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Partitioned Append on Identity Transform #555
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Original file line number | Diff line number | Diff line change | ||||
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@@ -16,13 +16,13 @@ | |||||
# under the License. | ||||||
from __future__ import annotations | ||||||
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import datetime | ||||||
import itertools | ||||||
import uuid | ||||||
import warnings | ||||||
from abc import ABC, abstractmethod | ||||||
from copy import copy | ||||||
from dataclasses import dataclass | ||||||
from datetime import datetime | ||||||
from enum import Enum | ||||||
from functools import cached_property, singledispatch | ||||||
from itertools import chain | ||||||
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@@ -72,6 +72,8 @@ | |||||
INITIAL_PARTITION_SPEC_ID, | ||||||
PARTITION_FIELD_ID_START, | ||||||
PartitionField, | ||||||
PartitionFieldValue, | ||||||
PartitionKey, | ||||||
PartitionSpec, | ||||||
_PartitionNameGenerator, | ||||||
_visit_partition_field, | ||||||
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@@ -711,7 +713,7 @@ def _(update: SetSnapshotRefUpdate, base_metadata: TableMetadata, context: _Tabl | |||||
if update.ref_name == MAIN_BRANCH: | ||||||
metadata_updates["current_snapshot_id"] = snapshot_ref.snapshot_id | ||||||
if "last_updated_ms" not in metadata_updates: | ||||||
metadata_updates["last_updated_ms"] = datetime_to_millis(datetime.datetime.now().astimezone()) | ||||||
metadata_updates["last_updated_ms"] = datetime_to_millis(datetime.now().astimezone()) | ||||||
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metadata_updates["snapshot_log"] = base_metadata.snapshot_log + [ | ||||||
SnapshotLogEntry( | ||||||
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@@ -1126,9 +1128,6 @@ def append(self, df: pa.Table, snapshot_properties: Dict[str, str] = EMPTY_DICT) | |||||
if not isinstance(df, pa.Table): | ||||||
raise ValueError(f"Expected PyArrow table, got: {df}") | ||||||
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if len(self.spec().fields) > 0: | ||||||
raise ValueError("Cannot write to partitioned tables") | ||||||
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_check_schema_compatible(self.schema(), other_schema=df.schema) | ||||||
# cast if the two schemas are compatible but not equal | ||||||
if self.schema().as_arrow() != df.schema: | ||||||
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@@ -2489,16 +2488,28 @@ def _add_and_move_fields( | |||||
class WriteTask: | ||||||
write_uuid: uuid.UUID | ||||||
task_id: int | ||||||
schema: Schema | ||||||
record_batches: List[pa.RecordBatch] | ||||||
sort_order_id: Optional[int] = None | ||||||
partition_key: Optional[PartitionKey] = None | ||||||
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# Later to be extended with partition information | ||||||
def generate_data_file_partition_path(self) -> str: | ||||||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. Nit: This function looks redundant. The check is being done in |
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if self.partition_key is None: | ||||||
raise ValueError("Cannot generate partition path based on non-partitioned WriteTask") | ||||||
return self.partition_key.to_path() | ||||||
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def generate_data_file_filename(self, extension: str) -> str: | ||||||
# Mimics the behavior in the Java API: | ||||||
# https://github.com/apache/iceberg/blob/a582968975dd30ff4917fbbe999f1be903efac02/core/src/main/java/org/apache/iceberg/io/OutputFileFactory.java#L92-L101 | ||||||
return f"00000-{self.task_id}-{self.write_uuid}.{extension}" | ||||||
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def generate_data_file_path(self, extension: str) -> str: | ||||||
if self.partition_key: | ||||||
file_path = f"{self.generate_data_file_partition_path()}/{self. generate_data_file_filename(extension)}" | ||||||
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return file_path | ||||||
else: | ||||||
return self.generate_data_file_filename(extension) | ||||||
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@dataclass(frozen=True) | ||||||
class AddFileTask: | ||||||
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@@ -2526,25 +2537,44 @@ def _dataframe_to_data_files( | |||||
""" | ||||||
from pyiceberg.io.pyarrow import bin_pack_arrow_table, write_file | ||||||
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if len([spec for spec in table_metadata.partition_specs if spec.spec_id != 0]) > 0: | ||||||
raise ValueError("Cannot write to partitioned tables") | ||||||
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counter = itertools.count(0) | ||||||
write_uuid = write_uuid or uuid.uuid4() | ||||||
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target_file_size = PropertyUtil.property_as_int( | ||||||
properties=table_metadata.properties, | ||||||
property_name=TableProperties.WRITE_TARGET_FILE_SIZE_BYTES, | ||||||
default=TableProperties.WRITE_TARGET_FILE_SIZE_BYTES_DEFAULT, | ||||||
) | ||||||
if target_file_size is None: | ||||||
raise ValueError( | ||||||
"Fail to get neither TableProperties.WRITE_TARGET_FILE_SIZE_BYTES nor WRITE_TARGET_FILE_SIZE_BYTES_DEFAULT for writing target data file." | ||||||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. I have mixed feelings about this exception check, because we are setting the default value of I understand why we are doing it though:
If we run into more of these type checking redundancies in the code base, where when we are using property values that are always expected to have a none-null default value, maybe we should refactor There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. property_as_int_with_default sounds better to me because all the exceptions raised due to missing default property could be centralized in the function? How do you feel about it There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. I like that as well, the There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. i just find the default value itself could be None: the original code for this target_file_size check just |
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) | ||||||
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# This is an iter, so we don't have to materialize everything every time | ||||||
# This will be more relevant when we start doing partitioned writes | ||||||
yield from write_file( | ||||||
io=io, | ||||||
table_metadata=table_metadata, | ||||||
tasks=iter([WriteTask(write_uuid, next(counter), batches) for batches in bin_pack_arrow_table(df, target_file_size)]), # type: ignore | ||||||
) | ||||||
if any(len(spec.fields) > 0 for spec in table_metadata.partition_specs): | ||||||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. It seems the old line was not checking whether the table is partitioned but was checking partition evolution? There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. Great find!
