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

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

Noam Asor created SPARK-20622:
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             Summary: 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


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("basePath", "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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