": 566, + "startTime": 1703354941837212, + "duration": 722, "tags": {}, "children": [] }, { "spanID": "964ddb776025f0ae", "operationName": "__split_adb_ids", - "duration": 529, + "startTime": 1703354941838250, + "duration": 658, "tags": {}, "children": [] }, { "spanID": "0247145f4a814d53", "operationName": "__process_adb_edge_type_df", - "duration": 1503, + "startTime": 1703354941840022, + "duration": 2171, "tags": { "edge_type": "[\"v1\",\"e0\",\"v0\"]", "edge_type_df_size": 922 @@ -1843,7 +2042,8 @@ { "spanID": "26a974652371ea2c", "operationName": "__set_pyg_data", - "duration": 324, + "startTime": 1703354941841766, + "duration": 421, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -1851,7 +2051,8 @@ { "spanID": "555a40854578bab3", "operationName": "__build_tensor_from_dataframe", - "duration": 292, + "startTime": 1703354941841800, + "duration": 375, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -1865,7 +2066,8 @@ { "spanID": "ca24be4d56672017", "operationName": "__process_adb_edge_type_df", - "duration": 903, + "startTime": 1703354941842217, + "duration": 1209, "tags": { "edge_type": "[\"v0\",\"e0\",\"v1\"]", "edge_type_df_size": 78 @@ -1874,7 +2076,8 @@ { "spanID": "b7ef941c5e00ea6d", "operationName": "__set_pyg_data", - "duration": 84, + "startTime": 1703354941843312, + "duration": 108, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -1882,7 +2085,8 @@ { "spanID": "5697f17c17fd3736", "operationName": "__build_tensor_from_dataframe", - "duration": 49, + "startTime": 1703354941843339, + "duration": 64, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -1898,7 +2102,8 @@ { "spanID": "9edb95f2c787ddfb", "operationName": "__process_adb_edge_df", - "duration": 3714, + "startTime": 1703354941851608, + "duration": 5078, "tags": { "edge_df_size": 1000 }, @@ -1906,21 +2111,24 @@ { "spanID": "0a8c46c709215f4f", "operationName": "__split_adb_ids", - "duration": 545, + "startTime": 1703354941851702, + "duration": 719, "tags": {}, "children": [] }, { "spanID": "29f2c3c74505f4f6", "operationName": "__split_adb_ids", - "duration": 512, + "startTime": 1703354941852728, + "duration": 651, "tags": {}, "children": [] }, { "spanID": "fb5eb8662640211e", "operationName": "__process_adb_edge_type_df", - "duration": 1618, + "startTime": 1703354941854395, + "duration": 2240, "tags": { "edge_type": "[\"v1\",\"e0\",\"v0\"]", "edge_type_df_size": 1000 @@ -1929,7 +2137,8 @@ { "spanID": "4a1eb1b7955d0e77", "operationName": "__set_pyg_data", - "duration": 354, + "startTime": 1703354941856060, + "duration": 568, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -1937,7 +2146,8 @@ { "spanID": "651116565c646036", "operationName": "__build_tensor_from_dataframe", - "duration": 319, + "startTime": 1703354941856092, + "duration": 501, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -1953,7 +2163,8 @@ { "spanID": "8c69778ffd42f697", "operationName": "__process_adb_edge_df", - "duration": 3665, + "startTime": 1703354941865563, + "duration": 4713, "tags": { "edge_df_size": 1000 }, @@ -1961,21 +2172,24 @@ { "spanID": "4b1cb8bd2130260c", "operationName": "__split_adb_ids", - "duration": 547, + "startTime": 1703354941865643, + "duration": 696, "tags": {}, "children": [] }, { "spanID": "7a62722e1d69d9fc", "operationName": "__split_adb_ids", - "duration": 517, + "startTime": 1703354941866628, + "duration": 652, "tags": {}, "children": [] }, { "spanID": "3d5d60bcbb0378eb", "operationName": "__process_adb_edge_type_df", - "duration": 1559, + "startTime": 1703354941868235, + "duration": 2007, "tags": { "edge_type": "[\"v1\",\"e0\",\"v0\"]", "edge_type_df_size": 1000 @@ -1984,7 +2198,8 @@ { "spanID": "0c5a876fef0a81ed", "operationName": "__set_pyg_data", - "duration": 358, + "startTime": 1703354941869775, + "duration": 461, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -1992,7 +2207,8 @@ { "spanID": "2df967474ed13553", "operationName": "__build_tensor_from_dataframe", - "duration": 323, + "startTime": 1703354941869804, + "duration": 400, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -2008,7 +2224,8 @@ { "spanID": "85e69ea9db66bfda", "operationName": "__process_adb_edge_df", - "duration": 3676, + "startTime": 1703354941879665, + "duration": 4946, "tags": { "edge_df_size": 1000 }, @@ -2016,21 +2233,24 @@ { "spanID": "122411e6ba8982dd", "operationName": "__split_adb_ids", - "duration": 543, + "startTime": 1703354941879758, + "duration": 891, "tags": {}, "children": [] }, { "spanID": "673617d94d7bd307", "operationName": "__split_adb_ids", - "duration": 511, + "startTime": 1703354941880963, + "duration": 710, "tags": {}, "children": [] }, { "spanID": "5419eefcd5e73e3f", "operationName": "__process_adb_edge_type_df", - "duration": 1600, + "startTime": 1703354941882608, + "duration": 1971, "tags": { "edge_type": "[\"v1\",\"e0\",\"v0\"]", "edge_type_df_size": 1000 @@ -2039,7 +2259,8 @@ { "spanID": "6a2b32004c9a0ae1", "operationName": "__set_pyg_data", - "duration": 362, + "startTime": 1703354941884128, + "duration": 444, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -2047,7 +2268,8 @@ { "spanID": "19724ce31bd09448", "operationName": "__build_tensor_from_dataframe", - "duration": 321, + "startTime": 1703354941884157, + "duration": 397, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -2063,7 +2285,8 @@ { "spanID": "e89dc8158f928dc5", "operationName": "__process_adb_edge_df", - "duration": 3666, + "startTime": 1703354941892912, + "duration": 4801, "tags": { "edge_df_size": 1000 }, @@ -2071,21 +2294,24 @@ { "spanID": "79585e697b2e1b82", "operationName": "__split_adb_ids", - "duration": 555, + "startTime": 1703354941892987, + "duration": 727, "tags": {}, "children": [] }, { "spanID": "d741d609564ae909", "operationName": "__split_adb_ids", - "duration": 531, + "startTime": 1703354941894028, + "duration": 644, "tags": {}, "children": [] }, { "spanID": "f9ea2c64cc417e7c", "operationName": "__process_adb_edge_type_df", - "duration": 1550, + "startTime": 1703354941895665, + "duration": 2016, "tags": { "edge_type": "[\"v1\",\"e0\",\"v0\"]", "edge_type_df_size": 1000 @@ -2094,7 +2320,8 @@ { "spanID": "57f98d1ecff4c56b", "operationName": "__set_pyg_data", - "duration": 359, + "startTime": 1703354941897235, + "duration": 440, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -2102,7 +2329,8 @@ { "spanID": "7aa56a181fd3c017", "operationName": "__build_tensor_from_dataframe", - "duration": 323, + "startTime": 1703354941897263, + "duration": 395, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -2118,7 +2346,8 @@ { "spanID": "b318ad4c1db2b452", "operationName": "__process_adb_edge_df", - "duration": 3800, + "startTime": 1703354941906243, + "duration": 5354, "tags": { "edge_df_size": 1000 }, @@ -2126,21 +2355,24 @@ { "spanID": "6d316b4a7f6b8793", "operationName": "__split_adb_ids", - "duration": 568, + "startTime": 1703354941906350, + "duration": 743, "tags": {}, "children": [] }, { "spanID": "4d4985dc09aedbd0", "operationName": "__split_adb_ids", - "duration": 532, + "startTime": 1703354941907447, + "duration": 701, "tags": {}, "children": [] }, { "spanID": "bc18a40b55c7ed9d", "operationName": "__process_adb_edge_type_df", - "duration": 1634, + "startTime": 1703354941909265, + "duration": 2297, "tags": { "edge_type": "[\"v1\",\"e0\",\"v0\"]", "edge_type_df_size": 1000 @@ -2149,7 +2381,8 @@ { "spanID": "e4f7625eafe6790a", "operationName": "__set_pyg_data", - "duration": 358, + "startTime": 1703354941911067, + "duration": 489, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -2157,7 +2390,8 @@ { "spanID": "eb70ba6527d99a23", "operationName": "__build_tensor_from_dataframe", - "duration": 319, + "startTime": 1703354941911103, + "duration": 431, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -2173,7 +2407,8 @@ { "spanID": "a0722aa02aa36cf7", "operationName": "__process_adb_edge_df", - "duration": 3821, + "startTime": 1703354941920648, + "duration": 4641, "tags": { "edge_df_size": 1000 }, @@ -2181,21 +2416,24 @@ { "spanID": "6025719990823eda", "operationName": "__split_adb_ids", - "duration": 567, + "startTime": 1703354941920719, + "duration": 681, "tags": {}, "children": [] }, { "spanID": "f97ccc57ce5dc807", "operationName": "__split_adb_ids", - "duration": 542, + "startTime": 1703354941921684, + "duration": 644, "tags": {}, "children": [] }, { "spanID": "a38d8afcfdd2ed7a", "operationName": "__process_adb_edge_type_df", - "duration": 1642, + "startTime": 1703354941923253, + "duration": 2000, "tags": { "edge_type": "[\"v1\",\"e0\",\"v0\"]", "edge_type_df_size": 1000 @@ -2204,7 +2442,8 @@ { "spanID": "10da8a9516408169", "operationName": "__set_pyg_data", - "duration": 371, + "startTime": 1703354941924779, + "duration": 468, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -2212,7 +2451,8 @@ { "spanID": "15ace7a1ceca2ee3", "operationName": "__build_tensor_from_dataframe", - "duration": 333, + "startTime": 1703354941924807, + "duration": 420, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -2228,7 +2468,8 @@ { "spanID": "bff773ce32b2c492", "operationName": "__process_adb_edge_df", - "duration": 3867, + "startTime": 1703354941934555, + "duration": 4997, "tags": { "edge_df_size": 1000 }, @@ -2236,21 +2477,24 @@ { "spanID": "0fa7ee0538974df5", "operationName": "__split_adb_ids", - "duration": 567, + "startTime": 1703354941934686, + "duration": 794, "tags": {}, "children": [] }, { "spanID": "0202861c62830869", "operationName": "__split_adb_ids", - "duration": 536, + "startTime": 1703354941935819, + "duration": 784, "tags": {}, "children": [] }, { "spanID": "64d09913191b8adf", "operationName": "__process_adb_edge_type_df", - "duration": 1681, + "startTime": 1703354941937574, + "duration": 1946, "tags": { "edge_type": "[\"v1\",\"e0\",\"v0\"]", "edge_type_df_size": 1000 @@ -2259,7 +2503,8 @@ { "spanID": "84dd6da68e751eb7", "operationName": "__set_pyg_data", - "duration": 377, + "startTime": 1703354941939061, + "duration": 453, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -2267,7 +2512,8 @@ { "spanID": "72d3cc5d4a31b243", "operationName": "__build_tensor_from_dataframe", - "duration": 335, + "startTime": 1703354941939089, + "duration": 401, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -2283,7 +2529,8 @@ { "spanID": "7d161f29eb8f2056", "operationName": "__process_adb_edge_df", - "duration": 4643, + "startTime": 1703354941950105, + "duration": 5997, "tags": { "edge_df_size": 1000 }, @@ -2291,21 +2538,24 @@ { "spanID": "95bb440dc9cd4af9", "operationName": "__split_adb_ids", - "duration": 570, + "startTime": 1703354941950205, + "duration": 800, "tags": {}, "children": [] }, { "spanID": "ade6c5e9b6e355f6", "operationName": "__split_adb_ids", - "duration": 536, + "startTime": 1703354941951352, + "duration": 736, "tags": {}, "children": [] }, { "spanID": "6c4c3935379deda1", "operationName": "__process_adb_edge_type_df", - "duration": 1380, + "startTime": 1703354941953086, + "duration": 1643, "tags": { "edge_type": "[\"v1\",\"e0\",\"v1\"]", "edge_type_df_size": 743 @@ -2314,7 +2564,8 @@ { "spanID": "5e4af862156af458", "operationName": "__set_pyg_data", - "duration": 291, + "startTime": 1703354941954377, + "duration": 346, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -2322,7 +2573,8 @@ { "spanID": "fd0ba70e385af463", "operationName": "__build_tensor_from_dataframe", - "duration": 259, + "startTime": 1703354941954406, + "duration": 307, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -2336,7 +2588,8 @@ { "spanID": "42cb6d1dffc573d5", "operationName": "__process_adb_edge_type_df", - "duration": 1072, + "startTime": 1703354941954752, + "duration": 1272, "tags": { "edge_type": "[\"v1\",\"e0\",\"v0\"]", "edge_type_df_size": 257 @@ -2345,7 +2598,8 @@ { "spanID": "c6f0093395d18051", "operationName": "__set_pyg_data", - "duration": 145, + "startTime": 1703354941955845, + "duration": 173, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -2353,7 +2607,8 @@ { "spanID": "6e6480432aa50f4e", "operationName": "__build_tensor_from_dataframe", - "duration": 106, + "startTime": 1703354941955871, + "duration": 131, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -2369,7 +2624,8 @@ { "spanID": "5bc7fdeb31234efe", "operationName": "__process_adb_edge_df", - "duration": 4038, + "startTime": 1703354941965302, + "duration": 5610, "tags": { "edge_df_size": 1000 }, @@ -2377,21 +2633,24 @@ { "spanID": "1058fe8c1d7173e5", "operationName": "__split_adb_ids", - "duration": 561, + "startTime": 1703354941965399, + "duration": 765, "tags": {}, "children": [] }, { "spanID": "dd138266d26d5396", "operationName": "__split_adb_ids", - "duration": 774, + "startTime": 1703354941966501, + "duration": 1112, "tags": {}, "children": [] }, { "spanID": "b3b68b57da54f267", "operationName": "__process_adb_edge_type_df", - "duration": 1623, + "startTime": 1703354941968749, + "duration": 2125, "tags": { "edge_type": "[\"v1\",\"e0\",\"v1\"]", "edge_type_df_size": 1000 @@ -2400,7 +2659,8 @@ { "spanID": "e72bb5b707120911", "operationName": "__set_pyg_data", - "duration": 372, + "startTime": 1703354941970396, + "duration": 472, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -2408,7 +2668,8 @@ { "spanID": "739cd488869bdbd2", "operationName": "__build_tensor_from_dataframe", - "duration": 335, + "startTime": 1703354941970430, + "duration": 415, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -2424,7 +2685,8 @@ { "spanID": "ad4ab155c09fcd8f", "operationName": "__process_adb_edge_df", - "duration": 3802, + "startTime": 1703354941979139, + "duration": 4912, "tags": { "edge_df_size": 1000 }, @@ -2432,21 +2694,24 @@ { "spanID": "1e70e79933a1d1c2", "operationName": "__split_adb_ids", - "duration": 575, + "startTime": 1703354941979227, + "duration": 729, "tags": {}, "children": [] }, { "spanID": "65e049937f411fed", "operationName": "__split_adb_ids", - "duration": 539, + "startTime": 1703354941980274, + "duration": 678, "tags": {}, "children": [] }, { "spanID": "350d278d41a8a6e1", "operationName": "__process_adb_edge_type_df", - "duration": 1588, + "startTime": 1703354941981955, + "duration": 2063, "tags": { "edge_type": "[\"v1\",\"e0\",\"v1\"]", "edge_type_df_size": 1000 @@ -2455,7 +2720,8 @@ { "spanID": "0ac728b4a41865bf", "operationName": "__set_pyg_data", - "duration": 370, + "startTime": 1703354941983559, + "duration": 452, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -2463,7 +2729,8 @@ { "spanID": "f2ad985fff3e0ba1", "operationName": "__build_tensor_from_dataframe", - "duration": 333, + "startTime": 1703354941983589, + "duration": 401, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -2479,7 +2746,8 @@ { "spanID": "3744da64cc249558", "operationName": "__process_adb_edge_df", - "duration": 3838, + "startTime": 1703354941992697, + "duration": 4979, "tags": { "edge_df_size": 1000 }, @@ -2487,21 +2755,24 @@ { "spanID": "25777cf09f982188", "operationName": "__split_adb_ids", - "duration": 569, + "startTime": 1703354941992793, + "duration": 742, "tags": {}, "children": [] }, { "spanID": "32ae2a201ac902ee", "operationName": "__split_adb_ids", - "duration": 540, + "startTime": 1703354941993849, + "duration": 680, "tags": {}, "children": [] }, { "spanID": "60c6b3ed755a3ac1", "operationName": "__process_adb_edge_type_df", - "duration": 1648, + "startTime": 1703354941995585, + "duration": 2056, "tags": { "edge_type": "[\"v1\",\"e0\",\"v1\"]", "edge_type_df_size": 1000 @@ -2510,7 +2781,8 @@ { "spanID": "8be04c3e5c949381", "operationName": "__set_pyg_data", - "duration": 371, + "startTime": 1703354941997176, + "duration": 459, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -2518,7 +2790,8 @@ { "spanID": "26bdd974d3b564b0", "operationName": "__build_tensor_from_dataframe", - "duration": 333, + "startTime": 1703354941997207, + "duration": 408, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -2534,7 +2807,8 @@ { "spanID": "fd1ac7ce1ad0a6f2", "operationName": "__process_adb_edge_df", - "duration": 4126, + "startTime": 1703354942006541, + "duration": 6205, "tags": { "edge_df_size": 1000 }, @@ -2542,21 +2816,24 @@ { "spanID": "fba52e5998a33736", "operationName": "__split_adb_ids", - "duration": 577, + "startTime": 1703354942006624, + "duration": 743, "tags": {}, "children": [] }, { "spanID": "25fdacbe7ce71b48", "operationName": "__split_adb_ids", - "duration": 806, + "startTime": 1703354942007658, + "duration": 1420, "tags": {}, "children": [] }, { "spanID": "67e98363905c053b", "operationName": "__process_adb_edge_type_df", - "duration": 1624, + "startTime": 1703354942010409, + "duration": 2298, "tags": { "edge_type": "[\"v1\",\"e0\",\"v1\"]", "edge_type_df_size": 1000 @@ -2565,7 +2842,8 @@ { "spanID": "ae0fdbc8a36bcb01", "operationName": "__set_pyg_data", - "duration": 373, + "startTime": 1703354942012231, + "duration": 469, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -2573,7 +2851,8 @@ { "spanID": "e0ae1a1b6c596216", "operationName": "__build_tensor_from_dataframe", - "duration": 333, + "startTime": 1703354942012264, + "duration": 406, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -2589,7 +2868,8 @@ { "spanID": "7ed2ec2f856f3d95", "operationName": "__process_adb_edge_df", - "duration": 4021, + "startTime": 1703354942024342, + "duration": 4879, "tags": { "edge_df_size": 1000 }, @@ -2597,21 +2877,24 @@ { "spanID": "eac39204ade7cef3", "operationName": "__split_adb_ids", - "duration": 588, + "startTime": 1703354942024428, + "duration": 753, "tags": {}, "children": [] }, { "spanID": "528cc241e345ac72", "operationName": "__split_adb_ids", - "duration": 552, + "startTime": 1703354942025506, + "duration": 662, "tags": {}, "children": [] }, { "spanID": "7f99d273d5627386", "operationName": "__process_adb_edge_type_df", - "duration": 1665, + "startTime": 1703354942027189, + "duration": 1999, "tags": { "edge_type": "[\"v1\",\"e0\",\"v1\"]", "edge_type_df_size": 1000 @@ -2620,7 +2903,8 @@ { "spanID": "7fa74d8aff88ec82", "operationName": "__set_pyg_data", - "duration": 381, + "startTime": 1703354942028733, + "duration": 449, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -2628,7 +2912,8 @@ { "spanID": "ab899605a2939b3b", "operationName": "__build_tensor_from_dataframe", - "duration": 339, + "startTime": 1703354942028761, + "duration": 400, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -2644,7 +2929,8 @@ { "spanID": "33b5b3cedfec4623", "operationName": "__process_adb_edge_df", - "duration": 4042, + "startTime": 1703354942040096, + "duration": 4563, "tags": { "edge_df_size": 1000 }, @@ -2652,21 +2938,24 @@ { "spanID": "9c19ed348af58903", "operationName": "__split_adb_ids", - "duration": 580, + "startTime": 1703354942040164, + "duration": 683, "tags": {}, "children": [] }, { "spanID": "38018399ee6a8e2f", "operationName": "__split_adb_ids", - "duration": 525, + "startTime": 1703354942041122, + "duration": 649, "tags": {}, "children": [] }, { "spanID": "5718ada2027c013f", "operationName": "__process_adb_edge_type_df", - "duration": 1704, + "startTime": 1703354942042695, + "duration": 1929, "tags": { "edge_type": "[\"v1\",\"e0\",\"v1\"]", "edge_type_df_size": 1000 @@ -2675,7 +2964,8 @@ { "spanID": "f66ac168b4a1ca79", "operationName": "__set_pyg_data", - "duration": 363, + "startTime": 1703354942044159, + "duration": 459, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -2683,7 +2973,8 @@ { "spanID": "e6256403bf3df0bb", "operationName": "__build_tensor_from_dataframe", - "duration": 322, + "startTime": 1703354942044188, + "duration": 410, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -2699,7 +2990,8 @@ { "spanID": "d17034ce51797350", "operationName": "__process_adb_edge_df", - "duration": 4203, + "startTime": 1703354942054661, + "duration": 5436, "tags": { "edge_df_size": 1000 }, @@ -2707,21 +2999,24 @@ { "spanID": "091472ad52631db9", "operationName": "__split_adb_ids", - "duration": 569, + "startTime": 1703354942054770, + "duration": 759, "tags": {}, "children": [] }, { "spanID": "25fb5f3d866d7002", "operationName": "__split_adb_ids", - "duration": 797, + "startTime": 1703354942055852, + "duration": 1076, "tags": {}, "children": [] }, { "spanID": "41c30359dfde2281", "operationName": "__process_adb_edge_type_df", - "duration": 1670, + "startTime": 1703354942058106, + "duration": 1957, "tags": { "edge_type": "[\"v1\",\"e0\",\"v1\"]", "edge_type_df_size": 1000 @@ -2730,7 +3025,8 @@ { "spanID": "c8bf23fb9a431f7a", "operationName": "__set_pyg_data", - "duration": 371, + "startTime": 1703354942059605, + "duration": 452, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -2738,7 +3034,8 @@ { "spanID": "d7a3283c27e969e2", "operationName": "__build_tensor_from_dataframe", - "duration": 326, + "startTime": 1703354942059633, + "duration": 399, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -2754,7 +3051,8 @@ { "spanID": "953c178e61067a8c", "operationName": "__process_adb_edge_df", - "duration": 4769, + "startTime": 1703354942069500, + "duration": 6219, "tags": { "edge_df_size": 1000 }, @@ -2762,21 +3060,24 @@ { "spanID": "b7d779cc4b5ca436", "operationName": "__split_adb_ids", - "duration": 563, + "startTime": 1703354942069604, + "duration": 742, "tags": {}, "children": [] }, { "spanID": "ce9b2e70b4d4dfcc", "operationName": "__split_adb_ids", - "duration": 530, + "startTime": 1703354942070709, + "duration": 677, "tags": {}, "children": [] }, { "spanID": "10fce97d786e30ef", "operationName": "__process_adb_edge_type_df", - "duration": 1353, + "startTime": 1703354942072483, + "duration": 1639, "tags": { "edge_type": "[\"v0\",\"e0\",\"v2\"]", "edge_type_df_size": 584 @@ -2785,7 +3086,8 @@ { "spanID": "15ab2c21ccc93ff7", "operationName": "__set_pyg_data", - "duration": 235, + "startTime": 1703354942073818, + "duration": 298, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -2793,7 +3095,8 @@ { "spanID": "de6fec4b843b2a7d", "operationName": "__build_tensor_from_dataframe", - "duration": 202, + "startTime": 1703354942073849, + "duration": 254, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -2807,7 +3110,8 @@ { "spanID": "0a1727f7ea5f24b6", "operationName": "__process_adb_edge_type_df", - "duration": 1191, + "startTime": 1703354942074146, + "duration": 1479, "tags": { "edge_type": "[\"v1\",\"e0\",\"v1\"]", "edge_type_df_size": 416 @@ -2816,7 +3120,8 @@ { "spanID": "399f8a8f10fc9eee", "operationName": "__set_pyg_data", - "duration": 262, + "startTime": 1703354942075382, + "duration": 237, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -2824,7 +3129,8 @@ { "spanID": "0a66dc4e21681081", "operationName": "__build_tensor_from_dataframe", - "duration": 158, + "startTime": 1703354942075411, + "duration": 189, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -2840,7 +3146,8 @@ { "spanID": "03e9ba024cea2df0", "operationName": "__process_adb_edge_df", - "duration": 3681, + "startTime": 1703354942086930, + "duration": 5151, "tags": { "edge_df_size": 1000 }, @@ -2848,21 +3155,24 @@ { "spanID": "d80d6a1cc2472fd6", "operationName": "__split_adb_ids", - "duration": 550, + "startTime": 1703354942087027, + "duration": 892, "tags": {}, "children": [] }, { "spanID": "54a1d50572d6bc20", "operationName": "__split_adb_ids", - "duration": 509, + "startTime": 1703354942088234, + "duration": 729, "tags": {}, "children": [] }, { "spanID": "2922fbd8dca5b353", "operationName": "__process_adb_edge_type_df", - "duration": 1550, + "startTime": 1703354942089934, + "duration": 2101, "tags": { "edge_type": "[\"v0\",\"e0\",\"v2\"]", "edge_type_df_size": 1000 @@ -2871,7 +3181,8 @@ { "spanID": "261908b9ccf719ab", "operationName": "__set_pyg_data", - "duration": 349, + "startTime": 1703354942091553, + "duration": 475, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -2879,7 +3190,8 @@ { "spanID": "a7f5195cde62d43f", "operationName": "__build_tensor_from_dataframe", - "duration": 312, + "startTime": 1703354942091589, + "duration": 419, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -2895,7 +3207,8 @@ { "spanID": "f7f60e7f75f2bc20", "operationName": "__process_adb_edge_df", - "duration": 4132, + "startTime": 1703354942102457, + "duration": 5462, "tags": { "edge_df_size": 1000 }, @@ -2903,21 +3216,24 @@ { "spanID": "8147a8f45f0ef320", "operationName": "__split_adb_ids", - "duration": 908, + "startTime": 1703354942102557, + "duration": 1306, "tags": {}, "children": [] }, { "spanID": "e6addd9e61d9fe39", "operationName": "__split_adb_ids", - "duration": 524, + "startTime": 1703354942104193, + "duration": 708, "tags": {}, "children": [] }, { "spanID": "809f292387a1798f", "operationName": "__process_adb_edge_type_df", - "duration": 1591, + "startTime": 1703354942105865, + "duration": 2021, "tags": { "edge_type": "[\"v0\",\"e0\",\"v2\"]", "edge_type_df_size": 1000 @@ -2926,7 +3242,8 @@ { "spanID": "92e94e89089b30a0", "operationName": "__set_pyg_data", - "duration": 365, + "startTime": 1703354942107424, + "duration": 456, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -2934,7 +3251,8 @@ { "spanID": "adb6da351734a26c", "operationName": "__build_tensor_from_dataframe", - "duration": 329, + "startTime": 1703354942107457, + "duration": 405, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -2950,7 +3268,8 @@ { "spanID": "ce1bb02acb4d18d6", "operationName": "__process_adb_edge_df", - "duration": 4210, + "startTime": 1703354942118334, + "duration": 5384, "tags": { "edge_df_size": 1000 }, @@ -2958,21 +3277,24 @@ { "spanID": "c202387b849b8a44", "operationName": "__split_adb_ids", - "duration": 621, + "startTime": 1703354942118463, + "duration": 750, "tags": {}, "children": [] }, { "spanID": "fd938adc99a2ecb1", "operationName": "__split_adb_ids", - "duration": 532, + "startTime": 1703354942119636, + "duration": 785, "tags": {}, "children": [] }, { "spanID": "bf391fbb138c3460", "operationName": "__process_adb_edge_type_df", - "duration": 1743, + "startTime": 1703354942121531, + "duration": 2147, "tags": { "edge_type": "[\"v0\",\"e0\",\"v2\"]", "edge_type_df_size": 1000 @@ -2981,7 +3303,8 @@ { "spanID": "e7e13ed86d265dd8", "operationName": "__set_pyg_data", - "duration": 383, + "startTime": 1703354942123192, + "duration": 479, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -2989,7 +3312,8 @@ { "spanID": "34c3494ac12ea9b8", "operationName": "__build_tensor_from_dataframe", - "duration": 335, + "startTime": 1703354942123222, + "duration": 419, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -3005,7 +3329,8 @@ { "spanID": "89110af04a276dda", "operationName": "__process_adb_edge_df", - "duration": 4079, + "startTime": 1703354942132971, + "duration": 5058, "tags": { "edge_df_size": 1000 }, @@ -3013,21 +3338,24 @@ { "spanID": "993ec8c6e6b106e2", "operationName": "__split_adb_ids", - "duration": 606, + "startTime": 1703354942133068, + "duration": 741, "tags": {}, "children": [] }, { "spanID": "d360da696af79ad2", "operationName": "__split_adb_ids", - "duration": 533, + "startTime": 1703354942134144, + "duration": 684, "tags": {}, "children": [] }, { "spanID": "7b72590bf8f8f071", "operationName": "__process_adb_edge_type_df", - "duration": 1693, + "startTime": 1703354942135989, + "duration": 2006, "tags": { "edge_type": "[\"v0\",\"e0\",\"v2\"]", "edge_type_df_size": 1000 @@ -3036,7 +3364,8 @@ { "spanID": "ca819c6fd872298c", "operationName": "__set_pyg_data", - "duration": 367, + "startTime": 1703354942137538, + "duration": 451, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -3044,7 +3373,8 @@ { "spanID": "63794035f8e45086", "operationName": "__build_tensor_from_dataframe", - "duration": 326, + "startTime": 1703354942137566, + "duration": 398, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -3060,7 +3390,8 @@ { "spanID": "961d8dcf9b8086da", "operationName": "__process_adb_edge_df", - "duration": 4215, + "startTime": 1703354942147957, + "duration": 5546, "tags": { "edge_df_size": 1000 }, @@ -3068,21 +3399,24 @@ { "spanID": "d9efe28b3bcb50b3", "operationName": "__split_adb_ids", - "duration": 880, + "startTime": 1703354942148061, + "duration": 1143, "tags": {}, "children": [] }, { "spanID": "cc4da021dd620222", "operationName": "__split_adb_ids", - "duration": 523, + "startTime": 1703354942149528, + "duration": 737, "tags": {}, "children": [] }, { "spanID": "a83023ab053e4b42", "operationName": "__process_adb_edge_type_df", - "duration": 1688, + "startTime": 1703354942151444, + "duration": 2025, "tags": { "edge_type": "[\"v0\",\"e0\",\"v2\"]", "edge_type_df_size": 1000 @@ -3091,7 +3425,8 @@ { "spanID": "000fc63de2a01335", "operationName": "__set_pyg_data", - "duration": 372, + "startTime": 1703354942153019, + "duration": 444, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -3099,7 +3434,8 @@ { "spanID": "2e9583eabda17da2", "operationName": "__build_tensor_from_dataframe", - "duration": 329, + "startTime": 1703354942153047, + "duration": 395, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -3115,7 +3451,8 @@ { "spanID": "81c16e984d6cd782", "operationName": "__process_adb_edge_df", - "duration": 3833, + "startTime": 1703354942163989, + "duration": 5273, "tags": { "edge_df_size": 1000 }, @@ -3123,21 +3460,24 @@ { "spanID": "4124405b91fcfe88", "operationName": "__split_adb_ids", - "duration": 566, + "startTime": 1703354942164096, + "duration": 925, "tags": {}, "children": [] }, { "spanID": "10cc8711552ae5ca", "operationName": "__split_adb_ids", - "duration": 527, + "startTime": 1703354942165360, + "duration": 732, "tags": {}, "children": [] }, { "spanID": "dc2151e17e56ac3d", "operationName": "__process_adb_edge_type_df", - "duration": 1632, + "startTime": 1703354942167173, + "duration": 2000, "tags": { "edge_type": "[\"v0\",\"e0\",\"v2\"]", "edge_type_df_size": 1000 @@ -3146,7 +3486,8 @@ { "spanID": "f164f9d84312ece2", "operationName": "__set_pyg_data", - "duration": 361, + "startTime": 1703354942168709, + "duration": 458, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -3154,7 +3495,8 @@ { "spanID": "4d849ec5d334886f", "operationName": "__build_tensor_from_dataframe", - "duration": 322, + "startTime": 1703354942168736, + "duration": 413, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -3170,7 +3512,8 @@ { "spanID": "68777babc5c14262", "operationName": "__process_adb_edge_df", - "duration": 4035, + "startTime": 1703354942179678, + "duration": 5390, "tags": { "edge_df_size": 1000 }, @@ -3178,21 +3521,24 @@ { "spanID": "cf5e9ea362584ab3", "operationName": "__split_adb_ids", - "duration": 584, + "startTime": 1703354942179790, + "duration": 773, "tags": {}, "children": [] }, { "spanID": "0ff030b86238d0a0", "operationName": "__split_adb_ids", - "duration": 561, + "startTime": 1703354942180907, + "duration": 677, "tags": {}, "children": [] }, { "spanID": "a417956f29ee7f3d", "operationName": "__process_adb_edge_type_df", - "duration": 1782, + "startTime": 1703354942182810, + "duration": 2215, "tags": { "edge_type": "[\"v0\",\"e0\",\"v2\"]", "edge_type_df_size": 1000 @@ -3201,7 +3547,8 @@ { "spanID": "209818d1ef7e85ec", "operationName": "__set_pyg_data", - "duration": 370, + "startTime": 1703354942184534, + "duration": 484, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -3209,7 +3556,8 @@ { "spanID": "497e9f1a3d2bf042", "operationName": "__build_tensor_from_dataframe", - "duration": 328, + "startTime": 1703354942184573, + "duration": 418, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -3225,7 +3573,8 @@ { "spanID": "d476fe38babd4745", "operationName": "__process_adb_edge_df", - "duration": 4233, + "startTime": 1703354942195282, + "duration": 5649, "tags": { "edge_df_size": 1000 }, @@ -3233,21 +3582,24 @@ { "spanID": "0e3705265582a3bd", "operationName": "__split_adb_ids", - "duration": 859, + "startTime": 1703354942195380, + "duration": 1173, "tags": {}, "children": [] }, { "spanID": "0932f5b6f11ddff7", "operationName": "__split_adb_ids", - "duration": 547, + "startTime": 1703354942196876, + "duration": 670, "tags": {}, "children": [] }, { "spanID": "6af944e07b38785b", "operationName": "__process_adb_edge_type_df", - "duration": 1698, + "startTime": 1703354942198623, + "duration": 2267, "tags": { "edge_type": "[\"v0\",\"e0\",\"v2\"]", "edge_type_df_size": 1000 @@ -3256,7 +3608,8 @@ { "spanID": "7de8a2342412579d", "operationName": "__set_pyg_data", - "duration": 362, + "startTime": 1703354942200402, + "duration": 481, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -3264,7 +3617,8 @@ { "spanID": "dd02e100e3d48408", "operationName": "__build_tensor_from_dataframe", - "duration": 319, + "startTime": 1703354942200435, + "duration": 415, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -3280,7 +3634,8 @@ { "spanID": "b799ae8e9a1a7d6f", "operationName": "__process_adb_edge_df", - "duration": 3779, + "startTime": 1703354942207277, + "duration": 4947, "tags": { "edge_df_size": 1000 }, @@ -3288,21 +3643,24 @@ { "spanID": "ac6d5df814e5064c", "operationName": "__split_adb_ids", - "duration": 578, + "startTime": 1703354942207361, + "duration": 711, "tags": {}, "children": [] }, { "spanID": "26c06e67b2ddc481", "operationName": "__split_adb_ids", - "duration": 522, + "startTime": 1703354942208392, + "duration": 717, "tags": {}, "children": [] }, { "spanID": "fc98c279cf6f111c", "operationName": "__process_adb_edge_type_df", - "duration": 1619, + "startTime": 1703354942210129, + "duration": 2061, "tags": { "edge_type": "[\"v0\",\"e0\",\"v2\"]", "edge_type_df_size": 1000 @@ -3311,7 +3669,8 @@ { "spanID": "69407be75a4f4145", "operationName": "__set_pyg_data", - "duration": 364, + "startTime": 1703354942211727, + "duration": 457, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -3319,7 +3678,8 @@ { "spanID": "9c9d03f309018aee", "operationName": "__build_tensor_from_dataframe", - "duration": 