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From "Apache Spark (JIRA)" <j...@apache.org>
Subject [jira] [Assigned] (SPARK-23325) DataSourceV2 readers should always produce InternalRow.
Date Fri, 20 Apr 2018 20:29:00 GMT

     [ https://issues.apache.org/jira/browse/SPARK-23325?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Apache Spark reassigned SPARK-23325:
------------------------------------

    Assignee: Apache Spark

> DataSourceV2 readers should always produce InternalRow.
> -------------------------------------------------------
>
>                 Key: SPARK-23325
>                 URL: https://issues.apache.org/jira/browse/SPARK-23325
>             Project: Spark
>          Issue Type: Sub-task
>          Components: SQL
>    Affects Versions: 2.3.0
>            Reporter: Ryan Blue
>            Assignee: Apache Spark
>            Priority: Major
>
> DataSourceV2 row-oriented implementations are limited to producing either {{Row}} instances or
{{UnsafeRow}} instances by implementing {{SupportsScanUnsafeRow}}. Instead, I think that implementations
should always produce {{InternalRow}}.
> The problem with the choice between {{Row}} and {{UnsafeRow}} is that neither one is
appropriate for implementers.
> File formats don't produce {{Row}} instances or the data values used by {{Row}}, like {{java.sql.Timestamp}}
and {{java.sql.Date}}. An implementation that uses {{Row}} instances must produce data that
is immediately translated from the representation that was just produced by Spark. In my experience,
it made little sense to translate a timestamp in microseconds to a (milliseconds, nanoseconds)
pair, create a {{Timestamp}} instance, and pass that instance to Spark for immediate translation
back.
> On the other hand, {{UnsafeRow}} is very difficult to produce unless data is already
held in memory. Even the Parquet support built into Spark deserializes to {{InternalRow}} and
then uses {{UnsafeProjection}} to produce unsafe rows. When I went to build an implementation
that deserializes Parquet or Avro directly to {{UnsafeRow}} (I tried both), I found that
it couldn't be done without first deserializing into memory because the size of an array must
be known before any values are written.
> I ended up deciding to deserialize to {{InternalRow}} and use {{UnsafeProjection}}
to convert to unsafe. There are two problems with this: first, this is Scala and was difficult
to call from Java (it required reflection), and second, this causes double projection in
the physical plan (a copy for unsafe to unsafe) if there is a projection that wasn't fully
pushed to the data source.
> I think the solution is to have a single interface for readers that expects {{InternalRow}}.
Then, a projection should be added in the Spark plan to convert to unsafe and avoid projection
in the plan and in the data source. If the data source already produces unsafe rows by deserializing
directly, this still minimizes the number of copies because the unsafe projection will check
whether the incoming data is already {{UnsafeRow}}.
> Using {{InternalRow}} would also match the interface on the write side.



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