From 4752ce27275c9d9b12a59076584c53274716163a Mon Sep 17 00:00:00 2001 From: Nico Ritschel Date: Mon, 17 Aug 2026 16:30:12 -0700 Subject: [PATCH] Bound composite-key merge predicates --- README.md | 18 ++- src/dlt_iceberg/destination_client.py | 213 ++++++++++++++++++------- src/dlt_iceberg/merge_utils.py | 42 ++++- tests/test_merge_batching.py | 214 ++++++++++++++++++++++++++ tests/test_merge_utils.py | 32 +++- uv.lock | 145 ++++++++++++----- 6 files changed, 560 insertions(+), 104 deletions(-) create mode 100644 tests/test_merge_batching.py diff --git a/README.md b/README.md index 477f615..8af4999 100644 --- a/README.md +++ b/README.md @@ -118,7 +118,8 @@ iceberg_rest( # Performance tuning max_retries=5, # Retry attempts for transient failures retry_backoff_base=2.0, # Exponential backoff multiplier - merge_batch_size=500000, # Rows per batch for merge operations + merge_batch_size=500000, # General maximum rows per merge batch + merge_composite_key_batch_size=500, # Safety cap for composite keys strict_casting=False, # Fail on potential data loss # Table management @@ -341,6 +342,12 @@ Truncates table and inserts new data. ``` Deletes matching rows then inserts new data. Single atomic transaction. +Composite-key delete predicates are staged in batches of at most +`min(merge_batch_size, merge_composite_key_batch_size)` keys to bound +PyIceberg's nested expression size. Single-key merges keep the general +`merge_batch_size` limit. All delete batches and the append are committed +together, so readers see the table-level change atomically. + #### Upsert Strategy ```python @dlt.resource( @@ -350,6 +357,15 @@ Deletes matching rows then inserts new data. Single atomic transaction. ``` Updates existing rows, inserts new rows. +Upserts are staged inside one PyIceberg transaction. Single-key upserts use +`merge_batch_size` (default 500,000); composite-key upserts also apply +`merge_composite_key_batch_size` (default 500) because PyIceberg builds a +nested OR-of-ANDs match expression for those keys. Lower values reduce peak +native memory and expression depth at the cost of more scans and staged +snapshot updates. The final metadata commit is atomic for a table, including +hard deletes, but a dlt load that touches multiple tables is not atomic across +those tables. + #### Hard Deletes Mark rows for deletion by setting the `_dlt_deleted_at` column: diff --git a/src/dlt_iceberg/destination_client.py b/src/dlt_iceberg/destination_client.py index dde202c..2df56a0 100644 --- a/src/dlt_iceberg/destination_client.py +++ b/src/dlt_iceberg/destination_client.py @@ -51,7 +51,10 @@ log_error_with_context, get_user_friendly_error_message, ) -from .merge_utils import build_primary_key_delete_filter +from .merge_utils import ( + iter_primary_key_delete_filters, + unique_primary_key_values, +) from pyiceberg.io.pyarrow import schema_to_pyarrow # MODULE-LEVEL STATE: Accumulate files across client instances @@ -147,9 +150,14 @@ class IcebergRestConfiguration(DestinationClientConfiguration): # Schema casting configuration strict_casting: bool = False - # Merge batch size (for upsert operations to avoid memory issues) + # Maximum rows per staged merge operation (legacy/default behavior). merge_batch_size: int = 500000 + # Additional safety cap for composite keys. PyIceberg represents a + # composite match as an OR tree of AND predicates, so this keeps that tree + # bounded without penalizing the much flatter single-key IN predicate. + merge_composite_key_batch_size: int = 500 + # Table location layout - controls directory structure for table files # Supports patterns: {namespace}, {dataset_name}, {table_name} # Example: "{namespace}/{table_name}" or "warehouse/{dataset_name}/{table_name}" @@ -1198,7 +1206,7 @@ def _execute_delete_insert( combined_table: