Writing a Dataframe to a partitioned table ignores partitioning

Describe the bug

After registering a parquet table with a partition column I write a dataframe to that table then attempt to read back the data from the table. This results in an error index out of bounds: the len is 0 but the index is 0. Upon inspecting the table path there is a written parquet file. This parquet file is readable although when being read directly it still contains the column for partitioning which to my understanding shouldn't be there if hive_partitioning is used. Additionally, the directory for the table contains no partitions and the parquet is at the top level.

./data
└── metrics
    └── hUnp6bKNqkJv4VHK_0.parquet

Also two things ofof note:

the compression is not included in the file suffix which is generally expected in the spark world
I believe the standard for spark is that the filename is actually a uuid

To Reproduce

This code is missing some of the supporting methods but is the gist of where the problem is.

    let records: Vec<VectorMetric> = serde_json::from_str(json).unwrap();

    let batch = VectorMetric::to_record_batch(records);

    let schema = Schema::new(VectorMetric::fields());
    let ctx = SessionContext::new();
    ctx.register_parquet(
        "metrics",
        "data/metrics",
        ParquetReadOptions::default()
            .table_partition_cols(vec![(
                "time_bucket".to_string(),
                DataType::Timestamp(TimeUnit::Second, None),
            )])
            .schema(&schema),
    )
    .await?;
    ctx.register_batch("batch", batch)?;

    let write_options =
        DataFrameWriteOptions::default().with_compression(CompressionTypeVariant::ZSTD);

    ctx.sql("SELECT *, DATE_TRUNC('DAY', timestamp) AS time_bucket FROM batch")
        .await?
        .write_table("metrics", write_options)
        .await?;

    let df = ctx.sql("SELECT * FROM metrics").await?.collect().await?;
    println!("{}", pretty_format_batches(&df).unwrap());

Expected behavior

Tables with partition cols should auto partition when writing to them.
Compression codec should be included in the parquet file suffix

Additional context

I can sanitize data and send the rest of the example if needed.

@theelderbeever

Thanks for the report @theelderbeever! Partitioned writes are not supported yet, so you should have received an error on the write_table call. There appears to be a bug in the logic detecting the attempt to write to a partitioned table.

I just so happened to open a PR adding support for partitioned writes recently (#7801), which should resolve this issue as a consequence. If you are so inclined, feel free to try out #7801 to see if you get the expected behavior with respect to partitioned writes.

#7801 does not add support for writing the compression codec in the file suffix. I think it would be a good idea to open an additional issue to track that for future work.

@devinjdangelo

@devinjdangelo Nice thats awesome on the timing! Ran against your branch and the only problem was lack of timestamp support. Coercing to a TEXT type causing a schema error unfortunately.

Error: Custom { kind: Other, error: NotImplemented("it is not yet supported to write to hive partitions with datatype Timestamp(Second, None)") }

Ran against your branch and the only problem was lack of timestamp support. Coercing to a TEXT type causing a schema error unfortunately.

How did you coerce? If you define your table so that the partition columns are of type STRING like below and also convert incoming timestamps to strings it should work. Basically the DDL of the table and the schema of the data to be written must both be string for any partition column currently. Some auto casting between common partitioning types would definitely be a nice improvement in the future.

CREATE EXTERNAL TABLE
partitioned_insert_test_pq(a string, b bigint)
STORED AS parquet
LOCATION 'test_files/scratch/insert_to_external/insert_to_partitioned_pq/'
PARTITIONED BY (a) 
OPTIONS(
create_local_path 'true',
insert_mode 'append_new_files',
);

INSERT INTO partitioned_insert_test_pq values (1, 2), (3, 4), (5, 6), (1, 2), (3, 4), (5, 6);
----
6

select * from partitioned_insert_test_pq order by a ASC, b ASC
----
1 2
1 2
3 4
3 4
5 6
5 6

@devinjdangelo

@devinjdangelo The clunky workaround though is to register the table with the timestamp partition column as a Utf8 when writing and re-register it with the correct datatypes when reading.

Also... Not sure if this was introduced on your branch or not but, the number of fields isn't being determined correctly when I have a Map column. I get this

ArrowError(InvalidArgumentError("number of columns(8) must match number of fields(11) in schema"))
however, removing the Map column fixes the problem. I didn't get this error on the main branch without hive partitioning working.

@devinjdangelo

@devinjdangelo jinx... but yeah defining the table to write to using strings then just manually casting the timestamp worked.

    let schema = Schema::new(VectorMetric::fields());
    let ctx = SessionContext::new();
    ctx.register_parquet(
        "metrics",
        "data/metrics",
        ParquetReadOptions::default()
            .table_partition_cols(vec![(
                "time_bucket".to_string(),
                // DataType::Timestamp(TimeUnit::Second, None),
                DataType::Utf8,
            )])
            .schema(&schema),
    )
    .await
    .unwrap();
    ctx.register_batch("batch", batch).unwrap();

    let write_options = DataFrameWriteOptions::default()
        .with_compression(CompressionTypeVariant::ZSTD)
        .with_single_file_output(false);

    let df = ctx
        .sql(
            r#"
        SELECT *, DATE_TRUNC('DAY', timestamp)::TEXT AS time_bucket FROM batch
        "#,
        )
        .await
        .unwrap()
        .write_table("metrics", write_options)
        .await
        .unwrap();

@theelderbeever

@theelderbeever agreed that that workaround is clunky...

Could you share a create table statement or example schema definition that is giving you issues, including with the map type? I can try to replicate and see if I can fix and possibly handle the type casting transparently.

@devinjdangelo

@devinjdangelo Sorry in advance for how gross this is... Its a sandbox right now

Clone this -> https://github.com/theelderbeever/datafusion-testing

The run mkdir data/metrics && cargo run and that should work.

The Cargo.toml is already pointed at your branch.

Commenting out L140 and L183 will make the write successful. Having them uncommented results in the error.

@theelderbeever

@theelderbeever thank you for this reproducible example! I was able to replicate the error and identify the root cause. It was indeed a flaw in how I was handling nested columns. I just pushed up a fix, which now handles nested columns correctly.

Let me know if the latest state of the branch is working for you a bit more smoothly now. I did not get to improving auto type casting to string type, but I plan to cut an issue for that if the branch is merged before I get to it (cc @alamb).

Navigation Menu

Repository navigation

Writing a Dataframe to a partitioned table ignores partitioning #7860

Describe the bug

To Reproduce

Expected behavior

Additional context

Metadata

Assignees

Labels

Type

Projects

Milestone

Relationships

Development

Writing a Dataframe to a partitioned table ignores partitioning #7860

Description

Describe the bug

To Reproduce

Expected behavior

Additional context

Activity

devinjdangelo commented on Oct 18, 2023

theelderbeever commented on Oct 18, 2023

devinjdangelo commented on Oct 18, 2023

theelderbeever commented on Oct 18, 2023

theelderbeever commented on Oct 18, 2023

devinjdangelo commented on Oct 18, 2023

theelderbeever commented on Oct 18, 2023

devinjdangelo commented on Oct 20, 2023

Metadata

Metadata

Assignees

Labels

Type

Projects

Milestone

Relationships

Development

Issue actions