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Posted to issues@spark.apache.org by "Ryan Blue (JIRA)" <ji...@apache.org> on 2019/03/05 18:15:00 UTC
[jira] [Updated] (SPARK-23521) SPIP: Standardize SQL logical plans
with DataSourceV2
[ https://issues.apache.org/jira/browse/SPARK-23521?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Ryan Blue updated SPARK-23521:
------------------------------
Attachment: SPIP_ Standardize logical plans.pdf
> SPIP: Standardize SQL logical plans with DataSourceV2
> -----------------------------------------------------
>
> Key: SPARK-23521
> URL: https://issues.apache.org/jira/browse/SPARK-23521
> Project: Spark
> Issue Type: Sub-task
> Components: SQL
> Affects Versions: 2.3.0
> Reporter: Ryan Blue
> Priority: Major
> Labels: SPIP
> Attachments: SPIP_ Standardize logical plans.pdf
>
>
> Executive Summary: This SPIP is based on [discussion about the DataSourceV2 implementation|https://lists.apache.org/thread.html/55676ec1f5039d3deaf347d391cf82fe8574b8fa4eeab70110ed5b2b@%3Cdev.spark.apache.org%3E] on the dev list. The proposal is to standardize the logical plans used for write operations to make the planner more maintainable and to make Spark's write behavior predictable and reliable. It proposes the following principles:
> # Use well-defined logical plan nodes for all high-level operations: insert, create, CTAS, overwrite table, etc.
> # Use planner rules that match on these high-level nodes, so that it isn’t necessary to create rules to match each eventual code path individually.
> # Clearly define Spark’s behavior for these logical plan nodes. Physical nodes should implement that behavior so that all code paths eventually make the same guarantees.
> # Specialize implementation when creating a physical plan, not logical plans. This will avoid behavior drift and ensure planner code is shared across physical implementations.
> The SPIP doc presents a small but complete set of those high-level logical operations, most of which are already defined in SQL or implemented by some write path in Spark.
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