feat: add support for multiple partition columns and filters in to_pyarrow_dataset() and OR filters in write_datalake() #1722

ldacey · 2023-10-13T04:41:12Z

Description

This provides support for passing lists of DNF tuples to the to_pyarrow_dataset() method. I didn't want to touch the dataset_partitions() Rust code, so this can probably be further simplified and improved. My use case was to be able to create a pyarrow dataset from the DeltaTable and then use polars.scan_pyarrow_dataset().

Related Issue(s)

Documentation

Here are some examples (which are part of the pytests as well):

Lists of lists of tuples in DNF format will create OR expressions:

    multiple_partitions_multiple_columns = [
        [("year", "=", "2020"), ("month", "=", "2"), ("day", "=", "5")],
        [("year", "=", "2021"), ("month", "=", "4"), ("day", "=", "5")],
        [("year", "=", "2021"), ("month", "=", "3"), ("day", "=", "1")],
    ]
    dataset_filtered = dt.to_pyarrow_dataset(
        partitions=multiple_partitions_multiple_columns
    )

A single list of tuples will create an AND expression:

    single_partition_multiple_columns = [("month", "=", "2"), ("day", "=", "5")]
    dataset_filtered = dt.to_pyarrow_dataset(
        partitions=single_partition_multiple_columns
    )

… to_pyarrow_dataset() - Partitions with multiple columns can be passed as lists of tuples in DNF format - Multiple partition filters can be passed

- Add tests for various filter/partition scenarios which can be passted to to_pyarrow_dataset()

wjones127 · 2023-10-13T04:44:34Z

My use case was to be able to create a pyarrow dataset from the DeltaTable and then use polars.scan_pyarrow_dataset().

If you are using polars lazy api, have you considered just writing the filters in Polars instead? They should be pushed down into the PyArrow dataset automatically.

ldacey · 2023-10-13T05:30:37Z

Yeah, I am doing that currently. I pass the DNF filters between Airflow tasks and convert them into polars filter expressions.

When testing earlier I noticed the partitions argument in to_pyarrow_dataset was not working with multiple partitions or multiple columns so it seemed like a reasonable addition.

My Airflow tasks do not always read data into polars (only for transformation and removing duplicates), so being able to stick with a single solution is nice. Pass the DNF filters between tasks and return a filtered dataset. Then scan that with polars or duckdb or turn it into a table etc.

Ref delta-io#1479

- Tests partition filters based on AND and OR conditions using a single and multiple partition columns

ldacey · 2023-10-14T19:17:18Z

Can you check my latest changes? This is my attempt to tackle #1479 and adding support for overwriting multiple partitions (without touching the Rust code). This allows me to update a dataset with AND and OR filter logic by using lists of lists of DNF tuples, or a list of DNF tuples. The test I added shows a few scenarios.

@MrPowers - this is what I was trying to accomplish during our chat on Slack the other day. Basically, I need to be able to overwrite partitions to with deduplicated data.

Many of my sources are partitioned by some category and some temporal field, so for example [("account", "=", "x"), ("date", "=", "2023-10-10")] would overwrite partitions where the account is X and the date is 2023-10-10.

Or some tasks download data based on updated_at timestamps but the data is partitioned by created_date. In this case I might need to overwrite all of the unique created_date partitions in the in-memory data which results in OR filters like [[("created_date", "=", "2023-01-01")], [("created_date", "=", "2023-08-23")], [("created_date", "=", "2023-10-15")]]

wjones127

This is making the logic in reading and writing really complicated. Could you pull out the DNF filter parsing into some separate functions? If we can normalize them to the same general type, then we can pass that down into Rust and have it handle that.

wjones127 · 2023-10-14T20:51:45Z

python/deltalake/writer.py

+                        table._table.create_write_transaction(
+                            filtered_add_actions,
+                            mode,
+                            partition_by or [],
+                            schema,
+                            partition_filter,
+                        )


Why are we creating a write transaction per filter?

It seemed like the create_write_transaction() was not able to handle a list of lists of tuples (OR conditions), so I had to treat each condition separately and loop through them.

[id_values0-account_values0-created_date_values0-updated_at_values0-value_values0-partition_by0-partition_filters0] TypeError: argument 'partitions_filters': 'list' object cannot be converted to 'PyTuple' [909, 1]

