From bca48a3b3abb3435fa5753b290a4dac0e2d287f4 Mon Sep 17 00:00:00 2001 From: Beinan Wang Date: Thu, 4 Jun 2026 21:54:32 -0700 Subject: [PATCH 1/2] feat: implement distributed segmented BTREE index building using Lance segment API This change introduces parallel/distributed segmented BTREE index building in daft-lance. By building full independent index segments per worker and committing them atomically via the dataset's API, we resolve the issue where describe_indices failed on distributed indexes due to empty index_details metadata. Co-Authored-By: Beinan Wang --- daft_lance/_lance.py | 12 + daft_lance/lance_scalar_index.py | 190 +- pyproject.toml | 2 +- tests/io/lancedb/test_lancedb_scalar_index.py | 246 +++ uv.lock | 1660 ++++++++--------- 5 files changed, 1275 insertions(+), 835 deletions(-) diff --git a/daft_lance/_lance.py b/daft_lance/_lance.py index 95a67bf..21b7c67 100644 --- a/daft_lance/_lance.py +++ b/daft_lance/_lance.py @@ -374,6 +374,7 @@ def create_scalar_index( fragment_group_size: int | None = None, num_partitions: int | None = None, max_concurrency: int | None = None, + segmented: bool = False, **kwargs: Any, ) -> None: """Build a distributed scalar index using Daft's distributed execution. @@ -406,6 +407,11 @@ def create_scalar_index( greater than 1 enable additional parallelism on distributed runners; values <= 1 or None will use the default partitioning. max_concurrency: Maximum number of concurrent tasks to use for processing fragment batches. If None, Daft will use its default concurrency setting. Must be a positive integer. + segmented: If True and ``index_type`` is ``"BTREE"``, use the segmented index + workflow where each worker builds a fully independent index segment and the + coordinator commits them atomically via ``commit_existing_index_segments``. + This produces proper ``index_details`` metadata so ``describe_indices()`` + works correctly. Defaults to False (legacy partitioned-and-merged flow). **kwargs: Additional keyword arguments forwarded to ``lance.LanceDataset.create_scalar_index``. Returns: @@ -436,6 +442,11 @@ def create_scalar_index( ... "s3://my-bucket/dataset/", column="price", index_type="BTREE", name="price_idx" ... ) + Create a segmented BTREE index (supports describe_indices): + >>> daft_lance.create_scalar_index( + ... "s3://my-bucket/dataset/", column="price", index_type="BTREE", segmented=True + ... ) + Create an index with custom fragment grouping and partitioning: >>> daft_lance.create_scalar_index( ... "s3://my-bucket/dataset/", column="description", fragment_group_size=8, num_partitions=16 @@ -470,6 +481,7 @@ def create_scalar_index( fragment_group_size=fragment_group_size, num_partitions=num_partitions, max_concurrency=max_concurrency, + segmented=segmented, **kwargs, ) diff --git a/daft_lance/lance_scalar_index.py b/daft_lance/lance_scalar_index.py index 4146f3a..36329dd 100644 --- a/daft_lance/lance_scalar_index.py +++ b/daft_lance/lance_scalar_index.py @@ -1,6 +1,7 @@ from __future__ import annotations import logging +import pickle import uuid from typing import TYPE_CHECKING, Any, cast @@ -58,6 +59,58 @@ def __call__(self, fragment_ids: list[int]) -> bool: return True +class SegmentedFragmentIndexHandler: + """Handler for segmented scalar index creation on fragment batches. + + Unlike ``FragmentIndexHandler``, which writes partial index files sharing + a single UUID, this handler builds a fully independent index segment per + worker via the low-level ``_ds.create_index`` binding. The returned + ``lance.Index`` metadata (including ``index_details``) is serialised + (pickled) so it can cross Daft process/serialisation boundaries. The + coordinator then commits all segments atomically with + ``commit_existing_index_segments``. + """ + + def __init__( + self, + lance_ds: lance.LanceDataset, + column: str, + index_type: str, + name: str, + replace: bool, + **kwargs: Any, + ) -> None: + self.lance_ds = lance_ds + self.column = column + self.index_type = index_type + self.name = name + self.replace = replace + self.kwargs = kwargs + + def __call__(self, fragment_ids: list[int]) -> bytes: + """Build an independent index segment and return its pickled metadata.""" + logger.info( + "Building segmented index segment for fragments %s (column=%s, type=%s)", + fragment_ids, + self.column, + self.index_type, + ) + + # _ds.create_index returns a lance.Index dataclass when fragment_ids + # is provided (uncommitted segment mode). + index_meta: lance.Index = self.lance_ds._ds.create_index( # type: ignore[call-arg, arg-type] + [self.column], + self.index_type, + name=self.name, + replace=self.replace, + train=True, + storage_options=None, + kwargs={"fragment_ids": fragment_ids, **self.kwargs}, + ) + + return pickle.dumps(index_meta) + + def create_scalar_index_internal( lance_ds: lance.LanceDataset, uri: str | pathlib.Path, @@ -70,6 +123,7 @@ def create_scalar_index_internal( fragment_group_size: int | None = None, num_partitions: int | None = None, max_concurrency: int | None = None, + segmented: bool = False, **kwargs: Any, ) -> None: """Internal implementation of distributed scalar index creation. @@ -78,6 +132,13 @@ def create_scalar_index_internal( merge_index_metadata, then commit). ``FTS`` is normalized to ``INVERTED`` (same Lance index); see Lance Rust/Python bindings: ``INVERTED`` and ``FTS`` map to the same inverted full-text index type. + + When ``segmented=True`` and ``index_type`` is ``BTREE``, a cleaner segmented workflow + is used instead: each worker builds a fully independent index segment via the low-level + ``_ds.create_index`` binding (which returns ``lance.Index`` metadata including + ``index_details``), and the coordinator commits them atomically with + ``commit_existing_index_segments``. This resolves a known issue where ``index_details`` + was left empty in the legacy path, preventing ``describe_indices()`` from working. """ if not column: raise ValueError("Column name cannot be empty") @@ -166,17 +227,138 @@ def create_scalar_index_internal( logger.info("No fragments found for dataset at %s; skipping scalar index creation.", uri) return - # Generate unique index ID - index_id = str(uuid.uuid4()) - logger.info( - "Starting distributed scalar index creation: column=%s, type=%s, name=%s, fragment_group_size=%s, max_concurrency=%s", + "Starting distributed scalar index creation: column=%s, type=%s, name=%s, fragment_group_size=%s, max_concurrency=%s, segmented=%s", column, index_type, name, fragment_group_size, max_concurrency, + segmented, + ) + + # Choose between the segmented workflow and the legacy partitioned-and-merged + # workflow. Segmented mode produces proper ``index_details`` so + # ``describe_indices()`` works correctly. + if segmented and index_type == "BTREE": + _create_segmented_index( + lance_ds=lance_ds, + uri=uri, + column=column, + index_type=index_type, + name=name, + replace=replace, + storage_options=storage_options, + fragment_data=fragment_data, + fragment_ids_to_use=fragment_ids_to_use, + num_partitions=num_partitions, + max_concurrency=max_concurrency, + **kwargs, + ) + else: + _create_partitioned_index( + lance_ds=lance_ds, + uri=uri, + column=column, + index_type=index_type, + name=name, + replace=replace, + storage_options=storage_options, + fragment_data=fragment_data, + fragment_ids_to_use=fragment_ids_to_use, + num_partitions=num_partitions, + max_concurrency=max_concurrency, + **kwargs, + ) + + +def _create_segmented_index( + lance_ds: lance.LanceDataset, + uri: str | pathlib.Path, + *, + column: str, + index_type: str, + name: str, + replace: bool, + storage_options: dict[str, Any] | None, + fragment_data: list[dict[str, list[int]]], + fragment_ids_to_use: list[int], + num_partitions: int | None, + max_concurrency: int | None, + **kwargs: Any, +) -> None: + """Segmented index workflow: each worker builds an independent segment. + + Workers call the low-level ``_ds.create_index`` binding (which returns + ``lance.Index`` metadata with ``index_details`` populated), pickle the + result so it can traverse Daft serialisation boundaries, and return it. + The coordinator unpickles all segments and commits them atomically via + ``commit_existing_index_segments``. + """ + handler_cls = daft.cls( + SegmentedFragmentIndexHandler, + max_concurrency=max_concurrency, ) + handler = handler_cls( + lance_ds=lance_ds, + column=column, + index_type=index_type, + name=name, + replace=replace, + **kwargs, + ) + + with execution_config_ctx(maintain_order=False): + if num_partitions is not None and num_partitions > 1: + df = from_pylist(fragment_data).repartition(num_partitions) + else: + df = from_pylist(fragment_data) + + df = df.select(handler(df["fragment_ids"]).alias("index_meta")) + collected = df.collect() + + # Deserialise the Index metadata returned by each worker. + index_metas: list[lance.Index | lance.indices.IndexSegment] = [ + pickle.loads(raw) for raw in collected.to_pydict()["index_meta"] + ] + + logger.info( + "Collected %d index segments; committing as segmented index %s", + len(index_metas), + name, + ) + + # Reload dataset to pick up the latest version (segment files were written + # by workers against the version that was current at their invocation time). + lance_ds = lance.LanceDataset(uri, storage_options=storage_options) + lance_ds.commit_existing_index_segments(name, column, index_metas) + + logger.info("Segmented index %s committed successfully", name) + + +def _create_partitioned_index( + lance_ds: lance.LanceDataset, + uri: str | pathlib.Path, + *, + column: str, + index_type: str, + name: str, + replace: bool, + storage_options: dict[str, Any] | None, + fragment_data: list[dict[str, list[int]]], + fragment_ids_to_use: list[int], + num_partitions: int | None, + max_concurrency: int | None, + **kwargs: Any, +) -> None: + """Legacy partitioned-and-merged index workflow. + + Workers build partial index files sharing the same UUID, then the + coordinator merges them with ``merge_index_metadata`` and commits via a + manual ``CreateIndex`` transaction. + """ + # Generate unique index ID (shared across all partitions) + index_id = str(uuid.uuid4()) handler_cls = daft.cls( FragmentIndexHandler, diff --git a/pyproject.toml b/pyproject.toml index c2dcdc4..35779b3 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -8,7 +8,7 @@ readme = "README.md" dependencies = [ "lance-namespace>=0.6.0", "lance-namespace-urllib3-client>=0.6.0", - "pylance>=6.0.0" + "pylance>=7.0.0" ] [dependency-groups] diff --git a/tests/io/lancedb/test_lancedb_scalar_index.py b/tests/io/lancedb/test_lancedb_scalar_index.py index 4de1836..ffa011f 100644 --- a/tests/io/lancedb/test_lancedb_scalar_index.py +++ b/tests/io/lancedb/test_lancedb_scalar_index.py @@ -596,3 +596,249 @@ def test_build_distributed_index_zonemap_invalid_string_column(self, temp_dir): column="text", index_type="ZONEMAP", ) + + +class TestSegmentedBTreeIndex: + """Test cases for segmented BTree index functionality.""" + + def test_segmented_btree_basic(self, temp_dir): + """Test basic segmented BTree index creation and query.""" + data = { + "id": [1, 2, 3, 4, 5, 6, 7, 8], + "price": [10.5, 20.75, 30.0, 40.25, 50.5, 60.75, 70.0, 80.25], + "name": ["item1", "item2", "item3", "item4", "item5", "item6", "item7", "item8"], + } + dataset = daft.from_pydict(data) + path = Path(temp_dir) / "segmented_btree_basic.lance" + dataset.write_lance(uri=path, max_rows_per_file=2) + + create_scalar_index( + uri=path, + column="price", + index_type="BTREE", + name="price_seg_idx", + segmented=True, + max_concurrency=2, + ) + + updated_dataset = lance.dataset(path) + + # describe_indices must work (the whole point of the segmented flow) + described = updated_dataset.describe_indices() + assert len(described) == 1 + assert described[0].name == "price_seg_idx" + assert "BTree" in described[0].type_url + + # Query must work + results = updated_dataset.scanner( + filter="price > 50.0", + columns=["id", "price"], + ).to_table() + assert results.num_rows == 4 + prices = sorted(results.column("price").to_pylist()) + assert prices == [50.5, 60.75, 70.0, 80.25] + + def test_segmented_btree_multiple_segments(self, temp_dir): + """Test segmented BTree with small fragment_group_size to force multiple segments.""" + data = { + "id": list(range(16)), + "score": [i * 10.0 for i in range(16)], + } + dataset = daft.from_pydict(data) + path = Path(temp_dir) / "segmented_btree_multi.lance" + dataset.write_lance(uri=path, max_rows_per_file=2) + + create_scalar_index( + uri=path, + column="score", + index_type="BTREE", + name="score_seg_idx", + segmented=True, + fragment_group_size=2, + max_concurrency=2, + ) + + updated_dataset = lance.dataset(path) + described = updated_dataset.describe_indices() + assert len(described) == 1 + assert len(described[0].segments) >= 2, "Expected multiple segments with fragment_group_size=2" + + # Query must work across segment boundaries + results = updated_dataset.scanner( + filter="score >= 100.0", + columns=["id", "score"], + ).to_table() + assert results.num_rows == 6 # ids 10..15 + + def test_segmented_btree_describe_indices_works(self, temp_dir): + """Test that describe_indices returns valid details for segmented index. + + This is the core regression that the segmented flow resolves: the legacy + partitioned-and-merged flow produces indices with empty index_details, + causing describe_indices() to fail. + """ + data = { + "id": [1, 2, 3, 4], + "value": [100, 200, 300, 400], + } + dataset = daft.from_pydict(data) + path = Path(temp_dir) / "segmented_describe.lance" + dataset.write_lance(uri=path, max_rows_per_file=2) + + create_scalar_index( + uri=path, + column="value", + index_type="BTREE", + name="value_idx", + segmented=True, + ) + + updated_dataset = lance.dataset(path) + + # This must not raise — it would with the legacy flow. + described = updated_dataset.describe_indices() + assert len(described) == 1 + desc = described[0] + assert desc.name == "value_idx" + assert desc.type_url == "/lance.table.BTreeIndexDetails" + assert desc.num_rows_indexed == 4 + + def test_segmented_btree_replace_existing(self, temp_dir): + """Test replacing an existing segmented BTree index.""" + data = { + "id": [1, 2, 3, 4, 5, 6, 7, 8], + "price": [10.5, 20.75, 30.0, 40.25, 50.5, 60.75, 70.0, 80.25], + } + dataset = daft.from_pydict(data) + path = Path(temp_dir) / "segmented_btree_replace.lance" + dataset.write_lance(uri=path, max_rows_per_file=2) + + # Create initial index + create_scalar_index( + uri=path, + column="price", + index_type="BTREE", + name="price_idx", + segmented=True, + ) + + ds1 = lance.dataset(path) + assert len(ds1.describe_indices()) == 1 + + # Replace it + create_scalar_index( + uri=path, + column="price", + index_type="BTREE", + name="price_idx", + segmented=True, + replace=True, + ) + + ds2 = lance.dataset(path) + described = ds2.describe_indices() + assert len(described) == 1 + assert described[0].name == "price_idx" + + # Query still works after replacement + results = ds2.scanner(filter="price > 50.0", columns=["id", "price"]).to_table() + assert results.num_rows == 4 + + def test_segmented_btree_string_column(self, temp_dir): + """Test segmented BTree index on a string column.""" + import pyarrow as pa + + # Explicitly use pa.string() (not large_string) to satisfy the BTREE type check. + table = pa.table( + { + "id": pa.array([1, 2, 3, 4], type=pa.int64()), + "category": pa.array(["alpha", "beta", "gamma", "delta"], type=pa.string()), + } + ) + path = Path(temp_dir) / "segmented_btree_string.lance" + lance.write_dataset(table, str(path), max_rows_per_file=2) + + create_scalar_index( + uri=path, + column="category", + index_type="BTREE", + name="cat_idx", + segmented=True, + ) + + updated_dataset = lance.dataset(path) + described = updated_dataset.describe_indices() + assert len(described) == 1 + assert described[0].name == "cat_idx" + assert "BTree" in described[0].type_url + + def test_segmented_btree_integer_column(self, temp_dir): + """Test