hnswpsql.py

__copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"

import copy
import inspect
import threading
import time
import traceback
from contextlib import nullcontext
from datetime import datetime, timezone
from threading import Thread
from typing import Optional, Tuple, Dict, Union

import numpy as np
from jina import Executor, requests, DocumentArray, Document
from jina.logging.logger import JinaLogger

from .hnswlib_searcher import HnswlibSearcher, DEFAULT_METRIC
from .postgres_indexer import PostgreSQLStorage


def _get_method_args():
    frame = inspect.currentframe().f_back
    keys, _, _, values = inspect.getargvalues(frame)
    kwargs = {}
    for key in keys:
        if key != 'self':
            kwargs[key] = values[key]
    return kwargs


class HNSWPostgresIndexer(Executor):
    """
    Production-ready, scalable Indexer for the Jina neural search framework.

    Combines the reliability of PostgreSQL with the speed and efficiency of the
    HNSWlib nearest neighbor library.
    """

    def __init__(
        self,
        total_shards: Optional[int] = None,
        startup_sync: bool = True,
        sync_interval: Optional[int] = None,
        limit: int = 10,
        metric: str = DEFAULT_METRIC,
        dim: int = 0,
        max_elements: int = 1_000_000,
        ef_construction: int = 400,
        ef_query: int = 50,
        max_connection: int = 64,
        is_distance: bool = True,
        num_threads: int = -1,
        traversal_paths: str = '@r',
        hostname: str = '127.0.0.1',
        port: int = 5432,
        username: str = 'postgres',
        password: str = '123456',
        database: str = 'postgres',
        table: str = 'default_table',
        return_embeddings: bool = True,
        dry_run: bool = False,
        partitions: int = 128,
        mute_unique_warnings: bool = False,
        **kwargs,
    ):
        """
        :param startup_sync: whether to sync from PSQL into HNSW on start-up
        :param total_shards: the total nr of shards that this shard is part of.
        :param sync_interval: the interval between automatic background PSQL-HNSW
        syncs
        (if None, sync will be turned off)
        :param limit: (HNSW) Number of results to get for each query document in
        search
        :param metric: (HNSW) Distance metric type. Can be 'euclidean',
        'inner_product', or 'cosine'
        :param dim: (HNSW) dimensionality of vectors to index
        :param max_elements: (HNSW) maximum number of elements (vectors) to index
        :param ef_construction: (HNSW) construction time/accuracy trade-off
        :param ef_query: (HNSW) query time accuracy/speed trade-off. High is more
        accurate but slower
        :param max_connection: (HNSW) The maximum number of outgoing connections in
        the graph (the "M" parameter)
        :param is_distance: (HNSW) if distance metric needs to be reinterpreted as
        similarity
        :param last_timestamp: (HNSW) the last time we synced into this HNSW index
        :param num_threads: (HNSW) nr of threads to use during indexing. -1 is default
        :param traversal_paths: (PSQL) default traversal paths on docs
        (used for indexing, delete and update), e.g. '@r', '@c', '@r,c'
        :param hostname: (PSQL) hostname of the machine
        :param port: (PSQL) the port
        :param username: (PSQL) the username to authenticate
        :param password: (PSQL) the password to authenticate
        :param database: (PSQL) the database
        :param table: (PSQL) the table to use
        :param return_embeddings: (PSQL) whether to return embeddings on
        search
        :param dry_run: (PSQL) If True, no database connection will be built
        :param partitions: (PSQL) the number of shards to distribute
         the data (used when syncing into HNSW)
        :param mute_unique_warnings: (PSQL) whether to mute warnings about unique
        ids constraint failing (useful when indexing with shards and polling = 'all')

        NOTE:

        - `total_shards` is REQUIRED in k8s, since there
        `runtime_args.parallel` is always 1
        - some arguments are passed to the inner classes. They are documented
        here for easier reference
        """
        super().__init__(**kwargs)
        self.logger = JinaLogger(getattr(self.metas, 'name', self.__class__.__name__))

        # TODO is there a way to improve this?
        # done because we want to have the args exposed in hub website
        # but we want to avoid having to manually pass every arg to the classes
        self._init_kwargs = _get_method_args()
        self._init_kwargs.update(kwargs)
        self.sync_interval = sync_interval
        self.lock = nullcontext()

        self._prepare_shards(total_shards)

        self._kv_indexer: Optional[PostgreSQLStorage] = None
        self._vec_indexer: Optional[HnswlibSearcher] = None