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partitions = partition(table_metadata, df) | ||||||
yield from write_file( | ||||||
io=io, | ||||||
table_metadata=table_metadata, | ||||||
tasks=iter([ | ||||||
WriteTask( | ||||||
write_uuid=write_uuid, | ||||||
task_id=next(counter), | ||||||
record_batches=batches, | ||||||
partition_key=partition.partition_key, | ||||||
schema=table_metadata.schema(), | ||||||
) | ||||||
for partition in partitions | ||||||
for batches in bin_pack_arrow_table(partition.arrow_table_partition, target_file_size) | ||||||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. This looks very nice! |
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]), | ||||||
) | ||||||
else: | ||||||
yield from write_file( | ||||||
io=io, | ||||||
table_metadata=table_metadata, | ||||||
tasks=iter([ | ||||||
WriteTask(write_uuid=write_uuid, task_id=next(counter), record_batches=batches, schema=table_metadata.schema()) | ||||||
for batches in bin_pack_arrow_table(df, target_file_size) | ||||||
]), | ||||||
) | ||||||
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def _parquet_files_to_data_files(table_metadata: TableMetadata, file_paths: List[str], io: FileIO) -> Iterable[DataFile]: | ||||||
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@@ -3096,7 +3126,7 @@ def snapshots(self) -> "pa.Table": | |||||
additional_properties = None | ||||||
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snapshots.append({ | ||||||
'committed_at': datetime.datetime.utcfromtimestamp(snapshot.timestamp_ms / 1000.0), | ||||||
'committed_at': datetime.utcfromtimestamp(snapshot.timestamp_ms / 1000.0), | ||||||
'snapshot_id': snapshot.snapshot_id, | ||||||
'parent_id': snapshot.parent_snapshot_id, | ||||||
'operation': str(operation), | ||||||
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@@ -3108,3 +3138,127 @@ def snapshots(self) -> "pa.Table": | |||||
snapshots, | ||||||
schema=snapshots_schema, | ||||||
) | ||||||
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@dataclass(frozen=True) | ||||||
class TablePartition: | ||||||
partition_key: PartitionKey | ||||||
arrow_table_partition: pa.Table | ||||||
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def _get_partition_sort_order(partition_columns: list[str], reverse: bool = False) -> dict[str, Any]: | ||||||
order = 'ascending' if not reverse else 'descending' | ||||||
null_placement = 'at_start' if reverse else 'at_end' | ||||||
return {'sort_keys': [(column_name, order) for column_name in partition_columns], 'null_placement': null_placement} | ||||||
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def group_by_partition_scheme( | ||||||
iceberg_table_metadata: TableMetadata, arrow_table: pa.Table, partition_columns: list[str] | ||||||
) -> pa.Table: | ||||||
"""Given a table sort it by current partition scheme with all transform functions supported.""" | ||||||
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from pyiceberg.transforms import IdentityTransform | ||||||
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supported = {IdentityTransform} | ||||||
if not all( | ||||||
type(field.transform) in supported for field in iceberg_table_metadata.spec().fields if field in partition_columns | ||||||
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): | ||||||
raise ValueError( | ||||||
f"Not all transforms are supported, get: {[transform in supported for transform in iceberg_table_metadata.spec().fields]}." | ||||||
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) | ||||||
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# only works for identity | ||||||
sort_options = _get_partition_sort_order(partition_columns, reverse=False) | ||||||
sorted_arrow_table = arrow_table.sort_by(sorting=sort_options['sort_keys'], null_placement=sort_options['null_placement']) | ||||||
return sorted_arrow_table | ||||||
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def get_partition_columns(iceberg_table_metadata: TableMetadata, arrow_table: pa.Table) -> list[str]: | ||||||
arrow_table_cols = set(arrow_table.column_names) | ||||||
partition_cols = [] | ||||||
for transform_field in iceberg_table_metadata.spec().fields: | ||||||
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column_name = iceberg_table_metadata.schema().find_column_name(transform_field.source_id) | ||||||
if not column_name: | ||||||
raise ValueError(f"{transform_field=} could not be found in {iceberg_table_metadata.schema()}.") | ||||||
if column_name not in arrow_table_cols: | ||||||
continue | ||||||
partition_cols.append(column_name) | ||||||
return partition_cols | ||||||
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def _get_table_partitions( | ||||||
arrow_table: pa.Table, | ||||||
partition_spec: PartitionSpec, | ||||||
schema: Schema, | ||||||
slice_instructions: list[dict[str, Any]], | ||||||
) -> list[TablePartition]: | ||||||
sorted_slice_instructions = sorted(slice_instructions, key=lambda x: x['offset']) | ||||||
