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https://issues.apache.org/jira/browse/SPARK-20622?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16040515#comment-16040515
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Noam Asor commented on SPARK-20622:
-----------------------------------
The provided pull request is not complete and is rather in a POC state.
If it will be useful enough to be looked at and considered as part of Spark
than it should get polished first.
> 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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