        (
            self._kv_indexer,
            self._vec_indexer,
        ) = self._init_executors(self._init_kwargs)
        if startup_sync:
            self._sync()

        if self.sync_interval:
            self.lock = threading.Lock()
            self.stop_sync_thread = False
            self._start_auto_sync()

    def _prepare_shards(self, total_shards):
        warning_issued = False
        if total_shards is None:
            self.total_shards = getattr(self.runtime_args, 'shards', None)
        else:
            self.total_shards = total_shards
        if self.total_shards is None:
            self.logger.warning(
                'total_shards was None. '
                'Setting it to 1 to allow non-sharded syncing. '
                'This can happen when running Executor outside a Flow or on k8s'
            )
            self.total_shards = 1
            warning_issued = True

        if not hasattr(self.runtime_args, 'shard_id'):
            self.runtime_args.shard_id = 0
            if not warning_issued:
                self.logger.warning(
                    'shard_id was None. '
                    'setting it to 1 to allow non-sharded syncing. '
                    'This can happen when running the Executor outside a Flow'
                )

        else:
            # shards is passed as str from Flow.add in yaml
            self.total_shards = int(self.total_shards)

    @requests(on='/sync')
    def sync(self, parameters: Dict, **kwargs):
        """
        Perform a sync between PSQL and HNSW

        :param parameters: dictionary with options for sync

            Keys accepted:

            - 'rebuild' (bool): whether to rebuild HNSW or do
            incremental syncing
            - 'timestamp' (str): ISO-formatted timestamp string. Time
            from which to get data for syncing into HNSW
            - 'batch_size' (int): The batch size for indexing in HNSW
        """
        self._sync(**parameters)

    def _sync(
        self,
        rebuild: bool = False,
        timestamp: str = None,
        batch_size: int = 100,
        **kwargs,
    ):
        timestamp: Optional[datetime] = self._compute_timestamp_for_sync(
            timestamp, rebuild
        )
        if timestamp is None:
            return

        iterator = self._kv_indexer._get_delta(
            shard_id=self.runtime_args.shard_id,
            total_shards=self.total_shards,
            timestamp=timestamp,
        )

        # we prevent race conditions with search
        with self.lock:
            if rebuild or self._vec_indexer.size == 0:
                # call with just indexing
                self._vec_indexer = HnswlibSearcher(**self._init_kwargs)
                self._vec_indexer.index_sync(iterator, batch_size)
                self.logger.info(
                    f'Rebuilt HNSW index with {self._vec_indexer.size} docs'
                )

            else:
                prev_size = self._vec_indexer.size
                self._vec_indexer.sync(iterator)
                if prev_size != self._vec_indexer.size:
                    self.logger.info(
                        f'Synced HNSW index from {prev_size} docs to '
                        f'{self._vec_indexer.size}'
                    )
                else:
                    self.logger.info(
                        f'Performed empty sync. HNSW index size is still {prev_size}'
                    )

    def _init_executors(
        self, _init_kwargs
    ) -> Tuple[PostgreSQLStorage, HnswlibSearcher]:
        kv_indexer = PostgreSQLStorage(dump_dtype=np.float32, **_init_kwargs)
        vec_indexer = HnswlibSearcher(**_init_kwargs)
        return kv_indexer, vec_indexer

    @requests(on='/index')
    def index(self, docs: DocumentArray, parameters: Dict, **kwargs):
        """Index new documents

        NOTE: PSQL has a uniqueness constraint on ID

        :param docs: the Documents to index
        :param parameters: dictionary with options for indexing

        Keys accepted:

            - 'traversal_paths' (str): traversal path for the docs
        """
        self._kv_indexer.add(docs, parameters, **kwargs)

    @requests(on='/update')
    def update(self, docs: DocumentArray, parameters: Dict, **kwargs):
        """Update existing documents

        :param docs: the Documents to update
        :param parameters: dictionary with options for updating

        Keys accepted:

            - 'traversal_paths' (str): traversal path for the docs
        """
        self._kv_indexer.update(docs, parameters, **kwargs)

    @requests(on='/delete')
    def delete(self, docs: DocumentArray, parameters: Dict, **kwargs):
        """Delete existing documents, by id

        :param docs: the Documents to delete
        :param parameters: dictionary with options for deleting

        Keys accepted:

            - 'traversal_paths' (str): traversal path for the docs
            - 'soft_delete' (bool, default `True`): whether to perform soft delete
            (doc is marked as empty but still exists in db, for retrieval purposes)
        """
        if 'soft_delete' not in parameters:
            parameters['soft_delete'] = True

        self._kv_indexer.delete(docs, parameters, **kwargs)

    @requests(on='/clear')
    def clear(self, **kwargs):
        """
        Delete all data from PSQL and HNSW

