[jira] [Updated] (SPARK-20622) Parquet partition discovery for non key=value named directories

["Noam Asor (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-20622?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Noam Asor updated SPARK-20622:
------------------------------
    Priority: Minor  (was: Major)

> Parquet partition discovery for non key=value named directories
> ---------------------------------------------------------------
>
>                 Key: SPARK-20622
>                 URL: https://issues.apache.org/jira/browse/SPARK-20622
>             Project: Spark
>          Issue Type: Improvement
>          Components: SQL
>    Affects Versions: 2.2.0
>            Reporter: Noam Asor
>            Priority: Minor
>
> h4. Why
> There are cases where traditional M/R jobs and RDD based Spark jobs writes out partitioned parquet in 'value only' named directories i.e. {{hdfs:///some/base/path/2017/05/06}} and not in 'key=value' named directories i.e. {{hdfs:///some/base/path/year=2017/month=05/day=06}} which prevents users from leveraging Spark SQL parquet partition discovery when reading the former back.
> h4. What
> This issue is a proposal for a solution which will allow Spark SQL to discover parquet partitions for 'value only' named directories.
> h4. How
> By introducing a new Spark SQL read option *partitionTemplate*.
> *partitionTemplate* is in a Path form and it should include base path followed by the missing 'key=' as a template for transforming 'value only' named dirs to 'key=value' named dirs. In the example above this will look like: 
> {{hdfs:///some/base/path/year=/month=/day=/}}.
> To simplify the solution this option should be tied with *basePath* option, meaning that *partitionTemplate* option is valid only if *basePath* is set also.
> In the end for the above scenario, this will look something like:
> {code}
> spark.read
>   .option("basePath", "hdfs:///some/base/path")
>   .option("partitionTemplate", "hdfs:///some/base/path/year=/month=/day=/")
>   .parquet(...)
> {code}
> which will allow Spark SQL to do parquet partition discovery on the following directory tree:
> {code}
> some
>   |--base
>        |--path
>              |--2016
>                   |--...
>              |--2017
>                    |--01
>                    |--02
>                        |--...
>                        |--15
>                        |--...
>                    |--...
> {code}
> adding to the schema of the resulted DataFrame the columns year, month, day and their respective values as expected.



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