[jira] [Resolved] (SPARK-34194) Queries that only touch partition columns shouldn't scan through all files

["Nicholas Chammas (Jira)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-34194?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Nicholas Chammas resolved SPARK-34194.
--------------------------------------
    Resolution: Won't Fix

> Queries that only touch partition columns shouldn't scan through all files
> --------------------------------------------------------------------------
>
>                 Key: SPARK-34194
>                 URL: https://issues.apache.org/jira/browse/SPARK-34194
>             Project: Spark
>          Issue Type: Improvement
>          Components: SQL
>    Affects Versions: 3.2.0
>            Reporter: Nicholas Chammas
>            Priority: Minor
>
> When querying only the partition columns of a partitioned table, it seems that Spark nonetheless scans through all files in the table, even though it doesn't need to.
> Here's an example:
> {code:python}
> >>> data = spark.read.option('mergeSchema', 'false').parquet('s3a://some/dataset')
> [Stage 0:==================>                                  (407 + 12) / 1158]
> {code}
> Note the 1158 tasks. This matches the number of partitions in the table, which is partitioned on a single field named {{file_date}}:
> {code:sh}
> $ aws s3 ls s3://some/dataset | head -n 3
>                            PRE file_date=2017-05-01/
>                            PRE file_date=2017-05-02/
>                            PRE file_date=2017-05-03/
> $ aws s3 ls s3://some/dataset | wc -l
>     1158
> {code}
> The table itself has over 138K files, though:
> {code:sh}
> $ aws s3 ls --recursive --human --summarize s3://some/dataset
> ...
> Total Objects: 138708
>    Total Size: 3.7 TiB
> {code}
> Now let's try to query just the {{file_date}} field and see what Spark does.
> {code:python}
> >>> data.select('file_date').orderBy('file_date', ascending=False).limit(1).explain()
> == Physical Plan ==
> TakeOrderedAndProject(limit=1, orderBy=[file_date#11 DESC NULLS LAST], output=[file_date#11])
> +- *(1) ColumnarToRow
>    +- FileScan parquet [file_date#11] Batched: true, DataFilters: [], Format: Parquet, Location: InMemoryFileIndex[s3a://some/dataset], PartitionFilters: [], PushedFilters: [], ReadSchema: struct<>
> >>> data.select('file_date').orderBy('file_date', ascending=False).limit(1).show()
> [Stage 2:>                                                   (179 + 12) / 41011]
> {code}
> Notice that Spark has spun up 41,011 tasks. Maybe more will be needed as the job progresses? I'm not sure.
> What I do know is that this operation takes a long time (~20 min) running from my laptop, whereas to list the top-level {{file_date}} partitions via the AWS CLI take a second or two.
> Spark appears to be going through all the files in the table, when it just needs to list the partitions captured in the S3 "directory" structure. The query is only touching {{file_date}}, after all.
> The current workaround for this performance problem / optimizer wastefulness, is to [query the catalog directly|https://stackoverflow.com/a/65724151/877069]. It works, but is a lot of extra work compared to the elegant query against {{file_date}} that users actually intend.
> Spark should somehow know when it is only querying partition fields and skip iterating through all the individual files in a table.
> Tested on Spark 3.0.1.



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