320, + "startTime": 1703354942211756, + "duration": 407, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -3335,7 +3695,8 @@ { "spanID": "62fda854775e0ec3", "operationName": "__process_adb_edge_df", - "duration": 2759, + "startTime": 1703354942215868, + "duration": 3500, "tags": { "edge_df_size": 450 }, @@ -3343,21 +3704,24 @@ { "spanID": "0c0a59677579501a", "operationName": "__split_adb_ids", - "duration": 295, + "startTime": 1703354942215942, + "duration": 375, "tags": {}, "children": [] }, { "spanID": "788c31f619faa06e", "operationName": "__split_adb_ids", - "duration": 270, + "startTime": 1703354942216583, + "duration": 342, "tags": {}, "children": [] }, { "spanID": "26c00984c734bb05", "operationName": "__process_adb_edge_type_df", - "duration": 1204, + "startTime": 1703354942217802, + "duration": 1545, "tags": { "edge_type": "[\"v0\",\"e0\",\"v2\"]", "edge_type_df_size": 450 @@ -3366,7 +3730,8 @@ { "spanID": "084fa819052daad3", "operationName": "__set_pyg_data", - "duration": 202, + "startTime": 1703354942219089, + "duration": 252, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -3374,7 +3739,8 @@ { "spanID": "9e0df45b992a34a1", "operationName": "__build_tensor_from_dataframe", - "duration": 164, + "startTime": 1703354942219117, + "duration": 205, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" diff --git a/benchmark/traces/master/pyg_to_arangodb.json b/benchmark/traces/master/pyg_to_arangodb.json index c54feab..e55e782 100644 --- a/benchmark/traces/master/pyg_to_arangodb.json +++ b/benchmark/traces/master/pyg_to_arangodb.json @@ -1,7 +1,8 @@ { "spanID": "40212ef7cca5a5a1", "operationName": "pyg_to_arangodb", - "duration": 1149783, + "startTime": 1703354939820545, + "duration": 1491957, "tags": { "name": "FakeHeteroGraphBenchmark" }, @@ -9,27 +10,31 @@ { "spanID": "e8e5216afcbd04c3", "operationName": "__get_node_and_edge_types", - "duration": 18, + "startTime": 1703354939820612, + "duration": 12, "tags": {}, "children": [] }, { "spanID": "fb97d43588561712", "operationName": "__create_adb_graph", - "duration": 7667, + "startTime": 1703354939820654, + "duration": 8549, "tags": {}, "children": [ { "spanID": "cf6a659eb4862b21", "operationName": "__etypes_to_edefinitions", - "duration": 17, + "startTime": 1703354939824427, + "duration": 21, "tags": {}, "children": [] }, { "spanID": "e6f4590b9a164106", "operationName": "__ntypes_to_ocollections", - "duration": 7, + "startTime": 1703354939824477, + "duration": 8, "tags": {}, "children": [] } @@ -38,7 +43,8 @@ { "spanID": "4f65d4d9259f4329", "operationName": "__process_pyg_n_type", - "duration": 129517, + "startTime": 1703354939830920, + "duration": 151250, "tags": { "n_type": "v0", "n_type_size": 1008 @@ -47,7 +53,8 @@ { "spanID": "bad640fb19488dec", "operationName": "__process_pyg_node_batch", - "duration": 10941, + "startTime": 1703354939830962, + "duration": 13101, "tags": { "start_index": 0, "end_index": 1008 @@ -56,7 +63,8 @@ { "spanID": "e61a441c12e0c8b2", "operationName": "__set_adb_data", - "duration": 8076, + "startTime": 1703354939832173, + "duration": 9474, "tags": { "meta": "{}" }, @@ -64,20 +72,22 @@ { "spanID": "af19922ad9b8a714", "operationName": "__build_dataframe_from_tensor", - "duration": 3497, + "startTime": 1703354939832364, + "duration": 4942, "tags": { - "meta_key": "y", - "meta_val": "y" + "meta_key": "x", + "meta_val": "x" }, "children": [] }, { "spanID": "78de58575487ce1e", "operationName": "__build_dataframe_from_tensor", - "duration": 1515, + "startTime": 1703354939838226, + "duration": 573, "tags": { - "meta_key": "x", - "meta_val": "x" + "meta_key": "y", + "meta_val": "y" }, "children": [] } @@ -88,7 +98,8 @@ { "spanID": "19c78df48f4ff31e", "operationName": "__insert_adb_docs", - "duration": 118460, + "startTime": 1703354939844161, + "duration": 137979, "tags": { "col": "v0", "size": 1008 @@ -100,7 +111,8 @@ { "spanID": "6f25e2a25a921187", "operationName": "__process_pyg_n_type", - "duration": 77546, + "startTime": 1703354939984119, + "duration": 97803, "tags": { "n_type": "v1", "n_type_size": 821 @@ -109,7 +121,8 @@ { "spanID": "9c6316b950f24455", "operationName": "__process_pyg_node_batch", - "duration": 2240, + "startTime": 1703354939984160, + "duration": 2664, "tags": { "start_index": 0, "end_index": 821 @@ -118,7 +131,8 @@ { "spanID": "e9bb17bca3f2c9bf", "operationName": "__set_adb_data", - "duration": 1327, + "startTime": 1703354939984398, + "duration": 1577, "tags": { "meta": "{}" }, @@ -126,7 +140,8 @@ { "spanID": "f77383c13458a748", "operationName": "__build_dataframe_from_tensor", - "duration": 1000, + "startTime": 1703354939984466, + "duration": 1177, "tags": { "meta_key": "x", "meta_val": "x" @@ -140,7 +155,8 @@ { "spanID": "7a1d50068d723104", "operationName": "__insert_adb_docs", - "duration": 75209, + "startTime": 1703354939986867, + "duration": 95027, "tags": { "col": "v1", "size": 821 @@ -152,7 +168,8 @@ { "spanID": "dd84f39e71545a13", "operationName": "__process_pyg_n_type", - "duration": 73123, + "startTime": 1703354940083949, + "duration": 95862, "tags": { "n_type": "v2", "n_type_size": 894 @@ -161,7 +178,8 @@ { "spanID": "42af9fc385776e9a", "operationName": "__process_pyg_node_batch", - "duration": 2076, + "startTime": 1703354940083988, + "duration": 2578, "tags": { "start_index": 0, "end_index": 894 @@ -170,7 +188,8 @@ { "spanID": "ce164dba0ff18e02", "operationName": "__set_adb_data", - "duration": 1175, + "startTime": 1703354940084213, + "duration": 1486, "tags": { "meta": "{}" }, @@ -178,7 +197,8 @@ { "spanID": "8c778ea6eb2083e6", "operationName": "__build_dataframe_from_tensor", - "duration": 869, + "startTime": 1703354940084281, + "duration": 1109, "tags": { "meta_key": "x", "meta_val": "x" @@ -192,7 +212,8 @@ { "spanID": "03983ca8ea7e9d49", "operationName": "__insert_adb_docs", - "duration": 70960, + "startTime": 1703354940086606, + "duration": 93165, "tags": { "col": "v2", "size": 894 @@ -204,7 +225,8 @@ { "spanID": "b83e90ec17e0aa3c", "operationName": "__process_pyg_e_type", - "duration": 135382, + "startTime": 1703354940182160, + "duration": 175054, "tags": { "e_type": "[\"v2\",\"e0\",\"v1\"]", "e_type_size": 8895 @@ -213,7 +235,8 @@ { "spanID": "66194cb1d71037d1", "operationName": "__process_pyg_edge_batch", - "duration": 6889, + "startTime": 1703354940182209, + "duration": 8649, "tags": { "start_index": 0, "end_index": 8895 @@ -222,7 +245,8 @@ { "spanID": "d3290a4cb5d32b16", "operationName": "__set_adb_data", - "duration": 1731, + "startTime": 1703354940185657, + "duration": 2570, "tags": { "meta": "{}" }, @@ -230,7 +254,8 @@ { "spanID": "ab0c1681c8f8e3d0", "operationName": "__build_dataframe_from_tensor", - "duration": 1314, + "startTime": 1703354940185715, + "duration": 2015, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -244,7 +269,8 @@ { "spanID": "004ae545a0116be5", "operationName": "__insert_adb_docs", - "duration": 128374, + "startTime": 1703354940190917, + "duration": 166269, "tags": { "col": "e0", "size": 8895 @@ -256,7 +282,8 @@ { "spanID": "7e5b1e7f9ca5499d", "operationName": "__process_pyg_e_type", - "duration": 116710, + "startTime": 1703354940359096, + "duration": 145459, "tags": { "e_type": "[\"v1\",\"e0\",\"v2\"]", "e_type_size": 8161 @@ -265,7 +292,8 @@ { "spanID": "de1b372ad3fbf47a", "operationName": "__process_pyg_edge_batch", - "duration": 4913, + "startTime": 1703354940359142, + "duration": 5921, "tags": { "start_index": 0, "end_index": 8161 @@ -274,7 +302,8 @@ { "spanID": "3e70f16a55485822", "operationName": "__set_adb_data", - "duration": 1661, + "startTime": 1703354940362018, + "duration": 2004, "tags": { "meta": "{}" }, @@ -282,7 +311,8 @@ { "spanID": "534097cabaf3897a", "operationName": "__build_dataframe_from_tensor", - "duration": 1252, + "startTime": 1703354940362070, + "duration": 1530, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -296,7 +326,8 @@ { "spanID": "ded733e8b421eaeb", "operationName": "__insert_adb_docs", - "duration": 111671, + "startTime": 1703354940365115, + "duration": 139411, "tags": { "col": "e0", "size": 8161 @@ -308,7 +339,8 @@ { "spanID": "30e9c5cc101fbccc", "operationName": "__process_pyg_e_type", - "duration": 199804, + "startTime": 1703354940506405, + "duration": 247974, "tags": { "e_type": "[\"v0\",\"e0\",\"v1\"]", "e_type_size": 10022 @@ -317,7 +349,8 @@ { "spanID": "9148624feac1c14f", "operationName": "__process_pyg_edge_batch", - "duration": 5482, + "startTime": 1703354940506450, + "duration": 7089, "tags": { "start_index": 0, "end_index": 10022 @@ -326,7 +359,8 @@ { "spanID": "3d15eef738c1962e", "operationName": "__set_adb_data", - "duration": 1820, + "startTime": 1703354940510095, + "duration": 2325, "tags": { "meta": "{}" }, @@ -334,7 +368,8 @@ { "spanID": "f7b0b7d2cda8056c", "operationName": "__build_dataframe_from_tensor", - "duration": 1439, + "startTime": 1703354940510149, + "duration": 1824, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -348,7 +383,8 @@ { "spanID": "cd9d2b7d247a8333", "operationName": "__insert_adb_docs", - "duration": 194210, + "startTime": 1703354940513590, + "duration": 240750, "tags": { "col": "e0", "size": 10022 @@ -360,7 +396,8 @@ { "spanID": "72ae22448b0163c1", "operationName": "__process_pyg_e_type", - "duration": 124746, + "startTime": 1703354940759178, + "duration": 211050, "tags": { "e_type": "[\"v1\",\"e0\",\"v0\"]", "e_type_size": 8179 @@ -369,7 +406,8 @@ { "spanID": "149818d11759edc3", "operationName": "__process_pyg_edge_batch", - "duration": 4521, + "startTime": 1703354940759236, + "duration": 54068, "tags": { "start_index": 0, "end_index": 8179 @@ -378,7 +416,8 @@ { "spanID": "51ef1922fe43c49e", "operationName": "__set_adb_data", - "duration": 1521, + "startTime": 1703354940809338, + "duration": 2884, "tags": { "meta": "{}" }, @@ -386,7 +425,8 @@ { "spanID": "820865d6e005b860", "operationName": "__build_dataframe_from_tensor", - "duration": 1164, + "startTime": 1703354940809740, + "duration": 2000, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -400,7 +440,8 @@ { "spanID": "eece328bff7b118e", "operationName": "__insert_adb_docs", - "duration": 120125, + "startTime": 1703354940813375, + "duration": 156823, "tags": { "col": "e0", "size": 8179 @@ -412,7 +453,8 @@ { "spanID": "1beb37117d41e602", "operationName": "__process_pyg_e_type", - "duration": 120860, + "startTime": 1703354940972247, + "duration": 155663, "tags": { "e_type": "[\"v1\",\"e0\",\"v1\"]", "e_type_size": 8159 @@ -421,7 +463,8 @@ { "spanID": "8d1fd9b74d2b9deb", "operationName": "__process_pyg_edge_batch", - "duration": 5168, + "startTime": 1703354940972290, + "duration": 6041, "tags": { "start_index": 0, "end_index": 8159 @@ -430,7 +473,8 @@ { "spanID": "b4e1357d4a84eb03", "operationName": "__set_adb_data", - "duration": 1667, + "startTime": 1703354940975250, + "duration": 2092, "tags": { "meta": "{}" }, @@ -438,7 +482,8 @@ { "spanID": "8c25166a1ff39849", "operationName": "__build_dataframe_from_tensor", - "duration": 1240, + "startTime": 1703354940975304, + "duration": 1621, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -452,7 +497,8 @@ { "spanID": "d080e66e552f233a", "operationName": "__insert_adb_docs", - "duration": 115575, + "startTime": 1703354940978376, + "duration": 149505, "tags": { "col": "e0", "size": 8159 @@ -464,7 +510,8 @@ { "spanID": "8a5006c1ec188efb", "operationName": "__process_pyg_e_type", - "duration": 148804, + "startTime": 1703354941129788, + "duration": 181631, "tags": { "e_type": "[\"v0\",\"e0\",\"v2\"]", "e_type_size": 10034 @@ -473,7 +520,8 @@ { "spanID": "f6be1f723405095c", "operationName": "__process_pyg_edge_batch", - "duration": 5613, + "startTime": 1703354941129836, + "duration": 7327, "tags": { "start_index": 0, "end_index": 10034 @@ -482,7 +530,8 @@ { "spanID": "9a6a5f92cca74147", "operationName": "__set_adb_data", - "duration": 1902, + "startTime": 1703354941133467, + "duration": 2550, "tags": { "meta": "{}" }, @@ -490,7 +539,8 @@ { "spanID": "966e12778c1745a7", "operationName": "__build_dataframe_from_tensor", - "duration": 1509, + "startTime": 1703354941133519, + "duration": 1999, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -504,7 +554,8 @@ { "spanID": "71eacd0549a3e80e", "operationName": "__insert_adb_docs", - "duration": 143086, + "startTime": 1703354941137235, + "duration": 174154, "tags": { "col": "e0", "size": 10034 From f3e268a7ddb85ef4111067724caa93060117d209 Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Sat, 23 Dec 2023 13:29:00 -0500 Subject: [PATCH 35/73] update `benchmark/write.py` --- benchmark/write.py | 12 ++++++++---- 1 file changed, 8 insertions(+), 4 deletions(-) diff --git a/benchmark/write.py b/benchmark/write.py index 11b7f95..6bf1753 100644 --- a/benchmark/write.py +++ b/benchmark/write.py @@ -38,13 +38,15 @@ class JaegerSpan: def __init__( self, - spanID: str, - operationName: str, + span_id: str, + operation_name: str, + start_time: int, duration: int, tags: list[dict[str, str]], ): - self.span_id = spanID - self.operation_name = operationName + self.span_id = span_id + self.operation_name = operation_name + self.start_time = start_time self.duration = duration self.tags = { tag["key"]: tag["value"] @@ -65,6 +67,7 @@ def to_dict(self) -> dict[str, Any]: return { "spanID": self.span_id, "operationName": self.operation_name, + "startTime": self.start_time, "duration": self.duration, "tags": self.tags, "children": [child.to_dict() for child in self.children.values()], @@ -110,6 +113,7 @@ def __build_span_tree(self) -> None: span_object = JaegerSpan( span_id, operation_name, + span["startTime"], span["duration"], span["tags"], ) From 38dd8ef3be14b91d5326920c0a1c30049d29a61c Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Sat, 23 Dec 2023 14:00:23 -0500 Subject: [PATCH 36/73] cleanup compare.py --- benchmark/compare.py | 170 ++++++++++++++++++++++++++++--------------- 1 file changed, 111 insertions(+), 59 deletions(-) diff --git a/benchmark/compare.py b/benchmark/compare.py index 9ad89db..1d55fdc 100644 --- a/benchmark/compare.py +++ b/benchmark/compare.py @@ -1,85 +1,137 @@ import json import pathlib +from typing import Optional + + +def sort_children_by_start_time(children): + return sorted(children, key=lambda span: span["startTime"]) + + +def compare_span(master_child: Optional[dict], branch_child: Optional[dict]): + if master_child and branch_child: + assert master_child.get("operationName") == branch_child.get("operationName") + assert master_child.get("tags") == branch_child.get("tags") + + operation_name = ( + master_child.get("operationName") + if master_child + else branch_child.get("operationName") + ) + + master_duration = master_child.get("duration") if master_child else None + branch_duration = branch_child.get("duration") if branch_child else None + improvement = ( + f"{round((1 - branch_duration / master_duration) * 100)}%" + if master_duration and branch_duration + else None + ) + + comparison = { + "operationName": operation_name, + "master_duration": master_duration, + "branch_duration": branch_duration, + "improvement": improvement, + "tags": master_child.get("tags") if master_child else branch_child.get("tags"), + "children": [], + } + + if master_child and branch_child: + comparison["children"] = compare_children( + master_child["children"], branch_child["children"] + ) + return comparison -class DiffSpan: - def __init__( - self, - span_id: str, - operation_name: str, - master_duration: int, - branch_duration: int, - ): - self.span_id = span_id - self.operation_name = operation_name - self.master_duration = master_duration - self.branch_duration = branch_duration - self.improvement = f"{round((1 - branch_duration / master_duration) * 100)}%" - self.children: dict[str, "DiffSpan"] = {} - - def add_child(self, span_id: str, child: "DiffSpan"): - self.children[span_id] = child - - def to_dict(self, include_children: bool = True): - res = { - "span_id": self.span_id, - "operation_name": self.operation_name, - "master_duration": self.master_duration, - "branch_duration": self.branch_duration, - "improvement": self.improvement, - } - if include_children: - res["children"] = [child.to_dict() for child in self.children.values()] +def match_children( + master_child: dict, + branch_child: dict, + master_children: list[dict], + branch_children: list[dict], +): + # Attempt to find a matching child in Branch Children for the current Master Child + for i, branch_candidate in enumerate(branch_children): + if branch_candidate.get("operationName") == master_child.get("operationName"): + branch_children.pop(i) + return master_child, branch_candidate - return res + # Attempt to find a matching child in Master Children for the current Branch Child + for i, master_candidate in enumerate(master_children): + if master_candidate.get("operationName") == branch_child.get("operationName"): + master_children.pop(i) + return master_candidate, branch_child + return master_child, branch_child -class DiffTree: - def __init__(self, master_trace: dict, branch_trace: dict): - self.root_span = self.__build_diff_tree(master_trace, branch_trace) - def __build_diff_tree(self, master_trace: dict, branch_trace: dict): - assert master_trace["operationName"] == branch_trace["operationName"] +def compare_children(master_children: list[dict], branch_children: list[dict]): + result = [] + master_children_sorted = sort_children_by_start_time(master_children) + branch_children_sorted = sort_children_by_start_time(branch_children) - diff_span = DiffSpan( - master_trace["spanID"], - master_trace["operationName"], - master_trace["duration"], - branch_trace["duration"], - ) + while master_children_sorted or branch_children_sorted: + master_child = master_children_sorted.pop(0) if master_children_sorted else None + branch_child = branch_children_sorted.pop(0) if branch_children_sorted else None - # Recursively build the tree for child spans - for head_child_span, branch_child_span in zip( - master_trace["children"], branch_trace["children"] + if ( + master_child + and branch_child + and master_child.get("operationName") != branch_child.get("operationName") ): - child_span = self.__build_diff_tree(head_child_span, branch_child_span) - diff_span.add_child(head_child_span["spanID"], child_span) - - return diff_span - - def to_dict(self): - return self.root_span.to_dict() + # Find the matching pair if they are out of order + master_child, branch_child = match_children( + master_child, + branch_child, + master_children_sorted, + branch_children_sorted, + ) + + result.append(compare_span(master_child, branch_child)) + + return result + + +def compare_traces(master_trace: dict, branch_trace: dict): + assert master_trace.get("operationName") == branch_trace.get("operationName") + assert master_trace.get("tags") == branch_trace.get("tags") + + result = { + "operationName": master_trace.get("operationName"), + "master_duration": master_trace["duration"], + "branch_duration": branch_trace["duration"], + "improvement": f"{round((1 - branch_trace['duration'] / master_trace['duration']) * 100)}%", + "tags": master_trace.get("tags"), + "children": compare_children( + master_trace["children"], branch_trace["children"] + ), + } - def to_json_file(self, operation: str): - current_dir = pathlib.Path(__file__).parent.absolute() - with open(f"{current_dir}/diff/{operation}.json", "w") as file: - file.write(json.dumps(self.to_dict(), indent=4)) + return result def main(): current_dir = pathlib.Path(__file__).parent.absolute() + root_span_diffs = {} for operation in ["pyg_to_arangodb", "arangodb_to_pyg"]: master_trace = json.load(open(f"{current_dir}/traces/master/{operation}.json")) branch_trace = json.load(open(f"{current_dir}/traces/branch/{operation}.json")) + diff_trace = compare_traces(master_trace, branch_trace) + + with open(f"{current_dir}/diff/{operation}.json", "w") as file: + file.write(json.dumps(diff_trace, indent=4)) + + root_span_diffs[operation] = { + "master_duration": diff_trace["master_duration"], + "branch_duration": diff_trace["branch_duration"], + "improvement": diff_trace["improvement"], + } - diff_tree = DiffTree(master_trace, branch_trace) - diff_tree.to_json_file(operation) + print("-" * 50) + print(json.dumps(root_span_diffs, indent=4)) + print("-" * 50) - print("-" * 50) - print(json.dumps(diff_tree.root_span.to_dict(include_children=False), indent=4)) - print("-" * 50) + return root_span_diffs if __name__ == "__main__": From a6856373b606b353a737a2731e0bbdf642e352fd Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Sat, 23 Dec 2023 15:08:14 -0500 Subject: [PATCH 37/73] tracing cleanup --- adbpyg_adapter/adapter.py | 9 ++++----- adbpyg_adapter/tracing.py | 10 ++++++++++ 2 files changed, 14 insertions(+), 5 deletions(-) diff --git a/adbpyg_adapter/adapter.py b/adbpyg_adapter/adapter.py index 2cfe26b..fd0c08c 100644 --- a/adbpyg_adapter/adapter.py +++ b/adbpyg_adapter/adapter.py @@ -852,7 +852,7 @@ def get_aql_return_value( """ col_size: int = self.__db.collection(col).count() - TracingManager.set_attributes(col=col, col_size=col_size, meta=str(meta)) + TracingManager.set_attributes(col=col, col_size=col_size, meta=meta) with get_export_spinner_progress(f"ADB Export: '{col}' ({col_size})") as p: p.add_task(col) @@ -1018,8 +1018,7 @@ def __process_adb_edge_df( with start_as_current_span("__process_adb_edge_type_df"): TracingManager.set_attributes( - edge_type=edge_type, - edge_type_df_size=count, + edge_type=edge_type, edge_type_df_size=count ) # 3. Check for partial Edge Collection import @@ -1139,7 +1138,7 @@ def __build_tensor_from_dataframe( :rtype: torch.Tensor :raise adbpyg_adapter.exceptions.ADBMetagraphError: If invalid **meta_val**. """ - TracingManager.set_attributes(meta_key=meta_key, meta_val=str(meta_val)) + TracingManager.set_attributes(meta_key=meta_key, meta_val=meta_val) logger.debug(f"__build_tensor_from_dataframe(df, {meta_key}, {str(meta_val)})") if type(meta_val) is str: @@ -1666,7 +1665,7 @@ def __set_adb_data( valid_meta: Dict[Any, PyGMetagraphValues] valid_meta = meta if type(meta) is dict else {m: m for m in meta} - TracingManager.set_attributes(meta=str(valid_meta)) + TracingManager.set_attributes(meta=valid_meta) pyg_keys = ( set(valid_meta.keys()) diff --git a/adbpyg_adapter/tracing.py b/adbpyg_adapter/tracing.py index ea0bde5..aeb0d03 100644 --- a/adbpyg_adapter/tracing.py +++ b/adbpyg_adapter/tracing.py @@ -34,6 +34,16 @@ def set_attributes(self, **attributes: Any) -> None: if TRACING_ENABLED and self.__tracer is not None: current_span = trace.get_current_span() for k, v in attributes.items(): + if isinstance(v, set): + v = list(sorted(v)) + + elif isinstance(v, dict): + v = str(dict(sorted(v.items()))) + + # 2D+ List + elif isinstance(v, list) and any(isinstance(item, list) for item in v): + v = str(v) + current_span.set_attribute(k, v) From 63ee34534cf2649cfa08ca2d43e6a5f63ac7853b Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Sat, 23 Dec 2023 15:40:32 -0500 Subject: [PATCH 38/73] cleanup tracing (again) --- adbpyg_adapter/adapter.py | 2 +- adbpyg_adapter/tracing.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/adbpyg_adapter/adapter.py b/adbpyg_adapter/adapter.py index fd0c08c..c981cab 100644 --- a/adbpyg_adapter/adapter.py +++ b/adbpyg_adapter/adapter.py @@ -1103,7 +1103,7 @@ def __set_pyg_data( """ valid_meta: Dict[str, ADBMetagraphValues] valid_meta = meta if type(meta) is dict else {m: m for m in meta} - TracingManager.set_attributes(meta=str(valid_meta)) + TracingManager.set_attributes(meta=valid_meta) for k, v in valid_meta.items(): t = self.__build_tensor_from_dataframe(df, k, v) diff --git a/adbpyg_adapter/tracing.py b/adbpyg_adapter/tracing.py index aeb0d03..29cde28 100644 --- a/adbpyg_adapter/tracing.py +++ b/adbpyg_adapter/tracing.py @@ -35,7 +35,7 @@ def set_attributes(self, **attributes: Any) -> None: current_span = trace.get_current_span() for k, v in attributes.items(): if isinstance(v, set): - v = list(sorted(v)) + v = str(sorted(v)) elif isinstance(v, dict): v = str(dict(sorted(v.items()))) From c81fbc5a214ef850ca8ca1293cb296d105289b4a Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Sat, 23 Dec 2023 15:40:48 -0500 Subject: [PATCH 39/73] fix `compare.py` --- benchmark/compare.py | 46 +++++++++++++++++++++++++------------------- 1 file changed, 26 insertions(+), 20 deletions(-) diff --git a/benchmark/compare.py b/benchmark/compare.py index 1d55fdc..4be5451 100644 --- a/benchmark/compare.py +++ b/benchmark/compare.py @@ -51,13 +51,19 @@ def match_children( ): # Attempt to find a matching child in Branch Children for the current Master Child for i, branch_candidate in enumerate(branch_children): - if branch_candidate.get("operationName") == master_child.get("operationName"): + name_match = master_child["operationName"] == branch_candidate["operationName"] + tags_match = master_child["tags"] == branch_candidate["tags"] + + if name_match and tags_match: branch_children.pop(i) return master_child, branch_candidate # Attempt to find a matching child in Master Children for the current Branch Child for i, master_candidate in enumerate(master_children): - if master_candidate.get("operationName") == branch_child.get("operationName"): + name_match = master_candidate["operationName"] == branch_child["operationName"] + tags_match = master_candidate["tags"] == branch_child["tags"] + + if name_match and tags_match: master_children.pop(i) return master_candidate, branch_child @@ -73,20 +79,24 @@ def compare_children(master_children: list[dict], branch_children: list[dict]): master_child = master_children_sorted.pop(0) if master_children_sorted else None branch_child = branch_children_sorted.pop(0) if branch_children_sorted else None - if ( - master_child - and branch_child - and master_child.get("operationName") != branch_child.get("operationName") - ): - # Find the matching pair if they are out of order - master_child, branch_child = match_children( - master_child, - branch_child, - master_children_sorted, - branch_children_sorted, - ) - - result.append(compare_span(master_child, branch_child)) + if master_child and branch_child: + name_match = master_child["operationName"] == branch_child["operationName"] + tags_match = master_child["tags"] == branch_child["tags"] + + if not (name_match and tags_match): + # Find the matching pair if they are out of order + master_child, branch_child = match_children( + master_child, + branch_child, + master_children_sorted, + branch_children_sorted, + ) + + if master_child or branch_child: + result.append(compare_span(master_child, branch_child)) + else: + # If both are None, break out of the loop to prevent appending None values + break return result @@ -127,11 +137,7 @@ def main(): "improvement": diff_trace["improvement"], } - print("-" * 50) print(json.dumps(root_span_diffs, indent=4)) - print("-" * 50) - - return root_span_diffs if __name__ == "__main__": From cf95b8a88b5d90380a632341adab42f68adb6c46 Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Sat, 23 Dec 2023 15:40:54 -0500 Subject: [PATCH 40/73] new master traces --- benchmark/traces/master/arangodb_to_pyg.json | 1478 +++++++++--------- benchmark/traces/master/pyg_to_arangodb.json | 212 +-- 2 files changed, 845 insertions(+), 845 deletions(-) diff --git a/benchmark/traces/master/arangodb_to_pyg.json b/benchmark/traces/master/arangodb_to_pyg.json index d734b40..fd6134f 100644 --- a/benchmark/traces/master/arangodb_to_pyg.json +++ b/benchmark/traces/master/arangodb_to_pyg.json @@ -1,8 +1,8 @@ { "spanID": "935ddd725129fb7c", "operationName": "arangodb_to_pyg", - "startTime": 1703354941312582, - "duration": 907787, + "startTime": 1703363922836354, + "duration": 906241, "tags": { "name": "FakeHeteroGraphBenchmark" }, @@ -10,8 +10,8 @@ { "spanID": "4a5308cc3dfabc08", "operationName": "__process_adb_v_col", - "startTime": 1703354941312652, - "duration": 53370, + "startTime": 1703363922836426, + "duration": 55595, "tags": { "v_col": "v0" }, @@ -19,27 +19,27 @@ { "spanID": "307bf3262f120554", "operationName": "__fetch_adb_docs", - "startTime": 1703354941312676, - "duration": 37020, + "startTime": 1703363922836453, + "duration": 37678, "tags": { "col": "v0", "col_size": 1008, - "meta": "{'x', 'y'}" + "meta": "['x', 'y']" }, "children": [] }, { "spanID": "2fcd81b5d24bace4", "operationName": "__process_adb_cursor", - "startTime": 1703354941349765, - "duration": 16240, + "startTime": 1703363922874201, + "duration": 17807, "tags": {}, "children": [ { "spanID": "9cdeb3e60870e15c", "operationName": "__process_adb_vertex_df", - "startTime": 1703354941352153, - "duration": 8414, + "startTime": 1703363922877910, + "duration": 8373, "tags": { "i": 0, "vertex_df_size": 1000 @@ -48,8 +48,8 @@ { "spanID": "a81ad477fb3675b8", "operationName": "__set_pyg_data", - "startTime": 1703354941352398, - "duration": 8163, + "startTime": 1703363922878172, + "duration": 8105, "tags": { "meta": "{'x': 'x', 'y': 'y'}" }, @@ -57,22 +57,22 @@ { "spanID": "79fdef7c42930b33", "operationName": "__build_tensor_from_dataframe", - "startTime": 1703354941352427, - "duration": 7880, + "startTime": 1703363922878206, + "duration": 188, "tags": { - "meta_key": "x", - "meta_val": "x" + "meta_key": "y", + "meta_val": "y" }, "children": [] }, { "spanID": "16febaa011af923d", "operationName": "__build_tensor_from_dataframe", - "startTime": 1703354941360374, - "duration": 176, + "startTime": 1703363922878434, + "duration": 7823, "tags": { - "meta_key": "y", - "meta_val": "y" + "meta_key": "x", + "meta_val": "x" }, "children": [] } @@ -83,8 +83,8 @@ { "spanID": "c1f254b8adc0da7a", "operationName": "__process_adb_vertex_df", - "startTime": 1703354941363673, - "duration": 1458, + "startTime": 1703363922889433, + "duration": 1690, "tags": { "i": 1000, "vertex_df_size": 8 @@ -93,8 +93,8 @@ { "spanID": "e07405eb215663ab", "operationName": "__set_pyg_data", - "startTime": 1703354941363771, - "duration": 1353, + "startTime": 1703363922889535, + "duration": 1577, "tags": { "meta": "{'x': 'x', 'y': 'y'}" }, @@ -102,22 +102,22 @@ { "spanID": "ec62b2c82648ee38", "operationName": "__build_tensor_from_dataframe", - "startTime": 1703354941363804, - "duration": 120, + "startTime": 1703363922889566, + "duration": 64, "tags": { - "meta_key": "x", - "meta_val": "x" + "meta_key": "y", + "meta_val": "y" }, "children": [] }, { "spanID": "d7ab792809e469e6", "operationName": "__build_tensor_from_dataframe", - "startTime": 1703354941365013, - "duration": 79, + "startTime": 1703363922890273, + "duration": 152, "tags": { - "meta_key": "y", - "meta_val": "y" + "meta_key": "x", + "meta_val": "x" }, "children": [] } @@ -132,8 +132,8 @@ { "spanID": "e5eeac76148b2758", "operationName": "__process_adb_v_col", - "startTime": 1703354941366089, - "duration": 71304, + "startTime": 1703363922892077, + "duration": 37759, "tags": { "v_col": "v1" }, @@ -141,27 +141,27 @@ { "spanID": "ec4f217bb306d1a8", "operationName": "__fetch_adb_docs", - "startTime": 1703354941366122, - "duration": 62232, + "startTime": 1703363922892112, + "duration": 28421, "tags": { "col": "v1", "col_size": 821, - "meta": "{'x'}" + "meta": "['x']" }, "children": [] }, { "spanID": "8a64c1b9d450fe4a", "operationName": "__process_adb_cursor", - "startTime": 1703354941428423, - "duration": 8950, + "startTime": 1703363922920599, + "duration": 9216, "tags": {}, "children": [ { "spanID": "642bfa42aef9c00b", "operationName": "__process_adb_vertex_df", - "startTime": 1703354941429933, - "duration": 6105, + "startTime": 1703363922922150, + "duration": 6370, "tags": { "i": 0, "vertex_df_size": 821 @@ -170,8 +170,8 @@ { "spanID": "b48d73f1d67e55fd", "operationName": "__set_pyg_data", - "startTime": 1703354941430171, - "duration": 5861, + "startTime": 1703363922922408, + "duration": 6106, "tags": { "meta": "{'x': 'x'}" }, @@ -179,8 +179,8 @@ { "spanID": "468ff53d864a7a50", "operationName": "__build_tensor_from_dataframe", - "startTime": 1703354941430199, - "duration": 5810, + "startTime": 1703363922922444, + "duration": 6048, "tags": { "meta_key": "x", "meta_val": "x" @@ -198,8 +198,8 @@ { "spanID": "cfc6e62585940927", "operationName": "__process_adb_v_col", - "startTime": 1703354941437474, - "duration": 38361, + "startTime": 1703363922929928, + "duration": 36396, "tags": { "v_col": "v2" }, @@ -207,27 +207,27 @@ { "spanID": "d977e9933c49d76f", "operationName": "__fetch_adb_docs", - "startTime": 1703354941437507, - "duration": 29308, + "startTime": 1703363922929964, + "duration": 27407, "tags": { "col": "v2", "col_size": 894, - "meta": "{'x'}" + "meta": "['x']" }, "children": [] }, { "spanID": "e521460637176e84", "operationName": "__process_adb_cursor", - "startTime": 1703354941466891, - "duration": 8926, + "startTime": 1703363922957436, + "duration": 8867, "tags": {}, "children": [ { "spanID": "96fd35d0adf20806", "operationName": "__process_adb_vertex_df", - "startTime": 1703354941468418, - "duration": 6024, + "startTime": 1703363922959093, + "duration": 5887, "tags": { "i": 0, "vertex_df_size": 894 @@ -236,8 +236,8 @@ { "spanID": "f323ca74d3447490", "operationName": "__set_pyg_data", - "startTime": 1703354941468664, - "duration": 5771, + "startTime": 1703363922959350, + "duration": 5620, "tags": { "meta": "{'x': 'x'}" }, @@ -245,8 +245,8 @@ { "spanID": "9466e4726b5f5241", "operationName": "__build_tensor_from_dataframe", - "startTime": 1703354941468691, - "duration": 5715, + "startTime": 1703363922959382, + "duration": 5558, "tags": { "meta_key": "x", "meta_val": "x" @@ -264,8 +264,8 @@ { "spanID": "73581a8146743741", "operationName": "__process_adb_e_col", - "startTime": 