paimon-python/pypaimon/execution/python/read/search/vector_search_scan.py

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# to you under the Apache License, Version 2.0 (the
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# with the License.  You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
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# software distributed under the License is distributed on an
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# KIND, either express or implied.  See the License for the
# specific language governing permissions and limitations
# under the License.


from pypaimon.table.source.search_types import (VectorSearchScanPlan, VectorSearchScan)
"""Vector search scan to scan index files."""

from collections import defaultdict

from pypaimon.common.options.core_options import CoreOptions, GlobalIndexSearchMode
from pypaimon.common.options.options import Options
from pypaimon.common.options.vector_index_options import VectorIndexOptions
from pypaimon.execution.python.globalindex.data_evolution_global_index_coverage import DataEvolutionGlobalIndexCoverage
from pypaimon.execution.python.globalindex.data_evolution_global_index_scanner import (
    is_supported_scalar_index,
)
from pypaimon.table.source.vector_search_split import (
    IndexVectorSearchSplit,
    RawVectorSearchSplit,
)
from pypaimon.table.source.vector_search_split import VectorSearchSplit  # noqa F401
from pypaimon.utils.range import Range


class DataEvolutionVectorScan(VectorSearchScan):
    """Implementation for VectorSearchScan."""

    def __init__(
        self,
        table,
        vector_column,
        filter_=None,
        partition_filter=None,
        options=None,
    ):
        self._table = table
        self._vector_column = vector_column
        self._filter = filter_
        self._partition_filter = partition_filter
        self._options = dict(options or {})

    def scan(self):
        # type: () -> VectorSearchScanPlan
        from pypaimon.index.index_file_handler import IndexFileHandler
        from pypaimon.read.push_down_utils import _get_all_fields
        from pypaimon.snapshot.time_travel_util import TimeTravelUtil

        vector_column = self._vector_column

        # Field IDs referenced by the scalar filter — used to pull in scalar
        # index files for pre-filtering.
        filter_field_ids = set()
        if self._filter is not None:
            referenced = _get_all_fields(self._filter)
            field_by_name = {f.name: f for f in self._table.fields}
            for name in referenced:
                field = field_by_name.get(name)
                if field is not None:
                    filter_field_ids.add(field.id)

        snapshot = TimeTravelUtil.resolve_snapshot(self._table)
        if snapshot is None:
            return VectorSearchScanPlan([])

        index_file_handler = IndexFileHandler(table=self._table)

        partition_filter = self._partition_filter

        def contains_field(global_index_meta, field_id):
            if global_index_meta.index_field_id == field_id:
                return True
            if global_index_meta.extra_field_ids is not None:
                return field_id in global_index_meta.extra_field_ids
            return False

        def index_file_filter(entry):
            if partition_filter is not None:
                if not partition_filter.test(entry.partition):
                    return False
            global_index_meta = entry.index_file.global_index_meta
            if global_index_meta is None:
                return False
            field_id = global_index_meta.index_field_id
            if vector_column.id == field_id:
                return True
            if not is_supported_scalar_index(entry.index_file):
                return False
            for filter_field_id in filter_field_ids:
                if contains_field(global_index_meta, filter_field_id):
                    return True
            return False

        entries = index_file_handler.scan(snapshot, index_file_filter)
        all_index_files = [entry.index_file for entry in entries]

        # Group vector index files by (rowRangeStart, rowRangeEnd).
        vector_by_range = defaultdict(list)
        vector_index_files = []
        for index_file in all_index_files:
            meta = index_file.global_index_meta
            assert meta is not None
            if meta.index_field_id != vector_column.id:
                continue
            range_key = Range(meta.row_range_start, meta.row_range_end)
            vector_by_range[range_key].append(index_file)
            vector_index_files.append(index_file)

        vector_index_type = _vector_index_type(vector_column, all_index_files)
        if vector_index_type is None:
            vector_index_type = _configured_vector_index_type(
                self._table, vector_column, self._options)