segmented BTree index on an integer column.""" + data = { + "id": [1, 2, 3, 4, 5, 6, 7, 8], + "count": [100, 200, 300, 400, 500, 600, 700, 800], + } + dataset = daft.from_pydict(data) + path = Path(temp_dir) / "segmented_btree_int.lance" + dataset.write_lance(uri=path, max_rows_per_file=2) + + create_scalar_index( + uri=path, + column="count", + index_type="BTREE", + name="count_idx", + segmented=True, + ) + + updated_dataset = lance.dataset(path) + described = updated_dataset.describe_indices() + assert len(described) == 1 + assert described[0].name == "count_idx" + + results = updated_dataset.scanner(filter="count > 500", columns=["id", "count"]).to_table() + assert results.num_rows == 3 # 600, 700, 800 + + def test_segmented_false_uses_legacy_flow(self, temp_dir): + """Test that segmented=False still uses the legacy partitioned-and-merged flow.""" + data = { + "id": [1, 2, 3, 4, 5, 6, 7, 8], + "price": [10.5, 20.75, 30.0, 40.25, 50.5, 60.75, 70.0, 80.25], + } + dataset = daft.from_pydict(data) + path = Path(temp_dir) / "segmented_false.lance" + dataset.write_lance(uri=path, max_rows_per_file=2) + + # segmented=False (default) should use the legacy flow + create_scalar_index( + uri=path, + column="price", + index_type="BTREE", + name="price_legacy_idx", + segmented=False, + ) + + updated_dataset = lance.dataset(path) + indices = updated_dataset.list_indices() + index_names = [idx["name"] for idx in indices] + assert "price_legacy_idx" in index_names + + def test_segmented_inverted_falls_back_to_legacy(self, multi_fragment_lance_dataset): + """Test that segmented=True with INVERTED still uses the legacy flow. + + The segmented workflow currently only supports BTREE. + """ + dataset_uri = multi_fragment_lance_dataset + + # segmented=True but INVERTED => legacy flow is used + create_scalar_index( + uri=dataset_uri, + column="text", + index_type="INVERTED", + name="text_inv_idx", + segmented=True, + ) + + updated_dataset = lance.dataset(dataset_uri) + indices = updated_dataset.list_indices() + index_names = [idx["name"] for idx in indices] + assert "text_inv_idx" in index_names diff --git a/uv.lock b/uv.lock index 8544765..31121cf 100644 --- a/uv.lock +++ b/uv.lock @@ -2,1146 +2,1146 @@ version = 1 revision = 3 requires-python = ">=3.10" resolution-markers = [ - "python_full_version >= '3.14' and sys_platform == 'win32'", - "python_full_version >= '3.14' and sys_platform == 'emscripten'", - "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version >= '3.11' and 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c9a7f51481384dda51a8393f4970d2be2822715b Mon Sep 17 00:00:00 2001 From: Beinan Wang Date: Thu, 4 Jun 2026 23:14:47 -0700 Subject: [PATCH 2/2] style: fix mypy unused-ignore warning in lance_scalar_index.py Refactors the `type: ignore` comment in `daft_lance/lance_scalar_index.py` at line 101 to omit `arg-type` as keyword arguments are now used and the warning is no longer emitted. Co-Authored-By: Beinan Wang --- daft_lance/lance_scalar_index.py | 2 +- uv.lock | 1654 +++++++++++++++--------------- 2 files changed, 828 insertions(+), 828 deletions(-) diff --git a/daft_lance/lance_scalar_index.py b/daft_lance/lance_scalar_index.py index 36329dd..7f63115 100644 --- a/daft_lance/lance_scalar_index.py +++ b/daft_lance/lance_scalar_index.py @@ -98,7 +98,7 @@ def __call__(self, fragment_ids: list[int]) -> bytes: # _ds.create_index returns a lance.Index dataclass when fragment_ids # is provided (uncommitted segment mode). - index_meta: lance.Index = self.lance_ds._ds.create_index( # type: ignore[call-arg, arg-type] + index_meta: lance.Index = self.lance_ds._ds.create_index( # type: ignore[call-arg] [self.column], self.index_type, name=self.name, diff --git a/uv.lock b/uv.lock index 31121cf..c7b50fb 100644 --- a/uv.lock +++ b/uv.lock @@ -2,1146 +2,1146 @@ version = 1 revision = 3 requires-python = ">=3.10" resolution-markers = [ - "python_full_version >= '3.14' and 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