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Posted to issues@spark.apache.org by "Reynold Xin (JIRA)" <ji...@apache.org> on 2016/11/01 20:41:58 UTC
[jira] [Updated] (SPARK-15687) Columnar execution engine
[ https://issues.apache.org/jira/browse/SPARK-15687?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Reynold Xin updated SPARK-15687:
--------------------------------
Target Version/s: (was: 2.1.0)
> Columnar execution engine
> -------------------------
>
> Key: SPARK-15687
> URL: https://issues.apache.org/jira/browse/SPARK-15687
> Project: Spark
> Issue Type: New Feature
> Components: SQL
> Reporter: Reynold Xin
> Priority: Critical
>
> This ticket tracks progress in making the entire engine columnar, especially in the context of nested data type support.
> In Spark 2.0, we have used the internal column batch interface in Parquet reading (via a vectorized Parquet decoder) and low cardinality aggregation. Other parts of the engine are already using whole-stage code generation, which is in many ways more efficient than a columnar execution engine for flat data types.
> The goal here is to figure out a story to work towards making column batch the common data exchange format between operators outside whole-stage code generation, as well as with external systems (e.g. Pandas).
> Some of the important questions to answer are:
> From the architectural perspective:
> - What is the end state architecture?
> - Should aggregation be columnar?
> - Should sorting be columnar?
> - How do we encode nested data? What are the operations on nested data, and how do we handle these operations in a columnar format?
> - What is the transition plan towards the end state?
> From an external API perspective:
> - Can we expose a more efficient column batch user-defined function API?
> - How do we leverage this to integrate with 3rd party tools?
> - Can we have a spec for a fixed version of the column batch format that can be externalized and use that in data source API v2?
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