1703354941475895, - "duration": 744361, + "startTime": 1703363922966374, + "duration": 776069, "tags": { "e_col": "e0" }, @@ -273,8 +273,8 @@ { "spanID": "a905d7507e1ea9c5", "operationName": "__fetch_adb_docs", - "startTime": 1703354941475931, - "duration": 10377, + "startTime": 1703363922966406, + "duration": 10465, "tags": { "col": "e0", "col_size": 53450, @@ -285,15 +285,15 @@ { "spanID": "ff0ac0f1a425799a", "operationName": "__process_adb_cursor", - "startTime": 1703354941486371, - "duration": 733870, + "startTime": 1703363922976933, + "duration": 765495, "tags": {}, "children": [ { "spanID": "eabca8d0b341facd", "operationName": "__process_adb_edge_df", - "startTime": 1703354941487877, - "duration": 9804, + "startTime": 1703363922978544, + "duration": 10606, "tags": { "edge_df_size": 1000 }, @@ -301,24 +301,24 @@ { "spanID": "cb175a5afb82860d", "operationName": "__split_adb_ids", - "startTime": 1703354941487990, - "duration": 814, + "startTime": 1703363922978649, + "duration": 1909, "tags": {}, "children": [] }, { "spanID": "151665705b7c709a", "operationName": "__split_adb_ids", - "startTime": 1703354941489141, - "duration": 654, + "startTime": 1703363922980973, + "duration": 753, "tags": {}, "children": [] }, { "spanID": "9cdf5a865306f3f5", "operationName": "__process_adb_edge_type_df", - "startTime": 1703354941492873, - "duration": 4770, + "startTime": 1703363922984230, + "duration": 4882, "tags": { "edge_type": "[\"v2\",\"e0\",\"v1\"]", "edge_type_df_size": 1000 @@ -327,8 +327,8 @@ { "spanID": "7c879b741d878f9f", "operationName": "__set_pyg_data", - "startTime": 1703354941497151, - "duration": 485, + "startTime": 1703363922988627, + "duration": 478, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -336,8 +336,8 @@ { "spanID": "a1515607964a870c", "operationName": "__build_tensor_from_dataframe", - "startTime": 1703354941497190, - "duration": 430, + "startTime": 1703363922988667, + "duration": 421, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -353,8 +353,8 @@ { "spanID": "d857010255d44936", "operationName": "__process_adb_edge_df", - "startTime": 1703354941503809, - "duration": 5345, + "startTime": 1703363922995539, + "duration": 5226, "tags": { "edge_df_size": 1000 }, @@ -362,24 +362,24 @@ { "spanID": "3e37952d30bcab0e", "operationName": "__split_adb_ids", - "startTime": 1703354941503909, - "duration": 710, + "startTime": 1703363922995644, + "duration": 741, "tags": {}, "children": [] }, { "spanID": "bb42e0b20426465e", "operationName": "__split_adb_ids", - "startTime": 1703354941504946, - "duration": 643, + "startTime": 1703363922996741, + "duration": 722, "tags": {}, "children": [] }, { "spanID": "1dfc83524562be7f", "operationName": "__process_adb_edge_type_df", - "startTime": 1703354941506930, - "duration": 2187, + "startTime": 1703363922998581, + "duration": 2145, "tags": { "edge_type": "[\"v2\",\"e0\",\"v1\"]", "edge_type_df_size": 1000 @@ -388,8 +388,8 @@ { "spanID": "38701a14b490b608", "operationName": "__set_pyg_data", - "startTime": 1703354941508640, - "duration": 470, + "startTime": 1703363923000244, + "duration": 475, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -397,8 +397,8 @@ { "spanID": "cb69ca385f3f5638", "operationName": "__build_tensor_from_dataframe", - "startTime": 1703354941508673, - "duration": 412, + "startTime": 1703363923000282, + "duration": 415, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -414,8 +414,8 @@ { "spanID": "552116dd2ba4b180", "operationName": "__process_adb_edge_df", - "startTime": 1703354941515203, - "duration": 5046, + "startTime": 1703363923006433, + "duration": 4664, "tags": { "edge_df_size": 1000 }, @@ -423,24 +423,24 @@ { "spanID": "d0dfae436d16ee18", "operationName": "__split_adb_ids", - "startTime": 1703354941515305, - "duration": 751, + "startTime": 1703363923006514, + "duration": 694, "tags": {}, "children": [] }, { "spanID": "19c16a0d0febd845", "operationName": "__split_adb_ids", - "startTime": 1703354941516393, - "duration": 655, + "startTime": 1703363923007504, + "duration": 646, "tags": {}, "children": [] }, { "spanID": "2577bffac87a7463", "operationName": "__process_adb_edge_type_df", - "startTime": 1703354941518109, - "duration": 2106, + "startTime": 1703363923009113, + "duration": 1952, "tags": { "edge_type": "[\"v2\",\"e0\",\"v1\"]", "edge_type_df_size": 1000 @@ -449,8 +449,8 @@ { "spanID": "b29a8b06daf66c5f", "operationName": "__set_pyg_data", - "startTime": 1703354941519735, - "duration": 473, + "startTime": 1703363923010608, + "duration": 451, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -458,8 +458,8 @@ { "spanID": "0b9475b138018b47", "operationName": "__build_tensor_from_dataframe", - "startTime": 1703354941519765, - "duration": 420, + "startTime": 1703363923010639, + "duration": 402, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -475,8 +475,8 @@ { "spanID": "92e8e269d12ecbc4", "operationName": "__process_adb_edge_df", - "startTime": 1703354941535957, - "duration": 5092, + "startTime": 1703363923016265, + "duration": 4745, "tags": { "edge_df_size": 1000 }, @@ -484,24 +484,24 @@ { "spanID": "e8f6cf32a25b59fd", "operationName": "__split_adb_ids", - "startTime": 1703354941536070, - "duration": 738, + "startTime": 1703363923016347, + "duration": 689, "tags": {}, "children": [] }, { "spanID": "88c132adefbfc19e", "operationName": "__split_adb_ids", - "startTime": 1703354941537135, - "duration": 655, + "startTime": 1703363923017330, + "duration": 651, "tags": {}, "children": [] }, { "spanID": "ae3b16ec9a27d858", "operationName": "__process_adb_edge_type_df", - "startTime": 1703354941538861, - "duration": 2156, + "startTime": 1703363923018944, + "duration": 2032, "tags": { "edge_type": "[\"v2\",\"e0\",\"v1\"]", "edge_type_df_size": 1000 @@ -510,8 +510,8 @@ { "spanID": "06d599e812f175ff", "operationName": "__set_pyg_data", - "startTime": 1703354941540565, - "duration": 447, + "startTime": 1703363923020510, + "duration": 459, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -519,8 +519,8 @@ { "spanID": "a28f5ab01fdb8b32", "operationName": "__build_tensor_from_dataframe", - "startTime": 1703354941540595, - "duration": 398, + "startTime": 1703363923020542, + "duration": 403, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -536,8 +536,8 @@ { "spanID": "9b38fe803042e325", "operationName": "__process_adb_edge_df", - "startTime": 1703354941546965, - "duration": 5033, + "startTime": 1703363923026860, + "duration": 4933, "tags": { "edge_df_size": 1000 }, @@ -545,24 +545,24 @@ { "spanID": "9371a71fd480865f", "operationName": "__split_adb_ids", - "startTime": 1703354941547079, - "duration": 736, + "startTime": 1703363923026954, + "duration": 714, "tags": {}, "children": [] }, { "spanID": "64264cd51ea45cd6", "operationName": "__split_adb_ids", - "startTime": 1703354941548148, - "duration": 648, + "startTime": 1703363923028004, + "duration": 656, "tags": {}, "children": [] }, { "spanID": "5ec17dbe176ea1b1", "operationName": "__process_adb_edge_type_df", - "startTime": 1703354941549891, - "duration": 2075, + "startTime": 1703363923029736, + "duration": 2022, "tags": { "edge_type": "[\"v2\",\"e0\",\"v1\"]", "edge_type_df_size": 1000 @@ -571,7 +571,7 @@ { "spanID": "fb0323a1d576d415", "operationName": "__set_pyg_data", - "startTime": 1703354941551503, + "startTime": 1703363923031294, "duration": 457, "tags": { "meta": "{'edge_attr': 'edge_attr'}" @@ -580,8 +580,8 @@ { "spanID": "0950fd131db53334", "operationName": "__build_tensor_from_dataframe", - "startTime": 1703354941551536, - "duration": 401, + "startTime": 1703363923031327, + "duration": 403, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -597,8 +597,8 @@ { "spanID": "0589f8779b025244", "operationName": "__process_adb_edge_df", - "startTime": 1703354941557204, - "duration": 4655, + "startTime": 1703363923036865, + "duration": 5013, "tags": { "edge_df_size": 1000 }, @@ -606,24 +606,24 @@ { "spanID": "f606254131d0b664", "operationName": "__split_adb_ids", - "startTime": 1703354941557283, - "duration": 699, + "startTime": 1703363923036948, + "duration": 746, "tags": {}, "children": [] }, { "spanID": "2f5a522af87f43fd", "operationName": "__split_adb_ids", - "startTime": 1703354941558270, - "duration": 638, + "startTime": 1703363923038000, + "duration": 656, "tags": {}, "children": [] }, { "spanID": "1fb797fab7d6467b", "operationName": "__process_adb_edge_type_df", - "startTime": 1703354941559854, - "duration": 1971, + "startTime": 1703363923039698, + "duration": 2143, "tags": { "edge_type": "[\"v2\",\"e0\",\"v1\"]", "edge_type_df_size": 1000 @@ -632,8 +632,8 @@ { "spanID": "35e8579a7aaf0e89", "operationName": "__set_pyg_data", - "startTime": 1703354941561364, - "duration": 455, + "startTime": 1703363923041361, + "duration": 473, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -641,8 +641,8 @@ { "spanID": "ccfdba9bba26d851", "operationName": "__build_tensor_from_dataframe", - "startTime": 1703354941561393, - "duration": 405, + "startTime": 1703363923041396, + "duration": 413, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -658,8 +658,8 @@ { "spanID": "efdd35f80fa34266", "operationName": "__process_adb_edge_df", - "startTime": 1703354941567044, - "duration": 4722, + "startTime": 1703363923047379, + "duration": 5234, "tags": { "edge_df_size": 1000 }, @@ -667,24 +667,24 @@ { "spanID": "05d51433ade9b2b4", "operationName": "__split_adb_ids", - "startTime": 1703354941567135, - "duration": 701, + "startTime": 1703363923047482, + "duration": 759, "tags": {}, "children": [] }, { "spanID": "6cf55b158b53031d", "operationName": "__split_adb_ids", - "startTime": 1703354941568142, - "duration": 656, + "startTime": 1703363923048563, + "duration": 681, "tags": {}, "children": [] }, { "spanID": "19fbeb1d9edfa3da", "operationName": "__process_adb_edge_type_df", - "startTime": 1703354941569766, - "duration": 1968, + "startTime": 1703363923050279, + "duration": 2296, "tags": { "edge_type": "[\"v2\",\"e0\",\"v1\"]", "edge_type_df_size": 1000 @@ -693,8 +693,8 @@ { "spanID": "428a1c22d5fdb76a", "operationName": "__set_pyg_data", - "startTime": 1703354941571281, - "duration": 445, + "startTime": 1703363923052100, + "duration": 468, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -702,8 +702,8 @@ { "spanID": "3888447911ebcd49", "operationName": "__build_tensor_from_dataframe", - "startTime": 1703354941571310, - "duration": 397, + "startTime": 1703363923052136, + "duration": 409, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -719,8 +719,8 @@ { "spanID": "a59cec98126cbc8f", "operationName": "__process_adb_edge_df", - "startTime": 1703354941577551, - "duration": 4899, + "startTime": 1703363923058534, + "duration": 4965, "tags": { "edge_df_size": 1000 }, @@ -728,24 +728,24 @@ { "spanID": "59acdd984d125e7f", "operationName": "__split_adb_ids", - "startTime": 1703354941577635, - "duration": 696, + "startTime": 1703363923058633, + "duration": 718, "tags": {}, "children": [] }, { "spanID": "2e2950656fa231e9", "operationName": "__split_adb_ids", - "startTime": 1703354941578629, - "duration": 648, + "startTime": 1703363923059675, + "duration": 663, "tags": {}, "children": [] }, { "spanID": "80ee526e0fa07a3f", "operationName": "__process_adb_edge_type_df", - "startTime": 1703354941580252, - "duration": 2044, + "startTime": 1703363923061447, + "duration": 2018, "tags": { "edge_type": "[\"v2\",\"e0\",\"v1\"]", "edge_type_df_size": 1000 @@ -754,8 +754,8 @@ { "spanID": "0a14b90a7795e986", "operationName": "__set_pyg_data", - "startTime": 1703354941581790, - "duration": 493, + "startTime": 1703363923063002, + "duration": 457, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -763,8 +763,8 @@ { "spanID": "19d5f97098b33c6e", "operationName": "__build_tensor_from_dataframe", - "startTime": 1703354941581820, - "duration": 444, + "startTime": 1703363923063037, + "duration": 401, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -780,8 +780,8 @@ { "spanID": "fcfcfa81b306d700", "operationName": "__process_adb_edge_df", - "startTime": 1703354941587725, - "duration": 7314, + "startTime": 1703363923069008, + "duration": 7771, "tags": { "edge_df_size": 1000 }, @@ -789,24 +789,24 @@ { "spanID": "3308fb2e642aad48", "operationName": "__split_adb_ids", - "startTime": 1703354941587807, - "duration": 705, + "startTime": 1703363923069113, + "duration": 726, "tags": {}, "children": [] }, { "spanID": "5bca47be429817c5", "operationName": "__split_adb_ids", - "startTime": 1703354941588814, - "duration": 652, + "startTime": 1703363923070158, + "duration": 661, "tags": {}, "children": [] }, { "spanID": "bb4a06cbe786ab37", "operationName": "__process_adb_edge_type_df", - "startTime": 1703354941591893, - "duration": 1965, + "startTime": 1703363923073278, + "duration": 2237, "tags": { "edge_type": "[\"v2\",\"e0\",\"v1\"]", "edge_type_df_size": 895 @@ -815,8 +815,8 @@ { "spanID": "d69c91c278601602", "operationName": "__set_pyg_data", - "startTime": 1703354941593442, - "duration": 410, + "startTime": 1703363923075085, + "duration": 424, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -824,8 +824,8 @@ { "spanID": "eb21a3f6e6fd68e8", "operationName": "__build_tensor_from_dataframe", - "startTime": 1703354941593472, - "duration": 361, + "startTime": 1703363923075117, + "duration": 368, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -839,8 +839,8 @@ { "spanID": "2b5f693291dc59ef", "operationName": "__process_adb_edge_type_df", - "startTime": 1703354941593882, - "duration": 1138, + "startTime": 1703363923075538, + "duration": 1225, "tags": { "edge_type": "[\"v1\",\"e0\",\"v2\"]", "edge_type_df_size": 105 @@ -849,8 +849,8 @@ { "spanID": "ac322c12b29c467d", "operationName": "__set_pyg_data", - "startTime": 1703354941594899, - "duration": 115, + "startTime": 1703363923076644, + "duration": 113, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -858,8 +858,8 @@ { "spanID": "f76fbfb83412fc12", "operationName": "__build_tensor_from_dataframe", - "startTime": 1703354941594926, - "duration": 78, + "startTime": 1703363923076672, + "duration": 74, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -875,8 +875,8 @@ { "spanID": "0edc6d2bc470f0e7", "operationName": "__process_adb_edge_df", - "startTime": 1703354941600365, - "duration": 4600, + "startTime": 1703363923082232, + "duration": 5045, "tags": { "edge_df_size": 1000 }, @@ -884,24 +884,24 @@ { "spanID": "ad1b8f60c9e4dab2", "operationName": "__split_adb_ids", - "startTime": 1703354941600439, - "duration": 674, + "startTime": 1703363923082335, + "duration": 766, "tags": {}, "children": [] }, { "spanID": "d86dbf1128805c5d", "operationName": "__split_adb_ids", - "startTime": 1703354941601402, - "duration": 647, + "startTime": 1703363923083448, + "duration": 685, "tags": {}, "children": [] }, { "spanID": "57a1cb712975d279", "operationName": "__process_adb_edge_type_df", - "startTime": 1703354941603003, - "duration": 1931, + "startTime": 1703363923085249, + "duration": 1994, "tags": { "edge_type": "[\"v1\",\"e0\",\"v2\"]", "edge_type_df_size": 1000 @@ -910,8 +910,8 @@ { "spanID": "402d0baf878b9f6b", "operationName": "__set_pyg_data", - "startTime": 1703354941604471, - "duration": 457, + "startTime": 1703363923086785, + "duration": 452, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -919,8 +919,8 @@ { "spanID": "98c752051e01a934", "operationName": "__build_tensor_from_dataframe", - "startTime": 1703354941604500, - "duration": 411, + "startTime": 1703363923086817, + "duration": 401, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -936,8 +936,8 @@ { "spanID": "713b7e05ebe21368", "operationName": "__process_adb_edge_df", - "startTime": 1703354941610461, - "duration": 4671, + "startTime": 1703363923093130, + "duration": 5038, "tags": { "edge_df_size": 1000 }, @@ -945,24 +945,24 @@ { "spanID": "2cc0f859aa6524ab", "operationName": "__split_adb_ids", - "startTime": 1703354941610541, - "duration": 680, + "startTime": 1703363923093213, + "duration": 709, "tags": {}, "children": [] }, { "spanID": "78bc71750361524c", "operationName": "__split_adb_ids", - "startTime": 1703354941611510, - "duration": 639, + "startTime": 1703363923094217, + "duration": 651, "tags": {}, "children": [] }, { "spanID": "68ef8f5fae68690a", "operationName": "__process_adb_edge_type_df", - "startTime": 1703354941613106, - "duration": 1997, + "startTime": 1703363923095931, + "duration": 2200, "tags": { "edge_type": "[\"v1\",\"e0\",\"v2\"]", "edge_type_df_size": 1000 @@ -971,8 +971,8 @@ { "spanID": "91b15f5de66cd36e", "operationName": "__set_pyg_data", - "startTime": 1703354941614647, - "duration": 450, + "startTime": 1703363923097665, + "duration": 459, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -980,8 +980,8 @@ { "spanID": "82339e23dff3334b", "operationName": "__build_tensor_from_dataframe", - "startTime": 1703354941614676, - "duration": 404, + "startTime": 1703363923097700, + "duration": 405, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -997,8 +997,8 @@ { "spanID": "4fbaecc0eae2025e", "operationName": "__process_adb_edge_df", - "startTime": 1703354941620324, - "duration": 4625, + "startTime": 1703363923103974, + "duration": 5287, "tags": { "edge_df_size": 1000 }, @@ -1006,24 +1006,24 @@ { "spanID": "5b6e4ae7a6208143", "operationName": "__split_adb_ids", - "startTime": 1703354941620406, - "duration": 690, + "startTime": 1703363923104073, + "duration": 751, "tags": {}, "children": [] }, { "spanID": "d670f668637e0edc", "operationName": "__split_adb_ids", - "startTime": 1703354941621386, - "duration": 649, + "startTime": 1703363923105166, + "duration": 707, "tags": {}, "children": [] }, { "spanID": "403d1f83a859890c", "operationName": "__process_adb_edge_type_df", - "startTime": 1703354941622986, - "duration": 1932, + "startTime": 1703363923106970, + "duration": 2258, "tags": { "edge_type": "[\"v1\",\"e0\",\"v2\"]", "edge_type_df_size": 1000 @@ -1032,8 +1032,8 @@ { "spanID": "8f837ef727460f22", "operationName": "__set_pyg_data", - "startTime": 1703354941624469, - "duration": 444, + "startTime": 1703363923108707, + "duration": 496, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -1041,8 +1041,8 @@ { "spanID": "032f06cab0d9c2aa", "operationName": "__build_tensor_from_dataframe", - "startTime": 1703354941624498, - "duration": 398, + "startTime": 1703363923108744, + "duration": 435, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -1058,8 +1058,8 @@ { "spanID": "bdd7d19b753c7c99", "operationName": "__process_adb_edge_df", - "startTime": 1703354941630063, - "duration": 4626, + "startTime": 1703363923115041, + "duration": 4878, "tags": { "edge_df_size": 1000 }, @@ -1067,24 +1067,24 @@ { "spanID": "55fea08e143e2e04", "operationName": "__split_adb_ids", - "startTime": 1703354941630139, - "duration": 692, + "startTime": 1703363923115135, + "duration": 716, "tags": {}, "children": [] }, { "spanID": "0bb2c3f0bd30291a", "operationName": "__split_adb_ids", - "startTime": 1703354941631120, - "duration": 644, + "startTime": 1703363923116156, + "duration": 681, "tags": {}, "children": [] }, { "spanID": "47e7f5938b5885ca", "operationName": "__process_adb_edge_type_df", - "startTime": 1703354941632699, - "duration": 1958, + "startTime": 1703363923117845, + "duration": 2036, "tags": { "edge_type": "[\"v1\",\"e0\",\"v2\"]", "edge_type_df_size": 1000 @@ -1093,8 +1093,8 @@ { "spanID": "3d792fa12284b7a4", "operationName": "__set_pyg_data", - "startTime": 1703354941634195, - "duration": 456, + "startTime": 1703363923119393, + "duration": 481, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -1102,8 +1102,8 @@ { "spanID": "f40048d7c31d5a97", "operationName": "__build_tensor_from_dataframe", - "startTime": 1703354941634224, - "duration": 410, + "startTime": 1703363923119432, + "duration": 419, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -1119,8 +1119,8 @@ { "spanID": "5a2b745b7b59051b", "operationName": "__process_adb_edge_df", - "startTime": 1703354941640359, - "duration": 4824, + "startTime": 1703363923125883, + "duration": 5082, "tags": { "edge_df_size": 1000 }, @@ -1128,24 +1128,24 @@ { "spanID": "49b25ded9c31d9b2", "operationName": "__split_adb_ids", - "startTime": 1703354941640450, - "duration": 717, + "startTime": 1703363923125968, + "duration": 702, "tags": {}, "children": [] }, { "spanID": "5bf49c04ac642b4c", "operationName": "__split_adb_ids", - "startTime": 1703354941641487, - "duration": 650, + "startTime": 1703363923126975, + "duration": 678, "tags": {}, "children": [] }, { "spanID": "f2686baa971c702d", "operationName": "__process_adb_edge_type_df", - "startTime": 1703354941643137, - "duration": 2014, + "startTime": 1703363923128742, + "duration": 2186, "tags": { "edge_type": "[\"v1\",\"e0\",\"v2\"]", "edge_type_df_size": 1000 @@ -1154,8 +1154,8 @@ { "spanID": "a23d4c9de456697c", "operationName": "__set_pyg_data", - "startTime": 1703354941644698, - "duration": 448, + "startTime": 1703363923130433, + "duration": 488, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -1163,8 +1163,8 @@ { "spanID": "9efee464da90f534", "operationName": "__build_tensor_from_dataframe", - "startTime": 1703354941644728, - "duration": 400, + "startTime": 1703363923130474, + "duration": 424, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -1180,8 +1180,8 @@ { "spanID": "b732d46f21e15094", "operationName": "__process_adb_edge_df", - "startTime": 1703354941650394, - "duration": 5282, + "startTime": 1703363923139342, + "duration": 4938, "tags": { "edge_df_size": 1000 }, @@ -1189,24 +1189,24 @@ { "spanID": "635518f74f6fa985", "operationName": "__split_adb_ids", - "startTime": 1703354941650483, - "duration": 857, + "startTime": 1703363923139436, + "duration": 710, "tags": {}, "children": [] }, { "spanID": "6a174c1cbf9cc545", "operationName": "__split_adb_ids", - "startTime": 1703354941651708, - "duration": 736, + "startTime": 1703363923140478, + "duration": 704, "tags": {}, "children": [] }, { "spanID": "a69cfb85d432f8db", "operationName": "__process_adb_edge_type_df", - "startTime": 1703354941653491, - "duration": 2091, + "startTime": 1703363923142177, + "duration": 2066, "tags": { "edge_type": "[\"v1\",\"e0\",\"v2\"]", "edge_type_df_size": 1000 @@ -1215,8 +1215,8 @@ { "spanID": "0063e42f14aa451c", "operationName": "__set_pyg_data", - "startTime": 1703354941655111, - "duration": 464, + "startTime": 1703363923143755, + "duration": 480, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -1224,8 +1224,8 @@ { "spanID": "313b32b798363189", "operationName": "__build_tensor_from_dataframe", - "startTime": 1703354941655142, - "duration": 409, + "startTime": 1703363923143790, + "duration": 418, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -1241,8 +1241,8 @@ { "spanID": "559b5975b2d650af", "operationName": "__process_adb_edge_df", - "startTime": 1703354941660950, - "duration": 4707, + "startTime": 1703363923152485, + "duration": 5002, "tags": { "edge_df_size": 1000 }, @@ -1250,24 +1250,24 @@ { "spanID": "3d4a5d5128fafd04", "operationName": "__split_adb_ids", - "startTime": 1703354941661033, - "duration": 692, + "startTime": 1703363923152574, + "duration": 714, "tags": {}, "children": [] }, { "spanID": "a32c9b6f391cf046", "operationName": "__split_adb_ids", - "startTime": 1703354941662022, - "duration": 648, + "startTime": 1703363923153632, + "duration": 708, "tags": {}, "children": [] }, { "spanID": "60ef147172b8ff39", "operationName": "__process_adb_edge_type_df", - "startTime": 1703354941663645, - "duration": 1979, + "startTime": 1703363923155367, + "duration": 2086, "tags": { "edge_type": "[\"v1\",\"e0\",\"v2\"]", "edge_type_df_size": 1000 @@ -1276,8 +1276,8 @@ { "spanID": "e01bbf50b5d97ef7", "operationName": "__set_pyg_data", - "startTime": 1703354941665172, - "duration": 446, + "startTime": 1703363923156963, + "duration": 484, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -1285,8 +1285,8 @@ { "spanID": "91725f0aac7c8803", "operationName": "__build_tensor_from_dataframe", - "startTime": 1703354941665202, - "duration": 398, + "startTime": 1703363923156994, + "duration": 432, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -1302,8 +1302,8 @@ { "spanID": "6a1689addfe1b307", "operationName": "__process_adb_edge_df", - "startTime": 1703354941716880, - "duration": 4931, + "startTime": 1703363923214021, + "duration": 5102, "tags": { "edge_df_size": 1000 }, @@ -1311,24 +1311,24 @@ { "spanID": "66faf98908135d58", "operationName": "__split_adb_ids", - "startTime": 1703354941716978, - "duration": 710, + "startTime": 1703363923214122, + "duration": 735, "tags": {}, "children": [] }, { "spanID": "b3ab1b2cdf26f517", "operationName": "__split_adb_ids", - "startTime": 1703354941718007, - "duration": 643, + "startTime": 1703363923215185, + "duration": 664, "tags": {}, "children": [] }, { "spanID": "6b10e53a9145de05", "operationName": "__process_adb_edge_type_df", - "startTime": 1703354941719718, - "duration": 2060, + "startTime": 1703363923216951, + "duration": 2135, "tags": { "edge_type": "[\"v1\",\"e0\",\"v2\"]", "edge_type_df_size": 1000 @@ -1337,8 +1337,8 @@ { "spanID": "a985ab61c5adf681", "operationName": "__set_pyg_data", - "startTime": 1703354941721315, - "duration": 457, + "startTime": 1703363923218596, + "duration": 483, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -1346,8 +1346,8 @@ { "spanID": "0bf9c0efb5816b74", "operationName": "__build_tensor_from_dataframe", - "startTime": 1703354941721344, - "duration": 403, + "startTime": 1703363923218631, + "duration": 423, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -1363,8 +1363,8 @@ { "spanID": "720299e32a69acc7", "operationName": "__process_adb_edge_df", - "startTime": 1703354941727395, - "duration": 5699, + "startTime": 1703363923229318, + "duration": 7216, "tags": { "edge_df_size": 1000 }, @@ -1372,24 +1372,24 @@ { "spanID": "425cb200105ada6b", "operationName": "__split_adb_ids", - "startTime": 1703354941727494, - "duration": 732, + "startTime": 1703363923229434, + "duration": 839, "tags": {}, "children": [] }, { "spanID": "285e25b4b3969057", "operationName": "__split_adb_ids", - "startTime": 1703354941728515, - "duration": 640, + "startTime": 1703363923230648, + "duration": 661, "tags": {}, "children": [] }, { "spanID": "870f084c7244f536", "operationName": "__process_adb_edge_type_df", - "startTime": 1703354941730093, - "duration": 1859, + "startTime": 1703363923232348, + "duration": 2898, "tags": { "edge_type": "[\"v0\",\"e0\",\"v1\"]", "edge_type_df_size": 944 @@ -1398,8 +1398,8 @@ { "spanID": "7cbd7025e28bc9ff", "operationName": "__set_pyg_data", - "startTime": 1703354941731526, - "duration": 420, + "startTime": 1703363923234788, + "duration": 451, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -1407,8 +1407,8 @@ { "spanID": "8fb83babe8754cd3", "operationName": "__build_tensor_from_dataframe", - "startTime": 1703354941731556, - "duration": 379, + "startTime": 1703363923234825, + "duration": 398, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -1422,8 +1422,8 @@ { "spanID": "c167733f9a9e4310", "operationName": "__process_adb_edge_type_df", - "startTime": 1703354941731976, - "duration": 1104, + "startTime": 1703363923235270, + "duration": 1245, "tags": { "edge_type": "[\"v1\",\"e0\",\"v2\"]", "edge_type_df_size": 56 @@ -1432,8 +1432,8 @@ { "spanID": "e245a4600004884c", "operationName": "__set_pyg_data", - "startTime": 1703354941732974, - "duration": 100, + "startTime": 1703363923236400, + "duration": 110, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -1441,8 +1441,8 @@ { "spanID": "7e9cf84f09f6048f", "operationName": "__build_tensor_from_dataframe", - "startTime": 1703354941733001, - "duration": 56, + "startTime": 1703363923236430, + "duration": 58, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -1458,8 +1458,8 @@ { "spanID": "4fe30c9a53710f57", "operationName": "__process_adb_edge_df", - "startTime": 1703354941738338, - "duration": 5418, + "startTime": 1703363923245907, + "duration": 5177, "tags": { "edge_df_size": 1000 }, @@ -1467,24 +1467,24 @@ { "spanID": "77863fe5d675ebf7", "operationName": "__split_adb_ids", - "startTime": 1703354941738413, - "duration": 678, + "startTime": 1703363923246005, + "duration": 743, "tags": {}, "children": [] }, { "spanID": "cf1da1100cc36d8c", "operationName": "__split_adb_ids", - "startTime": 1703354941739379, - "duration": 647, + "startTime": 1703363923247086, + "duration": 728, "tags": {}, "children": [] }, { "spanID": "e00111e5d29dc5df", "operationName": "__process_adb_edge_type_df", - "startTime": 1703354941740969, - "duration": 2755, + "startTime": 1703363923248893, + "duration": 2155, "tags": { "edge_type": "[\"v0\",\"e0\",\"v1\"]", "edge_type_df_size": 1000 @@ -1493,8 +1493,8 @@ { "spanID": "cffa6cddf963a7ef", "operationName": "__set_pyg_data", - "startTime": 1703354941743226, - "duration": 492, + "startTime": 1703363923250574, + "duration": 467, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -1502,8 +1502,8 @@ { "spanID": "3020da5c6a46721a", "operationName": "__build_tensor_from_dataframe", - "startTime": 1703354941743255, - "duration": 444, + "startTime": 1703363923250609, + "duration": 412, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -1519,8 +1519,8 @@ { "spanID": "ffda03368c6e9037", "operationName": "__process_adb_edge_df", - "startTime": 1703354941749202, - "duration": 4930, + "startTime": 1703363923260390, + "duration": 5009, "tags": { "edge_df_size": 1000 }, @@ -1528,24 +1528,24 @@ { "spanID": "a2121ac5f689a4a5", "operationName": "__split_adb_ids", - "startTime": 1703354941749302, - "duration": 760, + "startTime": 1703363923260493, + "duration": 729, "tags": {}, "children": [] }, { "spanID": "155e18b1fa83ada4", "operationName": "__split_adb_ids", - "startTime": 1703354941750412, - "duration": 663, + "startTime": 1703363923261559, + "duration": 661, "tags": {}, "children": [] }, { "spanID": "b9bdee2dd663049d", "operationName": "__process_adb_edge_type_df", - "startTime": 1703354941752116, - "duration": 1985, + "startTime": 1703363923263281, + "duration": 2083, "tags": { "edge_type": "[\"v0\",\"e0\",\"v1\"]", "edge_type_df_size": 1000 @@ -1554,8 +1554,8 @@ { "spanID": "fca055362169df82", "operationName": "__set_pyg_data", - "startTime": 1703354941753648, - "duration": 447, + "startTime": 1703363923264883, + "duration": 475, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -1563,8 +1563,8 @@ { "spanID": "66dd779403c54c71", "operationName": "__build_tensor_from_dataframe", - "startTime": 1703354941753677, - "duration": 400, + "startTime": 1703363923264915, + "duration": 420, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -1580,8 +1580,8 @@ { "spanID": "adb328cbf3158c0c", "operationName": "__process_adb_edge_df", - "startTime": 1703354941759461, - "duration": 5212, + "startTime": 1703363923274145, + "duration": 4968, "tags": { "edge_df_size": 1000 }, @@ -1589,24 +1589,24 @@ { "spanID": "50f0fc2b6ae04d52", "operationName": "__split_adb_ids", - "startTime": 1703354941759552, - "duration": 736, + "startTime": 1703363923274241, + "duration": 715, "tags": {}, "children": [] }, { "spanID": "36a98d7400de59f5", "operationName": "__split_adb_ids", - "startTime": 1703354941760633, - "duration": 700, + "startTime": 1703363923275287, + "duration": 688, "tags": {}, "children": [] }, { "spanID": "b7a28e0a03a89879", "operationName": "__process_adb_edge_type_df", - "startTime": 1703354941762509, - "duration": 2129, + "startTime": 1703363923276996, + "duration": 2085, "tags": { "edge_type": "[\"v0\",\"e0\",\"v1\"]", "edge_type_df_size": 1000 @@ -1615,8 +1615,8 @@ { "spanID": "009a815bc1378be5", "operationName": "__set_pyg_data", - "startTime": 1703354941764163, - "duration": 468, + "startTime": 1703363923278625, + "duration": 450, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -1624,8 +1624,8 @@ { "spanID": "d29e8693faf1501b", "operationName": "__build_tensor_from_dataframe", - "startTime": 1703354941764195, - "duration": 415, + "startTime": 1703363923278655, + "duration": 402, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -1641,8 +1641,8 @@ { "spanID": "8741ae91acfebb4b", "operationName": "__process_adb_edge_df", - "startTime": 1703354941770352, - "duration": 5490, + "startTime": 1703363923287999, + "duration": 5004, "tags": { "edge_df_size": 1000 }, @@ -1650,24 +1650,24 @@ { "spanID": "190865159cb017c1", "operationName": "__split_adb_ids", - "startTime": 1703354941770438, - "duration": 720, + "startTime": 1703363923288093, + "duration": 760, "tags": {}, "children": [] }, { "spanID": "1e707c5230c1fb6a", "operationName": "__split_adb_ids", - "startTime": 1703354941771690, - "duration": 792, + "startTime": 1703363923289197, + "duration": 690, "tags": {}, "children": [] }, { "spanID": "a636425c9bbd750d", "operationName": "__process_adb_edge_type_df", - "startTime": 1703354941773583, - "duration": 2223, + "startTime": 1703363923290941, + "duration": 2030, "tags": { "edge_type": "[\"v0\",\"e0\",\"v1\"]", "edge_type_df_size": 1000 @@ -1676,8 +1676,8 @@ { "spanID": "dfa7c6ed32d1f81b", "operationName": "__set_pyg_data", - "startTime": 1703354941775337, - "duration": 463, + "startTime": 1703363923292518, + "duration": 448, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -1685,8 +1685,8 @@ { "spanID": "47acf2f64d6b234f", "operationName": "__build_tensor_from_dataframe", - "startTime": 1703354941775369, - "duration": 407, + "startTime": 1703363923292548, + "duration": 398, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -1702,8 +1702,8 @@ { "spanID": "fa7ff8bfb044284a", "operationName": "__process_adb_edge_df", - "startTime": 1703354941782451, - "duration": 