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# to you under the Apache License, Version 2.0 (the
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#
# http://www.apache.org/licenses/LICENSE-2.0
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"""
StreamReadBuilder for building streaming table scans and reads.
This module provides a builder for configuring streaming reads from Paimon
tables, similar to ReadBuilder but for continuous streaming use cases.
"""
from typing import Callable, List, Optional, Set
from pypaimon.common.predicate import Predicate
from pypaimon.common.predicate_builder import PredicateBuilder
from pypaimon.read.streaming_table_scan import AsyncStreamingTableScan
from pypaimon.read.table_read import TableRead
from pypaimon.schema.data_types import DataField
from pypaimon.table.special_fields import SpecialFields
class StreamReadBuilder:
"""
Builder for streaming reads from Paimon tables.
Usage:
stream_builder = table.new_stream_read_builder()
stream_builder.with_poll_interval_ms(500)
scan = stream_builder.new_streaming_scan()
table_read = stream_builder.new_read()
async for plan in scan.stream():
arrow_table = table_read.to_arrow(plan.splits())
process(arrow_table)
"""
def __init__(self, table):
"""Initialize the StreamReadBuilder."""
from pypaimon.table.file_store_table import FileStoreTable
self.table: FileStoreTable = table
self._predicate: Optional[Predicate] = None
self._projection: Optional[List[str]] = None
self._poll_interval_ms: int = 1000
self._include_row_kind: bool = False
self._bucket_filter: Optional[Callable[[int], bool]] = None
self._consumer_id: Optional[str] = None
def with_filter(self, predicate: Predicate) -> 'StreamReadBuilder':
"""Set a filter predicate for the streaming read."""
self._predicate = predicate
return self
def with_projection(self, projection: List[str]) -> 'StreamReadBuilder':
"""Set column projection for the streaming read."""
self._projection = projection
return self
def with_poll_interval_ms(self, poll_interval_ms: int) -> 'StreamReadBuilder':
"""Set the poll interval in ms for checking new snapshots (default: 1000)."""
self._poll_interval_ms = poll_interval_ms
return self
def with_include_row_kind(self, include: bool = True) -> 'StreamReadBuilder':
"""Include row kind column (_row_kind) in the output.
When enabled, the output will include a _row_kind column as the first
column with values: +I (insert), -U (update before), +U (update after),
-D (delete).
"""
self._include_row_kind = include
return self
def with_consumer_id(self, consumer_id: str) -> 'StreamReadBuilder':
"""Set a consumer ID for persisting streaming read progress."""
self._consumer_id = consumer_id
return self
def with_bucket_filter(
self,
bucket_filter: Callable[[int], bool]
) -> 'StreamReadBuilder':
"""Push bucket filter for parallel consumption.
Example:
builder.with_bucket_filter(lambda b: b % 2 == 0)
builder.with_bucket_filter(lambda b: b < 4)
"""
self._bucket_filter = bucket_filter
return self
def with_buckets(self, bucket_ids: List[int]) -> 'StreamReadBuilder':
"""Convenience method to read only specific buckets.
Example:
builder.with_buckets([0, 1, 2])
builder.with_buckets([3, 4, 5])
"""
bucket_set: Set[int] = set(bucket_ids)
return self.with_bucket_filter(lambda bucket: bucket in bucket_set)
def new_streaming_scan(self) -> AsyncStreamingTableScan:
"""Create a new AsyncStreamingTableScan with this builder's settings."""
scan = AsyncStreamingTableScan(
table=self.table,
predicate=self._predicate,
poll_interval_ms=self._poll_interval_ms,
bucket_filter=self._bucket_filter,
consumer_id=self._consumer_id
)
scan._read_type = self.read_type()
return scan
def new_read(self) -> TableRead:
"""Create a new TableRead with this builder's settings."""
return TableRead(
table=self.table,
predicate=self._predicate,
read_type=self.read_type(),
include_row_kind=self._include_row_kind
)
def new_predicate_builder(self) -> PredicateBuilder:
"""Create a PredicateBuilder for building filter predicates."""
return PredicateBuilder(self.read_type())
def read_type(self) -> List[DataField]:
"""Get the read schema fields, applying projection if set."""
table_fields = self.table.fields
if not self._projection:
return table_fields
else:
if self.table.options.row_tracking_enabled():
table_fields = SpecialFields.row_type_with_row_tracking(table_fields)
field_map = {field.name: field for field in table_fields}
return [field_map[name] for name in self._projection if name in field_map]