        # For each vector range, attach matching scalar index files whose
        # row range intersects the vector range.
        splits = []
        for range_key, vector_files in vector_by_range.items():
            scalar_files = []
            for index_file in all_index_files:
                meta = index_file.global_index_meta
                assert meta is not None
                if (meta.index_field_id == vector_column.id
                        or not is_supported_scalar_index(index_file)):
                    continue
                scalar_range = Range(meta.row_range_start, meta.row_range_end)
                if range_key.overlaps(scalar_range):
                    scalar_files.append(index_file)
            splits.append(
                IndexVectorSearchSplit(
                    range_key.from_,
                    range_key.to,
                    vector_files,
                    scalar_files,
                )
            )

        # Java's DataEvolutionVectorScan overlays query options before resolving
        # the family search modes, without changing the table's configuration.
        effective_options = dict(self._table.options.options.to_map())
        effective_options.update(self._options)
        effective_core = CoreOptions(Options(effective_options))
        vector_search_mode = effective_core.vector_index_search_mode()
        raw_row_ranges = DataEvolutionGlobalIndexCoverage(
            self._table,
            snapshot,
            partition_filter,
            vector_index_files,
        ).unindexed_ranges(
            vector_column.id,
            search_mode=vector_search_mode,
        )
        scalar_index_files = [
            f for f in all_index_files
            if f.global_index_meta is not None
            and f.global_index_meta.index_field_id != vector_column.id
            and is_supported_scalar_index(f)
        ]
        if self._filter is not None:
            scalar_unindexed_ranges = DataEvolutionGlobalIndexCoverage(
                self._table,
                snapshot,
                partition_filter,
                scalar_index_files,
            ).unindexed_ranges(
                self._table.fields,
                self._filter,
                search_mode=effective_core.scalar_index_search_mode(),
            )
            if vector_search_mode == GlobalIndexSearchMode.FAST:
                scalar_unindexed_ranges = Range.and_(
                    scalar_unindexed_ranges,
                    Range.sort_and_merge_overlap(
                        list(vector_by_range.keys()), True),
                )
            raw_row_ranges = Range.sort_and_merge_overlap(
                raw_row_ranges + scalar_unindexed_ranges,
                True,
            )
        if raw_row_ranges:
            splits.append(
                RawVectorSearchSplit(
                    raw_row_ranges,
                    _scalar_index_files_for_ranges(
                        all_index_files,
                        raw_row_ranges,
                        vector_column.id,
                    ),
                    vector_index_type,
                )
            )

        return VectorSearchScanPlan(splits, snapshot)


def _has_intersection(ranges, row_range):
    for r in ranges:
        if r.overlaps(row_range):
            return True
    return False


def _scalar_index_files_for_ranges(all_index_files, row_ranges, vector_field_id):
    scalar_files = []
    for index_file in all_index_files:
        meta = index_file.global_index_meta
        if (meta is None
                or meta.index_field_id == vector_field_id
                or not is_supported_scalar_index(index_file)):
            continue
        if _has_intersection(row_ranges, Range(meta.row_range_start, meta.row_range_end)):
            scalar_files.append(index_file)
    return scalar_files


def _vector_index_type(vector_column, index_files):
    index_type = None
    for index_file in index_files:
        meta = index_file.global_index_meta
        if meta is None or meta.index_field_id != vector_column.id:
            continue
        if index_type is None:
            index_type = index_file.index_type
        elif index_type != index_file.index_type:
            raise ValueError(
                "Vector column '%s' has multiple index types: %s and %s."
                % (vector_column.name, index_type, index_file.index_type)
            )
    return index_type


def _configured_vector_index_type(table, vector_column, options):
    table_options = getattr(getattr(table, "options", None), "options", None)
    values = dict(table_options.to_map()) if table_options is not None else {}
    values.update(options)
    return VectorIndexOptions.resolve(Options(values), None, vector_column.name, default_metric=False).index_type