4845, + "startTime": 1703363923303283, + "duration": 5619, "tags": { "edge_df_size": 1000 }, @@ -1711,24 +1711,24 @@ { "spanID": "19a5711b2ea60b99", "operationName": "__split_adb_ids", - "startTime": 1703354941782536, - "duration": 736, + "startTime": 1703363923303388, + "duration": 765, "tags": {}, "children": [] }, { "spanID": "da9bb01779c147c7", "operationName": "__split_adb_ids", - "startTime": 1703354941783563, - "duration": 673, + "startTime": 1703363923304480, + "duration": 834, "tags": {}, "children": [] }, { "spanID": "658de17eec3aa314", "operationName": "__process_adb_edge_type_df", - "startTime": 1703354941785253, - "duration": 2009, + "startTime": 1703363923306491, + "duration": 2371, "tags": { "edge_type": "[\"v0\",\"e0\",\"v1\"]", "edge_type_df_size": 1000 @@ -1737,8 +1737,8 @@ { "spanID": "14d30dbca0acf4c9", "operationName": "__set_pyg_data", - "startTime": 1703354941786803, - "duration": 453, + "startTime": 1703363923308384, + "duration": 471, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -1746,8 +1746,8 @@ { "spanID": "4653a5600597aab6", "operationName": "__build_tensor_from_dataframe", - "startTime": 1703354941786831, - "duration": 404, + "startTime": 1703363923308421, + "duration": 411, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -1763,8 +1763,8 @@ { "spanID": "73f660d8e9f41cc0", "operationName": "__process_adb_edge_df", - "startTime": 1703354941793198, - "duration": 4837, + "startTime": 1703363923314786, + "duration": 5428, "tags": { "edge_df_size": 1000 }, @@ -1772,24 +1772,24 @@ { "spanID": "cad6e514ccc14d51", "operationName": "__split_adb_ids", - "startTime": 1703354941793292, - "duration": 714, + "startTime": 1703363923314909, + "duration": 753, "tags": {}, "children": [] }, { "spanID": "dc8215271da3b7e2", "operationName": "__split_adb_ids", - "startTime": 1703354941794317, - "duration": 648, + "startTime": 1703363923316002, + "duration": 719, "tags": {}, "children": [] }, { "spanID": "2227d96d41a93f90", "operationName": "__process_adb_edge_type_df", - "startTime": 1703354941795965, - "duration": 2038, + "startTime": 1703363923317856, + "duration": 2310, "tags": { "edge_type": "[\"v0\",\"e0\",\"v1\"]", "edge_type_df_size": 1000 @@ -1798,8 +1798,8 @@ { "spanID": "8557716aa7502a81", "operationName": "__set_pyg_data", - "startTime": 1703354941797542, - "duration": 455, + "startTime": 1703363923319636, + "duration": 523, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -1807,8 +1807,8 @@ { "spanID": "a699bae0d138d150", "operationName": "__build_tensor_from_dataframe", - "startTime": 1703354941797571, - "duration": 401, + "startTime": 1703363923319675, + "duration": 459, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -1824,8 +1824,8 @@ { "spanID": "58d87776a51ad4f3", "operationName": "__process_adb_edge_df", - "startTime": 1703354941803833, - "duration": 5048, + "startTime": 1703363923329264, + "duration": 4999, "tags": { "edge_df_size": 1000 }, @@ -1833,24 +1833,24 @@ { "spanID": "df3277fd1d77ce40", "operationName": "__split_adb_ids", - "startTime": 1703354941803924, - "duration": 736, + "startTime": 1703363923329368, + "duration": 737, "tags": {}, "children": [] }, { "spanID": "4745dd9e27896389", "operationName": "__split_adb_ids", - "startTime": 1703354941804975, - "duration": 666, + "startTime": 1703363923330426, + "duration": 656, "tags": {}, "children": [] }, { "spanID": "04c14982d9ead926", "operationName": "__process_adb_edge_type_df", - "startTime": 1703354941806652, - "duration": 2196, + "startTime": 1703363923332099, + "duration": 2131, "tags": { "edge_type": "[\"v0\",\"e0\",\"v1\"]", "edge_type_df_size": 1000 @@ -1859,7 +1859,7 @@ { "spanID": "0a68e88e0ad40415", "operationName": "__set_pyg_data", - "startTime": 1703354941808386, + "startTime": 1703363923333768, "duration": 456, "tags": { "meta": "{'edge_attr': 'edge_attr'}" @@ -1868,8 +1868,8 @@ { "spanID": "ae55cdff34ab18fd", "operationName": "__build_tensor_from_dataframe", - "startTime": 1703354941808415, - "duration": 404, + "startTime": 1703363923333799, + "duration": 399, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -1885,8 +1885,8 @@ { "spanID": "8ef066d44279b14d", "operationName": "__process_adb_edge_df", - "startTime": 1703354941815068, - "duration": 5524, + "startTime": 1703363923340401, + "duration": 4690, "tags": { "edge_df_size": 1000 }, @@ -1894,24 +1894,24 @@ { "spanID": "f24dfdd850910bdc", "operationName": "__split_adb_ids", - "startTime": 1703354941815198, - "duration": 792, + "startTime": 1703363923340477, + "duration": 682, "tags": {}, "children": [] }, { "spanID": "f03d866a5decc06a", "operationName": "__split_adb_ids", - "startTime": 1703354941816331, - "duration": 856, + "startTime": 1703363923341440, + "duration": 643, "tags": {}, "children": [] }, { "spanID": "e8ec01b3914591ae", "operationName": "__process_adb_edge_type_df", - "startTime": 1703354941818389, - "duration": 2163, + "startTime": 1703363923343042, + "duration": 2015, "tags": { "edge_type": "[\"v0\",\"e0\",\"v1\"]", "edge_type_df_size": 1000 @@ -1920,8 +1920,8 @@ { "spanID": "0ac0cf0dd974c146", "operationName": "__set_pyg_data", - "startTime": 1703354941820051, - "duration": 494, + "startTime": 1703363923344587, + "duration": 464, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -1929,8 +1929,8 @@ { "spanID": "bfc74ca9d8ab0b30", "operationName": "__build_tensor_from_dataframe", - "startTime": 1703354941820086, - "duration": 420, + "startTime": 1703363923344617, + "duration": 409, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -1946,8 +1946,8 @@ { "spanID": "b38a05fbf61164ce", "operationName": "__process_adb_edge_df", - "startTime": 1703354941826433, - "duration": 4991, + "startTime": 1703363923350830, + "duration": 5318, "tags": { "edge_df_size": 1000 }, @@ -1955,24 +1955,24 @@ { "spanID": "a7c5cb879b8b71a1", "operationName": "__split_adb_ids", - "startTime": 1703354941826524, - "duration": 709, + "startTime": 1703363923350928, + "duration": 726, "tags": {}, "children": [] }, { "spanID": "b65d12267e969cf3", "operationName": "__split_adb_ids", - "startTime": 1703354941827545, - "duration": 650, + "startTime": 1703363923351978, + "duration": 724, "tags": {}, "children": [] }, { "spanID": "e7180322a4e695c9", "operationName": "__process_adb_edge_type_df", - "startTime": 1703354941829200, - "duration": 2191, + "startTime": 1703363923353805, + "duration": 2224, "tags": { "edge_type": "[\"v0\",\"e0\",\"v1\"]", "edge_type_df_size": 1000 @@ -1981,8 +1981,8 @@ { "spanID": "a3e04b3b756b0715", "operationName": "__set_pyg_data", - "startTime": 1703354941830882, - "duration": 502, + "startTime": 1703363923355531, + "duration": 491, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -1990,8 +1990,8 @@ { "spanID": "5f58d5b56f790959", "operationName": "__build_tensor_from_dataframe", - "startTime": 1703354941830912, - "duration": 447, + "startTime": 1703363923355568, + "duration": 428, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -2007,8 +2007,8 @@ { "spanID": "89b5b368df14c612", "operationName": "__process_adb_edge_df", - "startTime": 1703354941837118, - "duration": 6324, + "startTime": 1703363923362249, + "duration": 6855, "tags": { "edge_df_size": 1000 }, @@ -2016,24 +2016,24 @@ { "spanID": "353545792da44da1", "operationName": "__split_adb_ids", - "startTime": 1703354941837212, - "duration": 722, + "startTime": 1703363923362353, + "duration": 760, "tags": {}, "children": [] }, { "spanID": "964ddb776025f0ae", "operationName": "__split_adb_ids", - "startTime": 1703354941838250, - "duration": 658, + "startTime": 1703363923363448, + "duration": 700, "tags": {}, "children": [] }, { "spanID": "0247145f4a814d53", "operationName": "__process_adb_edge_type_df", - "startTime": 1703354941840022, - "duration": 2171, + "startTime": 1703363923365699, + "duration": 2085, "tags": { "edge_type": "[\"v1\",\"e0\",\"v0\"]", "edge_type_df_size": 922 @@ -2042,8 +2042,8 @@ { "spanID": "26a974652371ea2c", "operationName": "__set_pyg_data", - "startTime": 1703354941841766, - "duration": 421, + "startTime": 1703363923367331, + "duration": 447, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -2051,8 +2051,8 @@ { "spanID": "555a40854578bab3", "operationName": "__build_tensor_from_dataframe", - "startTime": 1703354941841800, - "duration": 375, + "startTime": 1703363923367367, + "duration": 395, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -2066,8 +2066,8 @@ { "spanID": "ca24be4d56672017", "operationName": "__process_adb_edge_type_df", - "startTime": 1703354941842217, - "duration": 1209, + "startTime": 1703363923367808, + "duration": 1278, "tags": { "edge_type": "[\"v0\",\"e0\",\"v1\"]", "edge_type_df_size": 78 @@ -2076,8 +2076,8 @@ { "spanID": "b7ef941c5e00ea6d", "operationName": "__set_pyg_data", - "startTime": 1703354941843312, - "duration": 108, + "startTime": 1703363923368953, + "duration": 127, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -2085,8 +2085,8 @@ { "spanID": "5697f17c17fd3736", "operationName": "__build_tensor_from_dataframe", - "startTime": 1703354941843339, - "duration": 64, + "startTime": 1703363923368984, + "duration": 66, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -2102,8 +2102,8 @@ { "spanID": "9edb95f2c787ddfb", "operationName": "__process_adb_edge_df", - "startTime": 1703354941851608, - "duration": 5078, + "startTime": 1703363923392501, + "duration": 9977, "tags": { "edge_df_size": 1000 }, @@ -2111,24 +2111,24 @@ { "spanID": "0a8c46c709215f4f", "operationName": "__split_adb_ids", - "startTime": 1703354941851702, - "duration": 719, + "startTime": 1703363923392624, + "duration": 2093, "tags": {}, "children": [] }, { "spanID": "29f2c3c74505f4f6", "operationName": "__split_adb_ids", - "startTime": 1703354941852728, - "duration": 651, + "startTime": 1703363923395284, + "duration": 790, "tags": {}, "children": [] }, { "spanID": "fb5eb8662640211e", "operationName": "__process_adb_edge_type_df", - "startTime": 1703354941854395, - "duration": 2240, + "startTime": 1703363923397706, + "duration": 4715, "tags": { "edge_type": "[\"v1\",\"e0\",\"v0\"]", "edge_type_df_size": 1000 @@ -2137,8 +2137,8 @@ { "spanID": "4a1eb1b7955d0e77", "operationName": "__set_pyg_data", - "startTime": 1703354941856060, - "duration": 568, + "startTime": 1703363923401134, + "duration": 1272, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -2146,8 +2146,8 @@ { "spanID": "651116565c646036", "operationName": "__build_tensor_from_dataframe", - "startTime": 1703354941856092, - "duration": 501, + "startTime": 1703363923401193, + "duration": 1078, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -2163,8 +2163,8 @@ { "spanID": "8c69778ffd42f697", "operationName": "__process_adb_edge_df", - "startTime": 1703354941865563, - "duration": 4713, + "startTime": 1703363923425626, + "duration": 14242, "tags": { "edge_df_size": 1000 }, @@ -2172,24 +2172,24 @@ { "spanID": "4b1cb8bd2130260c", "operationName": "__split_adb_ids", - "startTime": 1703354941865643, - "duration": 696, + "startTime": 1703363923426579, + "duration": 3186, "tags": {}, "children": [] }, { "spanID": "7a62722e1d69d9fc", "operationName": "__split_adb_ids", - "startTime": 1703354941866628, - "duration": 652, + "startTime": 1703363923432783, + "duration": 2964, "tags": {}, "children": [] }, { "spanID": "3d5d60bcbb0378eb", "operationName": "__process_adb_edge_type_df", - "startTime": 1703354941868235, - "duration": 2007, + "startTime": 1703363923437456, + "duration": 2298, "tags": { "edge_type": "[\"v1\",\"e0\",\"v0\"]", "edge_type_df_size": 1000 @@ -2198,8 +2198,8 @@ { "spanID": "0c5a876fef0a81ed", "operationName": "__set_pyg_data", - "startTime": 1703354941869775, - "duration": 461, + "startTime": 1703363923439238, + "duration": 509, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -2207,8 +2207,8 @@ { "spanID": "2df967474ed13553", "operationName": "__build_tensor_from_dataframe", - "startTime": 1703354941869804, - "duration": 400, + "startTime": 1703363923439276, + "duration": 442, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -2224,8 +2224,8 @@ { "spanID": "85e69ea9db66bfda", "operationName": "__process_adb_edge_df", - "startTime": 1703354941879665, - "duration": 4946, + "startTime": 1703363923460029, + "duration": 10424, "tags": { "edge_df_size": 1000 }, @@ -2233,24 +2233,24 @@ { "spanID": "122411e6ba8982dd", "operationName": "__split_adb_ids", - "startTime": 1703354941879758, - "duration": 891, + "startTime": 1703363923460915, + "duration": 1562, "tags": {}, "children": [] }, { "spanID": "673617d94d7bd307", "operationName": "__split_adb_ids", - "startTime": 1703354941880963, - "duration": 710, + "startTime": 1703363923463493, + "duration": 1626, "tags": {}, "children": [] }, { "spanID": "5419eefcd5e73e3f", "operationName": "__process_adb_edge_type_df", - "startTime": 1703354941882608, - "duration": 1971, + "startTime": 1703363923467120, + "duration": 3287, "tags": { "edge_type": "[\"v1\",\"e0\",\"v0\"]", "edge_type_df_size": 1000 @@ -2259,8 +2259,8 @@ { "spanID": "6a2b32004c9a0ae1", "operationName": "__set_pyg_data", - "startTime": 1703354941884128, - "duration": 444, + "startTime": 1703363923469906, + "duration": 494, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -2268,8 +2268,8 @@ { "spanID": "19724ce31bd09448", "operationName": "__build_tensor_from_dataframe", - "startTime": 1703354941884157, - "duration": 397, + "startTime": 1703363923469946, + "duration": 422, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -2285,8 +2285,8 @@ { "spanID": "e89dc8158f928dc5", "operationName": "__process_adb_edge_df", - "startTime": 1703354941892912, - "duration": 4801, + "startTime": 1703363923482447, + "duration": 9734, "tags": { "edge_df_size": 1000 }, @@ -2294,24 +2294,24 @@ { "spanID": "79585e697b2e1b82", "operationName": "__split_adb_ids", - "startTime": 1703354941892987, - "duration": 727, + "startTime": 1703363923482764, + "duration": 1091, "tags": {}, "children": [] }, { "spanID": "d741d609564ae909", "operationName": "__split_adb_ids", - "startTime": 1703354941894028, - "duration": 644, + "startTime": 1703363923484342, + "duration": 1249, "tags": {}, "children": [] }, { "spanID": "f9ea2c64cc417e7c", "operationName": "__process_adb_edge_type_df", - "startTime": 1703354941895665, - "duration": 2016, + "startTime": 1703363923487275, + "duration": 4851, "tags": { "edge_type": "[\"v1\",\"e0\",\"v0\"]", "edge_type_df_size": 1000 @@ -2320,8 +2320,8 @@ { "spanID": "57f98d1ecff4c56b", "operationName": "__set_pyg_data", - "startTime": 1703354941897235, - "duration": 440, + "startTime": 1703363923491431, + "duration": 687, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -2329,8 +2329,8 @@ { "spanID": "7aa56a181fd3c017", "operationName": "__build_tensor_from_dataframe", - "startTime": 1703354941897263, - "duration": 395, + "startTime": 1703363923491492, + "duration": 584, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -2346,8 +2346,8 @@ { "spanID": "b318ad4c1db2b452", "operationName": "__process_adb_edge_df", - "startTime": 1703354941906243, - "duration": 5354, + "startTime": 1703363923508267, + "duration": 7592, "tags": { "edge_df_size": 1000 }, @@ -2355,24 +2355,24 @@ { "spanID": "6d316b4a7f6b8793", "operationName": "__split_adb_ids", - "startTime": 1703354941906350, - "duration": 743, + "startTime": 1703363923508388, + "duration": 779, "tags": {}, "children": [] }, { "spanID": "4d4985dc09aedbd0", "operationName": "__split_adb_ids", - "startTime": 1703354941907447, - "duration": 701, + "startTime": 1703363923509530, + "duration": 682, "tags": {}, "children": [] }, { "spanID": "bc18a40b55c7ed9d", "operationName": "__process_adb_edge_type_df", - "startTime": 1703354941909265, - "duration": 2297, + "startTime": 1703363923511306, + "duration": 4468, "tags": { "edge_type": "[\"v1\",\"e0\",\"v0\"]", "edge_type_df_size": 1000 @@ -2381,8 +2381,8 @@ { "spanID": "e4f7625eafe6790a", "operationName": "__set_pyg_data", - "startTime": 1703354941911067, - "duration": 489, + "startTime": 1703363923515280, + "duration": 487, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -2390,8 +2390,8 @@ { "spanID": "eb70ba6527d99a23", "operationName": "__build_tensor_from_dataframe", - "startTime": 1703354941911103, - "duration": 431, + "startTime": 1703363923515322, + "duration": 418, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -2407,8 +2407,8 @@ { "spanID": "a0722aa02aa36cf7", "operationName": "__process_adb_edge_df", - "startTime": 1703354941920648, - "duration": 4641, + "startTime": 1703363923522239, + "duration": 5101, "tags": { "edge_df_size": 1000 }, @@ -2416,24 +2416,24 @@ { "spanID": "6025719990823eda", "operationName": "__split_adb_ids", - "startTime": 1703354941920719, - "duration": 681, + "startTime": 1703363923522334, + "duration": 735, "tags": {}, "children": [] }, { "spanID": "f97ccc57ce5dc807", "operationName": "__split_adb_ids", - "startTime": 1703354941921684, - "duration": 644, + "startTime": 1703363923523396, + "duration": 680, "tags": {}, "children": [] }, { "spanID": "a38d8afcfdd2ed7a", "operationName": "__process_adb_edge_type_df", - "startTime": 1703354941923253, - "duration": 2000, + "startTime": 1703363923525148, + "duration": 2157, "tags": { "edge_type": "[\"v1\",\"e0\",\"v0\"]", "edge_type_df_size": 1000 @@ -2442,8 +2442,8 @@ { "spanID": "10da8a9516408169", "operationName": "__set_pyg_data", - "startTime": 1703354941924779, - "duration": 468, + "startTime": 1703363923526792, + "duration": 507, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -2451,8 +2451,8 @@ { "spanID": "15ace7a1ceca2ee3", "operationName": "__build_tensor_from_dataframe", - "startTime": 1703354941924807, - "duration": 420, + "startTime": 1703363923526824, + "duration": 401, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -2468,8 +2468,8 @@ { "spanID": "bff773ce32b2c492", "operationName": "__process_adb_edge_df", - "startTime": 1703354941934555, - "duration": 4997, + "startTime": 1703363923533900, + "duration": 5319, "tags": { "edge_df_size": 1000 }, @@ -2477,24 +2477,24 @@ { "spanID": "0fa7ee0538974df5", "operationName": "__split_adb_ids", - "startTime": 1703354941934686, - "duration": 794, + "startTime": 1703363923534017, + "duration": 759, "tags": {}, "children": [] }, { "spanID": "0202861c62830869", "operationName": "__split_adb_ids", - "startTime": 1703354941935819, - "duration": 784, + "startTime": 1703363923535137, + "duration": 679, "tags": {}, "children": [] }, { "spanID": "64d09913191b8adf", "operationName": "__process_adb_edge_type_df", - "startTime": 1703354941937574, - "duration": 1946, + "startTime": 1703363923536934, + "duration": 2197, "tags": { "edge_type": "[\"v1\",\"e0\",\"v0\"]", "edge_type_df_size": 1000 @@ -2503,8 +2503,8 @@ { "spanID": "84dd6da68e751eb7", "operationName": "__set_pyg_data", - "startTime": 1703354941939061, - "duration": 453, + "startTime": 1703363923538640, + "duration": 484, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -2512,8 +2512,8 @@ { "spanID": "72d3cc5d4a31b243", "operationName": "__build_tensor_from_dataframe", - "startTime": 1703354941939089, - "duration": 401, + "startTime": 1703363923538676, + "duration": 419, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -2529,8 +2529,8 @@ { "spanID": "7d161f29eb8f2056", "operationName": "__process_adb_edge_df", - "startTime": 1703354941950105, - "duration": 5997, + "startTime": 1703363923545029, + "duration": 6319, "tags": { "edge_df_size": 1000 }, @@ -2538,24 +2538,24 @@ { "spanID": "95bb440dc9cd4af9", "operationName": "__split_adb_ids", - "startTime": 1703354941950205, - "duration": 800, + "startTime": 1703363923545219, + "duration": 823, "tags": {}, "children": [] }, { "spanID": "ade6c5e9b6e355f6", "operationName": "__split_adb_ids", - "startTime": 1703354941951352, - "duration": 736, + "startTime": 1703363923546383, + "duration": 684, "tags": {}, "children": [] }, { "spanID": "6c4c3935379deda1", "operationName": "__process_adb_edge_type_df", - "startTime": 1703354941953086, - "duration": 1643, + "startTime": 1703363923548112, + "duration": 1853, "tags": { "edge_type": "[\"v1\",\"e0\",\"v1\"]", "edge_type_df_size": 743 @@ -2564,8 +2564,8 @@ { "spanID": "5e4af862156af458", "operationName": "__set_pyg_data", - "startTime": 1703354941954377, - "duration": 346, + "startTime": 1703363923549567, + "duration": 392, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -2573,8 +2573,8 @@ { "spanID": "fd0ba70e385af463", "operationName": "__build_tensor_from_dataframe", - "startTime": 1703354941954406, - "duration": 307, + "startTime": 1703363923549600, + "duration": 346, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -2588,8 +2588,8 @@ { "spanID": "42cb6d1dffc573d5", "operationName": "__process_adb_edge_type_df", - "startTime": 1703354941954752, - "duration": 1272, + "startTime": 1703363923549987, + "duration": 1341, "tags": { "edge_type": "[\"v1\",\"e0\",\"v0\"]", "edge_type_df_size": 257 @@ -2598,8 +2598,8 @@ { "spanID": "c6f0093395d18051", "operationName": "__set_pyg_data", - "startTime": 1703354941955845, - "duration": 173, + "startTime": 1703363923551138, + "duration": 183, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -2607,8 +2607,8 @@ { "spanID": "6e6480432aa50f4e", "operationName": "__build_tensor_from_dataframe", - "startTime": 1703354941955871, - "duration": 131, + "startTime": 1703363923551167, + "duration": 128, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -2624,8 +2624,8 @@ { "spanID": "5bc7fdeb31234efe", "operationName": "__process_adb_edge_df", - "startTime": 1703354941965302, - "duration": 5610, + "startTime": 1703363923557061, + "duration": 5534, "tags": { "edge_df_size": 1000 }, @@ -2633,24 +2633,24 @@ { "spanID": "1058fe8c1d7173e5", "operationName": "__split_adb_ids", - "startTime": 1703354941965399, - "duration": 765, + "startTime": 1703363923557173, + "duration": 830, "tags": {}, "children": [] }, { "spanID": "dd138266d26d5396", "operationName": "__split_adb_ids", - "startTime": 1703354941966501, - "duration": 1112, + "startTime": 1703363923558326, + "duration": 982, "tags": {}, "children": [] }, { "spanID": "b3b68b57da54f267", "operationName": "__process_adb_edge_type_df", - "startTime": 1703354941968749, - "duration": 2125, + "startTime": 1703363923560430, + "duration": 2123, "tags": { "edge_type": "[\"v1\",\"e0\",\"v1\"]", "edge_type_df_size": 1000 @@ -2659,8 +2659,8 @@ { "spanID": "e72bb5b707120911", "operationName": "__set_pyg_data", - "startTime": 1703354941970396, - "duration": 472, + "startTime": 1703363923562048, + "duration": 498, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -2668,7 +2668,7 @@ { "spanID": "739cd488869bdbd2", "operationName": "__build_tensor_from_dataframe", - "startTime": 1703354941970430, + "startTime": 1703363923562107, "duration": 415, "tags": { "meta_key": "edge_attr", @@ -2685,8 +2685,8 @@ { "spanID": "ad4ab155c09fcd8f", "operationName": "__process_adb_edge_df", - "startTime": 1703354941979139, - "duration": 4912, + "startTime": 1703363923568206, + "duration": 5715, "tags": { "edge_df_size": 1000 }, @@ -2694,24 +2694,24 @@ { "spanID": "1e70e79933a1d1c2", "operationName": "__split_adb_ids", - "startTime": 1703354941979227, - "duration": 729, + "startTime": 1703363923568309, + "duration": 722, "tags": {}, "children": [] }, { "spanID": "65e049937f411fed", "operationName": "__split_adb_ids", - "startTime": 1703354941980274, - "duration": 678, + "startTime": 1703363923569372, + "duration": 673, "tags": {}, "children": [] }, { "spanID": "350d278d41a8a6e1", "operationName": "__process_adb_edge_type_df", - "startTime": 1703354941981955, - "duration": 2063, + "startTime": 1703363923571719, + "duration": 2167, "tags": { "edge_type": "[\"v1\",\"e0\",\"v1\"]", "edge_type_df_size": 1000 @@ -2720,8 +2720,8 @@ { "spanID": "0ac728b4a41865bf", "operationName": "__set_pyg_data", - "startTime": 1703354941983559, - "duration": 452, + "startTime": 1703363923573413, + "duration": 466, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -2729,8 +2729,8 @@ { "spanID": "f2ad985fff3e0ba1", "operationName": "__build_tensor_from_dataframe", - "startTime": 1703354941983589, - "duration": 401, + "startTime": 1703363923573449, + "duration": 407, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -2746,8 +2746,8 @@ { "spanID": "3744da64cc249558", "operationName": "__process_adb_edge_df", - "startTime": 1703354941992697, - "duration": 4979, + "startTime": 1703363923580144, + "duration": 4875, "tags": { "edge_df_size": 1000 }, @@ -2755,24 +2755,24 @@ { "spanID": "25777cf09f982188", "operationName": "__split_adb_ids", - "startTime": 1703354941992793, - "duration": 742, + "startTime": 1703363923580230, + "duration": 749, "tags": {}, "children": [] }, { "spanID": "32ae2a201ac902ee", "operationName": "__split_adb_ids", - "startTime": 1703354941993849, - "duration": 680, + "startTime": 1703363923581312, + "duration": 693, "tags": {}, "children": [] }, { "spanID": "60c6b3ed755a3ac1", "operationName": "__process_adb_edge_type_df", - "startTime": 1703354941995585, - "duration": 2056, + "startTime": 1703363923583013, + "duration": 1973, "tags": { "edge_type": "[\"v1\",\"e0\",\"v1\"]", "edge_type_df_size": 1000 @@ -2781,8 +2781,8 @@ { "spanID": "8be04c3e5c949381", "operationName": "__set_pyg_data", - "startTime": 1703354941997176, - "duration": 459, + "startTime": 1703363923584531, + "duration": 449, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -2790,8 +2790,8 @@ { "spanID": "26bdd974d3b564b0", "operationName": "__build_tensor_from_dataframe", - "startTime": 1703354941997207, - "duration": 408, + "startTime": 1703363923584561, + "duration": 400, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -2807,8 +2807,8 @@ { "spanID": "fd1ac7ce1ad0a6f2", "operationName": "__process_adb_edge_df", - "startTime": 1703354942006541, - "duration": 6205, + "startTime": 1703363923590779, + "duration": 5902, "tags": { "edge_df_size": 1000 }, @@ -2816,24 +2816,24 @@ { "spanID": "fba52e5998a33736", "operationName": "__split_adb_ids", - "startTime": 1703354942006624, - "duration": 743, + "startTime": 1703363923590884, + "duration": 799, "tags": {}, "children": [] }, { "spanID": "25fdacbe7ce71b48", "operationName": "__split_adb_ids", - "startTime": 1703354942007658, - "duration": 1420, + "startTime": 1703363923592050, + "duration": 1095, "tags": {}, "children": [] }, { "spanID": "67e98363905c053b", "operationName": "__process_adb_edge_type_df", - "startTime": 1703354942010409, - "duration": 2298, + "startTime": 1703363923594372, + "duration": 2263, "tags": { "edge_type": "[\"v1\",\"e0\",\"v1\"]", "edge_type_df_size": 1000 @@ -2842,8 +2842,8 @@ { "spanID": "ae0fdbc8a36bcb01", "operationName": "__set_pyg_data", - "startTime": 1703354942012231, - "duration": 469, + "startTime": 1703363923596144, + "duration": 484, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -2851,8 +2851,8 @@ { "spanID": "e0ae1a1b6c596216", "operationName": "__build_tensor_from_dataframe", - "startTime": 1703354942012264, - "duration": 406, + "startTime": 1703363923596183, + "duration": 425, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -2868,8 +2868,8 @@ { "spanID": "7ed2ec2f856f3d95", "operationName": "__process_adb_edge_df", - "startTime": 1703354942024342, - "duration": 4879, + "startTime": 1703363923602373, + "duration": 5056, "tags": { "edge_df_size": 1000 }, @@ -2877,24 +2877,24 @@ { "spanID": "eac39204ade7cef3", "operationName": "__split_adb_ids", - "startTime": 1703354942024428, - "duration": 753, + "startTime": 1703363923602474, + "duration": 758, "tags": {}, "children": [] }, { "spanID": "528cc241e345ac72", "operationName": "__split_adb_ids", - "startTime": 1703354942025506, - "duration": 662, + "startTime": 1703363923603575, + "duration": 676, "tags": {}, "children": [] }, { "spanID": "7f99d273d5627386", "operationName": "__process_adb_edge_type_df", - "startTime": 1703354942027189, - "duration": 1999, + "startTime": 1703363923605326, + "duration": 2064, "tags": { "edge_type": "[\"v1\",\"e0\",\"v1\"]", "edge_type_df_size": 1000 @@ -2903,8 +2903,8 @@ { "spanID": "7fa74d8aff88ec82", "operationName": "__set_pyg_data", - "startTime": 1703354942028733, - "duration": 449, + "startTime": 1703363923606916, + "duration": 468, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -2912,8 +2912,8 @@ { "spanID": "ab899605a2939b3b", "operationName": "__build_tensor_from_dataframe", - "startTime": 1703354942028761, - "duration": 400, + "startTime": 1703363923606949, + "duration": 409, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -2929,8 +2929,8 @@ { "spanID": "33b5b3cedfec4623", "operationName": "__process_adb_edge_df", - "startTime": 1703354942040096, - "duration": 4563, + "startTime": 1703363923613100, + "duration": 5029, "tags": { "edge_df_size": 1000 }, @@ -2938,24 +2938,24 @@ { "spanID": "9c19ed348af58903", "operationName": "__split_adb_ids", - "startTime": 1703354942040164, - "duration": 683, + "startTime": 1703363923613198, + "duration": 734, "tags": {}, "children": [] }, { "spanID": "38018399ee6a8e2f", "operationName": "__split_adb_ids", - "startTime": 1703354942041122, - "duration": 649, + "startTime": 1703363923614235, + "duration": 679, "tags": {}, "children": [] }, { "spanID": "5718ada2027c013f", "operationName": "__process_adb_edge_type_df", - "startTime": 1703354942042695, - "duration": 1929, + "startTime": 1703363923616009, + "duration": 2082, "tags": { "edge_type": "[\"v1\",\"e0\",\"v1\"]", "edge_type_df_size": 1000 @@ -2964,8 +2964,8 @@ { "spanID": "f66ac168b4a1ca79", "operationName": "__set_pyg_data", - "startTime": 1703354942044159, - "duration": 459, + "startTime": 1703363923617606, + "duration": 478, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -2973,8 +2973,8 @@ { "spanID": "e6256403bf3df0bb", "operationName": "__build_tensor_from_dataframe", - "startTime": 1703354942044188, - "duration": 410, + "startTime": 1703363923617641, + "duration": 417, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -2990,8 +2990,8 @@ { "spanID": "d17034ce51797350", "operationName": "__process_adb_edge_df", - "startTime": 1703354942054661, - "duration": 5436, + "startTime": 1703363923623546, + "duration": 5420, "tags": { "edge_df_size": 1000 }, @@ -2999,24 +2999,24 @@ { "spanID": "091472ad52631db9", "operationName": "__split_adb_ids", - "startTime": 1703354942054770, - "duration": 759, + "startTime": 1703363923623657, + "duration": 765, "tags": {}, "children": [] }, { "spanID": "25fb5f3d866d7002", "operationName": "__split_adb_ids", - "startTime": 1703354942055852, - "duration": 1076, + "startTime": 1703363923624746, + "duration": 1043, "tags": {}, "children": [] }, { "spanID": "41c30359dfde2281", "operationName": "__process_adb_edge_type_df", - "startTime": 1703354942058106, - "duration": 1957, + "startTime": 1703363923626871, + "duration": 2059, "tags": { "edge_type": "[\"v1\",\"e0\",\"v1\"]", "edge_type_df_size": 1000 @@ -3025,8 +3025,8 @@ { "spanID": "c8bf23fb9a431f7a", "operationName": "__set_pyg_data", - "startTime": 1703354942059605, - "duration": 452, + "startTime": 1703363923628457, + "duration": 466, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -3034,8 +3034,8 @@ { "spanID": "d7a3283c27e969e2", "operationName": "__build_tensor_from_dataframe", - "startTime": 1703354942059633, - "duration": 399, + "startTime": 1703363923628490, + "duration": 412, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -3051,8 +3051,8 @@ { "spanID": "953c178e61067a8c", "operationName": "__process_adb_edge_df", - "startTime": 1703354942069500, - "duration": 6219, + "startTime": 1703363923634520, + "duration": 5906, "tags": { "edge_df_size": 1000 }, @@ -3060,24 +3060,24 @@ { "spanID": "b7d779cc4b5ca436", "operationName": "__split_adb_ids", - "startTime": 1703354942069604, - "duration": 742, + "startTime": 1703363923634609, + "duration": 716, "tags": {}, "children": [] }, { "spanID": "ce9b2e70b4d4dfcc", "operationName": "__split_adb_ids", - "startTime": 1703354942070709, - "duration": 677, + "startTime": 1703363923635639, + "duration": 663, "tags": {}, "children": [] }, { "spanID": "10fce97d786e30ef", "operationName": "__process_adb_edge_type_df", - "startTime": 1703354942072483, - "duration": 1639, + "startTime": 1703363923637328, + "duration": 1666, "tags": { "edge_type": "[\"v0\",\"e0\",\"v2\"]", "edge_type_df_size": 584 @@ -3086,8 +3086,8 @@ { "spanID": "15ab2c21ccc93ff7", "operationName": "__set_pyg_data", - "startTime": 1703354942073818, - "duration": 298, + "startTime": 1703363923638696, + "duration": 292, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -3095,8 +3095,8 @@ { "spanID": "de6fec4b843b2a7d", "operationName": "__build_tensor_from_dataframe", - "startTime": 1703354942073849, - "duration": 254, + "startTime": 1703363923638727, + "duration": 249, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -3110,8 +3110,8 @@ { "spanID": "0a1727f7ea5f24b6", "operationName": "__process_adb_edge_type_df", - "startTime": 1703354942074146, - "duration": 1479, + "startTime": 1703363923639015, + "duration": 1389, "tags": { "edge_type": "[\"v1\",\"e0\",\"v1\"]", "edge_type_df_size": 416 @@ -3120,8 +3120,8 @@ { "spanID": "399f8a8f10fc9eee", "operationName": "__set_pyg_data", - "startTime": 1703354942075382, - "duration": 237, + "startTime": 1703363923640162, + "duration": 236, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -3129,8 +3129,8 @@ { "spanID": "0a66dc4e21681081", "operationName": "__build_tensor_from_dataframe", - "startTime": 1703354942075411, - "duration": 189, + "startTime": 1703363923640191, + "duration": 187, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -3146,8 +3146,8 @@ { "spanID": "03e9ba024cea2df0", "operationName": "__process_adb_edge_df", - "startTime": 1703354942086930, - "duration": 5151, + "startTime": 1703363923646080, + "duration": 5058, "tags": { "edge_df_size": 1000 }, @@ -3155,24 +3155,24 @@ { "spanID": "d80d6a1cc2472fd6", "operationName": "__split_adb_ids", - "startTime": 1703354942087027, - "duration": 892, + "startTime": 1703363923646184, + "duration": 870, "tags": {}, "children": [] }, { "spanID": "54a1d50572d6bc20", "operationName": "__split_adb_ids", - "startTime": 1703354942088234, - "duration": 729, + "startTime": 1703363923647386, + "duration": 738, "tags": {}, "children": [] }, { "spanID": "2922fbd8dca5b353", "operationName": "__process_adb_edge_type_df", - "startTime": 1703354942089934, - "duration": 2101, + "startTime": 1703363923649107, + "duration": 1999, "tags": { "edge_type": "[\"v0\",\"e0\",\"v2\"]", "edge_type_df_size": 1000 @@ -3181,8 +3181,8 @@ { "spanID": "261908b9ccf719ab", "operationName": "__set_pyg_data", - "startTime": 1703354942091553, - "duration": 475, + "startTime": 1703363923650649, + "duration": 451, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -3190,8 +3190,8 @@ { "spanID": "a7f5195cde62d43f", "operationName": "__build_tensor_from_dataframe", - "startTime": 1703354942091589, - "duration": 419, + "startTime": 1703363923650681, + "duration": 400, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -3207,8 +3207,8 @@ { "spanID": "f7f60e7f75f2bc20", "operationName": "__process_adb_edge_df", - "startTime": 1703354942102457, - "duration": 5462, + "startTime": 1703363923656733, + "duration": 5227, "tags": { "edge_df_size": 1000 }, @@ -3216,24 +3216,24 @@ { "spanID": "8147a8f45f0ef320", "operationName": "__split_adb_ids", - "startTime": 1703354942102557, - "duration": 1306, + "startTime": 1703363923656825, + "duration": 1043, "tags": {}, "children": [] }, { "spanID": "e6addd9e61d9fe39", "operationName": "__split_adb_ids", - "startTime": 1703354942104193, - "duration": 708, + "startTime": 1703363923658176, + "duration": 658, "tags": {}, "children": [] }, { "spanID": "809f292387a1798f", "operationName": "__process_adb_edge_type_df", - "startTime": 1703354942105865, - "duration": 2021, + "startTime": 1703363923659809, + "duration": 2120, "tags": { "edge_type": "[\"v0\",\"e0\",\"v2\"]", "edge_type_df_size": 1000 @@ -3242,7 +3242,7 @@ { "spanID": "92e94e89089b30a0", "operationName": "__set_pyg_data", - "startTime": 1703354942107424, + "startTime": 1703363923661467, "duration": 456, "tags": { "meta": "{'edge_attr': 'edge_attr'}" @@ -3251,8 +3251,8 @@ { "spanID": "adb6da351734a26c", "operationName": "__build_tensor_from_dataframe", - "startTime": 1703354942107457, - "duration": 405, + "startTime": 1703363923661497, + "duration": 408, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -3268,8 +3268,8 @@ { "spanID": "ce1bb02acb4d18d6", "operationName": "__process_adb_edge_df", - "startTime": 1703354942118334, - "duration": 5384, + "startTime": 1703363923667582, + "duration": 5137, "tags": { "edge_df_size": 1000 }, @@ -3277,24 +3277,24 @@ { "spanID": "c202387b849b8a44", "operationName": "__split_adb_ids", - "startTime": 1703354942118463, - "duration": 750, + "startTime": 1703363923667685, + "duration": 729, "tags": {}, "children": [] }, { "spanID": "fd938adc99a2ecb1", "operationName": "__split_adb_ids", - "startTime": 1703354942119636, - "duration": 785, + "startTime": 1703363923668733, + "duration": 748, "tags": {}, "children": [] }, { "spanID": "bf391fbb138c3460", "operationName": "__process_adb_edge_type_df", - "startTime": 1703354942121531, - "duration": 2147, + "startTime": 1703363923670495, + "duration": 2184, "tags": { "edge_type": "[\"v0\",\"e0\",\"v2\"]", "edge_type_df_size": 1000 @@ -3303,8 +3303,8 @@ { "spanID": "e7e13ed86d265dd8", "operationName": "__set_pyg_data", - "startTime": 1703354942123192, - "duration": 479, + "startTime": 1703363923672173, + "duration": 499, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -3312,8 +3312,8 @@ { "spanID": "34c3494ac12ea9b8", "operationName": "__build_tensor_from_dataframe", - "startTime": 1703354942123222, - "duration": 419, + "startTime": 1703363923672209, + "duration": 410, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -3329,8 +3329,8 @@ { "spanID": "89110af04a276dda", "operationName": "__process_adb_edge_df", - "startTime": 1703354942132971, - "duration": 5058, + "startTime": 1703363923678244, + "duration": 5007, "tags": { "edge_df_size": 1000 }, @@ -3338,24 +3338,24 @@ { "spanID": "993ec8c6e6b106e2", "operationName": "__split_adb_ids", - "startTime": 1703354942133068, - "duration": 741, + "startTime": 1703363923678329, + "duration": 759, "tags": {}, "children": [] }, { "spanID": "d360da696af79ad2", "operationName": "__split_adb_ids", - "startTime": 1703354942134144, - "duration": 684, + "startTime": 1703363923679413, + "duration": 697, "tags": {}, "children": [] }, { "spanID": "7b72590bf8f8f071", "operationName": "__process_adb_edge_type_df", - "startTime": 1703354942135989, - "duration": 2006, + "startTime": 1703363923681140, + "duration": 2075, "tags": { "edge_type": "[\"v0\",\"e0\",\"v2\"]", "edge_type_df_size": 1000 @@ -3364,8 +3364,8 @@ { "spanID": "ca819c6fd872298c", "operationName": "__set_pyg_data", - "startTime": 1703354942137538, - "duration": 451, + "startTime": 1703363923682754, + "duration": 455, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -3373,8 +3373,8 @@ { "spanID": "63794035f8e45086", "operationName": "__build_tensor_from_dataframe", - "startTime": 1703354942137566, - "duration": 398, + "startTime": 1703363923682784, + "duration": 402, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -3390,8 +3390,8 @@ { "spanID": "961d8dcf9b8086da", "operationName": "__process_adb_edge_df", - "startTime": 1703354942147957, - "duration": 5546, + "startTime": 1703363923688673, + "duration": 5168, "tags": { "edge_df_size": 1000 }, @@ -3399,24 +3399,24 @@ { "spanID": "d9efe28b3bcb50b3", "operationName": "__split_adb_ids", - "startTime": 1703354942148061, - "duration": 1143, + "startTime": 1703363923688758, + "duration": 1041, "tags": {}, "children": [] }, { "spanID": "cc4da021dd620222", "operationName": "__split_adb_ids", - "startTime": 1703354942149528, - "duration": 737, + "startTime": 1703363923690094, + "duration": 652, "tags": {}, "children": [] }, { "spanID": "a83023ab053e4b42", "operationName": "__process_adb_edge_type_df", - "startTime": 1703354942151444, - "duration": 2025, + "startTime": 1703363923691700, + "duration": 2109, "tags": { "edge_type": "[\"v0\",\"e0\",\"v2\"]", "edge_type_df_size": 1000 @@ -3425,8 +3425,8 @@ { "spanID": "000fc63de2a01335", "operationName": "__set_pyg_data", - "startTime": 1703354942153019, - "duration": 444, + "startTime": 1703363923693342, + "duration": 462, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -3434,8 +3434,8 @@ { "spanID": "2e9583eabda17da2", "operationName": "__build_tensor_from_dataframe", - "startTime": 1703354942153047, - "duration": 395, + "startTime": 1703363923693376, + "duration": 409, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -3451,8 +3451,8 @@ { "spanID": "81c16e984d6cd782", "operationName": "__process_adb_edge_df", - "startTime": 1703354942163989, - "duration": 5273, + "startTime": 1703363923699083, + "duration": 4804, "tags": { "edge_df_size": 1000 }, @@ -3460,24 +3460,24 @@ { "spanID": "4124405b91fcfe88", "operationName": "__split_adb_ids", - "startTime": 1703354942164096, - "duration": 925, + "startTime": 1703363923699165, + "duration": 691, "tags": {}, "children": [] }, { "spanID": "10cc8711552ae5ca", "operationName": "__split_adb_ids", - "startTime": 1703354942165360, - "duration": 732, + "startTime": 1703363923700142, + "duration": 660, "tags": {}, "children": [] }, { "spanID": "dc2151e17e56ac3d", "operationName": "__process_adb_edge_type_df", - "startTime": 1703354942167173, - "duration": 2000, + "startTime": 1703363923701814, + "duration": 2040, "tags": { "edge_type": "[\"v0\",\"e0\",\"v2\"]", "edge_type_df_size": 1000 @@ -3486,8 +3486,8 @@ { "spanID": "f164f9d84312ece2", "operationName": "__set_pyg_data", - "startTime": 1703354942168709, - "duration": 458, + "startTime": 1703363923703392, + "duration": 455, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -3495,8 +3495,8 @@ { "spanID": "4d849ec5d334886f", "operationName": "__build_tensor_from_dataframe", - "startTime": 1703354942168736, - "duration": 413, + "startTime": 1703363923703422, + "duration": 407, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -3512,8 +3512,8 @@ { "spanID": "68777babc5c14262", "operationName": "__process_adb_edge_df", - "startTime": 1703354942179678, - "duration": 5390, + "startTime": 1703363923709119, + "duration": 4904, "tags": { "edge_df_size": 1000 }, @@ -3521,24 +3521,24 @@ { "spanID": "cf5e9ea362584ab3", "operationName": "__split_adb_ids", - "startTime": 1703354942179790, - "duration": 773, + "startTime": 1703363923709198, + "duration": 702, "tags": {}, "children": [] }, { "spanID": "0ff030b86238d0a0", "operationName": "__split_adb_ids", - "startTime": 1703354942180907, - "duration": 677, + "startTime": 1703363923710192, + "duration": 697, "tags": {}, "children": [] }, { "spanID": "a417956f29ee7f3d", "operationName": "__process_adb_edge_type_df", - "startTime": 1703354942182810, - "duration": 2215, + "startTime": 1703363923711923, + "duration": 2064, "tags": { "edge_type": "[\"v0\",\"e0\",\"v2\"]", "edge_type_df_size": 1000 @@ -3547,8 +3547,8 @@ { "spanID": "209818d1ef7e85ec", "operationName": "__set_pyg_data", - "startTime": 1703354942184534, - "duration": 484, + "startTime": 1703363923713525, + "duration": 455, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -3556,8 +3556,8 @@ { "spanID": "497e9f1a3d2bf042", "operationName": "__build_tensor_from_dataframe", - "startTime": 1703354942184573, - "duration": 418, + "startTime": 1703363923713557, + "duration": 402, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -3573,8 +3573,8 @@ { "spanID": "d476fe38babd4745", "operationName": "__process_adb_edge_df", - "startTime": 1703354942195282, - "duration": 5649, + "startTime": 1703363923719431, + "duration": 5272, "tags": { "edge_df_size": 1000 }, @@ -3582,24 +3582,24 @@ { "spanID": "0e3705265582a3bd", "operationName": "__split_adb_ids", - "startTime": 1703354942195380, - "duration": 1173, + "startTime": 1703363923719522, + "duration": 1083, "tags": {}, "children": [] }, { "spanID": "0932f5b6f11ddff7", "operationName": "__split_adb_ids", - "startTime": 1703354942196876, - "duration": 670, + "startTime": 1703363923720934, + "duration": 684, "tags": {}, "children": [] }, { "spanID": "6af944e07b38785b", "operationName": "__process_adb_edge_type_df", - "startTime": 1703354942198623, - "duration": 2267, + "startTime": 1703363923722601, + "duration": 2058, "tags": { "edge_type": "[\"v0\",\"e0\",\"v2\"]", "edge_type_df_size": 1000 @@ -3608,8 +3608,8 @@ { "spanID": "7de8a2342412579d", "operationName": "__set_pyg_data", - "startTime": 1703354942200402, - "duration": 481, + "startTime": 1703363923724167, + "duration": 485, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -3617,8 +3617,8 @@ { "spanID": "dd02e100e3d48408", "operationName": "__build_tensor_from_dataframe", - "startTime": 1703354942200435, - "duration": 415, + "startTime": 1703363923724198, + "duration": 421, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -3634,8 +3634,8 @@ { "spanID": "b799ae8e9a1a7d6f", "operationName": "__process_adb_edge_df", - "startTime": 1703354942207277, - "duration": 4947, + "startTime": 1703363923729496, + "duration": 5113, "tags": { "edge_df_size": 1000 }, @@ -3643,24 +3643,24 @@ { "spanID": "ac6d5df814e5064c", "operationName": "__split_adb_ids", - "startTime": 1703354942207361, - "duration": 711, + "startTime": 1703363923729585, + "duration": 759, "tags": {}, "children": [] }, { "spanID": "26c06e67b2ddc481", "operationName": "__split_adb_ids", - "startTime": 1703354942208392, - "duration": 717, + "startTime": 1703363923730678, + "duration": 700, "tags": {}, "children": [] }, { "spanID": "fc98c279cf6f111c", "operationName": "__process_adb_edge_type_df", - "startTime": 1703354942210129, - "duration": 2061, + "startTime": 1703363923732409, + "duration": 2163, "tags": { "edge_type": "[\"v0\",\"e0\",\"v2\"]", "edge_type_df_size": 1000 @@ -3669,8 +3669,8 @@ { "spanID": "69407be75a4f4145", "operationName": "__set_pyg_data", - "startTime": 1703354942211727, - "duration": 457, + "startTime": 1703363923734101, + "duration": 464, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -3678,8 +3678,8 @@ { "spanID": "9c9d03f309018aee", "operationName": "__build_tensor_from_dataframe", - "startTime": 1703354942211756, - "duration": 407, + "startTime": 1703363923734133, + "duration": 404, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -3695,8 +3695,8 @@ { "spanID": "62fda854775e0ec3", "operationName": "__process_adb_edge_df", - "startTime": 1703354942215868, - "duration": 3500, + "startTime": 1703363923737710, + "duration": 3772, "tags": { "edge_df_size": 450 }, @@ -3704,24 +3704,24 @@ { "spanID": "0c0a59677579501a", "operationName": "__split_adb_ids", - "startTime": 1703354942215942, - "duration": 375, + "startTime": 1703363923737801, + "duration": 397, "tags": {}, "children": [] }, { "spanID": "788c31f619faa06e", "operationName": "__split_adb_ids", - "startTime": 1703354942216583, - "duration": 342, + "startTime": 1703363923738492, + "duration": 370, "tags": {}, "children": [] }, { "spanID": "26c00984c734bb05", "operationName": "__process_adb_edge_type_df", - "startTime": 1703354942217802, - "duration": 1545, + "startTime": 1703363923739829, + "duration": 1630, "tags": { "edge_type": "[\"v0\",\"e0\",\"v2\"]", "edge_type_df_size": 450 @@ -3730,8 +3730,8 @@ { "spanID": "084fa819052daad3", "operationName": "__set_pyg_data", - "startTime": 1703354942219089, - "duration": 252, + "startTime": 1703363923741187, + "duration": 266, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, @@ -3739,8 +3739,8 @@ { "spanID": "9e0df45b992a34a1", "operationName": "__build_tensor_from_dataframe", - "startTime": 1703354942219117, - "duration": 205, + "startTime": 1703363923741218, + "duration": 202, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" diff --git a/benchmark/traces/master/pyg_to_arangodb.json b/benchmark/traces/master/pyg_to_arangodb.json index e55e782..5011f24 100644 --- a/benchmark/traces/master/pyg_to_arangodb.json +++ b/benchmark/traces/master/pyg_to_arangodb.json @@ -1,8 +1,8 @@ { "spanID": "40212ef7cca5a5a1", "operationName": "pyg_to_arangodb", - "startTime": 1703354939820545, - "duration": 1491957, + "startTime": 1703363921366132, + "duration": 1470129, "tags": { "name": "FakeHeteroGraphBenchmark" }, @@ -10,31 +10,31 @@ { "spanID": "e8e5216afcbd04c3", "operationName": "__get_node_and_edge_types", - "startTime": 1703354939820612, - "duration": 12, + "startTime": 1703363921366221, + "duration": 17, "tags": {}, "children": [] }, { "spanID": "fb97d43588561712", "operationName": "__create_adb_graph", - "startTime": 1703354939820654, - "duration": 8549, + "startTime": 1703363921366281, + "duration": 5596, "tags": {}, "children": [ { "spanID": "cf6a659eb4862b21", "operationName": "__etypes_to_edefinitions", - "startTime": 1703354939824427, - "duration": 21, + "startTime": 1703363921368952, + "duration": 23, "tags": {}, "children": [] }, { "spanID": "e6f4590b9a164106", "operationName": "__ntypes_to_ocollections", - "startTime": 1703354939824477, - "duration": 8, + "startTime": 1703363921369004, + "duration": 9, "tags": {}, "children": [] } @@ -43,8 +43,8 @@ { "spanID": "4f65d4d9259f4329", "operationName": "__process_pyg_n_type", - "startTime": 1703354939830920, - "duration": 151250, + "startTime": 1703363921373624, + "duration": 154737, "tags": { "n_type": "v0", "n_type_size": 1008 @@ -53,8 +53,8 @@ { "spanID": "bad640fb19488dec", "operationName": "__process_pyg_node_batch", - "startTime": 1703354939830962, - "duration": 13101, + "startTime": 1703363921373669, + "duration": 11623, "tags": { "start_index": 0, "end_index": 1008 @@ -63,8 +63,8 @@ { "spanID": "e61a441c12e0c8b2", "operationName": "__set_adb_data", - "startTime": 1703354939832173, - "duration": 9474, + "startTime": 1703363921374931, + "duration": 8928, "tags": { "meta": "{}" }, @@ -72,22 +72,22 @@ { "spanID": "af19922ad9b8a714", "operationName": "__build_dataframe_from_tensor", - "startTime": 1703354939832364, - "duration": 4942, + "startTime": 1703363921375218, + "duration": 3766, "tags": { - "meta_key": "x", - "meta_val": "x" + "meta_key": "y", + "meta_val": "y" }, "children": [] }, { "spanID": "78de58575487ce1e", "operationName": "__build_dataframe_from_tensor", - "startTime": 1703354939838226, - "duration": 573, + "startTime": 1703363921379833, + "duration": 1975, "tags": { - "meta_key": "y", - "meta_val": "y" + "meta_key": "x", + "meta_val": "x" }, "children": [] } @@ -98,8 +98,8 @@ { "spanID": "19c78df48f4ff31e", "operationName": "__insert_adb_docs", - "startTime": 1703354939844161, - "duration": 137979, + "startTime": 1703363921385369, + "duration": 142960, "tags": { "col": "v0", "size": 1008 @@ -111,8 +111,8 @@ { "spanID": "6f25e2a25a921187", "operationName": "__process_pyg_n_type", - "startTime": 1703354939984119, - "duration": 97803, + "startTime": 1703363921530445, + "duration": 98388, "tags": { "n_type": "v1", "n_type_size": 821 @@ -121,8 +121,8 @@ { "spanID": "9c6316b950f24455", "operationName": "__process_pyg_node_batch", - "startTime": 1703354939984160, - "duration": 2664, + "startTime": 1703363921530486, + "duration": 2707, "tags": { "start_index": 0, "end_index": 821 @@ -131,8 +131,8 @@ { "spanID": "e9bb17bca3f2c9bf", "operationName": "__set_adb_data", - "startTime": 1703354939984398, - "duration": 1577, + "startTime": 1703363921530734, + "duration": 1610, "tags": { "meta": "{}" }, @@ -140,8 +140,8 @@ { "spanID": "f77383c13458a748", "operationName": "__build_dataframe_from_tensor", - "startTime": 1703354939984466, - "duration": 1177, + "startTime": 1703363921530803, + "duration": 1223, "tags": { "meta_key": "x", "meta_val": "x" @@ -155,8 +155,8 @@ { "spanID": "7a1d50068d723104", "operationName": "__insert_adb_docs", - "startTime": 1703354939986867, - "duration": 95027, + "startTime": 1703363921533238, + "duration": 95566, "tags": { "col": "v1", "size": 821 @@ -168,8 +168,8 @@ { "spanID": "dd84f39e71545a13", "operationName": "__process_pyg_n_type", - "startTime": 1703354940083949, - "duration": 95862, + "startTime": 1703363921630746, + "duration": 94787, "tags": { "n_type": "v2", "n_type_size": 894 @@ -178,8 +178,8 @@ { "spanID": "42af9fc385776e9a", "operationName": "__process_pyg_node_batch", - "startTime": 1703354940083988, - "duration": 2578, + "startTime": 1703363921630788, + "duration": 2623, "tags": { "start_index": 0, "end_index": 894 @@ -188,8 +188,8 @@ { "spanID": "ce164dba0ff18e02", "operationName": "__set_adb_data", - "startTime": 1703354940084213, - "duration": 1486, + "startTime": 1703363921631030, + "duration": 1502, "tags": { "meta": "{}" }, @@ -197,8 +197,8 @@ { "spanID": "8c778ea6eb2083e6", "operationName": "__build_dataframe_from_tensor", - "startTime": 1703354940084281, - "duration": 1109, + "startTime": 1703363921631098, + "duration": 1122, "tags": { "meta_key": "x", "meta_val": "x" @@ -212,8 +212,8 @@ { "spanID": "03983ca8ea7e9d49", "operationName": "__insert_adb_docs", - "startTime": 1703354940086606, - "duration": 93165, + "startTime": 1703363921633455, + "duration": 92050, "tags": { "col": "v2", "size": 894 @@ -225,8 +225,8 @@ { "spanID": "b83e90ec17e0aa3c", "operationName": "__process_pyg_e_type", - "startTime": 1703354940182160, - "duration": 175054, + "startTime": 1703363921727597, + "duration": 191512, "tags": { "e_type": "[\"v2\",\"e0\",\"v1\"]", "e_type_size": 8895 @@ -235,8 +235,8 @@ { "spanID": "66194cb1d71037d1", "operationName": "__process_pyg_edge_batch", - "startTime": 1703354940182209, - "duration": 8649, + "startTime": 1703363921727648, + "duration": 8551, "tags": { "start_index": 0, "end_index": 8895 @@ -245,8 +245,8 @@ { "spanID": "d3290a4cb5d32b16", "operationName": "__set_adb_data", - "startTime": 1703354940185657, - "duration": 2570, + "startTime": 1703363921731472, + "duration": 2670, "tags": { "meta": "{}" }, @@ -254,8 +254,8 @@ { "spanID": "ab0c1681c8f8e3d0", "operationName": "__build_dataframe_from_tensor", - "startTime": 1703354940185715, - "duration": 2015, + "startTime": 1703363921731557, + "duration": 1888, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -269,8 +269,8 @@ { "spanID": "004ae545a0116be5", "operationName": "__insert_adb_docs", - "startTime": 1703354940190917, - "duration": 166269, + "startTime": 1703363921736269, + "duration": 182809, "tags": { "col": "e0", "size": 8895 @@ -282,8 +282,8 @@ { "spanID": "7e5b1e7f9ca5499d", "operationName": "__process_pyg_e_type", - "startTime": 1703354940359096, - "duration": 145459, + "startTime": 1703363921921310, + "duration": 156678, "tags": { "e_type": "[\"v1\",\"e0\",\"v2\"]", "e_type_size": 8161 @@ -292,8 +292,8 @@ { "spanID": "de1b372ad3fbf47a", "operationName": "__process_pyg_edge_batch", - "startTime": 1703354940359142, - "duration": 5921, + "startTime": 1703363921921358, + "duration": 6323, "tags": { "start_index": 0, "end_index": 8161 @@ -302,8 +302,8 @@ { "spanID": "3e70f16a55485822", "operationName": "__set_adb_data", - "startTime": 1703354940362018, - "duration": 2004, + "startTime": 1703363921924293, + "duration": 2166, "tags": { "meta": "{}" }, @@ -311,8 +311,8 @@ { "spanID": "534097cabaf3897a", "operationName": "__build_dataframe_from_tensor", - "startTime": 1703354940362070, - "duration": 1530, + "startTime": 1703363921924356, + "duration": 1601, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -326,8 +326,8 @@ { "spanID": "ded733e8b421eaeb", "operationName": "__insert_adb_docs", - "startTime": 1703354940365115, - "duration": 139411, + "startTime": 1703363921927749, + "duration": 150209, "tags": { "col": "e0", "size": 8161 @@ -339,8 +339,8 @@ { "spanID": "30e9c5cc101fbccc", "operationName": "__process_pyg_e_type", - "startTime": 1703354940506405, - "duration": 247974, + "startTime": 1703363922079865, + "duration": 249886, "tags": { "e_type": "[\"v0\",\"e0\",\"v1\"]", "e_type_size": 10022 @@ -349,8 +349,8 @@ { "spanID": "9148624feac1c14f", "operationName": "__process_pyg_edge_batch", - "startTime": 1703354940506450, - "duration": 7089, + "startTime": 1703363922079911, + "duration": 7285, "tags": { "start_index": 0, "end_index": 10022 @@ -359,8 +359,8 @@ { "spanID": "3d15eef738c1962e", "operationName": "__set_adb_data", - "startTime": 1703354940510095, - "duration": 2325, + "startTime": 1703363922083694, + "duration": 2405, "tags": { "meta": "{}" }, @@ -368,8 +368,8 @@ { "spanID": "f7b0b7d2cda8056c", "operationName": "__build_dataframe_from_tensor", - "startTime": 1703354940510149, - "duration": 1824, + "startTime": 1703363922083769, + "duration": 1877, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -383,8 +383,8 @@ { "spanID": "cd9d2b7d247a8333", "operationName": "__insert_adb_docs", - "startTime": 1703354940513590, - "duration": 240750, + "startTime": 1703363922087247, + "duration": 242475, "tags": { "col": "e0", "size": 10022 @@ -396,8 +396,8 @@ { "spanID": "72ae22448b0163c1", "operationName": "__process_pyg_e_type", - "startTime": 1703354940759178, - "duration": 211050, + "startTime": 1703363922331731, + "duration": 154780, "tags": { "e_type": "[\"v1\",\"e0\",\"v0\"]", "e_type_size": 8179 @@ -406,8 +406,8 @@ { "spanID": "149818d11759edc3", "operationName": "__process_pyg_edge_batch", - "startTime": 1703354940759236, - "duration": 54068, + "startTime": 1703363922331779, + "duration": 6392, "tags": { "start_index": 0, "end_index": 8179 @@ -416,8 +416,8 @@ { "spanID": "51ef1922fe43c49e", "operationName": "__set_adb_data", - "startTime": 1703354940809338, - "duration": 2884, + "startTime": 1703363922334788, + "duration": 2280, "tags": { "meta": "{}" }, @@ -425,8 +425,8 @@ { "spanID": "820865d6e005b860", "operationName": "__build_dataframe_from_tensor", - "startTime": 1703354940809740, - "duration": 2000, + "startTime": 1703363922334852, + "duration": 1690, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -440,8 +440,8 @@ { "spanID": "eece328bff7b118e", "operationName": "__insert_adb_docs", - "startTime": 1703354940813375, - "duration": 156823, + "startTime": 1703363922338234, + "duration": 148247, "tags": { "col": "e0", "size": 8179 @@ -453,8 +453,8 @@ { "spanID": "1beb37117d41e602", "operationName": "__process_pyg_e_type", - "startTime": 1703354940972247, - "duration": 155663, + "startTime": 1703363922488590, + "duration": 156159, "tags": { "e_type": "[\"v1\",\"e0\",\"v1\"]", "e_type_size": 8159 @@ -463,8 +463,8 @@ { "spanID": "8d1fd9b74d2b9deb", "operationName": "__process_pyg_edge_batch", - "startTime": 1703354940972290, - "duration": 6041, + "startTime": 1703363922488644, + "duration": 6517, "tags": { "start_index": 0, "end_index": 8159 @@ -473,8 +473,8 @@ { "spanID": "b4e1357d4a84eb03", "operationName": "__set_adb_data", - "startTime": 1703354940975250, - "duration": 2092, + "startTime": 1703363922491712, + "duration": 2232, "tags": { "meta": "{}" }, @@ -482,8 +482,8 @@ { "spanID": "8c25166a1ff39849", "operationName": "__build_dataframe_from_tensor", - "startTime": 1703354940975304, - "duration": 1621, + "startTime": 1703363922491781, + "duration": 1669, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -497,8 +497,8 @@ { "spanID": "d080e66e552f233a", "operationName": "__insert_adb_docs", - "startTime": 1703354940978376, - "duration": 149505, + "startTime": 1703363922495223, + "duration": 149497, "tags": { "col": "e0", "size": 8159 @@ -510,8 +510,8 @@ { "spanID": "8a5006c1ec188efb", "operationName": "__process_pyg_e_type", - "startTime": 1703354941129788, - "duration": 181631, + "startTime": 1703363922646634, + "duration": 188557, "tags": { "e_type": "[\"v0\",\"e0\",\"v2\"]", "e_type_size": 10034 @@ -520,8 +520,8 @@ { "spanID": "f6be1f723405095c", "operationName": "__process_pyg_edge_batch", - "startTime": 1703354941129836, - "duration": 7327, + "startTime": 1703363922646683, + "duration": 7481, "tags": { "start_index": 0, "end_index": 10034 @@ -530,8 +530,8 @@ { "spanID": "9a6a5f92cca74147", "operationName": "__set_adb_data", - "startTime": 1703354941133467, - "duration": 2550, + "startTime": 1703363922650275, + "duration": 2651, "tags": { "meta": "{}" }, @@ -539,8 +539,8 @@ { "spanID": "966e12778c1745a7", "operationName": "__build_dataframe_from_tensor", - "startTime": 1703354941133519, - "duration": 1999, + "startTime": 1703363922650330, + "duration": 2067, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -554,8 +554,8 @@ { "spanID": "71eacd0549a3e80e", "operationName": "__insert_adb_docs", - "startTime": 1703354941137235, - "duration": 174154, + "startTime": 1703363922654225, + "duration": 180936, "tags": { "col": "e0", "size": 10034 From b3eeb86a9800add0e87b46139ee0416bba9f58e5 Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Sat, 23 Dec 2023 15:45:20 -0500 Subject: [PATCH 41/73] pragma no cover --- adbpyg_adapter/tracing.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/adbpyg_adapter/tracing.py b/adbpyg_adapter/tracing.py index 29cde28..069e67a 100644 --- a/adbpyg_adapter/tracing.py +++ b/adbpyg_adapter/tracing.py @@ -30,7 +30,7 @@ def set_tracer(cls, tracer: "Tracer") -> None: cls.__tracer = tracer @classmethod - def set_attributes(self, **attributes: Any) -> None: + def set_attributes(self, **attributes: Any) -> None: # pragma: no cover if TRACING_ENABLED and self.__tracer is not None: current_span = trace.get_current_span() for k, v in attributes.items(): From 265c3b2ee8af611d0a22babc3921bb211301727d Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Sat, 23 Dec 2023 15:45:51 -0500 Subject: [PATCH 42/73] Update benchmark.yml --- .github/workflows/benchmark.yml | 59 ++++++++++++++++++++++++++++++--- 1 file changed, 54 insertions(+), 5 deletions(-) diff --git a/.github/workflows/benchmark.yml b/.github/workflows/benchmark.yml index 2074df0..eac1812 100644 --- a/.github/workflows/benchmark.yml +++ b/.github/workflows/benchmark.yml @@ -105,10 +105,59 @@ jobs: run: python benchmark/write.py --output_dir branch - name: Compare PR traces against Master traces - run: python benchmark/compare.py + id: python_script + run: | + echo "ROOT_SPAN_COMPARISON<> $GITHUB_ENV + python benchmark/compare.py >> $GITHUB_ENV + echo "EOF" >> $GITHUB_ENV + + - name: Echo PyG to ArangoDB Diff + run: | + echo "PYG_TO_ARANGODB_DIFF<> $GITHUB_ENV + cat benchmark/diff/pyg_to_arangodb.json >> $GITHUB_ENV + echo "EOF" >> $GITHUB_ENV - - name: Echo PyG to ArangoDB Comparison - run: cat benchmark/diff/pyg_to_arangodb.json | jq + - name: Echo ArangoDB to PyG Diff + run: | + echo "ARANGODB_TO_PYG_DIFF<> $GITHUB_ENV + cat benchmark/diff/arangodb_to_pyg.json >> $GITHUB_ENV + echo "EOF" >> $GITHUB_ENV - - name: Echo ArangoDB to PyG Comparison - run: cat benchmark/diff/arangodb_to_pyg.json | jq \ No newline at end of file + - name: Prepare PR Message + run: | + echo "MESSAGE<> $GITHUB_ENV + echo '**Benchmark for Commit ${GITHUB_SHA}**' >> $GITHUB_ENV + echo '' >> $GITHUB_ENV + echo 'Root Span Comparisons:' >> $GITHUB_ENV + echo '```json' >> $GITHUB_ENV + cat $ROOT_SPAN_COMPARISON >> $GITHUB_ENV + echo '```' >> $GITHUB_ENV + echo '' >> $GITHUB_ENV + echo '
PyG to ArangoDB Comparison' >> $GITHUB_ENV + echo '' >> $GITHUB_ENV + echo '```json' >> $GITHUB_ENV + cat $PYG_TO_ARANGODB_DIFF >> $GITHUB_ENV + echo '```' >> $GITHUB_ENV + echo '
' >> $GITHUB_ENV + echo '' >> $GITHUB_ENV + echo '
ArangoDB to PyG Comparison' >> $GITHUB_ENV + echo '' >> $GITHUB_ENV + echo '```json' >> $GITHUB_ENV + cat $ARANGODB_TO_PYG_DIFF >> $GITHUB_ENV + echo '```' >> $GITHUB_ENV + echo '
' >> $GITHUB_ENV + echo "EOF" >> $GITHUB_ENV + + - name: Comment on PR + uses: actions/github-script@v5 + with: + github-token: ${{secrets.GITHUB_TOKEN}} + script: | + const prNumber = context.payload.pull_request.number; + const message = process.env.MESSAGE; + github.rest.issues.createComment({ + owner: context.repo.owner, + repo: context.repo.repo, + issue_number: prNumber, + body: message + }); \ No newline at end of file From a8567966c350e22272eb547c74f6eb9dbc19920c Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Sat, 23 Dec 2023 15:50:44 -0500 Subject: [PATCH 43/73] Update benchmark.yml --- .github/workflows/benchmark.yml | 61 +++++++++++++++++++-------------- 1 file changed, 36 insertions(+), 25 deletions(-) diff --git a/.github/workflows/benchmark.yml b/.github/workflows/benchmark.yml index eac1812..9ee03ee 100644 --- a/.github/workflows/benchmark.yml +++ b/.github/workflows/benchmark.yml @@ -111,43 +111,54 @@ jobs: python benchmark/compare.py >> $GITHUB_ENV echo "EOF" >> $GITHUB_ENV - - name: Echo PyG to ArangoDB Diff - run: | - echo "PYG_TO_ARANGODB_DIFF<> $GITHUB_ENV - cat benchmark/diff/pyg_to_arangodb.json >> $GITHUB_ENV - echo "EOF" >> $GITHUB_ENV + # - name: Echo PyG to ArangoDB Diff + # run: | + # echo "PYG_TO_ARANGODB_DIFF<> $GITHUB_ENV + # cat benchmark/diff/pyg_to_arangodb.json | jq >> $GITHUB_ENV + # echo "EOF" >> $GITHUB_ENV - - name: Echo ArangoDB to PyG Diff - run: | - echo "ARANGODB_TO_PYG_DIFF<> $GITHUB_ENV - cat benchmark/diff/arangodb_to_pyg.json >> $GITHUB_ENV - echo "EOF" >> $GITHUB_ENV + # - name: Echo ArangoDB to PyG Diff + # run: | + # echo "ARANGODB_TO_PYG_DIFF<> $GITHUB_ENV + # cat benchmark/diff/arangodb_to_pyg.json | jq >> $GITHUB_ENV + # echo "EOF" >> $GITHUB_ENV - name: Prepare PR Message run: | echo "MESSAGE<> $GITHUB_ENV - echo '**Benchmark for Commit ${GITHUB_SHA}**' >> $GITHUB_ENV + echo '**Benchmark (${GITHUB_SHA})**' >> $GITHUB_ENV echo '' >> $GITHUB_ENV echo 'Root Span Comparisons:' >> $GITHUB_ENV echo '```json' >> $GITHUB_ENV cat $ROOT_SPAN_COMPARISON >> $GITHUB_ENV echo '```' >> $GITHUB_ENV - echo '' >> $GITHUB_ENV - echo '
PyG to ArangoDB Comparison' >> $GITHUB_ENV - echo '' >> $GITHUB_ENV - echo '```json' >> $GITHUB_ENV - cat $PYG_TO_ARANGODB_DIFF >> $GITHUB_ENV - echo '```' >> $GITHUB_ENV - echo '
' >> $GITHUB_ENV - echo '' >> $GITHUB_ENV - echo '
ArangoDB to PyG Comparison' >> $GITHUB_ENV - echo '' >> $GITHUB_ENV - echo '```json' >> $GITHUB_ENV - cat $ARANGODB_TO_PYG_DIFF >> $GITHUB_ENV - echo '```' >> $GITHUB_ENV - echo '
' >> $GITHUB_ENV echo "EOF" >> $GITHUB_ENV + # - name: Prepare PR Message + # run: | + # echo "MESSAGE<> $GITHUB_ENV + # echo '**Benchmark (${GITHUB_SHA})**' >> $GITHUB_ENV + # echo '' >> $GITHUB_ENV + # echo 'Root Span Comparisons:' >> $GITHUB_ENV + # echo '```json' >> $GITHUB_ENV + # cat $ROOT_SPAN_COMPARISON >> $GITHUB_ENV + # echo '```' >> $GITHUB_ENV + # echo '' >> $GITHUB_ENV + # echo '
PyG to ArangoDB Comparison' >> $GITHUB_ENV + # echo '' >> $GITHUB_ENV + # echo '```json' >> $GITHUB_ENV + # cat $PYG_TO_ARANGODB_DIFF >> $GITHUB_ENV + # echo '```' >> $GITHUB_ENV + # echo '
' >> $GITHUB_ENV + # echo '' >> $GITHUB_ENV + # echo '
ArangoDB to PyG Comparison' >> $GITHUB_ENV + # echo '' >> $GITHUB_ENV + # echo '```json' >> $GITHUB_ENV + # cat $ARANGODB_TO_PYG_DIFF >> $GITHUB_ENV + # echo '```' >> $GITHUB_ENV + # echo '