pa.Table, primary_keys: List[str], identifier: str, - hard_delete_filter=None, + hard_delete_rows: Optional[pa.Table] = None, ) -> Tuple[int, int, int]: """Execute delete-insert merge strategy with optional hard deletes. @@ -1210,14 +1218,14 @@ def _execute_delete_insert( combined_table: Arrow table with data to merge primary_keys: List of primary key column names identifier: Table identifier for logging - hard_delete_filter: Optional filter for hard deletes (rows to permanently remove) + hard_delete_rows: Optional rows whose keys should be permanently removed Returns: Tuple of (rows_deleted_estimate, rows_inserted, hard_deleted) """ - # Build delete filter from primary key values in incoming data - delete_filter, deleted_estimate = build_primary_key_delete_filter( - combined_table, primary_keys + batch_size = self._merge_batch_size(primary_keys) + deleted_estimate = len( + unique_primary_key_values(combined_table, primary_keys) ) logger.info( @@ -1225,34 +1233,129 @@ def _execute_delete_insert( f"matching rows, inserting {len(combined_table)} rows" ) - # Execute atomic hard-delete + delete + append using single transaction + # Each staged delete has a bounded predicate, while the transaction makes + # all hard-delete, delete, and append changes visible atomically. with iceberg_table.transaction() as txn: - # Hard deletes first (permanent removal) - if hard_delete_filter is not None: - txn.delete(hard_delete_filter) - # Then delete-insert for merge - txn.delete(delete_filter) + if hard_delete_rows is not None: + self._delete_primary_key_batches( + txn, hard_delete_rows, primary_keys, batch_size, identifier, + operation="hard delete", + ) + self._delete_primary_key_batches( + txn, combined_table, primary_keys, batch_size, identifier, + operation="delete-insert", + ) txn.append(combined_table) - return (deleted_estimate, len(combined_table), 1 if hard_delete_filter else 0) + return ( + deleted_estimate, + len(combined_table), + len(hard_delete_rows) if hard_delete_rows is not None else 0, + ) + + def _merge_batch_size(self, primary_keys: List[str]) -> int: + """Return the configured batch size with the composite-key safety cap.""" + batch_size = self.config.merge_batch_size + if batch_size <= 0: + raise ValueError("merge_batch_size must be greater than 0") + if len(primary_keys) > 1: + composite_batch_size = self.config.merge_composite_key_batch_size + if composite_batch_size <= 0: + raise ValueError( + "merge_composite_key_batch_size must be greater than 0" + ) + return min(batch_size, composite_batch_size) + return batch_size + + def _delete_primary_key_batches( + self, + txn, + rows: pa.Table, + primary_keys: List[str], + batch_size: int, + identifier: str, + operation: str, + ) -> int: + """Stage bounded primary-key delete predicates in an open transaction.""" + deleted_estimate = 0 + for batch_number, (delete_filter, key_count) in enumerate( + iter_primary_key_delete_filters(rows, primary_keys, batch_size), start=1 + ): + logger.info( + f"Staging {operation} batch {batch_number} for {identifier}: " + f"up to {key_count} keys" + ) + txn.delete(delete_filter) + deleted_estimate += key_count + return deleted_estimate + + def _execute_upsert( + self, + iceberg_table, + combined_table: pa.Table, + primary_keys: List[str], + identifier: str, + hard_delete_rows: Optional[pa.Table] = None, + ) -> Tuple[int, int, int]: + """Stage bounded upserts and optional hard deletes in one transaction.""" + batch_size = self._merge_batch_size(primary_keys) + unique_key_count = len( + unique_primary_key_values(combined_table, primary_keys) + ) + if unique_key_count != len(combined_table): + raise ValueError( + "Duplicate rows found in source dataset based on the key columns. " + "No upsert executed" + ) + + total_updated = 0 + total_inserted = 0 + batch_count = 0 + with iceberg_table.transaction() as txn: + if hard_delete_rows is not None: + self._delete_primary_key_batches( + txn, hard_delete_rows, primary_keys, batch_size, identifier, + operation="hard delete", + ) + + for batch_number, batch_start in enumerate( + range(0, len(combined_table), batch_size), start=1 + ): + batch_count = batch_number + batch = combined_table.slice(batch_start, batch_size) + logger.info( + f"Staging upsert batch {batch_number} for {identifier}: " + f"rows {batch_start} to {batch_start + len(batch)} " + f"({len(batch)} rows)" + ) + upsert_result = txn.upsert( + df=batch, + join_cols=primary_keys, + when_matched_update_all=True, + when_not_matched_insert_all=True, + ) + total_updated += upsert_result.rows_updated + total_inserted += upsert_result.rows_inserted + + return total_updated, total_inserted, batch_count def _prepare_hard_deletes( self, combined_table: pa.Table, primary_keys: List[str], - ) -> Tuple[pa.Table, Optional[Any], int]: + ) -> Tuple[pa.Table, Optional[pa.Table], int]: """ Prepare hard deletes from incoming data (does not execute). Rows with the hard_delete_column set (non-null) will be deleted. - Returns the filter expression to use in a transaction. + Returns the marked rows so their key predicates can be built in bounded batches. Args: combined_table: Arrow table with data including possible delete markers primary_keys: List of primary key column names Returns: - Tuple of (remaining_rows, delete_filter_or_none, num_to_delete) + Tuple of (remaining_rows, delete_rows_or_none, num_to_delete) """ hard_delete_col = self.config.hard_delete_column @@ -1273,10 +1376,7 @@ def _prepare_hard_deletes( if len(rows_to_delete) == 0: return rows_to_keep, None, 0 - # Build delete filter from primary keys of rows to delete - delete_filter, _ = build_primary_key_delete_filter(rows_to_delete, primary_keys) - - return rows_to_keep, delete_filter, len(rows_to_delete) + return rows_to_keep, rows_to_delete, len(rows_to_delete) def _commit_table_files( self, @@ -1428,17 +1528,30 @@ def _commit_table_files( iceberg_table.append(combined_table) else: # Prepare hard deletes (rows marked for deletion) - remaining_rows, hard_delete_filter, num_hard_deletes = self._prepare_hard_deletes( - combined_table, primary_keys - ) + ( + remaining_rows, + hard_delete_rows, + num_hard_deletes, + ) = self._prepare_hard_deletes(combined_table, primary_keys) if num_hard_deletes > 0: logger.info(f"Prepared {num_hard_deletes} rows for hard delete") # If all rows were hard deletes, just execute the delete if len(remaining_rows) == 0: - if hard_delete_filter is not None: - iceberg_table.delete(hard_delete_filter) - logger.info(f"Executed {num_hard_deletes} hard deletes (no merge needed)") + if hard_delete_rows is not None: + with iceberg_table.transaction() as txn: + self._delete_primary_key_batches( + txn, + hard_delete_rows, + primary_keys, + self._merge_batch_size(primary_keys), + identifier, + operation="hard delete", + ) + logger.info( + f"Executed {num_hard_deletes} hard deletes " + f"(no merge needed)" + ) return # Get merge strategy @@ -1452,45 +1565,31 @@ def _commit_table_files( # Atomic hard-delete + delete + insert in single transaction deleted, inserted, _ = self._execute_delete_insert( iceberg_table, remaining_rows, primary_keys, identifier, - hard_delete_filter=hard_delete_filter + hard_delete_rows=hard_delete_rows ) logger.info( f"Delete-insert completed: ~{deleted} deleted, " f"{inserted} inserted" ) else: - # Default: upsert strategy - # Execute hard deletes first (separate transaction since upsert is atomic) - if hard_delete_filter is not None: - iceberg_table.delete(hard_delete_filter) - logger.info(f"Executed {num_hard_deletes} hard deletes before upsert") - - batch_size = self.config.merge_batch_size - total_updated = 0 - total_inserted = 0 - - for batch_start in range(0, len(remaining_rows), batch_size): - batch_end = min(batch_start + batch_size, len(remaining_rows)) - batch = remaining_rows.slice(batch_start, batch_end - batch_start) - - logger.info( - f"Upserting batch {batch_start//batch_size + 1}: " - f"rows {batch_start} to {batch_end} ({len(batch)} rows)" - ) - - upsert_result = iceberg_table.upsert( - df=batch, - join_cols=primary_keys, - when_matched_update_all=True, - when_not_matched_insert_all=True, - ) - - total_updated += upsert_result.rows_updated - total_inserted += upsert_result.rows_inserted + # The transaction keeps all bounded upsert and hard-delete + # batches atomic at the table level. + ( + total_updated, + total_inserted, + batch_count, + ) = self._execute_upsert( + iceberg_table, + remaining_rows, + primary_keys, + identifier, + hard_delete_rows=hard_delete_rows, + ) logger.info( f"Upsert completed: {total_updated} updated, " - f"{total_inserted} inserted across {(len(remaining_rows) + batch_size - 1) // batch_size} batches" + f"{total_inserted} inserted across " + f"{batch_count} batches" ) else: raise ValueError(f"Unknown write disposition: {disposition_type}") diff --git a/src/dlt_iceberg/merge_utils.py b/src/dlt_iceberg/merge_utils.py index 25c9fd3..f497cc1 100644 --- a/src/dlt_iceberg/merge_utils.py +++ b/src/dlt_iceberg/merge_utils.py @@ -1,6 +1,7 @@ """Utilities shared by merge strategies.""" -from typing import Any, List, Tuple +from itertools import islice +from typing import Any, Iterable, Iterator, List, Sequence, Tuple import pyarrow as pa @@ -14,17 +15,19 @@ def unique_primary_key_values(table: pa.Table, primary_keys: List[str]) -> set: return set(zip(*key_columns)) -def build_primary_key_delete_filter(table: pa.Table, primary_keys: List[str]) -> Tuple[Any, int]: - """Build a PyIceberg delete expression for the unique primary keys in table.""" +def _build_primary_key_delete_filter( + primary_keys: Sequence[str], unique_key_values: Iterable[Any] +) -> Tuple[Any, int]: + """Build a PyIceberg delete expression from already-unique key values.""" from pyiceberg.expressions import AlwaysFalse, And, EqualTo, In, Or - unique_key_values = unique_primary_key_values(table, primary_keys) + key_values = list(unique_key_values) if len(primary_keys) == 1: - return In(primary_keys[0], list(unique_key_values)), len(unique_key_values) + return In(primary_keys[0], key_values), len(key_values) conditions = [] - for pk_tuple in unique_key_values: + for pk_tuple in key_values: and_conditions = [ EqualTo(pk, val) for pk, val in zip(primary_keys, pk_tuple) ] @@ -37,6 +40,29 @@ def build_primary_key_delete_filter(table: pa.Table, primary_keys: List[str]) -> return AlwaysFalse(), 0 if len(conditions) == 1: - return conditions[0], len(unique_key_values) + return conditions[0], len(key_values) - return Or(*conditions), len(unique_key_values) + return Or(*conditions), len(key_values) + + +def build_primary_key_delete_filter( + table: pa.Table, primary_keys: List[str] +) -> Tuple[Any, int]: + """Build one PyIceberg delete expression for the unique primary keys in table.""" + return _build_primary_key_delete_filter( + primary_keys, unique_primary_key_values(table, primary_keys) + ) + + +def iter_primary_key_delete_filters( + table: pa.Table, + primary_keys: List[str], + batch_size: int, +) -> Iterator[Tuple[Any, int]]: + """Yield delete expressions whose unique-key count is bounded by ``batch_size``.""" + if batch_size <= 0: + raise ValueError("merge_batch_size must be greater than 0") + + unique_values = iter(unique_primary_key_values(table, primary_keys)) + while key_batch := list(islice(unique_values, batch_size)): + yield _build_primary_key_delete_filter(primary_keys, key_batch) diff --git a/tests/test_merge_batching.py