Looping through each filter results in this:

partition_filters=[ [("created_date", "=", "2023-08-25")], [("created_date", "=", "2023-09-07")], [("created_date", "=", "2023-09-21")], ], ic| partition_filter: [('created_date', '=', '2023-08-25')] ic| filtered_add_actions: [AddAction(path='created_date=2023-08-25/2-a542e805-94c2-4e57-a957-0944117d293d-0.parquet', size=3715, partition_values={'created_date': '2023-08-25'}, modification_time=1697321774036, data_change=True, stats='{"numRecords": 1, "minValues": {"id": 1, "account": ' '"account_a", "updated_at": "2023-10-14T22:16:13.921783", ' '"value": 44.5}, "maxValues": {"id": 1, "account": ' '"account_a", "updated_at": "2023-10-14T22:16:13.921783", ' '"value": 44.5}, "nullCount": {"id": 0, "account": 0, ' '"updated_at": 0, "value": 0}}')] ic| 'update_incremental' ic| partition_filter: [('created_date', '=', '2023-09-07')] ic| filtered_add_actions: [AddAction(path='created_date=2023-09-07/2-a542e805-94c2-4e57-a957-0944117d293d-0.parquet', size=3715, partition_values={'created_date': '2023-09-07'}, modification_time=1697321774034, data_change=True, stats='{"numRecords": 1, "minValues": {"id": 3, "account": ' '"account_b", "updated_at": "2023-10-14T22:16:13.921786", ' '"value": 68.0}, "maxValues": {"id": 3, "account": ' '"account_b", "updated_at": "2023-10-14T22:16:13.921786", ' '"value": 68.0}, "nullCount": {"id": 0, "account": 0, ' '"updated_at": 0, "value": 0}}')] ic| 'update_incremental' ic| partition_filter: [('created_date', '=', '2023-09-21')] ic| filtered_add_actions: [AddAction(path='created_date=2023-09-21/2-a542e805-94c2-4e57-a957-0944117d293d-0.parquet', size=3715, partition_values={'created_date': '2023-09-21'}, modification_time=1697321774034, data_change=True, stats='{"numRecords": 1, "minValues": {"id": 4, "account": ' '"account_a", "updated_at": "2023-10-14T22:16:13.921786", ' '"value": 11.5}, "maxValues": {"id": 4, "account": ' '"account_a", "updated_at": "2023-10-14T22:16:13.921786", ' '"value": 11.5}, "nullCount": {"id": 0, "account": 0, ' '"updated_at": 0, "value": 0}}')] ic| 'update_incremental'

If we use a single list of tuples (AND condition) like the example below, there is still only one call to table.update_incremental().

ic| partition_filter: [('created_date', '>', '2023-08-01'), ('created_date', '<', '2023-12-31')] ic| filtered_add_actions: [AddAction(path='created_date=2023-08-25/account=account_a/2-6ce878e9-572d-4d78-9baa-8b34fe0b855e-0.parquet', size=3383, partition_values={'account': 'account_a', 'created_date': '2023-08-25'}, modification_time=1697322195775, data_change=True, stats='{"numRecords": 1, "minValues": {"id": 1, "updated_at": ' '"2023-10-14T22:23:15.400092", "value": 0.1}, "maxValues": ' '{"id": 1, "updated_at": "2023-10-14T22:23:15.400092", ' '"value": 0.1}, "nullCount": {"id": 0, "updated_at": 0, ' '"value": 0}}'), AddAction(path='created_date=2023-09-05/account=account_b/2-6ce878e9-572d-4d78-9baa-8b34fe0b855e-0.parquet', size=3383, partition_values={'account': 'account_b', 'created_date': '2023-09-05'}, modification_time=1697322195778, data_change=True, stats='{"numRecords": 1, "minValues": {"id": 2, "updated_at": ' '"2023-10-14T22:23:15.400093", "value": 0.2}, "maxValues": ' '{"id": 2, "updated_at": "2023-10-14T22:23:15.400093", ' '"value": 0.2}, "nullCount": {"id": 0, "updated_at": 0, ' '"value": 0}}'), AddAction(path='created_date=2023-10-02/account=account_b/2-6ce878e9-572d-4d78-9baa-8b34fe0b855e-0.parquet', size=3383, partition_values={'account': 'account_b', 'created_date': '2023-10-02'}, modification_time=1697322195780, data_change=True, stats='{"numRecords": 1, "minValues": {"id": 5, "updated_at": ' '"2023-10-14T22:23:15.400093", "value": 0.5}, "maxValues": ' '{"id": 5, "updated_at": "2023-10-14T22:23:15.400093", ' '"value": 0.5}, "nullCount": {"id": 0, "updated_at": 0, ' '"value": 0}}'), AddAction(path='created_date=2023-09-07/account=account_a/2-6ce878e9-572d-4d78-9baa-8b34fe0b855e-0.parquet', size=3383, partition_values={'account': 'account_a', 'created_date': '2023-09-07'}, modification_time=1697322195780, data_change=True, stats='{"numRecords": 1, "minValues": {"id": 3, "updated_at": ' '"2023-10-14T22:23:15.400093", "value": 0.3}, "maxValues": ' '{"id": 3, "updated_at": "2023-10-14T22:23:15.400093", ' '"value": 0.3}, "nullCount": {"id": 0, "updated_at": 0, ' '"value": 0}}'), AddAction(path='created_date=2023-09-21/account=account_c/2-6ce878e9-572d-4d78-9baa-8b34fe0b855e-0.parquet', size=3383, partition_values={'account': 'account_c', 'created_date': '2023-09-21'}, modification_time=1697322195781, data_change=True, stats='{"numRecords": 1, "minValues": {"id": 4, "updated_at": ' '"2023-10-14T22:23:15.400093", "value": 0.4}, "maxValues": ' '{"id": 4, "updated_at": "2023-10-14T22:23:15.400093", ' '"value": 0.4}, "nullCount": {"id": 0, "updated_at": 0, ' '"value": 0}}')] ic| 'update_incremental'