' >> $GITHUB_ENV + # echo "EOF" >> $GITHUB_ENV + - name: Comment on PR uses: actions/github-script@v5 with: From ae3b12af04a4316ef06b4a886fe0f8661a2d00f3 Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Sat, 23 Dec 2023 15:55:55 -0500 Subject: [PATCH 44/73] jq --- .github/workflows/benchmark.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/benchmark.yml b/.github/workflows/benchmark.yml index 9ee03ee..405543d 100644 --- a/.github/workflows/benchmark.yml +++ b/.github/workflows/benchmark.yml @@ -130,7 +130,7 @@ jobs: echo '' >> $GITHUB_ENV echo 'Root Span Comparisons:' >> $GITHUB_ENV echo '```json' >> $GITHUB_ENV - cat $ROOT_SPAN_COMPARISON >> $GITHUB_ENV + cat $ROOT_SPAN_COMPARISON | jq >> $GITHUB_ENV echo '```' >> $GITHUB_ENV echo "EOF" >> $GITHUB_ENV From 2399b857f5d7d48f369297282f51bf9ddfe33413 Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Sat, 23 Dec 2023 16:03:02 -0500 Subject: [PATCH 45/73] Update benchmark.yml --- .github/workflows/benchmark.yml | 25 +++++++------------------ 1 file changed, 7 insertions(+), 18 deletions(-) diff --git a/.github/workflows/benchmark.yml b/.github/workflows/benchmark.yml index 405543d..f4de148 100644 --- a/.github/workflows/benchmark.yml +++ b/.github/workflows/benchmark.yml @@ -111,28 +111,17 @@ jobs: python benchmark/compare.py >> $GITHUB_ENV echo "EOF" >> $GITHUB_ENV - # - name: Echo PyG to ArangoDB Diff - # run: | - # echo "PYG_TO_ARANGODB_DIFF<> $GITHUB_ENV - # cat benchmark/diff/pyg_to_arangodb.json | jq >> $GITHUB_ENV - # echo "EOF" >> $GITHUB_ENV - - # - name: Echo ArangoDB to PyG Diff - # run: | - # echo "ARANGODB_TO_PYG_DIFF<> $GITHUB_ENV - # cat benchmark/diff/arangodb_to_pyg.json | jq >> $GITHUB_ENV - # echo "EOF" >> $GITHUB_ENV + - run: echo "PYG_TO_ARANGODB_DIFF=$(jq -c . < benchmark/diff/pyg_to_arangodb.json)" >> $GITHUB_ENV + - run: echo "ARANGODB_TO_PYG_DIFF=$(jq -c . < benchmark/diff/arangodb_to_pyg.json)" >> $GITHUB_ENV - name: Prepare PR Message run: | - echo "MESSAGE<> $GITHUB_ENV - echo '**Benchmark (${GITHUB_SHA})**' >> $GITHUB_ENV - echo '' >> $GITHUB_ENV + echo 'MESSAGE<> $GITHUB_ENV + echo '**Benchmark ($GITHUB_SHA)**' >> $GITHUB_ENV echo 'Root Span Comparisons:' >> $GITHUB_ENV - echo '```json' >> $GITHUB_ENV - cat $ROOT_SPAN_COMPARISON | jq >> $GITHUB_ENV - echo '```' >> $GITHUB_ENV - echo "EOF" >> $GITHUB_ENV + echo "${{ env.ROOT_SPAN_COMPARISON }}" >> $GITHUB_ENV + echo 'EOF' >> $GITHUB_ENV + # - name: Prepare PR Message # run: | From 85a0865b4083b9a2fc299b4de704ec9fcfa74df1 Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Sat, 23 Dec 2023 16:09:41 -0500 Subject: [PATCH 46/73] Update benchmark.yml --- .github/workflows/benchmark.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/benchmark.yml b/.github/workflows/benchmark.yml index f4de148..eeccb63 100644 --- a/.github/workflows/benchmark.yml +++ b/.github/workflows/benchmark.yml @@ -117,9 +117,9 @@ jobs: - name: Prepare PR Message run: | echo 'MESSAGE<> $GITHUB_ENV - echo '**Benchmark ($GITHUB_SHA)**' >> $GITHUB_ENV + echo "Benchmark ${GITHUB_SHA}" >> $GITHUB_ENV echo 'Root Span Comparisons:' >> $GITHUB_ENV - echo "${{ env.ROOT_SPAN_COMPARISON }}" >> $GITHUB_ENV + echo "$ROOT_SPAN_COMPARISON" | jq . >> $GITHUB_ENV echo 'EOF' >> $GITHUB_ENV From cbb6392e76b5d418ee6ad7e9df80d7020ea9b868 Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Sat, 23 Dec 2023 16:29:31 -0500 Subject: [PATCH 47/73] Update benchmark.yml --- .github/workflows/benchmark.yml | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/.github/workflows/benchmark.yml b/.github/workflows/benchmark.yml index eeccb63..a4dd1fe 100644 --- a/.github/workflows/benchmark.yml +++ b/.github/workflows/benchmark.yml @@ -101,6 +101,10 @@ jobs: pip install torch-scatter torch-sparse -f https://data.pyg.org/whl/torch-$(python -c 'import torch; print(torch.__version__.split("+")[0])')+cpu.html pip install -e '.[dev, tracing]' + - name: Declare some variables + shell: bash + run: echo "sha_short=$(git rev-parse --short "$GITHUB_SHA")" >> "$GITHUB_ENV" + - name: Write PR traces run: python benchmark/write.py --output_dir branch @@ -117,9 +121,11 @@ jobs: - name: Prepare PR Message run: | echo 'MESSAGE<> $GITHUB_ENV - echo "Benchmark ${GITHUB_SHA}" >> $GITHUB_ENV + echo "Benchmark ${sha_short}" >> $GITHUB_ENV echo 'Root Span Comparisons:' >> $GITHUB_ENV + echo '```json' >> $GITHUB_ENV echo "$ROOT_SPAN_COMPARISON" | jq . >> $GITHUB_ENV + echo '```' >> $GITHUB_ENV echo 'EOF' >> $GITHUB_ENV From fc6b149c8c27cbec1909b9dd01f766cce68da6ac Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Sat, 23 Dec 2023 16:36:11 -0500 Subject: [PATCH 48/73] Update benchmark.yml --- .github/workflows/benchmark.yml | 9 +++------ 1 file changed, 3 insertions(+), 6 deletions(-) diff --git a/.github/workflows/benchmark.yml b/.github/workflows/benchmark.yml index a4dd1fe..aaa4aa9 100644 --- a/.github/workflows/benchmark.yml +++ b/.github/workflows/benchmark.yml @@ -101,10 +101,6 @@ jobs: pip install torch-scatter torch-sparse -f https://data.pyg.org/whl/torch-$(python -c 'import torch; print(torch.__version__.split("+")[0])')+cpu.html pip install -e '.[dev, tracing]' - - name: Declare some variables - shell: bash - run: echo "sha_short=$(git rev-parse --short "$GITHUB_SHA")" >> "$GITHUB_ENV" - - name: Write PR traces run: python benchmark/write.py --output_dir branch @@ -121,7 +117,8 @@ jobs: - name: Prepare PR Message run: | echo 'MESSAGE<> $GITHUB_ENV - echo "Benchmark ${sha_short}" >> $GITHUB_ENV + echo "Benchmark (${github.event.pull_request.head.sha})" >> $GITHUB_ENV + echo '' >> $GITHUB_ENV echo 'Root Span Comparisons:' >> $GITHUB_ENV echo '```json' >> $GITHUB_ENV echo "$ROOT_SPAN_COMPARISON" | jq . >> $GITHUB_ENV @@ -155,7 +152,7 @@ jobs: # echo "EOF" >> $GITHUB_ENV - name: Comment on PR - uses: actions/github-script@v5 + uses: actions/github-script@v7 with: github-token: ${{secrets.GITHUB_TOKEN}} script: | From d2a46c3c38960f9aeaae50bdd1e6b4db141ad925 Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Sat, 23 Dec 2023 16:39:27 -0500 Subject: [PATCH 49/73] fix typo --- .github/workflows/benchmark.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/benchmark.yml b/.github/workflows/benchmark.yml index aaa4aa9..dae6c30 100644 --- a/.github/workflows/benchmark.yml +++ b/.github/workflows/benchmark.yml @@ -117,7 +117,7 @@ jobs: - name: Prepare PR Message run: | echo 'MESSAGE<> $GITHUB_ENV - echo "Benchmark (${github.event.pull_request.head.sha})" >> $GITHUB_ENV + echo "Benchmark (${{ github.event.pull_request.head.sha }})" >> $GITHUB_ENV echo '' >> $GITHUB_ENV echo 'Root Span Comparisons:' >> $GITHUB_ENV echo '```json' >> $GITHUB_ENV From 42976c44b7d5b5f85b0eb6b0bfdd36e7c617c043 Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Sat, 23 Dec 2023 16:52:14 -0500 Subject: [PATCH 50/73] Update benchmark.yml --- .github/workflows/benchmark.yml | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/.github/workflows/benchmark.yml b/.github/workflows/benchmark.yml index dae6c30..affc99d 100644 --- a/.github/workflows/benchmark.yml +++ b/.github/workflows/benchmark.yml @@ -123,6 +123,13 @@ jobs: echo '```json' >> $GITHUB_ENV echo "$ROOT_SPAN_COMPARISON" | jq . >> $GITHUB_ENV echo '```' >> $GITHUB_ENV + echo '' >> $GITHUB_ENV + echo '
ArangoDB to PyG Comparison' >> $GITHUB_ENV + echo '' >> $GITHUB_ENV + echo '```json' >> $GITHUB_ENV + cat $ARANGODB_TO_PYG_DIFF >> $GITHUB_ENV + echo '```' >> $GITHUB_ENV + echo '
' >> $GITHUB_ENV echo 'EOF' >> $GITHUB_ENV From ef6a7461ba23aa1bf76c46a12b343427910f10df Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Sat, 23 Dec 2023 16:55:33 -0500 Subject: [PATCH 51/73] jq --- .github/workflows/benchmark.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/benchmark.yml b/.github/workflows/benchmark.yml index affc99d..b148f1b 100644 --- a/.github/workflows/benchmark.yml +++ b/.github/workflows/benchmark.yml @@ -127,7 +127,7 @@ jobs: echo '
ArangoDB to PyG Comparison' >> $GITHUB_ENV echo '' >> $GITHUB_ENV echo '```json' >> $GITHUB_ENV - cat $ARANGODB_TO_PYG_DIFF >> $GITHUB_ENV + cat $ARANGODB_TO_PYG_DIFF | jq . >> $GITHUB_ENV echo '```' >> $GITHUB_ENV echo '
' >> $GITHUB_ENV echo 'EOF' >> $GITHUB_ENV From 12adb6ea169279347907d2411ab683fdac5e0712 Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Sat, 23 Dec 2023 17:04:00 -0500 Subject: [PATCH 52/73] Update benchmark.yml --- .github/workflows/benchmark.yml | 56 +++++++++++++-------------------- 1 file changed, 22 insertions(+), 34 deletions(-) diff --git a/.github/workflows/benchmark.yml b/.github/workflows/benchmark.yml index b148f1b..ebedd35 100644 --- a/.github/workflows/benchmark.yml +++ b/.github/workflows/benchmark.yml @@ -49,10 +49,10 @@ jobs: run: python benchmark/write.py --output_dir master - name: Echo PyG to ArangoDB - run: cat benchmark/traces/master/pyg_to_arangodb.json | jq + run: cat benchmark/traces/master/pyg_to_arangodb.json | jq . - name: Echo ArangoDB to PyG - run: cat benchmark/traces/master/arangodb_to_pyg.json | jq + run: cat benchmark/traces/master/arangodb_to_pyg.json | jq . # - name: Make commit for auto-generated benchmark files # uses: EndBug/add-and-commit@v9 @@ -105,59 +105,47 @@ jobs: run: python benchmark/write.py --output_dir branch - name: Compare PR traces against Master traces - id: python_script run: | echo "ROOT_SPAN_COMPARISON<> $GITHUB_ENV python benchmark/compare.py >> $GITHUB_ENV echo "EOF" >> $GITHUB_ENV - - run: echo "PYG_TO_ARANGODB_DIFF=$(jq -c . < benchmark/diff/pyg_to_arangodb.json)" >> $GITHUB_ENV - - run: echo "ARANGODB_TO_PYG_DIFF=$(jq -c . < benchmark/diff/arangodb_to_pyg.json)" >> $GITHUB_ENV + - name: Extract PyG to ArangoDB Diff + run: | + echo "PYG_TO_ARANGODB_DIFF<> $GITHUB_ENV + cat benchmark/diff/pyg_to_arangodb.json | jq . >> $GITHUB_ENV + echo "EOF" >> $GITHUB_ENV + + - name: Extract ArangoDB to PyG Diff + run: | + echo "ARANGODB_TO_PYG_DIFF<> $GITHUB_ENV + cat benchmark/diff/arangodb_to_pyg.json | jq . >> $GITHUB_ENV + echo "EOF" >> $GITHUB_ENV - name: Prepare PR Message run: | echo 'MESSAGE<> $GITHUB_ENV echo "Benchmark (${{ github.event.pull_request.head.sha }})" >> $GITHUB_ENV echo '' >> $GITHUB_ENV - echo 'Root Span Comparisons:' >> $GITHUB_ENV echo '```json' >> $GITHUB_ENV echo "$ROOT_SPAN_COMPARISON" | jq . >> $GITHUB_ENV echo '```' >> $GITHUB_ENV echo '' >> $GITHUB_ENV - echo '
ArangoDB to PyG Comparison' >> $GITHUB_ENV + echo '
PyG to ArangoDB Diff' >> $GITHUB_ENV echo '' >> $GITHUB_ENV echo '```json' >> $GITHUB_ENV - cat $ARANGODB_TO_PYG_DIFF | jq . >> $GITHUB_ENV + echo "$PYG_TO_ARANGODB_DIFF" | jq . >> $GITHUB_ENV + echo '```' >> $GITHUB_ENV + echo '
' >> $GITHUB_ENV + echo '' >> $GITHUB_ENV + echo '
ArangoDB to PyG Diff' >> $GITHUB_ENV + echo '' >> $GITHUB_ENV + echo '```json' >> $GITHUB_ENV + echo "$ARANGODB_TO_PYG_DIFF" | jq . >> $GITHUB_ENV echo '```' >> $GITHUB_ENV echo '
' >> $GITHUB_ENV echo 'EOF' >> $GITHUB_ENV - - # - name: Prepare PR Message - # run: | - # echo "MESSAGE<> $GITHUB_ENV - # echo '**Benchmark (${GITHUB_SHA})**' >> $GITHUB_ENV - # echo '' >> $GITHUB_ENV - # echo 'Root Span Comparisons:' >> $GITHUB_ENV - # echo '```json' >> $GITHUB_ENV - # cat $ROOT_SPAN_COMPARISON >> $GITHUB_ENV - # echo '```' >> $GITHUB_ENV - # echo '' >> $GITHUB_ENV - # echo '
PyG to ArangoDB Comparison' >> $GITHUB_ENV - # echo '' >> $GITHUB_ENV - # echo '```json' >> $GITHUB_ENV - # cat $PYG_TO_ARANGODB_DIFF >> $GITHUB_ENV - # echo '```' >> $GITHUB_ENV - # echo '
' >> $GITHUB_ENV - # echo '' >> $GITHUB_ENV - # echo '
ArangoDB to PyG Comparison' >> $GITHUB_ENV - # echo '' >> $GITHUB_ENV - # echo '```json' >> $GITHUB_ENV - # cat $ARANGODB_TO_PYG_DIFF >> $GITHUB_ENV - # echo '```' >> $GITHUB_ENV - # echo '
' >> $GITHUB_ENV - # echo "EOF" >> $GITHUB_ENV - - name: Comment on PR uses: actions/github-script@v7 with: From ced88abf67173042928ba4b77cadd81ee753196d Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Sat, 23 Dec 2023 17:07:19 -0500 Subject: [PATCH 53/73] Update benchmark.yml --- .github/workflows/benchmark.yml | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/.github/workflows/benchmark.yml b/.github/workflows/benchmark.yml index ebedd35..0646fa9 100644 --- a/.github/workflows/benchmark.yml +++ b/.github/workflows/benchmark.yml @@ -104,7 +104,7 @@ jobs: - name: Write PR traces run: python benchmark/write.py --output_dir branch - - name: Compare PR traces against Master traces + - name: Compare PR & Master Traces run: | echo "ROOT_SPAN_COMPARISON<> $GITHUB_ENV python benchmark/compare.py >> $GITHUB_ENV @@ -122,7 +122,7 @@ jobs: cat benchmark/diff/arangodb_to_pyg.json | jq . >> $GITHUB_ENV echo "EOF" >> $GITHUB_ENV - - name: Prepare PR Message + - name: Prepare PR Comment run: | echo 'MESSAGE<> $GITHUB_ENV echo "Benchmark (${{ github.event.pull_request.head.sha }})" >> $GITHUB_ENV @@ -134,19 +134,19 @@ jobs: echo '
PyG to ArangoDB Diff' >> $GITHUB_ENV echo '' >> $GITHUB_ENV echo '```json' >> $GITHUB_ENV - echo "$PYG_TO_ARANGODB_DIFF" | jq . >> $GITHUB_ENV + echo "$PYG_TO_ARANGODB_DIFF" >> $GITHUB_ENV echo '```' >> $GITHUB_ENV echo '
' >> $GITHUB_ENV echo '' >> $GITHUB_ENV echo '
ArangoDB to PyG Diff' >> $GITHUB_ENV echo '' >> $GITHUB_ENV echo '```json' >> $GITHUB_ENV - echo "$ARANGODB_TO_PYG_DIFF" | jq . >> $GITHUB_ENV + echo "$ARANGODB_TO_PYG_DIFF" >> $GITHUB_ENV echo '```' >> $GITHUB_ENV echo '
' >> $GITHUB_ENV echo 'EOF' >> $GITHUB_ENV - - name: Comment on PR + - name: Post PR Comment uses: actions/github-script@v7 with: github-token: ${{secrets.GITHUB_TOKEN}} From a9d924985cbc25ced3a030c27e1987517fba9991 Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Sat, 23 Dec 2023 17:33:30 -0500 Subject: [PATCH 54/73] Update benchmark.yml --- .github/workflows/benchmark.yml | 34 ++++++++------------------------- 1 file changed, 8 insertions(+), 26 deletions(-) diff --git a/.github/workflows/benchmark.yml b/.github/workflows/benchmark.yml index 0646fa9..8e3477a 100644 --- a/.github/workflows/benchmark.yml +++ b/.github/workflows/benchmark.yml @@ -110,18 +110,6 @@ jobs: python benchmark/compare.py >> $GITHUB_ENV echo "EOF" >> $GITHUB_ENV - - name: Extract PyG to ArangoDB Diff - run: | - echo "PYG_TO_ARANGODB_DIFF<> $GITHUB_ENV - cat benchmark/diff/pyg_to_arangodb.json | jq . >> $GITHUB_ENV - echo "EOF" >> $GITHUB_ENV - - - name: Extract ArangoDB to PyG Diff - run: | - echo "ARANGODB_TO_PYG_DIFF<> $GITHUB_ENV - cat benchmark/diff/arangodb_to_pyg.json | jq . >> $GITHUB_ENV - echo "EOF" >> $GITHUB_ENV - - name: Prepare PR Comment run: | echo 'MESSAGE<> $GITHUB_ENV @@ -131,19 +119,7 @@ jobs: echo "$ROOT_SPAN_COMPARISON" | jq . >> $GITHUB_ENV echo '```' >> $GITHUB_ENV echo '' >> $GITHUB_ENV - echo '
PyG to ArangoDB Diff' >> $GITHUB_ENV - echo '' >> $GITHUB_ENV - echo '```json' >> $GITHUB_ENV - echo "$PYG_TO_ARANGODB_DIFF" >> $GITHUB_ENV - echo '```' >> $GITHUB_ENV - echo '
' >> $GITHUB_ENV - echo '' >> $GITHUB_ENV - echo '
ArangoDB to PyG Diff' >> $GITHUB_ENV - echo '' >> $GITHUB_ENV - echo '```json' >> $GITHUB_ENV - echo "$ARANGODB_TO_PYG_DIFF" >> $GITHUB_ENV - echo '```' >> $GITHUB_ENV - echo '
' >> $GITHUB_ENV + echo 'See the full diff [here](https://github.com/arangoml/pyg-adapter/actions/runs/$GITHUB_RUN_ID)' >> $GITHUB_ENV echo 'EOF' >> $GITHUB_ENV - name: Post PR Comment @@ -158,4 +134,10 @@ jobs: repo: context.repo.repo, issue_number: prNumber, body: message - }); \ No newline at end of file + }); + + - name: Echo Full PyG to ArangoDB Diff + run: cat benchmark/diff/pyg_to_arangodb.json | jq . + + - name: Echo Full ArangoDB to PyG Diff + run: cat benchmark/diff/arangodb_to_pyg.json | jq . From 28629efcf2826d6706bb11e113ad2cd44b7e1a49 Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Sat, 23 Dec 2023 17:41:04 -0500 Subject: [PATCH 55/73] fix double quotes --- .github/workflows/benchmark.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/benchmark.yml b/.github/workflows/benchmark.yml index 8e3477a..4bdf159 100644 --- a/.github/workflows/benchmark.yml +++ b/.github/workflows/benchmark.yml @@ -119,7 +119,7 @@ jobs: echo "$ROOT_SPAN_COMPARISON" | jq . >> $GITHUB_ENV echo '```' >> $GITHUB_ENV echo '' >> $GITHUB_ENV - echo 'See the full diff [here](https://github.com/arangoml/pyg-adapter/actions/runs/$GITHUB_RUN_ID)' >> $GITHUB_ENV + echo "See the full diff [here](https://github.com/arangoml/pyg-adapter/actions/runs/$GITHUB_RUN_ID)" >> $GITHUB_ENV echo 'EOF' >> $GITHUB_ENV - name: Post PR Comment From dc0e031afc59bc21389ea525128443b6c5573c94 Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Sat, 23 Dec 2023 18:11:13 -0500 Subject: [PATCH 56/73] Update write.py --- benchmark/write.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/benchmark/write.py b/benchmark/write.py index 6bf1753..f26a4ff 100644 --- a/benchmark/write.py +++ b/benchmark/write.py @@ -242,7 +242,7 @@ def run_arangodb_to_pyg(adapter: ADBPyG_Adapter, name: str) -> None: def main(): - service_name = f"adbpyg-adapter-benchmark" + service_name = "adbpyg-adapter-benchmark" # 1. Parse the arguments args = parse_args() From 17c4995419e2a4b1b41e4e33b77427d91cbb0aa4 Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Sat, 23 Dec 2023 18:21:28 -0500 Subject: [PATCH 57/73] cleanup --- .github/workflows/benchmark.yml | 47 ++++++++++++++------------- .gitignore | 2 +- benchmark/compare.py | 2 +- benchmark/{ => traces}/diff/README.md | 0 benchmark/write.py | 3 -- 5 files changed, 27 insertions(+), 27 deletions(-) rename benchmark/{ => traces}/diff/README.md (100%) diff --git a/.github/workflows/benchmark.yml b/.github/workflows/benchmark.yml index 4bdf159..d52af94 100644 --- a/.github/workflows/benchmark.yml +++ b/.github/workflows/benchmark.yml @@ -3,11 +3,14 @@ name: benchmark on: workflow_dispatch: pull_request: + paths: + - "adbpyg_adapter/*.py" + - "benchmark/*.py" push: branches: - master paths: - - "src/*.py" + - "adbpyg_adapter/*.py" - "benchmark/*.py" jobs: @@ -54,26 +57,26 @@ jobs: - name: Echo ArangoDB to PyG run: cat benchmark/traces/master/arangodb_to_pyg.json | jq . - # - name: Make commit for auto-generated benchmark files - # uses: EndBug/add-and-commit@v9 - # env: - # GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} - # with: - # add: "./benchmark/traces/master/*.json" - # new_branch: actions/benchmark - # message: "generate benchmark files for $GITHUB_SHA" - - # - name: Create pull request for the auto generated benchmark - # run: | - # echo "PR_URL=$(gh pr create \ - # --title "benchmark: $GITHUB_SHA" \ - # --body "beep boop, i am a robot" \ - # --label documentation)" >> $GITHUB_ENV - # env: - # GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + - name: Make commit for auto-generated benchmark files + uses: EndBug/add-and-commit@v9 + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + with: + add: "./benchmark/traces/master/*.json" + new_branch: actions/benchmark + message: "generate benchmark files for $GITHUB_SHA" + + - name: Create pull request for the auto generated benchmark + run: | + echo "PR_URL=$(gh pr create \ + --title "benchmark: $GITHUB_SHA" \ + --body "beep boop, i am a robot ($GITHUB_SHA)" \ + --label documentation)" >> $GITHUB_ENV + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} - # - name: Alert developer of open PR - # run: echo "Benchmark $PR_URL is ready to be merged by developer." + - name: Alert developer of open PR + run: echo "Benchmark $PR_URL is ready to be merged by developer." compare_traces: if: github.event_name == 'pull_request' @@ -137,7 +140,7 @@ jobs: }); - name: Echo Full PyG to ArangoDB Diff - run: cat benchmark/diff/pyg_to_arangodb.json | jq . + run: cat benchmark/traces/diff/pyg_to_arangodb.json | jq . - name: Echo Full ArangoDB to PyG Diff - run: cat benchmark/diff/arangodb_to_pyg.json | jq . + run: cat benchmark/traces/diff/arangodb_to_pyg.json | jq . diff --git a/.gitignore b/.gitignore index 6a59526..ee05a02 100644 --- a/.gitignore +++ b/.gitignore @@ -122,4 +122,4 @@ tests/data/pyg # "Current" Benchmark Results benchmark/traces/branch/*.json -benchmark/diff/*.json \ No newline at end of file +benchmark/traces/diff/*.json \ No newline at end of file diff --git a/benchmark/compare.py b/benchmark/compare.py index 4be5451..b09f420 100644 --- a/benchmark/compare.py +++ b/benchmark/compare.py @@ -128,7 +128,7 @@ def main(): branch_trace = json.load(open(f"{current_dir}/traces/branch/{operation}.json")) diff_trace = compare_traces(master_trace, branch_trace) - with open(f"{current_dir}/diff/{operation}.json", "w") as file: + with open(f"{current_dir}/traces/diff/{operation}.json", "w") as file: file.write(json.dumps(diff_trace, indent=4)) root_span_diffs[operation] = { diff --git a/benchmark/diff/README.md b/benchmark/traces/diff/README.md similarity index 100% rename from benchmark/diff/README.md rename to benchmark/traces/diff/README.md diff --git a/benchmark/write.py b/benchmark/write.py index f26a4ff..32a5ab8 100644 --- a/benchmark/write.py +++ b/benchmark/write.py @@ -13,9 +13,6 @@ from retry import retry from torch_geometric.datasets import FakeHeteroDataset -# import uuid - - try: from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter except ImportError: From 62efe151b30570f5f0a8335e6b1e8e9ec1b64db5 Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Sat, 23 Dec 2023 18:35:59 -0500 Subject: [PATCH 58/73] cleanup `compare.py` --- benchmark/compare.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/benchmark/compare.py b/benchmark/compare.py index b09f420..1b7e5a5 100644 --- a/benchmark/compare.py +++ b/benchmark/compare.py @@ -106,11 +106,11 @@ def compare_traces(master_trace: dict, branch_trace: dict): assert master_trace.get("tags") == branch_trace.get("tags") result = { - "operationName": master_trace.get("operationName"), + "operationName": master_trace["operationName"], "master_duration": master_trace["duration"], "branch_duration": branch_trace["duration"], "improvement": f"{round((1 - branch_trace['duration'] / master_trace['duration']) * 100)}%", - "tags": master_trace.get("tags"), + "tags": master_trace["tags"], "children": compare_children( master_trace["children"], branch_trace["children"] ), From bc50b6f91773b0e7d101e18e77c3aad9d6997789 Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Sat, 23 Dec 2023 18:39:23 -0500 Subject: [PATCH 59/73] update benchmark readme files --- benchmark/traces/branch/README.md | 4 ++-- benchmark/traces/diff/README.md | 4 ++-- benchmark/traces/master/README.md | 4 ++-- 3 files changed, 6 insertions(+), 6 deletions(-) diff --git a/benchmark/traces/branch/README.md b/benchmark/traces/branch/README.md index 4e9f9cd..b144637 100644 --- a/benchmark/traces/branch/README.md +++ b/benchmark/traces/branch/README.md @@ -1,3 +1,3 @@ -Empty directory to store the branche's trace files during Github Actions. +Empty directory to store the branch trace files generated during a Pull Request CI. -See .github/workflows/benchmark.yml and benchmark/write.py for more details. \ No newline at end of file +See [benchmark.yml](https://github.com/arangoml/pyg-adapter/blob/master/.github/workflows/benchmark.yml) and [write.py](https://github.com/arangoml/pyg-adapter/blob/master/benchmark/write.py) for more details. \ No newline at end of file diff --git a/benchmark/traces/diff/README.md b/benchmark/traces/diff/README.md index 0886721..b6fa89e 100644 --- a/benchmark/traces/diff/README.md +++ b/benchmark/traces/diff/README.md @@ -1,3 +1,3 @@ -Empty directory to store the diff files during Github Actions. +Empty directory to store the diff trace files generated during a Pull Request CI. -See .github/workflows/benchmark.yml and benchmark/compare.py for more details. \ No newline at end of file +See [benchmark.yml](https://github.com/arangoml/pyg-adapter/blob/master/.github/workflows/benchmark.yml) and [compare.py](https://github.com/arangoml/pyg-adapter/blob/master/benchmark/compare.py) for more details. \ No newline at end of file diff --git a/benchmark/traces/master/README.md b/benchmark/traces/master/README.md index c249cd5..84a9327 100644 --- a/benchmark/traces/master/README.md +++ b/benchmark/traces/master/README.md @@ -1,3 +1,3 @@ -Stores the traces for the current state of pyg-adapter@master. +Stores the traces for the current state of `pyg-adapter@master`. -See .github/workflows/benchmark.yml and benchmark/write.py for more details. \ No newline at end of file +See [benchmark.yml](https://github.com/arangoml/pyg-adapter/blob/master/.github/workflows/benchmark.yml) and [write.py](https://github.com/arangoml/pyg-adapter/blob/master/benchmark/write.py) for more details. \ No newline at end of file From 3d0e3de6ce14866f1ce159edbbbdcf689189bd8b Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Thu, 18 Jan 2024 12:55:22 -0500 Subject: [PATCH 60/73] new: `continue-on-error` --- .github/workflows/build.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 39f47e1..5a828d5 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -10,6 +10,7 @@ env: jobs: build: runs-on: ubuntu-latest + continue-on-error: true strategy: matrix: python: ["3.8", "3.9", "3.10", "3.11"] # "3.12" From ddbd3318e5efb8ee4e643608f0f3ca69f2230c33 Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Thu, 18 Jan 2024 12:56:19 -0500 Subject: [PATCH 61/73] fix: custom span names for `@with_tracing` decorator (private methods only) --- adbpyg_adapter/adapter.py | 48 +++++++++++++++++++-------------------- adbpyg_adapter/tracing.py | 23 +++++++++++-------- 2 files changed, 37 insertions(+), 34 deletions(-) diff --git a/adbpyg_adapter/adapter.py b/adbpyg_adapter/adapter.py index c981cab..accd05a 100644 --- a/adbpyg_adapter/adapter.py +++ b/adbpyg_adapter/adapter.py @@ -126,7 +126,7 @@ def set_tracer(self, tracer: Optional["Tracer"]) -> None: # Public: ArangoDB -> PyG # ########################### - @with_tracing + @with_tracing() def arangodb_to_pyg( self, name: str, @@ -363,7 +363,7 @@ def udf_v1_x(v1_df): logger.info(f"Created PyG '{name}' Graph") return data - @with_tracing + @with_tracing() def arangodb_collections_to_pyg( self, name: str, @@ -417,7 +417,7 @@ def arangodb_collections_to_pyg( name, metagraph, preserve_adb_keys, strict, **adb_export_kwargs ) - @with_tracing + @with_tracing() def arangodb_graph_to_pyg( self, name: str, @@ -469,7 +469,7 @@ def arangodb_graph_to_pyg( # Public: PyG -> ArangoDB # ########################### - @with_tracing + @with_tracing() def pyg_to_arangodb( self, name: str, @@ -696,7 +696,7 @@ def y_tensor_to_2_column_dataframe(pyg_tensor, adb_df): # Private: ArangoDB -> PyG # ############################ - @with_tracing + @with_tracing("__process_adb_v_col") def __process_adb_v_col( self, v_col: str, @@ -742,7 +742,7 @@ def __process_adb_v_col( node_data=node_data, ) - @with_tracing + @with_tracing("__process_adb_e_col") def __process_adb_e_col( self, e_col: str, @@ -801,7 +801,7 @@ def __process_adb_e_col( is_homogeneous=is_homogeneous, ) - @with_tracing + @with_tracing("__fetch_adb_docs") def __fetch_adb_docs( self, col: str, @@ -865,7 +865,7 @@ def get_aql_return_value( return cursor, col_size - @with_tracing + @with_tracing("__process_adb_cursor") def __process_adb_cursor( self, progress_color: str, @@ -916,7 +916,7 @@ def __process_adb_cursor( if cursor.has_more(): cursor.fetch() - @with_tracing + @with_tracing("__process_adb_vertex_df") def __process_adb_vertex_df( self, i: int, @@ -963,7 +963,7 @@ def __process_adb_vertex_df( return i - @with_tracing + @with_tracing("__process_adb_edge_df") def __process_adb_edge_df( self, _: int, @@ -1067,7 +1067,7 @@ def __process_adb_edge_df( return 1 # Useless return value, but needed for type hinting - @with_tracing + @with_tracing("__split_adb_ids") def __split_adb_ids(self, s: Series) -> Series: """AranogDB -> PyG: Helper method to split the ArangoDB IDs within a Series into two columns @@ -1080,7 +1080,7 @@ def __split_adb_ids(self, s: Series) -> Series: """ return s.str.split(pat="/", n=1, expand=True) - @with_tracing + @with_tracing("__set_pyg_data") def __set_pyg_data( self, meta: Union[Set[str], Dict[str, ADBMetagraphValues]], @@ -1116,7 +1116,7 @@ def __set_pyg_data( m = f"'{k}' key in PyG Data must point to a Tensor" raise TypeError(m) - @with_tracing + @with_tracing("__build_tensor_from_dataframe") def __build_tensor_from_dataframe( self, adb_df: DataFrame, @@ -1173,7 +1173,7 @@ def __build_tensor_from_dataframe( # Private: PyG -> ArangoDB # ############################ - @with_tracing + @with_tracing("__get_node_and_edge_types") def __get_node_and_edge_types( self, name: str, @@ -1217,7 +1217,7 @@ def __get_node_and_edge_types( return node_types, edge_types - @with_tracing + @with_tracing("__etypes_to_edefinitions") def __etypes_to_edefinitions(self, edge_types: List[EdgeType]) -> List[Json]: """PyG -> ArangoDB: Converts PyG edge_types to ArangoDB edge_definitions @@ -1262,7 +1262,7 @@ def __etypes_to_edefinitions(self, edge_types: List[EdgeType]) -> List[Json]: return edge_definitions - @with_tracing + @with_tracing("__ntypes_to_ocollections") def __ntypes_to_ocollections( self, node_types: List[str], edge_types: List[EdgeType] ) -> List[str]: @@ -1286,7 +1286,7 @@ def __ntypes_to_ocollections( orphan_collections = set(node_types) ^ non_orphan_collections return list(orphan_collections) - @with_tracing + @with_tracing("__create_adb_graph") def __create_adb_graph( self, name: str, @@ -1325,7 +1325,7 @@ def __create_adb_graph( orphan_collections, ) - @with_tracing + @with_tracing("__process_pyg_node_batch") def __process_pyg_node_batch( self, n_type: str, @@ -1385,7 +1385,7 @@ def __process_pyg_node_batch( return df - @with_tracing + @with_tracing("__process_pyg_edge_batch") def __process_pyg_edge_batch( self, e_type: EdgeType, @@ -1461,7 +1461,7 @@ def __process_pyg_edge_batch( return df - @with_tracing + @with_tracing("__process_pyg_n_type") def __process_pyg_n_type( self, n_type: str, @@ -1513,7 +1513,7 @@ def __process_pyg_n_type( adb_import_kwargs, ) - @with_tracing + @with_tracing("__process_pyg_e_type") def __process_pyg_e_type( self, e_type: EdgeType, @@ -1619,7 +1619,7 @@ def __process_batches( start_index = end_index end_index = min(end_index + batch_size, total_size) - @with_tracing + @with_tracing("__set_adb_data") def __set_adb_data( self, df: DataFrame, @@ -1705,7 +1705,7 @@ def __set_adb_data( return df - @with_tracing + @with_tracing("__build_dataframe_from_tensor") def __build_dataframe_from_tensor( self, pyg_tensor: Tensor, @@ -1782,7 +1782,7 @@ def __build_dataframe_from_tensor( raise PyGMetagraphError(f"Invalid {meta_val} type") # pragma: no cover - @with_tracing + @with_tracing("__insert_adb_docs") def __insert_adb_docs( self, spinner_progress: Progress, diff --git a/adbpyg_adapter/tracing.py b/adbpyg_adapter/tracing.py index 069e67a..1004882 100644 --- a/adbpyg_adapter/tracing.py +++ b/adbpyg_adapter/tracing.py @@ -50,19 +50,22 @@ def set_attributes(self, **attributes: Any) -> None: # pragma: no cover T = TypeVar("T", bound=Callable[..., Any]) -def with_tracing(method: T) -> T: - if not TRACING_ENABLED: - return method # pragma: no cover +def with_tracing(span_name: Optional[str] = None) -> Callable[[T], T]: + def decorator(method: T) -> T: + if not TRACING_ENABLED: + return method # pragma: no cover - @wraps(method) - def decorator(*args: Any, **kwargs: Any) -> Any: - if tracer := TracingManager.get_tracer(): - with tracer.start_as_current_span(method.__name__): - return method(*args, **kwargs) + @wraps(method) + def wrapper(*args: Any, **kwargs: Any) -> Any: + if tracer := TracingManager.get_tracer(): + with tracer.start_as_current_span(span_name or method.__name__): + return method(*args, **kwargs) - return method(*args, **kwargs) + return method(*args, **kwargs) - return cast(T, decorator) + return cast(T, wrapper) + + return decorator @contextmanager From 704cbbae45dfff634e2c991a7bad0b731b89e39b Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Fri, 19 Jan 2024 09:36:53 -0500 Subject: [PATCH 62/73] cleanup: `__fetch_adb_docs` --- adbpyg_adapter/adapter.py | 27 ++++++++++++--------------- 1 file changed, 12 insertions(+), 15 deletions(-) diff --git a/adbpyg_adapter/adapter.py b/adbpyg_adapter/adapter.py index accd05a..d111072 100644 --- a/adbpyg_adapter/adapter.py +++ b/adbpyg_adapter/adapter.py @@ -726,7 +726,7 @@ def __process_adb_v_col( # 1. Fetch ArangoDB vertices v_col_cursor, v_col_size = self.__fetch_adb_docs( - v_col, meta, **adb_export_kwargs + v_col, False, meta, **adb_export_kwargs ) # 2. Process ArangoDB vertices @@ -782,7 +782,7 @@ def __process_adb_e_col( # 1. Fetch ArangoDB edges e_col_cursor, e_col_size = self.__fetch_adb_docs( - e_col, meta, **adb_export_kwargs + e_col, True, meta, **adb_export_kwargs ) # 2. Process ArangoDB edges @@ -805,6 +805,7 @@ def __process_adb_e_col( def __fetch_adb_docs( self, col: str, + is_edge: bool, meta: Union[Set[str], Dict[str, ADBMetagraphValues]], **adb_export_kwargs: Any, ) -> Tuple[Cursor, int]: @@ -813,6 +814,8 @@ def __fetch_adb_docs( :param col: The ArangoDB collection. :type col: str + :param is_edge: True if **col** is an edge collection. + :type is_edge: bool :param meta: The MetaGraph associated to **col** :type meta: Set[str] | Dict[str, adbpyg_adapter.typings.ADBMetagraphValues] :param adb_export_kwargs: Keyword arguments to specify AQL query options @@ -822,34 +825,28 @@ def __fetch_adb_docs( :rtype: pandas.DataFrame """ - def get_aql_return_value( - meta: Union[Set[str], Dict[str, ADBMetagraphValues]] - ) -> str: + def get_aql_return_value() -> str: """Helper method to formulate the AQL `RETURN` value based on the document attributes specified in **meta** """ - attributes = [] + attributes = ["_key"] + attributes += ["_from", "_to"] if is_edge else [] if type(meta) is set: - attributes = list(meta) + attributes += list(meta) elif type(meta) is dict: for value in meta.values(): if type(value) is str: attributes.append(value) elif type(value) is dict: - attributes.extend(list(value.keys())) + attributes += list(value.keys()) elif callable(value): # Cannot determine which attributes to extract if UDFs are used # Therefore we just return the entire document return "doc" - return f""" - MERGE( - {{ _key: doc._key, _from: doc._from, _to: doc._to }}, - KEEP(doc, {list(attributes)}) - ) - """ + return f"KEEP(doc, {attributes})" col_size: int = self.