        """
        if self._kv_indexer.initialized:
            self._kv_indexer.clear()
        self._vec_indexer = HnswlibSearcher(**self._init_kwargs)
        self._vec_indexer.clear()
        assert self._kv_indexer.size == 0
        assert self._vec_indexer.size == 0

    @requests(on='/status')
    def status(self, **kwargs):
        """
        Get information on status of this Indexer inside a Document's tags

        :return: DocumentArray with one Document with tags 'psql_docs', 'hnsw_docs',
        'last_sync', 'shard_id'
        """
        psql_docs = None
        hnsw_docs = None
        last_sync = None

        if self._kv_indexer and self._kv_indexer.initialized:
            psql_docs = self._kv_indexer.size
        else:
            self.logger.warning(f'PSQL connection has not been initialized')

        if self._vec_indexer:
            hnsw_docs = self._vec_indexer.size
            last_sync = self._vec_indexer.last_timestamp
            last_sync = last_sync.isoformat()
        else:
            self.logger.warning(f'HNSW index has not been initialized')

        status = {
            'psql_docs': psql_docs,
            'hnsw_docs': hnsw_docs,
            'last_sync': last_sync,
            'shard_id': self.runtime_args.shard_id,
        }
        return DocumentArray([Document(tags=status)])

    @requests(on='/search')
    def search(self, docs: 'DocumentArray', parameters: Dict = None, **kwargs):
        """Search the vec embeddings in HNSW and then lookup the metadata in PSQL

        The `HNSWSearcher` attaches matches to the `Documents` sent as
            inputs with the id of the match, and its embedding.
            Then, the `PostgreSQLStorage` retrieves the full metadata
            (original text or image blob) and attaches
            those to the Document. You receive back the full Documents as matches
            to your search Documents.

        :param docs: `Document` with `.embedding` the same shape as the
            `Documents` stored in the `HNSW` index. The ids of the `Documents`
            stored in `HNSW` need to exist in the PSQL.
            Otherwise you will not get back the original metadata.
        :param parameters: dictionary for parameters for the search operation


            - 'traversal_paths' (str): traversal paths for the docs
            - 'limit' (int): nr of matches to get per Document
            - 'ef_query' (int): query time accuracy/speed trade-off. High is more
            accurate but slower
        """
        if self._kv_indexer and self._vec_indexer:
            # we prevent race conditions with sync
            with self.lock:
                self._vec_indexer.search(docs, parameters)

            kv_parameters = copy.deepcopy(parameters)
            kv_parameters['traversal_paths'] = ','.join(
                [
                    path + 'm'
                    for path in kv_parameters.get('traversal_paths', '@r').split(',')
                ]
            )
            self._kv_indexer.search(docs, kv_parameters)
        else:
            self.logger.warning('Indexers have not been initialized. Empty results')
            return

    @requests(on='/cleanup')
    def cleanup(self, **kwargs):
        """
        Completely remove the entries in PSQL that have been
        soft-deleted (via the /delete endpoint)
        """
        if self._kv_indexer:
            self._kv_indexer.cleanup()
        else:
            self.logger.warning(f'PSQL has not been initialized')

    def _start_auto_sync(self):
        self.sync_thread = Thread(target=self._sync_loop, daemon=False)
        self.sync_thread.start()

    def close(self) -> None:
        if hasattr(self, 'sync_thread'):
            # wait for sync thread to finish
            self.stop_sync_thread = True
            try:
                self.sync_thread.join()
            except Exception as e:
                self.logger.warning(f'Error when stopping sync thread: {e}')

    def _sync_loop(self):
        try:
            self.logger.warning(f'started sync thread')
            while True:
                self._sync(rebuild=False, timestamp=None, batch_size=100)
                self.logger.info(f'sync thread: Completed sync')
                time.sleep(self.sync_interval)
                if self.stop_sync_thread:
                    self.logger.info(f'Exiting sync thread')
                    return
        except Exception as e:
            self.logger.error(f'Sync thread failed: {e}')
            self.logger.error(traceback.format_exc())

    def _compute_timestamp_for_sync(
        self, timestamp: Union[datetime, str], rebuild: bool
    ) -> Optional[datetime]:
        if timestamp is None:
            if rebuild:
                # we assume all db timestamps are UTC +00
                timestamp = datetime.fromtimestamp(0, timezone.utc)
            elif self._vec_indexer.last_timestamp:
                timestamp = self._vec_indexer.last_timestamp
            else:
                self.logger.error(
                    f'No timestamp provided in parameters: '
                    f'and vec_indexer.last_timestamp'
                    f'was None. Cannot do sync'
                )
                return None
        else:
            timestamp = datetime.fromisoformat(timestamp)

        return timestamp