- validate_filters ensures partitions and filters are in DNF format (list of tuples, list of lists of tuples) and checks for empty lists - stringify_partition_values ensures values are converted from dates, ints, etc to string for partition columns

- Use pyarrow.parquet filters_to_expression instead of the custom implementation - Move __stringify_partition_values to _util to be able to test more easily - Move partition validation to validate_filters function - Move fragment building to separate method

…validate_filters

ldacey · 2023-10-17T05:16:25Z

This is making the logic in reading and writing really complicated. Could you pull out the DNF filter parsing into some separate functions? If we can normalize them to the same general type, then we can pass that down into Rust and have it handle that.

Agree. I was keeping the logic within the existing functions to not cause too many changes.

validate_filters can be used to ensure that the DNF format is correct (list of tuples, or list of list of tuples)
Moved the fragment creation to a separate method
I believe we can just use pq.filters_to_expression() which is a public method as of a few versions ago. This removes some of the more complex logic.
I added some tests for the filters_to_expression(), the validate_filters(), and the stringify_partition_values()

I still need add the validate_filters() to the writer.py file as well. I did not touch that yet.

- validated_filters is guaranteed to be a list of list of tuples

- Shows that the output will still be a list of lists of tuples

ldacey · 2023-10-17T19:06:09Z

The validate_filters() function I added ensures that the partition_filters is a list of list of tuples now, so I made updates to accomodate for that. I was not able to avoid looping through each OR condition.

I think that is all I can do on the Python side for now and I am not familar with Rust. I will take a look at the get_active_partitions() and create_write_transaction() Rust functions to see if I can adapt them to accept a list of list of tuples. Right now it looks like they only accept a list of tuples:

partitions_filters: Option<Vec<(&str, &str, PartitionFilterValue)>>

If that change is made, then further simplification can be done with the Python code.

ldacey added 2 commits October 13, 2023 12:26

Add support for multiple partition columns and multiple partitions in…

4c0551a

… to_pyarrow_dataset() - Partitions with multiple columns can be passed as lists of tuples in DNF format - Multiple partition filters can be passed

Add test_pyarrow_dataset_partitions pytest

b128bc3

- Add tests for various filter/partition scenarios which can be passted to to_pyarrow_dataset()

ldacey requested review from wjones127, fvaleye and roeap as code owners October 13, 2023 04:41

github-actions bot added the binding/python Issues for the Python package label Oct 13, 2023

ldacey added 3 commits October 15, 2023 02:57

Add support for overwriting partitions based on multiple conditions

133f246

Ref delta-io#1479

Add test_overwriting_multiple_partitions pytest

3b53dc4

- Tests partition filters based on AND and OR conditions using a single and multiple partition columns

Add check for empty lists in to_pyarrow_dataset partititions argument

f42494c

wjones127 requested changes Oct 14, 2023

View reviewed changes

ldacey added 4 commits October 17, 2023 12:23

Add tests for filters_to_expression, stringify_partition_values, and …

87e397f

…validate_filters

Add test_encode_partition_value and reorder type checks

32224d4

ldacey added 4 commits October 18, 2023 02:53

Clarify that the validate_filters returns a list of lists of tuples

1b52925

Update types and add validated_filters variable

e87a1ec

- validated_filters is guaranteed to be a list of list of tuples

Add @pytest.mark.pandas decorator to fix tests

87ce8e1

Add test for single filter

0b8b6bb

- Shows that the output will still be a list of lists of tuples

ldacey changed the title ~~feat: add support for multiple partition columns and filters in to_pyarrow_dataset()~~ feat: add support for multiple partition columns and filters in to_pyarrow_dataset() and OR filters in write_datalake() Oct 17, 2023

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feat: add support for multiple partition columns and filters in to_pyarrow_dataset() and OR filters in write_datalake() #1722

feat: add support for multiple partition columns and filters in to_pyarrow_dataset() and OR filters in write_datalake() #1722

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feat: add support for multiple partition columns and filters in to_pyarrow_dataset() and OR filters in write_datalake() #1722

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feat: add support for multiple partition columns and filters in to_pyarrow_dataset() and OR filters in write_datalake() #1722

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ldacey commented Oct 13, 2023

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