__db.collection(col).count() TracingManager.set_attributes(col=col, col_size=col_size, meta=meta) @@ -858,7 +855,7 @@ def get_aql_return_value( p.add_task(col) cursor: Cursor = self.__db.aql.execute( - f"FOR doc IN @@col RETURN {get_aql_return_value(meta)}", + f"FOR doc IN @@col RETURN {get_aql_return_value()}", bind_vars={"@col": col}, **{**adb_export_kwargs, "stream": True}, ) From bd3e06cfcf85be5bc2a9836731d8d9b598b9448e Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Fri, 19 Jan 2024 10:00:29 -0500 Subject: [PATCH 63/73] fix private method span names --- adbpyg_adapter/adapter.py | 40 +++++++++++++++++++-------------------- 1 file changed, 20 insertions(+), 20 deletions(-) diff --git a/adbpyg_adapter/adapter.py b/adbpyg_adapter/adapter.py index d111072..aeb2790 100644 --- a/adbpyg_adapter/adapter.py +++ b/adbpyg_adapter/adapter.py @@ -696,7 +696,7 @@ def y_tensor_to_2_column_dataframe(pyg_tensor, adb_df): # Private: ArangoDB -> PyG # ############################ - @with_tracing("__process_adb_v_col") + @with_tracing("process_adb_vertex_collection") def __process_adb_v_col( self, v_col: str, @@ -742,7 +742,7 @@ def __process_adb_v_col( node_data=node_data, ) - @with_tracing("__process_adb_e_col") + @with_tracing("process_adb_edge_collection") def __process_adb_e_col( self, e_col: str, @@ -801,7 +801,7 @@ def __process_adb_e_col( is_homogeneous=is_homogeneous, ) - @with_tracing("__fetch_adb_docs") + @with_tracing("fetch_adb_documents") def __fetch_adb_docs( self, col: str, @@ -862,7 +862,7 @@ def get_aql_return_value() -> str: return cursor, col_size - @with_tracing("__process_adb_cursor") + @with_tracing("process_adb_cursor") def __process_adb_cursor( self, progress_color: str, @@ -913,7 +913,7 @@ def __process_adb_cursor( if cursor.has_more(): cursor.fetch() - @with_tracing("__process_adb_vertex_df") + @with_tracing("process_adb_vertex_dataframe") def __process_adb_vertex_df( self, i: int, @@ -960,7 +960,7 @@ def __process_adb_vertex_df( return i - @with_tracing("__process_adb_edge_df") + @with_tracing("process_adb_edge_dataframe") def __process_adb_edge_df( self, _: int, @@ -1064,7 +1064,7 @@ def __process_adb_edge_df( return 1 # Useless return value, but needed for type hinting - @with_tracing("__split_adb_ids") + @with_tracing("split_adb_ids") def __split_adb_ids(self, s: Series) -> Series: """AranogDB -> PyG: Helper method to split the ArangoDB IDs within a Series into two columns @@ -1077,7 +1077,7 @@ def __split_adb_ids(self, s: Series) -> Series: """ return s.str.split(pat="/", n=1, expand=True) - @with_tracing("__set_pyg_data") + @with_tracing("set_pyg_data") def __set_pyg_data( self, meta: Union[Set[str], Dict[str, ADBMetagraphValues]], @@ -1113,7 +1113,7 @@ def __set_pyg_data( m = f"'{k}' key in PyG Data must point to a Tensor" raise TypeError(m) - @with_tracing("__build_tensor_from_dataframe") + @with_tracing("build_tensor_from_dataframe") def __build_tensor_from_dataframe( self, adb_df: DataFrame, @@ -1170,7 +1170,7 @@ def __build_tensor_from_dataframe( # Private: PyG -> ArangoDB # ############################ - @with_tracing("__get_node_and_edge_types") + @with_tracing("get_node_and_edge_types") def __get_node_and_edge_types( self, name: str, @@ -1214,7 +1214,7 @@ def __get_node_and_edge_types( return node_types, edge_types - @with_tracing("__etypes_to_edefinitions") + @with_tracing("edge_types_to_edge_definitions") def __etypes_to_edefinitions(self, edge_types: List[EdgeType]) -> List[Json]: """PyG -> ArangoDB: Converts PyG edge_types to ArangoDB edge_definitions @@ -1259,7 +1259,7 @@ def __etypes_to_edefinitions(self, edge_types: List[EdgeType]) -> List[Json]: return edge_definitions - @with_tracing("__ntypes_to_ocollections") + @with_tracing("node_types_to_orphan_collections") def __ntypes_to_ocollections( self, node_types: List[str], edge_types: List[EdgeType] ) -> List[str]: @@ -1283,7 +1283,7 @@ def __ntypes_to_ocollections( orphan_collections = set(node_types) ^ non_orphan_collections return list(orphan_collections) - @with_tracing("__create_adb_graph") + @with_tracing("create_adb_graph") def __create_adb_graph( self, name: str, @@ -1322,7 +1322,7 @@ def __create_adb_graph( orphan_collections, ) - @with_tracing("__process_pyg_node_batch") + @with_tracing("process_pyg_node_batch") def __process_pyg_node_batch( self, n_type: str, @@ -1382,7 +1382,7 @@ def __process_pyg_node_batch( return df - @with_tracing("__process_pyg_edge_batch") + @with_tracing("process_pyg_edge_batch") def __process_pyg_edge_batch( self, e_type: EdgeType, @@ -1458,7 +1458,7 @@ def __process_pyg_edge_batch( return df - @with_tracing("__process_pyg_n_type") + @with_tracing("process_pyg_node_type") def __process_pyg_n_type( self, n_type: str, @@ -1510,7 +1510,7 @@ def __process_pyg_n_type( adb_import_kwargs, ) - @with_tracing("__process_pyg_e_type") + @with_tracing("process_pyg_edge_type") def __process_pyg_e_type( self, e_type: EdgeType, @@ -1616,7 +1616,7 @@ def __process_batches( start_index = end_index end_index = min(end_index + batch_size, total_size) - @with_tracing("__set_adb_data") + @with_tracing("set_adb_data") def __set_adb_data( self, df: DataFrame, @@ -1702,7 +1702,7 @@ def __set_adb_data( return df - @with_tracing("__build_dataframe_from_tensor") + @with_tracing("build_dataframe_from_tensor") def __build_dataframe_from_tensor( self, pyg_tensor: Tensor, @@ -1779,7 +1779,7 @@ def __build_dataframe_from_tensor( raise PyGMetagraphError(f"Invalid {meta_val} type") # pragma: no cover - @with_tracing("__insert_adb_docs") + @with_tracing("insert_adb_documents") def __insert_adb_docs( self, spinner_progress: Progress, From 0744148cea94672fcc9675419511053ff8e3c0ca Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Fri, 19 Jan 2024 10:21:19 -0500 Subject: [PATCH 64/73] regenerate "master" traces --- benchmark/traces/master/arangodb_to_pyg.json | 2074 +++++++++--------- benchmark/traces/master/pyg_to_arangodb.json | 304 +-- 2 files changed, 1189 insertions(+), 1189 deletions(-) diff --git a/benchmark/traces/master/arangodb_to_pyg.json b/benchmark/traces/master/arangodb_to_pyg.json index fd6134f..40a4d88 100644 --- a/benchmark/traces/master/arangodb_to_pyg.json +++ b/benchmark/traces/master/arangodb_to_pyg.json @@ -1,26 +1,26 @@ { "spanID": "935ddd725129fb7c", "operationName": "arangodb_to_pyg", - "startTime": 1703363922836354, - "duration": 906241, + "startTime": 1705677351837714, + "duration": 836965, "tags": { "name": "FakeHeteroGraphBenchmark" }, "children": [ { "spanID": "4a5308cc3dfabc08", - "operationName": "__process_adb_v_col", - "startTime": 1703363922836426, - "duration": 55595, + "operationName": "process_adb_vertex_collection", + "startTime": 1705677351837783, + "duration": 85153, "tags": { "v_col": "v0" }, "children": [ { "spanID": "307bf3262f120554", - "operationName": "__fetch_adb_docs", - "startTime": 1703363922836453, - "duration": 37678, + "operationName": "fetch_adb_documents", + "startTime": 1705677351837805, + "duration": 32511, "tags": { "col": "v0", "col_size": 1008, @@ -30,16 +30,16 @@ }, { "spanID": "2fcd81b5d24bace4", - "operationName": "__process_adb_cursor", - "startTime": 1703363922874201, - "duration": 17807, + "operationName": "process_adb_cursor", + "startTime": 1705677351870494, + "duration": 52422, "tags": {}, "children": [ { "spanID": "9cdeb3e60870e15c", - "operationName": "__process_adb_vertex_df", - "startTime": 1703363922877910, - "duration": 8373, + "operationName": "process_adb_vertex_dataframe", + "startTime": 1705677351872646, + "duration": 8512, "tags": { "i": 0, "vertex_df_size": 1000 @@ -47,18 +47,18 @@ "children": [ { "spanID": "a81ad477fb3675b8", - "operationName": "__set_pyg_data", - "startTime": 1703363922878172, - "duration": 8105, + "operationName": "set_pyg_data", + "startTime": 1705677351873189, + "duration": 7962, "tags": { "meta": "{'x': 'x', 'y': 'y'}" }, "children": [ { "spanID": "79fdef7c42930b33", - "operationName": "__build_tensor_from_dataframe", - "startTime": 1703363922878206, - "duration": 188, + "operationName": "build_tensor_from_dataframe", + "startTime": 1705677351873226, + "duration": 241, "tags": { "meta_key": "y", "meta_val": "y" @@ -67,9 +67,9 @@ }, { "spanID": "16febaa011af923d", - "operationName": "__build_tensor_from_dataframe", - "startTime": 1703363922878434, - "duration": 7823, + "operationName": "build_tensor_from_dataframe", + "startTime": 1705677351873577, + "duration": 7532, "tags": { "meta_key": "x", "meta_val": "x" @@ -82,9 +82,9 @@ }, { "spanID": "c1f254b8adc0da7a", - "operationName": "__process_adb_vertex_df", - "startTime": 1703363922889433, - "duration": 1690, + "operationName": "process_adb_vertex_dataframe", + "startTime": 1705677351917792, + "duration": 2997, "tags": { "i": 1000, "vertex_df_size": 8 @@ -92,18 +92,18 @@ "children": [ { "spanID": "e07405eb215663ab", - "operationName": "__set_pyg_data", - "startTime": 1703363922889535, - "duration": 1577, + "operationName": "set_pyg_data", + "startTime": 1705677351918119, + "duration": 2433, "tags": { "meta": "{'x': 'x', 'y': 'y'}" }, "children": [ { "spanID": "ec62b2c82648ee38", - "operationName": "__build_tensor_from_dataframe", - "startTime": 1703363922889566, - "duration": 64, + "operationName": "build_tensor_from_dataframe", + "startTime": 1705677351918197, + "duration": 135, "tags": { "meta_key": "y", "meta_val": "y" @@ -112,8 +112,8 @@ }, { "spanID": "d7ab792809e469e6", - "operationName": "__build_tensor_from_dataframe", - "startTime": 1703363922890273, + "operationName": "build_tensor_from_dataframe", + "startTime": 1705677351919245, "duration": 152, "tags": { "meta_key": "x", @@ -131,18 +131,18 @@ }, { "spanID": "e5eeac76148b2758", - "operationName": "__process_adb_v_col", - "startTime": 1703363922892077, - "duration": 37759, + "operationName": "process_adb_vertex_collection", + "startTime": 1705677351923008, + "duration": 39611, "tags": { "v_col": "v1" }, "children": [ { "spanID": "ec4f217bb306d1a8", - "operationName": "__fetch_adb_docs", - "startTime": 1703363922892112, - "duration": 28421, + "operationName": "fetch_adb_documents", + "startTime": 1705677351923037, + "duration": 32213, "tags": { "col": "v1", "col_size": 821, @@ -152,16 +152,16 @@ }, { "spanID": "8a64c1b9d450fe4a", - "operationName": "__process_adb_cursor", - "startTime": 1703363922920599, - "duration": 9216, + "operationName": "process_adb_cursor", + "startTime": 1705677351955321, + "duration": 7278, "tags": {}, "children": [ { "spanID": "642bfa42aef9c00b", - "operationName": "__process_adb_vertex_df", - "startTime": 1703363922922150, - "duration": 6370, + "operationName": "process_adb_vertex_dataframe", + "startTime": 1705677351956719, + "duration": 4841, "tags": { "i": 0, "vertex_df_size": 821 @@ -169,18 +169,18 @@ "children": [ { "spanID": "b48d73f1d67e55fd", - "operationName": "__set_pyg_data", - "startTime": 1703363922922408, - "duration": 6106, + "operationName": "set_pyg_data", + "startTime": 1705677351956925, + "duration": 4629, "tags": { "meta": "{'x': 'x'}" }, "children": [ { "spanID": "468ff53d864a7a50", - "operationName": "__build_tensor_from_dataframe", - "startTime": 1703363922922444, - "duration": 6048, + "operationName": "build_tensor_from_dataframe", + "startTime": 1705677351956953, + "duration": 4573, "tags": { "meta_key": "x", "meta_val": "x" @@ -197,18 +197,18 @@ }, { "spanID": "cfc6e62585940927", - "operationName": "__process_adb_v_col", - "startTime": 1703363922929928, - "duration": 36396, + "operationName": "process_adb_vertex_collection", + "startTime": 1705677351962686, + "duration": 29280, "tags": { "v_col": "v2" }, "children": [ { "spanID": "d977e9933c49d76f", - "operationName": "__fetch_adb_docs", - "startTime": 1703363922929964, - "duration": 27407, + "operationName": "fetch_adb_documents", + "startTime": 1705677351962711, + "duration": 21677, "tags": { "col": "v2", "col_size": 894, @@ -218,16 +218,16 @@ }, { "spanID": "e521460637176e84", - "operationName": "__process_adb_cursor", - "startTime": 1703363922957436, - "duration": 8867, + "operationName": "process_adb_cursor", + "startTime": 1705677351984451, + "duration": 7495, "tags": {}, "children": [ { "spanID": "96fd35d0adf20806", - "operationName": "__process_adb_vertex_df", - "startTime": 1703363922959093, - "duration": 5887, + "operationName": "process_adb_vertex_dataframe", + "startTime": 1705677351985641, + "duration": 4967, "tags": { "i": 0, "vertex_df_size": 894 @@ -235,18 +235,18 @@ "children": [ { "spanID": "f323ca74d3447490", - "operationName": "__set_pyg_data", - "startTime": 1703363922959350, - "duration": 5620, + "operationName": "set_pyg_data", + "startTime": 1705677351985865, + "duration": 4735, "tags": { "meta": "{'x': 'x'}" }, "children": [ { "spanID": "9466e4726b5f5241", - "operationName": "__build_tensor_from_dataframe", - "startTime": 1703363922959382, - "duration": 5558, + "operationName": "build_tensor_from_dataframe", + "startTime": 1705677351985890, + "duration": 4662, "tags": { "meta_key": "x", "meta_val": "x" @@ -263,18 +263,18 @@ }, { "spanID": "73581a8146743741", - "operationName": "__process_adb_e_col", - "startTime": 1703363922966374, - "duration": 776069, + "operationName": "process_adb_edge_collection", + "startTime": 1705677351992022, + "duration": 682567, "tags": { "e_col": "e0" }, "children": [ { "spanID": "a905d7507e1ea9c5", - "operationName": "__fetch_adb_docs", - "startTime": 1703363922966406, - "duration": 10465, + "operationName": "fetch_adb_documents", + "startTime": 1705677351992050, + "duration": 7506, "tags": { "col": "e0", "col_size": 53450, @@ -284,41 +284,41 @@ }, { "spanID": "ff0ac0f1a425799a", - "operationName": "__process_adb_cursor", - "startTime": 1703363922976933, - "duration": 765495, + "operationName": "process_adb_cursor", + "startTime": 1705677351999603, + "duration": 674974, "tags": {}, "children": [ { "spanID": "eabca8d0b341facd", - "operationName": "__process_adb_edge_df", - "startTime": 1703363922978544, - "duration": 10606, + "operationName": "process_adb_edge_dataframe", + "startTime": 1705677352000767, + "duration": 9597, "tags": { "edge_df_size": 1000 }, "children": [ { "spanID": "cb175a5afb82860d", - "operationName": "__split_adb_ids", - "startTime": 1703363922978649, - "duration": 1909, + "operationName": "split_adb_ids", + "startTime": 1705677352000859, + "duration": 673, "tags": {}, "children": [] }, { "spanID": "151665705b7c709a", - "operationName": "__split_adb_ids", - "startTime": 1703363922980973, - "duration": 753, + "operationName": "split_adb_ids", + "startTime": 1705677352001982, + "duration": 608, "tags": {}, "children": [] }, { "spanID": "9cdf5a865306f3f5", "operationName": "__process_adb_edge_type_df", - "startTime": 1703363922984230, - "duration": 4882, + "startTime": 1705677352005840, + "duration": 4481, "tags": { "edge_type": "[\"v2\",\"e0\",\"v1\"]", "edge_type_df_size": 1000 @@ -326,18 +326,18 @@ "children": [ { "spanID": "7c879b741d878f9f", - "operationName": "__set_pyg_data", - "startTime": 1703363922988627, - "duration": 478, + "operationName": "set_pyg_data", + "startTime": 1705677352009886, + "duration": 430, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, "children": [ { "spanID": "a1515607964a870c", - "operationName": "__build_tensor_from_dataframe", - "startTime": 1703363922988667, - "duration": 421, + "operationName": "build_tensor_from_dataframe", + "startTime": 1705677352009924, + "duration": 378, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -352,34 +352,34 @@ }, { "spanID": "d857010255d44936", - "operationName": "__process_adb_edge_df", - "startTime": 1703363922995539, - "duration": 5226, + "operationName": "process_adb_edge_dataframe", + "startTime": 1705677352015125, + "duration": 3723, "tags": { "edge_df_size": 1000 }, "children": [ { "spanID": "3e37952d30bcab0e", - "operationName": "__split_adb_ids", - "startTime": 1703363922995644, - "duration": 741, + "operationName": "split_adb_ids", + "startTime": 1705677352015195, + "duration": 544, "tags": {}, "children": [] }, { "spanID": "bb42e0b20426465e", - "operationName": "__split_adb_ids", - "startTime": 1703363922996741, - "duration": 722, + "operationName": "split_adb_ids", + "startTime": 1705677352015980, + "duration": 491, "tags": {}, "children": [] }, { "spanID": "1dfc83524562be7f", "operationName": "__process_adb_edge_type_df", - "startTime": 1703363922998581, - "duration": 2145, + "startTime": 1705677352017258, + "duration": 1567, "tags": { "edge_type": "[\"v2\",\"e0\",\"v1\"]", "edge_type_df_size": 1000 @@ -387,18 +387,18 @@ "children": [ { "spanID": "38701a14b490b608", - "operationName": "__set_pyg_data", - "startTime": 1703363923000244, - "duration": 475, + "operationName": "set_pyg_data", + "startTime": 1705677352018455, + "duration": 365, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, "children": [ { "spanID": "cb69ca385f3f5638", - "operationName": "__build_tensor_from_dataframe", - "startTime": 1703363923000282, - "duration": 415, + "operationName": "build_tensor_from_dataframe", + "startTime": 1705677352018481, + "duration": 323, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -413,34 +413,34 @@ }, { "spanID": "552116dd2ba4b180", - "operationName": "__process_adb_edge_df", - "startTime": 1703363923006433, - "duration": 4664, + "operationName": "process_adb_edge_dataframe", + "startTime": 1705677352023643, + "duration": 3913, "tags": { "edge_df_size": 1000 }, "children": [ { "spanID": "d0dfae436d16ee18", - "operationName": "__split_adb_ids", - "startTime": 1703363923006514, - "duration": 694, + "operationName": "split_adb_ids", + "startTime": 1705677352023739, + "duration": 573, "tags": {}, "children": [] }, { "spanID": "19c16a0d0febd845", - "operationName": "__split_adb_ids", - "startTime": 1703363923007504, - "duration": 646, + "operationName": "split_adb_ids", + "startTime": 1705677352024589, + "duration": 507, "tags": {}, "children": [] }, { "spanID": "2577bffac87a7463", "operationName": "__process_adb_edge_type_df", - "startTime": 1703363923009113, - "duration": 1952, + "startTime": 1705677352025914, + "duration": 1610, "tags": { "edge_type": "[\"v2\",\"e0\",\"v1\"]", "edge_type_df_size": 1000 @@ -448,18 +448,18 @@ "children": [ { "spanID": "b29a8b06daf66c5f", - "operationName": "__set_pyg_data", - "startTime": 1703363923010608, - "duration": 451, + "operationName": "set_pyg_data", + "startTime": 1705677352027168, + "duration": 351, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, "children": [ { "spanID": "0b9475b138018b47", - "operationName": "__build_tensor_from_dataframe", - "startTime": 1703363923010639, - "duration": 402, + "operationName": "build_tensor_from_dataframe", + "startTime": 1705677352027193, + "duration": 309, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -474,34 +474,34 @@ }, { "spanID": "92e8e269d12ecbc4", - "operationName": "__process_adb_edge_df", - "startTime": 1703363923016265, - "duration": 4745, + "operationName": "process_adb_edge_dataframe", + "startTime": 1705677352032151, + "duration": 4199, "tags": { "edge_df_size": 1000 }, "children": [ { "spanID": "e8f6cf32a25b59fd", - "operationName": "__split_adb_ids", - "startTime": 1703363923016347, - "duration": 689, + "operationName": "split_adb_ids", + "startTime": 1705677352032248, + "duration": 584, "tags": {}, "children": [] }, { "spanID": "88c132adefbfc19e", - "operationName": "__split_adb_ids", - "startTime": 1703363923017330, - "duration": 651, + "operationName": "split_adb_ids", + "startTime": 1705677352033111, + "duration": 517, "tags": {}, "children": [] }, { "spanID": "ae3b16ec9a27d858", "operationName": "__process_adb_edge_type_df", - "startTime": 1703363923018944, - "duration": 2032, + "startTime": 1705677352034500, + "duration": 1812, "tags": { "edge_type": "[\"v2\",\"e0\",\"v1\"]", "edge_type_df_size": 1000 @@ -509,18 +509,18 @@ "children": [ { "spanID": "06d599e812f175ff", - "operationName": "__set_pyg_data", - "startTime": 1703363923020510, - "duration": 459, + "operationName": "set_pyg_data", + "startTime": 1705677352035929, + "duration": 377, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, "children": [ { "spanID": "a28f5ab01fdb8b32", - "operationName": "__build_tensor_from_dataframe", - "startTime": 1703363923020542, - "duration": 403, + "operationName": "build_tensor_from_dataframe", + "startTime": 1705677352035959, + "duration": 327, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -535,34 +535,34 @@ }, { "spanID": "9b38fe803042e325", - "operationName": "__process_adb_edge_df", - "startTime": 1703363923026860, - "duration": 4933, + "operationName": "process_adb_edge_dataframe", + "startTime": 1705677352042270, + "duration": 3887, "tags": { "edge_df_size": 1000 }, "children": [ { "spanID": "9371a71fd480865f", - "operationName": "__split_adb_ids", - "startTime": 1703363923026954, - "duration": 714, + "operationName": "split_adb_ids", + "startTime": 1705677352042365, + "duration": 575, "tags": {}, "children": [] }, { "spanID": "64264cd51ea45cd6", - "operationName": "__split_adb_ids", - "startTime": 1703363923028004, - "duration": 656, + "operationName": "split_adb_ids", + "startTime": 1705677352043200, + "duration": 493, "tags": {}, "children": [] }, { "spanID": "5ec17dbe176ea1b1", "operationName": "__process_adb_edge_type_df", - "startTime": 1703363923029736, - "duration": 2022, + "startTime": 1705677352044530, + "duration": 1601, "tags": { "edge_type": "[\"v2\",\"e0\",\"v1\"]", "edge_type_df_size": 1000 @@ -570,18 +570,18 @@ "children": [ { "spanID": "fb0323a1d576d415", - "operationName": "__set_pyg_data", - "startTime": 1703363923031294, - "duration": 457, + "operationName": "set_pyg_data", + "startTime": 1705677352045790, + "duration": 336, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, "children": [ { "spanID": "0950fd131db53334", - "operationName": "__build_tensor_from_dataframe", - "startTime": 1703363923031327, - "duration": 403, + "operationName": "build_tensor_from_dataframe", + "startTime": 1705677352045815, + "duration": 297, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -596,34 +596,34 @@ }, { "spanID": "0589f8779b025244", - "operationName": "__process_adb_edge_df", - "startTime": 1703363923036865, - "duration": 5013, + "operationName": "process_adb_edge_dataframe", + "startTime": 1705677352050495, + "duration": 4576, "tags": { "edge_df_size": 1000 }, "children": [ { "spanID": "f606254131d0b664", - "operationName": "__split_adb_ids", - "startTime": 1703363923036948, - "duration": 746, + "operationName": "split_adb_ids", + "startTime": 1705677352050558, + "duration": 542, "tags": {}, "children": [] }, { "spanID": "2f5a522af87f43fd", - "operationName": "__split_adb_ids", - "startTime": 1703363923038000, - "duration": 656, + "operationName": "split_adb_ids", + "startTime": 1705677352051326, + "duration": 501, "tags": {}, "children": [] }, { "spanID": "1fb797fab7d6467b", "operationName": "__process_adb_edge_type_df", - 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"operationName": "__process_adb_edge_df", - "startTime": 1703363923719431, - "duration": 5272, + "operationName": "process_adb_edge_dataframe", + "startTime": 1705677352653078, + "duration": 4483, "tags": { "edge_df_size": 1000 }, "children": [ { "spanID": "0e3705265582a3bd", - "operationName": "__split_adb_ids", - "startTime": 1703363923719522, - "duration": 1083, + "operationName": "split_adb_ids", + "startTime": 1705677352653164, + "duration": 924, "tags": {}, "children": [] }, { "spanID": "0932f5b6f11ddff7", - "operationName": "__split_adb_ids", - "startTime": 1703363923720934, - "duration": 684, + "operationName": "split_adb_ids", + "startTime": 1705677352654356, + "duration": 536, "tags": {}, "children": [] }, { "spanID": "6af944e07b38785b", "operationName": "__process_adb_edge_type_df", - "startTime": 1703363923722601, - "duration": 2058, + "startTime": 1705677352655760, + "duration": 1772, "tags": { "edge_type": "[\"v0\",\"e0\",\"v2\"]", "edge_type_df_size": 1000 @@ -3607,18 +3607,18 @@ "children": [ { "spanID": "7de8a2342412579d", - "operationName": "__set_pyg_data", - "startTime": 1703363923724167, - "duration": 485, + "operationName": "set_pyg_data", + "startTime": 1705677352657158, + "duration": 369, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, "children": [ { "spanID": "dd02e100e3d48408", - "operationName": "__build_tensor_from_dataframe", - "startTime": 1703363923724198, - "duration": 421, + "operationName": "build_tensor_from_dataframe", + "startTime": 1705677352657186, + "duration": 319, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -3633,34 +3633,34 @@ }, { "spanID": "b799ae8e9a1a7d6f", - "operationName": "__process_adb_edge_df", - "startTime": 1703363923729496, - "duration": 5113, + "operationName": "process_adb_edge_dataframe", + "startTime": 1705677352663511, + "duration": 4087, "tags": { "edge_df_size": 1000 }, "children": [ { "spanID": "ac6d5df814e5064c", - "operationName": "__split_adb_ids", - "startTime": 1703363923729585, - "duration": 759, + "operationName": "split_adb_ids", + "startTime": 1705677352663588, + "duration": 585, "tags": {}, "children": [] }, { "spanID": "26c06e67b2ddc481", - "operationName": "__split_adb_ids", - "startTime": 1703363923730678, - "duration": 700, + "operationName": "split_adb_ids", + "startTime": 1705677352664435, + "duration": 537, "tags": {}, "children": [] }, { "spanID": "fc98c279cf6f111c", "operationName": "__process_adb_edge_type_df", - "startTime": 1703363923732409, - "duration": 2163, + "startTime": 1705677352665837, + "duration": 1735, "tags": { "edge_type": "[\"v0\",\"e0\",\"v2\"]", "edge_type_df_size": 1000 @@ -3668,18 +3668,18 @@ "children": [ { "spanID": "69407be75a4f4145", - "operationName": "__set_pyg_data", - "startTime": 1703363923734101, - "duration": 464, + "operationName": "set_pyg_data", + "startTime": 1705677352667209, + "duration": 357, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, "children": [ { "spanID": "9c9d03f309018aee", - "operationName": "__build_tensor_from_dataframe", - "startTime": 1703363923734133, - "duration": 404, + "operationName": "build_tensor_from_dataframe", + "startTime": 1705677352667238, + "duration": 306, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -3694,34 +3694,34 @@ }, { "spanID": "62fda854775e0ec3", - "operationName": "__process_adb_edge_df", - "startTime": 1703363923737710, - "duration": 3772, + "operationName": "process_adb_edge_dataframe", + "startTime": 1705677352671044, + "duration": 2790, "tags": { "edge_df_size": 450 }, "children": [ { "spanID": "0c0a59677579501a", - "operationName": "__split_adb_ids", - "startTime": 1703363923737801, - "duration": 397, + "operationName": "split_adb_ids", + "startTime": 1705677352671106, + "duration": 300, "tags": {}, "children": [] }, { "spanID": "788c31f619faa06e", - "operationName": "__split_adb_ids", - "startTime": 1703363923738492, - "duration": 370, + "operationName": "split_adb_ids", + "startTime": 1705677352671621, + "duration": 267, "tags": {}, "children": [] }, { "spanID": "26c00984c734bb05", "operationName": "__process_adb_edge_type_df", - "startTime": 1703363923739829, - "duration": 1630, + "startTime": 1705677352672594, + "duration": 1221, "tags": { "edge_type": "[\"v0\",\"e0\",\"v2\"]", "edge_type_df_size": 450 @@ -3729,18 +3729,18 @@ "children": [ { "spanID": "084fa819052daad3", - "operationName": "__set_pyg_data", - "startTime": 1703363923741187, - "duration": 266, + "operationName": "set_pyg_data", + "startTime": 1705677352673607, + "duration": 203, "tags": { "meta": "{'edge_attr': 'edge_attr'}" }, "children": [ { "spanID": "9e0df45b992a34a1", - "operationName": "__build_tensor_from_dataframe", - "startTime": 1703363923741218, - "duration": 202, + "operationName": "build_tensor_from_dataframe", + "startTime": 1705677352673630, + "duration": 153, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" diff --git a/benchmark/traces/master/pyg_to_arangodb.json b/benchmark/traces/master/pyg_to_arangodb.json index 5011f24..6fb416f 100644 --- a/benchmark/traces/master/pyg_to_arangodb.json +++ b/benchmark/traces/master/pyg_to_arangodb.json @@ -1,40 +1,40 @@ { "spanID": "40212ef7cca5a5a1", "operationName": "pyg_to_arangodb", - "startTime": 1703363921366132, - "duration": 1470129, + "startTime": 1705677350412614, + "duration": 1424964, "tags": { "name": "FakeHeteroGraphBenchmark" }, "children": [ { "spanID": "e8e5216afcbd04c3", - "operationName": "__get_node_and_edge_types", - "startTime": 1703363921366221, - "duration": 17, + "operationName": "get_node_and_edge_types", + "startTime": 1705677350412677, + "duration": 12, "tags": {}, "children": [] }, { "spanID": "fb97d43588561712", - "operationName": "__create_adb_graph", - "startTime": 1703363921366281, - "duration": 5596, + "operationName": "create_adb_graph", + "startTime": 1705677350412716, + "duration": 5920, "tags": {}, "children": [ { "spanID": "cf6a659eb4862b21", - "operationName": "__etypes_to_edefinitions", - "startTime": 1703363921368952, - "duration": 23, + "operationName": "edge_types_to_edge_definitions", + "startTime": 1705677350415265, + "duration": 16, "tags": {}, "children": [] }, { "spanID": "e6f4590b9a164106", - "operationName": "__ntypes_to_ocollections", - "startTime": 1703363921369004, - "duration": 9, + "operationName": "node_types_to_orphan_collections", + "startTime": 1705677350415302, + "duration": 7, "tags": {}, "children": [] } @@ -42,9 +42,9 @@ }, { "spanID": "4f65d4d9259f4329", - "operationName": "__process_pyg_n_type", - "startTime": 1703363921373624, - "duration": 154737, + "operationName": "process_pyg_node_type", + "startTime": 1705677350419876, + "duration": 137646, "tags": { "n_type": "v0", "n_type_size": 1008 @@ -52,9 +52,9 @@ "children": [ { "spanID": "bad640fb19488dec", - "operationName": "__process_pyg_node_batch", - "startTime": 1703363921373669, - "duration": 11623, + "operationName": "process_pyg_node_batch", + "startTime": 1705677350419914, + "duration": 13309, "tags": { "start_index": 0, "end_index": 1008 @@ -62,18 +62,18 @@ "children": [ { "spanID": "e61a441c12e0c8b2", - "operationName": "__set_adb_data", - "startTime": 1703363921374931, - "duration": 8928, + "operationName": "set_adb_data", + "startTime": 1705677350421257, + "duration": 10150, "tags": { "meta": "{}" }, "children": [ { "spanID": "af19922ad9b8a714", - "operationName": "__build_dataframe_from_tensor", - "startTime": 1703363921375218, - "duration": 3766, + "operationName": "build_dataframe_from_tensor", + "startTime": 1705677350421620, + "duration": 2935, "tags": { "meta_key": "y", "meta_val": "y" @@ -82,9 +82,9 @@ }, { "spanID": "78de58575487ce1e", - "operationName": "__build_dataframe_from_tensor", - "startTime": 1703363921379833, - "duration": 1975, + "operationName": "build_dataframe_from_tensor", + "startTime": 1705677350426245, + "duration": 1702, "tags": { "meta_key": "x", "meta_val": "x" @@ -97,9 +97,9 @@ }, { "spanID": "19c78df48f4ff31e", - "operationName": "__insert_adb_docs", - "startTime": 1703363921385369, - "duration": 142960, + "operationName": "insert_adb_documents", + "startTime": 1705677350433306, + "duration": 124191, "tags": { "col": "v0", "size": 1008 @@ -110,9 +110,9 @@ }, { "spanID": "6f25e2a25a921187", - "operationName": "__process_pyg_n_type", - "startTime": 1703363921530445, - "duration": 98388, + "operationName": "process_pyg_node_type", + "startTime": 1705677350559298, + "duration": 82233, "tags": { "n_type": "v1", "n_type_size": 821 @@ -120,9 +120,9 @@ "children": [ { "spanID": "9c6316b950f24455", - "operationName": "__process_pyg_node_batch", - "startTime": 1703363921530486, - "duration": 2707, + "operationName": "process_pyg_node_batch", + "startTime": 1705677350559338, + "duration": 2455, "tags": { "start_index": 0, "end_index": 821 @@ -130,18 +130,18 @@ "children": [ { "spanID": "e9bb17bca3f2c9bf", - "operationName": "__set_adb_data", - "startTime": 1703363921530734, - "duration": 1610, + "operationName": "set_adb_data", + "startTime": 1705677350559603, + "duration": 1472, "tags": { "meta": "{}" }, "children": [ { "spanID": "f77383c13458a748", - "operationName": "__build_dataframe_from_tensor", - "startTime": 1703363921530803, - "duration": 1223, + "operationName": "build_dataframe_from_tensor", + "startTime": 1705677350559671, + "duration": 1114, "tags": { "meta_key": "x", "meta_val": "x" @@ -154,9 +154,9 @@ }, { "spanID": "7a1d50068d723104", - "operationName": "__insert_adb_docs", - "startTime": 1703363921533238, - "duration": 95566, + "operationName": "insert_adb_documents", + "startTime": 1705677350561831, + "duration": 79670, "tags": { "col": "v1", "size": 821 @@ -167,9 +167,9 @@ }, { "spanID": "dd84f39e71545a13", - "operationName": "__process_pyg_n_type", - "startTime": 1703363921630746, - "duration": 94787, + "operationName": "process_pyg_node_type", + "startTime": 1705677350643251, + "duration": 77198, "tags": { "n_type": "v2", "n_type_size": 894 @@ -177,9 +177,9 @@ "children": [ { "spanID": "42af9fc385776e9a", - "operationName": "__process_pyg_node_batch", - "startTime": 1703363921630788, - "duration": 2623, + "operationName": "process_pyg_node_batch", + "startTime": 1705677350643284, + "duration": 2235, "tags": { "start_index": 0, "end_index": 894 @@ -187,18 +187,18 @@ "children": [ { "spanID": "ce164dba0ff18e02", - "operationName": "__set_adb_data", - "startTime": 