b/tests/test_merge_batching.py new file mode 100644 index 0000000..077fd9f --- /dev/null +++ b/tests/test_merge_batching.py @@ -0,0 +1,214 @@ +from types import SimpleNamespace +from unittest.mock import Mock + +import pyarrow as pa +import pytest +from pyiceberg.catalog import load_catalog +from pyiceberg.schema import Schema +from pyiceberg.types import LongType, NestedField, StringType + +from dlt_iceberg.destination_client import ( + IcebergRestClient, + IcebergRestConfiguration, +) + + +class RecordingTransaction: + def __init__(self): + self.delete = Mock() + self.append = Mock() + self.upsert = Mock() + self.committed = False + + def __enter__(self): + return self + + def __exit__(self, exc_type, exc_value, traceback): + self.committed = exc_type is None + + +class RecordingTable: + def __init__(self, transaction): + self._transaction = transaction + self.transaction_calls = 0 + + def transaction(self): + self.transaction_calls += 1 + return self._transaction + + +def make_client(batch_size=500000, composite_key_batch_size=500): + client = object.__new__(IcebergRestClient) + client.config = SimpleNamespace( + merge_batch_size=batch_size, + merge_composite_key_batch_size=composite_key_batch_size, + ) + return client + + +def composite_table(row_count): + return pa.table( + { + "account_id": list(range(row_count)), + "event_id": [f"event-{index}" for index in range(row_count)], + "value": list(range(row_count)), + } + ) + + +def test_delete_insert_batches_reported_composite_key_load_atomically(): + client = make_client() + transaction = RecordingTransaction() + table = RecordingTable(transaction) + incoming = composite_table(1018) + + deleted, inserted, hard_deleted = client._execute_delete_insert( + table, + incoming, + ["account_id", "event_id"], + "analytics.events", + ) + + assert (deleted, inserted, hard_deleted) == (1018, 1018, 0) + assert transaction.delete.call_count == 3 + transaction.append.assert_called_once_with(incoming) + assert table.transaction_calls == 1 + assert transaction.committed + + +def test_upsert_batches_use_one_transaction_and_preserve_source_validation(): + client = make_client() + transaction = RecordingTransaction() + transaction.upsert.side_effect = [ + SimpleNamespace(rows_updated=100, rows_inserted=400), + SimpleNamespace(rows_updated=200, rows_inserted=300), + SimpleNamespace(rows_updated=10, rows_inserted=8), + ] + table = RecordingTable(transaction) + + updated, inserted, batch_count = client._execute_upsert( + table, + composite_table(1018), + ["account_id", "event_id"], + "analytics.events", + ) + + assert (updated, inserted) == (310, 708) + assert batch_count == 3 + assert [len(call.kwargs["df"]) for call in transaction.upsert.call_args_list] == [ + 500, + 500, + 18, + ] + assert table.transaction_calls == 1 + assert transaction.committed + + +def test_upsert_batch_failure_does_not_commit_transaction(): + client = make_client() + transaction = RecordingTransaction() + transaction.upsert.side_effect = [ + SimpleNamespace(rows_updated=0, rows_inserted=500), + RuntimeError("native merge failed"), + ] + table = RecordingTable(transaction) + + with pytest.raises(RuntimeError, match="native merge failed"): + client._execute_upsert( + table, + composite_table(501), + ["account_id", "event_id"], + "analytics.events", + ) + + assert transaction.upsert.call_count == 2 + assert table.transaction_calls == 1 + assert not transaction.committed + + +def test_upsert_rejects_duplicates_across_batch_boundaries_before_transaction(): + client = make_client(batch_size=2) + transaction = RecordingTransaction() + table = RecordingTable(transaction) + incoming = pa.table( + { + "account_id": [1, 2, 1], + "event_id": ["a", "b", "a"], + "value": [10, 20, 30], + } + ) + + with