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partition_fields = partition_spec.fields | ||||||
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offsets = [inst["offset"] for inst in sorted_slice_instructions] | ||||||
projected_and_filtered = { | ||||||
partition_field.source_id: arrow_table[schema.find_field(name_or_id=partition_field.source_id).name] | ||||||
.take(offsets) | ||||||
.to_pylist() | ||||||
for partition_field in partition_fields | ||||||
} | ||||||
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table_partitions = [] | ||||||
for inst in sorted_slice_instructions: | ||||||
partition_slice = arrow_table.slice(**inst) | ||||||
fieldvalues = [ | ||||||
PartitionFieldValue(partition_field, projected_and_filtered[partition_field.source_id][inst["offset"]]) | ||||||
for partition_field in partition_fields | ||||||
] | ||||||
partition_key = PartitionKey(raw_partition_field_values=fieldvalues, partition_spec=partition_spec, schema=schema) | ||||||
table_partitions.append(TablePartition(partition_key=partition_key, arrow_table_partition=partition_slice)) | ||||||
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return table_partitions | ||||||
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def partition(iceberg_table_metadata: TableMetadata, arrow_table: pa.Table) -> Iterable[TablePartition]: | ||||||
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"""Based on the iceberg table partition spec, slice the arrow table into partitions with their keys. | ||||||
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Example: | ||||||
Input: | ||||||
An arrow table with partition key of ['n_legs', 'year'] and with data of | ||||||
{'year': [2020, 2022, 2022, 2021, 2022, 2022, 2022, 2019, 2021], | ||||||
'n_legs': [2, 2, 2, 4, 4, 4, 4, 5, 100], | ||||||
'animal': ["Flamingo", "Parrot", "Parrot", "Dog", "Horse", "Horse", "Horse","Brittle stars", "Centipede"]}. | ||||||
The algrithm: | ||||||
Firstly we group the rows into partitions by sorting with sort order [('n_legs', 'descending'), ('year', 'descending')] | ||||||
and null_placement of "at_end". | ||||||
This gives the same table as raw input. | ||||||
Then we sort_indices using reverse order of [('n_legs', 'descending'), ('year', 'descending')] | ||||||
and null_placement : "at_start". | ||||||
This gives: | ||||||
[8, 7, 4, 5, 6, 3, 1, 2, 0] | ||||||
Based on this we get partition groups of indices: | ||||||
[{'offset': 8, 'length': 1}, {'offset': 7, 'length': 1}, {'offset': 4, 'length': 3}, {'offset': 3, 'length': 1}, {'offset': 1, 'length': 2}, {'offset': 0, 'length': 1}] | ||||||
We then retrieve the partition keys by offsets. | ||||||
And slice the arrow table by offsets and lengths of each partition. | ||||||
""" | ||||||
import pyarrow as pa | ||||||
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partition_columns = get_partition_columns(iceberg_table_metadata, arrow_table) | ||||||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. How do you feel about this suggestion? Most of this function's responsibility seems to lie in making sure that the partition field is provided in the arrow_table, but we seem to already be checking the schema in the write functions now.
Suggested change
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. it will be more useful when there are hidden partition columns. And the check is also for mypy check because find_column_name returns optional[str] |
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arrow_table = group_by_partition_scheme(iceberg_table_metadata, arrow_table, partition_columns) | ||||||
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reversing_sort_order_options = _get_partition_sort_order(partition_columns, reverse=True) | ||||||
reversed_indices = pa.compute.sort_indices(arrow_table, **reversing_sort_order_options).to_pylist() | ||||||
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slice_instructions: list[dict[str, Any]] = [] | ||||||
last = len(reversed_indices) | ||||||
reversed_indices_size = len(reversed_indices) | ||||||
ptr = 0 | ||||||
while ptr < reversed_indices_size: | ||||||
group_size = last - reversed_indices[ptr] | ||||||
offset = reversed_indices[ptr] | ||||||
slice_instructions.append({"offset": offset, "length": group_size}) | ||||||
last = reversed_indices[ptr] | ||||||
ptr = ptr + group_size | ||||||
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table_partitions: list[TablePartition] = _get_table_partitions( | ||||||
arrow_table, iceberg_table_metadata.spec(), iceberg_table_metadata.schema(), slice_instructions | ||||||
) | ||||||
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return table_partitions |
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This single-dispatch is there only for the
TimeType
it seems. Probably we should we should also convert those into a native type.There was a problem hiding this comment.
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fixed in the commit 82dd3ad
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Beautiful, thanks 👍