1703363921631030, - "duration": 1502, + "operationName": "set_adb_data", + "startTime": 1705677350643505, + "duration": 1306, "tags": { "meta": "{}" }, "children": [ { "spanID": "8c778ea6eb2083e6", - "operationName": "__build_dataframe_from_tensor", - "startTime": 1703363921631098, - "duration": 1122, + "operationName": "build_dataframe_from_tensor", + "startTime": 1705677350643563, + "duration": 963, "tags": { "meta_key": "x", "meta_val": "x" @@ -211,9 +211,9 @@ }, { "spanID": "03983ca8ea7e9d49", - "operationName": "__insert_adb_docs", - "startTime": 1703363921633455, - "duration": 92050, + "operationName": "insert_adb_documents", + "startTime": 1705677350645557, + "duration": 74861, "tags": { "col": "v2", "size": 894 @@ -224,9 +224,9 @@ }, { "spanID": "b83e90ec17e0aa3c", - "operationName": "__process_pyg_e_type", - "startTime": 1703363921727597, - "duration": 191512, + "operationName": "process_pyg_edge_type", + "startTime": 1705677350721993, + "duration": 199098, "tags": { "e_type": "[\"v2\",\"e0\",\"v1\"]", "e_type_size": 8895 @@ -234,9 +234,9 @@ "children": [ { "spanID": "66194cb1d71037d1", - "operationName": "__process_pyg_edge_batch", - "startTime": 1703363921727648, - "duration": 8551, + "operationName": "process_pyg_edge_batch", + "startTime": 1705677350722032, + "duration": 6419, "tags": { "start_index": 0, "end_index": 8895 @@ -244,18 +244,18 @@ "children": [ { "spanID": "d3290a4cb5d32b16", - "operationName": "__set_adb_data", - "startTime": 1703363921731472, - "duration": 2670, + "operationName": "set_adb_data", + "startTime": 1705677350724898, + "duration": 1762, "tags": { "meta": "{}" }, "children": [ { "spanID": "ab0c1681c8f8e3d0", - "operationName": "__build_dataframe_from_tensor", - "startTime": 1703363921731557, - "duration": 1888, + "operationName": "build_dataframe_from_tensor", + "startTime": 1705677350724946, + "duration": 1372, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -268,9 +268,9 @@ }, { "spanID": "004ae545a0116be5", - "operationName": "__insert_adb_docs", - "startTime": 1703363921736269, - "duration": 182809, + "operationName": "insert_adb_documents", + "startTime": 1705677350728509, + "duration": 192552, "tags": { "col": "e0", "size": 8895 @@ -281,9 +281,9 @@ }, { "spanID": "7e5b1e7f9ca5499d", - "operationName": "__process_pyg_e_type", - "startTime": 1703363921921310, - "duration": 156678, + "operationName": "process_pyg_edge_type", + "startTime": 1705677350922998, + "duration": 147111, "tags": { "e_type": "[\"v1\",\"e0\",\"v2\"]", "e_type_size": 8161 @@ -291,9 +291,9 @@ "children": [ { "spanID": "de1b372ad3fbf47a", - "operationName": "__process_pyg_edge_batch", - "startTime": 1703363921921358, - "duration": 6323, + "operationName": "process_pyg_edge_batch", + "startTime": 1705677350923056, + "duration": 5320, "tags": { "start_index": 0, "end_index": 8161 @@ -301,18 +301,18 @@ "children": [ { "spanID": "3e70f16a55485822", - "operationName": "__set_adb_data", - "startTime": 1703363921924293, - "duration": 2166, + "operationName": "set_adb_data", + "startTime": 1705677350925687, + "duration": 1779, "tags": { "meta": "{}" }, "children": [ { "spanID": "534097cabaf3897a", - "operationName": "__build_dataframe_from_tensor", - "startTime": 1703363921924356, - "duration": 1601, + "operationName": "build_dataframe_from_tensor", + "startTime": 1705677350925754, + "duration": 1309, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -325,9 +325,9 @@ }, { "spanID": "ded733e8b421eaeb", - "operationName": "__insert_adb_docs", - "startTime": 1703363921927749, - "duration": 150209, + "operationName": "insert_adb_documents", + "startTime": 1705677350928429, + "duration": 141653, "tags": { "col": "e0", "size": 8161 @@ -338,9 +338,9 @@ }, { "spanID": "30e9c5cc101fbccc", - "operationName": "__process_pyg_e_type", - "startTime": 1703363922079865, - "duration": 249886, + "operationName": "process_pyg_edge_type", + "startTime": 1705677351071917, + "duration": 205157, "tags": { "e_type": "[\"v0\",\"e0\",\"v1\"]", "e_type_size": 10022 @@ -348,9 +348,9 @@ "children": [ { "spanID": "9148624feac1c14f", - "operationName": "__process_pyg_edge_batch", - "startTime": 1703363922079911, - "duration": 7285, + "operationName": "process_pyg_edge_batch", + "startTime": 1705677351071962, + "duration": 5514, "tags": { "start_index": 0, "end_index": 10022 @@ -358,18 +358,18 @@ "children": [ { "spanID": "3d15eef738c1962e", - "operationName": "__set_adb_data", - "startTime": 1703363922083694, - "duration": 2405, + "operationName": "set_adb_data", + "startTime": 1705677351074725, + "duration": 1806, "tags": { "meta": "{}" }, "children": [ { "spanID": "f7b0b7d2cda8056c", - "operationName": "__build_dataframe_from_tensor", - "startTime": 1703363922083769, - "duration": 1877, + "operationName": "build_dataframe_from_tensor", + "startTime": 1705677351074773, + "duration": 1414, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -382,9 +382,9 @@ }, { "spanID": "cd9d2b7d247a8333", - "operationName": "__insert_adb_docs", - "startTime": 1703363922087247, - "duration": 242475, + "operationName": "insert_adb_documents", + "startTime": 1705677351077519, + "duration": 199528, "tags": { "col": "e0", "size": 10022 @@ -395,9 +395,9 @@ }, { "spanID": "72ae22448b0163c1", - "operationName": "__process_pyg_e_type", - "startTime": 1703363922331731, - "duration": 154780, + "operationName": "process_pyg_edge_type", + "startTime": 1705677351279085, + "duration": 125564, "tags": { "e_type": "[\"v1\",\"e0\",\"v0\"]", "e_type_size": 8179 @@ -405,9 +405,9 @@ "children": [ { "spanID": "149818d11759edc3", - "operationName": "__process_pyg_edge_batch", - "startTime": 1703363922331779, - "duration": 6392, + "operationName": "process_pyg_edge_batch", + "startTime": 1705677351279128, + "duration": 4637, "tags": { "start_index": 0, "end_index": 8179 @@ -415,18 +415,18 @@ "children": [ { "spanID": "51ef1922fe43c49e", - "operationName": "__set_adb_data", - "startTime": 1703363922334788, - "duration": 2280, + "operationName": "set_adb_data", + "startTime": 1705677351281404, + "duration": 1560, "tags": { "meta": "{}" }, "children": [ { "spanID": "820865d6e005b860", - "operationName": "__build_dataframe_from_tensor", - "startTime": 1703363922334852, - "duration": 1690, + "operationName": "build_dataframe_from_tensor", + "startTime": 1705677351281450, + "duration": 1175, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -439,9 +439,9 @@ }, { "spanID": "eece328bff7b118e", - "operationName": "__insert_adb_docs", - "startTime": 1703363922338234, - "duration": 148247, + "operationName": "insert_adb_documents", + "startTime": 1705677351283800, + "duration": 120819, "tags": { "col": "e0", "size": 8179 @@ -452,9 +452,9 @@ }, { "spanID": "1beb37117d41e602", - "operationName": "__process_pyg_e_type", - "startTime": 1703363922488590, - "duration": 156159, + "operationName": "process_pyg_edge_type", + "startTime": 1705677351406630, + "duration": 127494, "tags": { "e_type": "[\"v1\",\"e0\",\"v1\"]", "e_type_size": 8159 @@ -462,9 +462,9 @@ "children": [ { "spanID": "8d1fd9b74d2b9deb", - "operationName": "__process_pyg_edge_batch", - "startTime": 1703363922488644, - "duration": 6517, + "operationName": "process_pyg_edge_batch", + "startTime": 1705677351406674, + "duration": 4985, "tags": { "start_index": 0, "end_index": 8159 @@ -472,18 +472,18 @@ "children": [ { "spanID": "b4e1357d4a84eb03", - "operationName": "__set_adb_data", - "startTime": 1703363922491712, - "duration": 2232, + "operationName": "set_adb_data", + "startTime": 1705677351409196, + "duration": 1679, "tags": { "meta": "{}" }, "children": [ { "spanID": "8c25166a1ff39849", - "operationName": "__build_dataframe_from_tensor", - "startTime": 1703363922491781, - "duration": 1669, + "operationName": "build_dataframe_from_tensor", + "startTime": 1705677351409259, + "duration": 1257, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -496,9 +496,9 @@ }, { "spanID": "d080e66e552f233a", - "operationName": "__insert_adb_docs", - "startTime": 1703363922495223, - "duration": 149497, + "operationName": "insert_adb_documents", + "startTime": 1705677351411703, + "duration": 122399, "tags": { "col": "e0", "size": 8159 @@ -509,9 +509,9 @@ }, { "spanID": "8a5006c1ec188efb", - "operationName": "__process_pyg_e_type", - "startTime": 1703363922646634, - "duration": 188557, + "operationName": "process_pyg_edge_type", + "startTime": 1705677351535677, + "duration": 300614, "tags": { "e_type": "[\"v0\",\"e0\",\"v2\"]", "e_type_size": 10034 @@ -519,9 +519,9 @@ "children": [ { "spanID": "f6be1f723405095c", - "operationName": "__process_pyg_edge_batch", - "startTime": 1703363922646683, - "duration": 7481, + "operationName": "process_pyg_edge_batch", + "startTime": 1705677351535729, + "duration": 6490, "tags": { "start_index": 0, "end_index": 10034 @@ -529,18 +529,18 @@ "children": [ { "spanID": "9a6a5f92cca74147", - "operationName": "__set_adb_data", - "startTime": 1703363922650275, - "duration": 2651, + "operationName": "set_adb_data", + "startTime": 1705677351538806, + "duration": 2319, "tags": { "meta": "{}" }, "children": [ { "spanID": "966e12778c1745a7", - "operationName": "__build_dataframe_from_tensor", - "startTime": 1703363922650330, - "duration": 2067, + "operationName": "build_dataframe_from_tensor", + "startTime": 1705677351538873, + "duration": 1794, "tags": { "meta_key": "edge_attr", "meta_val": "edge_attr" @@ -553,9 +553,9 @@ }, { "spanID": "71eacd0549a3e80e", - "operationName": "__insert_adb_docs", - "startTime": 1703363922654225, - "duration": 180936, + "operationName": "insert_adb_documents", + "startTime": 1705677351542275, + "duration": 293944, "tags": { "col": "e0", "size": 10034 From 16ace5cb498865071f07e2c5e8997dc88c089869 Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Fri, 19 Jan 2024 10:21:24 -0500 Subject: [PATCH 65/73] add README --- benchmark/README.md | 22 ++++++++++++++++++++++ 1 file changed, 22 insertions(+) create mode 100644 benchmark/README.md diff --git a/benchmark/README.md b/benchmark/README.md new file mode 100644 index 0000000..e8ebcdf --- /dev/null +++ b/benchmark/README.md @@ -0,0 +1,22 @@ +# Benchmarking + +This directory contains the benchmarking scripts for the project. + +1. `compare.py` compares the benchmarking results of two branches. +2. `write.py`: writes the benchmarking results to a file: +```py +parser.add_argument("--url", type=str, default="http://localhost:8529") +parser.add_argument("--dbName", type=str, default="_system") +parser.add_argument("--username", type=str, default="root") +parser.add_argument("--password", type=str, default="") +parser.add_argument("--jaeger_endpoint", type=str, default="http://localhost:16686") +parser.add_argument("--otlp_endpoint", type=str, default="http://localhost:4317") +parser.add_argument( + "--output_dir", type=str, choices=["branch", "master"], required=True +) +``` + +Results are stored in: +- `benchmark/master` for the master results +- `benchmark/branch` for the branch results (added to `.gitignore`) +- `benchmark/diff` for the diff between the branch and master results (added to `.gitignore`) \ No newline at end of file From 3f3575594c03da6035a680d61d976ec7a181b384 Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Fri, 19 Jan 2024 10:21:31 -0500 Subject: [PATCH 66/73] fix benchmark assertions --- benchmark/compare.py | 22 ++++++++++++++++++---- 1 file changed, 18 insertions(+), 4 deletions(-) diff --git a/benchmark/compare.py b/benchmark/compare.py index 1b7e5a5..6a59e61 100644 --- a/benchmark/compare.py +++ b/benchmark/compare.py @@ -9,8 +9,15 @@ def sort_children_by_start_time(children): def compare_span(master_child: Optional[dict], branch_child: Optional[dict]): if master_child and branch_child: - assert master_child.get("operationName") == branch_child.get("operationName") - assert master_child.get("tags") == branch_child.get("tags") + assert master_child.get("operationName") == branch_child.get("operationName"), ( + f"Master Child Operation Name: {master_child.get('operationName')}\n" + f"Branch Child Operation Name: {branch_child.get('operationName')}" + ) + + assert master_child.get("tags") == branch_child.get("tags"), ( + f"Master Child Tags: {master_child.get('tags')}\n" + f"Branch Child Tags: {branch_child.get('tags')}" + ) operation_name = ( master_child.get("operationName") @@ -102,8 +109,15 @@ def compare_children(master_children: list[dict], branch_children: list[dict]): def compare_traces(master_trace: dict, branch_trace: dict): - assert master_trace.get("operationName") == branch_trace.get("operationName") - assert master_trace.get("tags") == branch_trace.get("tags") + assert master_trace.get("operationName") == branch_trace.get("operationName"), ( + f"Master Operation Name: {master_trace.get('operationName')}\n" + f"Branch Operation Name: {branch_trace.get('operationName')}" + ) + + assert master_trace.get("tags") == branch_trace.get("tags"), ( + f"Master Tags: {master_trace.get('tags')}\n" + f"Branch Tags: {branch_trace.get('tags')}" + ) result = { "operationName": master_trace["operationName"], From de16e8db0d24cb9d0ec68f9e8bdee9aa4177880c Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Fri, 26 Jan 2024 11:10:05 -0500 Subject: [PATCH 67/73] fix `start_as_current_span` --- adbpyg_adapter/adapter.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/adbpyg_adapter/adapter.py b/adbpyg_adapter/adapter.py index aeb2790..7eab1bf 100644 --- a/adbpyg_adapter/adapter.py +++ b/adbpyg_adapter/adapter.py @@ -1013,7 +1013,7 @@ def __process_adb_edge_df( ): edge_type = (from_col, e_col, to_col) - with start_as_current_span("__process_adb_edge_type_df"): + with start_as_current_span("process_adb_edge_type_df"): TracingManager.set_attributes( edge_type=edge_type, edge_type_df_size=count ) From 77b7a814a5d59a77963c80088c17540ca2f51adb Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Fri, 26 Jan 2024 11:10:51 -0500 Subject: [PATCH 68/73] fix: `operationName` related to de16e8d --- benchmark/traces/master/arangodb_to_pyg.json | 118 +++++++++---------- 1 file changed, 59 insertions(+), 59 deletions(-) diff --git a/benchmark/traces/master/arangodb_to_pyg.json b/benchmark/traces/master/arangodb_to_pyg.json index 40a4d88..0d638fc 100644 --- a/benchmark/traces/master/arangodb_to_pyg.json +++ b/benchmark/traces/master/arangodb_to_pyg.json @@ -316,7 +316,7 @@ }, { "spanID": "9cdf5a865306f3f5", - "operationName": "__process_adb_edge_type_df", + "operationName": "process_adb_edge_type_df", "startTime": 1705677352005840, "duration": 4481, "tags": { @@ -377,7 +377,7 @@ }, { "spanID": "1dfc83524562be7f", - "operationName": "__process_adb_edge_type_df", + "operationName": "process_adb_edge_type_df", "startTime": 1705677352017258, "duration": 1567, "tags": { @@ -438,7 +438,7 @@ }, { "spanID": "2577bffac87a7463", - "operationName": "__process_adb_edge_type_df", + "operationName": "process_adb_edge_type_df", "startTime": 1705677352025914, "duration": 1610, "tags": { @@ -499,7 +499,7 @@ }, { "spanID": "ae3b16ec9a27d858", - "operationName": "__process_adb_edge_type_df", + "operationName": "process_adb_edge_type_df", "startTime": 1705677352034500, "duration": 1812, "tags": { @@ -560,7 +560,7 @@ }, { "spanID": "5ec17dbe176ea1b1", - "operationName": "__process_adb_edge_type_df", + "operationName": "process_adb_edge_type_df", "startTime": 1705677352044530, "duration": 1601, "tags": { @@ -621,7 +621,7 @@ }, { "spanID": "1fb797fab7d6467b", - "operationName": "__process_adb_edge_type_df", + "operationName": "process_adb_edge_type_df", "startTime": 1705677352052573, "duration": 2449, "tags": { @@ -682,7 +682,7 @@ }, { "spanID": "19fbeb1d9edfa3da", - "operationName": "__process_adb_edge_type_df", + "operationName": "process_adb_edge_type_df", "startTime": 1705677352062419, "duration": 1591, "tags": { @@ -743,7 +743,7 @@ }, { "spanID": "80ee526e0fa07a3f", - "operationName": "__process_adb_edge_type_df", + "operationName": "process_adb_edge_type_df", "startTime": 1705677352071338, "duration": 2128, "tags": { @@ -804,7 +804,7 @@ }, { "spanID": "bb4a06cbe786ab37", - "operationName": "__process_adb_edge_type_df", + "operationName": "process_adb_edge_type_df", "startTime": 1705677352081353, "duration": 1826, "tags": { @@ -838,7 +838,7 @@ }, { "spanID": "2b5f693291dc59ef", - "operationName": "__process_adb_edge_type_df", + "operationName": "process_adb_edge_type_df", "startTime": 1705677352083198, "duration": 1000, "tags": { @@ -899,7 +899,7 @@ }, { "spanID": "57a1cb712975d279", - "operationName": "__process_adb_edge_type_df", + "operationName": "process_adb_edge_type_df", "startTime": 1705677352091558, "duration": 1623, "tags": { @@ -960,7 +960,7 @@ }, { "spanID": "68ef8f5fae68690a", - "operationName": "__process_adb_edge_type_df", + "operationName": "process_adb_edge_type_df", "startTime": 1705677352108876, "duration": 1657, "tags": { @@ -1021,7 +1021,7 @@ }, { "spanID": "403d1f83a859890c", - "operationName": "__process_adb_edge_type_df", + "operationName": "process_adb_edge_type_df", "startTime": 1705677352117507, "duration": 1913, "tags": { @@ -1082,7 +1082,7 @@ }, { "spanID": "47e7f5938b5885ca", - "operationName": "__process_adb_edge_type_df", + "operationName": "process_adb_edge_type_df", "startTime": 1705677352126533, "duration": 1570, "tags": { @@ -1143,7 +1143,7 @@ }, { "spanID": "f2686baa971c702d", - "operationName": "__process_adb_edge_type_df", + "operationName": "process_adb_edge_type_df", "startTime": 1705677352134800, "duration": 1970, "tags": { @@ -1204,7 +1204,7 @@ }, { "spanID": "a69cfb85d432f8db", - "operationName": "__process_adb_edge_type_df", + "operationName": "process_adb_edge_type_df", "startTime": 1705677352143909, "duration": 1588, "tags": { @@ -1265,7 +1265,7 @@ }, { "spanID": "60ef147172b8ff39", - "operationName": "__process_adb_edge_type_df", + "operationName": "process_adb_edge_type_df", "startTime": 1705677352152452, "duration": 1866, "tags": { @@ -1326,7 +1326,7 @@ }, { "spanID": "6b10e53a9145de05", - "operationName": "__process_adb_edge_type_df", + "operationName": "process_adb_edge_type_df", "startTime": 1705677352207070, "duration": 1961, "tags": { @@ -1387,7 +1387,7 @@ }, { "spanID": "870f084c7244f536", - "operationName": "__process_adb_edge_type_df", + "operationName": "process_adb_edge_type_df", "startTime": 1705677352218698, "duration": 1671, "tags": { @@ -1421,7 +1421,7 @@ }, { "spanID": "c167733f9a9e4310", - "operationName": "__process_adb_edge_type_df", + "operationName": "process_adb_edge_type_df", "startTime": 1705677352220388, "duration": 1153, "tags": { @@ -1482,7 +1482,7 @@ }, { "spanID": "e00111e5d29dc5df", - "operationName": "__process_adb_edge_type_df", + "operationName": "process_adb_edge_type_df", "startTime": 1705677352231887, "duration": 1594, "tags": { @@ -1543,7 +1543,7 @@ }, { "spanID": "b9bdee2dd663049d", - "operationName": "__process_adb_edge_type_df", + "operationName": "process_adb_edge_type_df", "startTime": 1705677352244513, "duration": 1666, "tags": { @@ -1604,7 +1604,7 @@ }, { "spanID": "b7a28e0a03a89879", - "operationName": "__process_adb_edge_type_df", + "operationName": "process_adb_edge_type_df", "startTime": 1705677352257895, "duration": 1837, "tags": { @@ -1665,7 +1665,7 @@ }, { "spanID": "a636425c9bbd750d", - "operationName": "__process_adb_edge_type_df", + "operationName": "process_adb_edge_type_df", "startTime": 1705677352269400, "duration": 1826, "tags": { @@ -1726,7 +1726,7 @@ }, { "spanID": "658de17eec3aa314", - "operationName": "__process_adb_edge_type_df", + "operationName": "process_adb_edge_type_df", "startTime": 1705677352283206, "duration": 1699, "tags": { @@ -1787,7 +1787,7 @@ }, { "spanID": "2227d96d41a93f90", - "operationName": "__process_adb_edge_type_df", + "operationName": "process_adb_edge_type_df", "startTime": 1705677352292098, "duration": 1663, "tags": { @@ -1848,7 +1848,7 @@ }, { "spanID": "04c14982d9ead926", - "operationName": "__process_adb_edge_type_df", + "operationName": "process_adb_edge_type_df", "startTime": 1705677352303318, "duration": 1696, "tags": { @@ -1909,7 +1909,7 @@ }, { "spanID": "e8ec01b3914591ae", - "operationName": "__process_adb_edge_type_df", + "operationName": "process_adb_edge_type_df", "startTime": 1705677352315906, "duration": 1594, "tags": { @@ -1970,7 +1970,7 @@ }, { "spanID": "e7180322a4e695c9", - "operationName": "__process_adb_edge_type_df", + "operationName": "process_adb_edge_type_df", "startTime": 1705677352324226, "duration": 1770, "tags": { @@ -2031,7 +2031,7 @@ }, { "spanID": "0247145f4a814d53", - "operationName": "__process_adb_edge_type_df", + "operationName": "process_adb_edge_type_df", "startTime": 1705677352336540, "duration": 1622, "tags": { @@ -2065,7 +2065,7 @@ }, { "spanID": "ca24be4d56672017", - "operationName": "__process_adb_edge_type_df", + "operationName": "process_adb_edge_type_df", "startTime": 1705677352338181, "duration": 1012, "tags": { @@ -2126,7 +2126,7 @@ }, { "spanID": "fb5eb8662640211e", - "operationName": "__process_adb_edge_type_df", + "operationName": "process_adb_edge_type_df", "startTime": 1705677352347788, "duration": 1528, "tags": { @@ -2187,7 +2187,7 @@ }, { "spanID": "3d5d60bcbb0378eb", - "operationName": "__process_adb_edge_type_df", + "operationName": "process_adb_edge_type_df", "startTime": 1705677352363161, "duration": 1665, "tags": { @@ -2248,7 +2248,7 @@ }, { "spanID": "5419eefcd5e73e3f", - "operationName": "__process_adb_edge_type_df", + "operationName": "process_adb_edge_type_df", "startTime": 1705677352374009, "duration": 1591, "tags": { @@ -2309,7 +2309,7 @@ }, { "spanID": "f9ea2c64cc417e7c", - "operationName": "__process_adb_edge_type_df", + "operationName": "process_adb_edge_type_df", "startTime": 1705677352383235, "duration": 1637, "tags": { @@ -2370,7 +2370,7 @@ }, { "spanID": "bc18a40b55c7ed9d", - "operationName": "__process_adb_edge_type_df", + "operationName": "process_adb_edge_type_df", "startTime": 1705677352397879, "duration": 1650, "tags": { @@ -2431,7 +2431,7 @@ }, { "spanID": "a38d8afcfdd2ed7a", - "operationName": "__process_adb_edge_type_df", + "operationName": "process_adb_edge_type_df", "startTime": 1705677352410965, "duration": 1655, "tags": { @@ -2492,7 +2492,7 @@ }, { "spanID": "64d09913191b8adf", - "operationName": "__process_adb_edge_type_df", + "operationName": "process_adb_edge_type_df", "startTime": 1705677352426569, "duration": 1636, "tags": { @@ -2553,7 +2553,7 @@ }, { "spanID": "6c4c3935379deda1", - "operationName": "__process_adb_edge_type_df", + "operationName": "process_adb_edge_type_df", "startTime": 1705677352445463, "duration": 1394, "tags": { @@ -2587,7 +2587,7 @@ }, { "spanID": "42cb6d1dffc573d5", - "operationName": "__process_adb_edge_type_df", + "operationName": "process_adb_edge_type_df", "startTime": 1705677352446875, "duration": 1028, "tags": { @@ -2648,7 +2648,7 @@ }, { "spanID": "b3b68b57da54f267", - "operationName": "__process_adb_edge_type_df", + "operationName": "process_adb_edge_type_df", "startTime": 1705677352463146, "duration": 1548, "tags": { @@ -2709,7 +2709,7 @@ }, { "spanID": "350d278d41a8a6e1", - "operationName": "__process_adb_edge_type_df", + "operationName": "process_adb_edge_type_df", "startTime": 1705677352478872, "duration": 1574, "tags": { @@ -2770,7 +2770,7 @@ }, { "spanID": "60c6b3ed755a3ac1", - "operationName": "__process_adb_edge_type_df", + "operationName": "process_adb_edge_type_df", "startTime": 1705677352494665, "duration": 1581, "tags": { @@ -2831,7 +2831,7 @@ }, { "spanID": "67e98363905c053b", - "operationName": "__process_adb_edge_type_df", + "operationName": "process_adb_edge_type_df", "startTime": 1705677352510019, "duration": 1599, "tags": { @@ -2892,7 +2892,7 @@ }, { "spanID": "7f99d273d5627386", - "operationName": "__process_adb_edge_type_df", + "operationName": "process_adb_edge_type_df", "startTime": 1705677352520788, "duration": 1680, "tags": { @@ -2953,7 +2953,7 @@ }, { "spanID": "5718ada2027c013f", - "operationName": "__process_adb_edge_type_df", + "operationName": "process_adb_edge_type_df", "startTime": 1705677352534000, "duration": 1737, "tags": { @@ -3014,7 +3014,7 @@ }, { "spanID": "41c30359dfde2281", - "operationName": "__process_adb_edge_type_df", + "operationName": "process_adb_edge_type_df", "startTime": 1705677352549261, "duration": 1630, "tags": { @@ -3075,7 +3075,7 @@ }, { "spanID": "10fce97d786e30ef", - "operationName": "__process_adb_edge_type_df", + "operationName": "process_adb_edge_type_df", "startTime": 1705677352561382, "duration": 1280, "tags": { @@ -3109,7 +3109,7 @@ }, { "spanID": "0a1727f7ea5f24b6", - "operationName": "__process_adb_edge_type_df", + "operationName": "process_adb_edge_type_df", "startTime": 1705677352562679, "duration": 1041, "tags": { @@ -3170,7 +3170,7 @@ }, { "spanID": "2922fbd8dca5b353", - "operationName": "__process_adb_edge_type_df", + "operationName": "process_adb_edge_type_df", "startTime": 1705677352580495, "duration": 1731, "tags": { @@ -3231,7 +3231,7 @@ }, { "spanID": "809f292387a1798f", - "operationName": "__process_adb_edge_type_df", + "operationName": "process_adb_edge_type_df", "startTime": 1705677352594216, "duration": 1552, "tags": { @@ -3292,7 +3292,7 @@ }, { "spanID": "bf391fbb138c3460", - "operationName": "__process_adb_edge_type_df", + "operationName": "process_adb_edge_type_df", "startTime": 1705677352606959, "duration": 1554, "tags": { @@ -3353,7 +3353,7 @@ }, { "spanID": "7b72590bf8f8f071", - "operationName": "__process_adb_edge_type_df", + "operationName": "process_adb_edge_type_df", "startTime": 1705677352619174, "duration": 1628, "tags": { @@ -3414,7 +3414,7 @@ }, { "spanID": "a83023ab053e4b42", - "operationName": "__process_adb_edge_type_df", + "operationName": "process_adb_edge_type_df", "startTime": 1705677352630897, "duration": 1626, "tags": { @@ -3475,7 +3475,7 @@ }, { "spanID": "dc2151e17e56ac3d", - "operationName": "__process_adb_edge_type_df", + "operationName": "process_adb_edge_type_df", "startTime": 1705677352638874, "duration": 1735, "tags": { @@ -3536,7 +3536,7 @@ }, { "spanID": "a417956f29ee7f3d", - "operationName": "__process_adb_edge_type_df", + "operationName": "process_adb_edge_type_df", "startTime": 1705677352647135, "duration": 1714, "tags": { @@ -3597,7 +3597,7 @@ }, { "spanID": "6af944e07b38785b", - "operationName": "__process_adb_edge_type_df", + "operationName": "process_adb_edge_type_df", "startTime": 1705677352655760, "duration": 1772, "tags": { @@ -3658,7 +3658,7 @@ }, { "spanID": "fc98c279cf6f111c", - "operationName": "__process_adb_edge_type_df", + "operationName": "process_adb_edge_type_df", "startTime": 1705677352665837, "duration": 1735, "tags": { @@ -3719,7 +3719,7 @@ }, { "spanID": "26c00984c734bb05", - "operationName": "__process_adb_edge_type_df", + "operationName": "process_adb_edge_type_df", "startTime": 1705677352672594, "duration": 1221, "tags": { From 67378e8b0ade5943ab8d7cb4a12158aaed54b4c9 Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Fri, 9 Feb 2024 09:34:36 -0500 Subject: [PATCH 69/73] fix: edge_index ref: https://github.com/arangoml/pyg-adapter/pull/31 --- adbpyg_adapter/adapter.py | 23 ++++++++++++++++------- 1 file changed, 16 insertions(+), 7 deletions(-) diff --git a/adbpyg_adapter/adapter.py b/adbpyg_adapter/adapter.py index 7eab1bf..7b3cc36 100644 --- a/adbpyg_adapter/adapter.py +++ b/adbpyg_adapter/adapter.py @@ -5,6 +5,7 @@ from math import ceil from typing import Any, Callable, DefaultDict, Dict, List, Optional, Set, Tuple, Union +import numpy as np import torch from arango.cursor import Cursor from arango.database import StandardDatabase @@ -1032,24 +1033,32 @@ def __process_adb_edge_df( ] # 5. Map each ArangoDB from/to _key to the corresponding PyG node id - from_nodes = et_df["from_key"].map(adb_map[from_col]).tolist() - to_nodes = et_df["to_key"].map(adb_map[to_col]).tolist() + # NOTE: map() is somehow converting int values to float... + # So we rely on astype(int) to convert the float back to int, + # but we also fill NaN values with -1 so that we can convert + # the entire column to int without any issues. Need to revisit... + from_n = et_df["from_key"].map(adb_map[from_col]).fillna(-1).astype(int) + to_n = et_df["to_key"].map(adb_map[to_col]).fillna(-1).astype(int) # 6. Set/Update the PyG Edge Index edge_data: EdgeStorage = data if is_homogeneous else data[edge_type] - existing_ei = edge_data.get("edge_index", tensor([])) - new_ei = tensor([from_nodes, to_nodes]) - edge_data.edge_index = torch.cat((existing_ei, new_ei), dim=1) + empty_tensor = tensor([], dtype=torch.int64) + existing_edge_index = edge_data.get("edge_index", empty_tensor) + new_edge_index = tensor( + np.array([from_n.to_numpy(), to_n.to_numpy()]), dtype=torch.int64 + ) + + edge_data.edge_index = cat((existing_edge_index, new_edge_index), dim=1) # 7. Deal with invalid edges - if torch.any(torch.isnan(edge_data.edge_index)): + if torch.any(edge_data.edge_index == -1): if strict: m = f"Invalid edges found in Edge Collection {e_col}, {from_col} -> {to_col}." # noqa: E501 raise InvalidADBEdgesError(m) else: # Remove the invalid edges edge_data.edge_index = edge_data.edge_index[ - :, ~torch.any(edge_data.edge_index.isnan(), dim=0) + :, ~torch.any(edge_data.edge_index == -1, dim=0) ] # 8. Set the PyG Edge Data From 36e33e5f10b65196bec6cf10e88fecbf5e4dcd71 Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Fri, 9 Feb 2024 09:43:55 -0500 Subject: [PATCH 70/73] empty commit to rerun benchmark From 91aed84e1d18817cee0d0fa8a9df261985144e8e Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Mon, 26 Feb 2024 21:26:22 -0500 Subject: [PATCH 71/73] attempt: insert_many instead of import_bulk --- adbpyg_adapter/adapter.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/adbpyg_adapter/adapter.py b/adbpyg_adapter/adapter.py index 7b3cc36..cb25b9d 100644 --- a/adbpyg_adapter/adapter.py +++ b/adbpyg_adapter/adapter.py @@ -1819,7 +1819,7 @@ def __insert_adb_docs( docs = df.to_dict("records") db = self.__async_db if use_async else self.__db - result = db.collection(col).import_bulk(docs, **adb_import_kwargs) + result = db.collection(col).insert_many(docs, **adb_import_kwargs) logger.debug(result) df.drop(df.index, inplace=True) From b8a19b33d32f13f9be128355bd09844089f8d48e Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Mon, 26 Feb 2024 21:36:59 -0500 Subject: [PATCH 72/73] temp: fix parameters for `insert_many`, skip test --- tests/test_adapter.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/tests/test_adapter.py b/tests/test_adapter.py index 827b2ca..11b2ff0 100644 --- a/tests/test_adapter.py +++ b/tests/test_adapter.py @@ -743,6 +743,8 @@ def test_adb_graph_to_pyg_to_arangodb_with_missing_document_and_permissive( def test_full_cycle_imdb_without_preserve_adb_keys() -> None: + pytest.skip("temporarily skipping") + name = "imdb" db.delete_graph(name, drop_collections=True, ignore_missing=True) arango_restore(con, "tests/data/adb/imdb_dump") @@ -822,7 +824,7 @@ def test_full_cycle_homogeneous_with_preserve_adb_keys() -> None: pyg_g["_v_key"].append(f"new-vertex-{num_nodes}") pyg_g.num_nodes = num_nodes + 1 - adbpyg_adapter.pyg_to_arangodb(name, pyg_g, on_duplicate="update") + adbpyg_adapter.pyg_to_arangodb(name, pyg_g, overwrite_mode="update") assert_pyg_to_adb(name, pyg_g, {}, False) assert db.collection("Homogeneous_N").get(f"new-vertex-{num_nodes}") is not None @@ -893,7 +895,7 @@ def test_full_cycle_imdb_with_preserve_adb_keys() -> None: pyg_g, pyg_to_adb_metagraph, explicit_metagraph=True, - on_duplicate="update", + overwrite_mode="update", ) assert_pyg_to_adb(name, pyg_g, pyg_to_adb_metagraph, True) From 7fb9f6721bcc368841940eae60b3fea89e44f837 Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Mon, 26 Feb 2024 21:40:40 -0500 Subject: [PATCH 73/73] switch back to `import_bulk` --- adbpyg_adapter/adapter.py | 2 +- tests/test_adapter.py | 6 ++---- 2 files changed, 3 insertions(+), 5 deletions(-) diff --git a/adbpyg_adapter/adapter.py b/adbpyg_adapter/adapter.py index cb25b9d..7b3cc36 100644 --- a/adbpyg_adapter/adapter.py +++ b/adbpyg_adapter/adapter.py @@ -1819,7 +1819,7 @@ def __insert_adb_docs( docs = df.to_dict("records") db = self.__async_db if use_async else self.__db - result = db.collection(col).insert_many(docs, **adb_import_kwargs) + result = db.collection(col).import_bulk(docs, **adb_import_kwargs) logger.debug(result) df.drop(df.index, inplace=True) diff --git a/tests/test_adapter.py b/tests/test_adapter.py index 11b2ff0..827b2ca 100644 --- a/tests/test_adapter.py +++ b/tests/test_adapter.py @@ -743,8 +743,6 @@ def test_adb_graph_to_pyg_to_arangodb_with_missing_document_and_permissive( def test_full_cycle_imdb_without_preserve_adb_keys() -> None: - pytest.skip("temporarily skipping") - name = "imdb" db.delete_graph(name, drop_collections=True, ignore_missing=True) arango_restore(con, "tests/data/adb/imdb_dump") @@ -824,7 +822,7 @@ def test_full_cycle_homogeneous_with_preserve_adb_keys() -> None: pyg_g["_v_key"].append(f"new-vertex-{num_nodes}") pyg_g.num_nodes = num_nodes + 1 - adbpyg_adapter.pyg_to_arangodb(name, pyg_g, overwrite_mode="update") + adbpyg_adapter.pyg_to_arangodb(name, pyg_g, on_duplicate="update") assert_pyg_to_adb(name, pyg_g, {}, False) assert db.collection("Homogeneous_N").get(f"new-vertex-{num_nodes}") is not None @@ -895,7 +893,7 @@ def test_full_cycle_imdb_with_preserve_adb_keys() -> None: pyg_g, pyg_to_adb_metagraph, explicit_metagraph=True, - overwrite_mode="update", + on_duplicate="update", ) assert_pyg_to_adb(name, pyg_g, pyg_to_adb_metagraph, True)