pytest.raises(ValueError, match="Duplicate rows found"): + client._execute_upsert( + table, + incoming, + ["account_id", "event_id"], + "analytics.events", + ) + + assert table.transaction_calls == 0 + + +def test_single_key_merge_keeps_general_batch_size(): + client = make_client(batch_size=500000, composite_key_batch_size=500) + + assert client._merge_batch_size(["id"]) == 500000 + assert client._merge_batch_size(["account_id", "event_id"]) == 500 + + +def test_merge_batch_configuration_defaults_are_strategy_specific(): + config = IcebergRestConfiguration() + + assert config.merge_batch_size == 500000 + assert config.merge_composite_key_batch_size == 500 + + +@pytest.mark.parametrize("strategy", ["upsert", "delete-insert"]) +def test_pyiceberg_composite_merge_regression_at_reported_row_count( + tmp_path, strategy +): + catalog = load_catalog( + f"regression_{strategy}", + type="sql", + uri=f"sqlite:///{tmp_path / 'catalog.db'}", + warehouse=f"file://{tmp_path / 'warehouse'}", + ) + catalog.create_namespace("analytics") + iceberg_table = catalog.create_table( + "analytics.events", + schema=Schema( + NestedField(1, "account_id", LongType(), required=False), + NestedField(2, "event_id", StringType(), required=False), + NestedField(3, "value", LongType(), required=False), + ), + ) + initial = composite_table(1018) + iceberg_table.append(initial) + incoming = initial.set_column( + 2, + "value", + pa.array([index + 10000 for index in range(1018)], type=pa.int64()), + ) + client = make_client() + + if strategy == "upsert": + updated, inserted, batch_count = client._execute_upsert( + iceberg_table, + incoming, + ["account_id", "event_id"], + "analytics.events", + ) + assert (updated, inserted, batch_count) == (1018, 0, 3) + else: + deleted, inserted, hard_deleted = client._execute_delete_insert( + iceberg_table, + incoming, + ["account_id", "event_id"], + "analytics.events", + ) + assert (deleted, inserted, hard_deleted) == (1018, 1018, 0) + + result = catalog.load_table("analytics.events").scan().to_arrow() + assert len(result) == 1018 + assert min(result.column("value").to_pylist()) == 10000 + assert max(result.column("value").to_pylist()) == 11017 diff --git a/tests/test_merge_utils.py b/tests/test_merge_utils.py index 630db5b..2475a96 100644 --- a/tests/test_merge_utils.py +++ b/tests/test_merge_utils.py @@ -1,4 +1,10 @@ -from dlt_iceberg.merge_utils import build_primary_key_delete_filter +import pyarrow as pa +import pytest + +from dlt_iceberg.merge_utils import ( + build_primary_key_delete_filter, + iter_primary_key_delete_filters, +) class CountingColumn: @@ -43,3 +49,27 @@ def test_composite_primary_key_filter_materializes_each_column_once(): assert unique_count == 3 assert table.columns["user_id"].to_pylist_calls == 1 assert table.columns["event_date"].to_pylist_calls == 1 + + +def test_composite_primary_key_filters_are_bounded_for_reported_row_count(): + table = pa.table( + { + "account_id": list(range(1018)), + "event_id": [f"event-{index}" for index in range(1018)], + } + ) + + batches = list( + iter_primary_key_delete_filters( + table, ["account_id", "event_id"], batch_size=500 + ) + ) + + assert [key_count for _, key_count in batches] == [500, 500, 18] + + +def test_primary_key_filter_batch_size_must_be_positive(): + table = pa.table({"id": [1]}) + + with pytest.raises(ValueError, match="merge_batch_size must be greater than 0"): + list(iter_primary_key_delete_filters(table, ["id"], batch_size=0)) diff --git a/uv.lock b/uv.lock index 9c606bd..fddcac9 100644 --- a/uv.lock +++ b/uv.lock @@ -358,7 +358,7 @@ wheels = [ [[package]] name = "dlt-iceberg" -version = "0.2.0" +version = "0.6.2" source = { editable = "." } dependencies = [ { name = "boto3" }, @@ -591,7 +591,6 @@ wheels = [ { url = 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