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https://issues.apache.org/jira/browse/SPARK-23521?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Ryan Blue updated SPARK-23521:
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Description:
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.
The SPIP doc presents a small set of operations, most of which are already
defined in SQL or implemented by some write path in Spark. This set is complete
enough to handle all the write cases.
was:
Executive Summary: This SPIP is based on discussion about the DataSourceV2
implementation 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.
The SPIP doc presents a small set of operations, most of which are already
defined in SQL or implemented by some write path in Spark. This set is complete
enough to handle all the write cases.
> SPIP: Standardize SQL logical plans with DataSourceV2
> -----------------------------------------------------
>
> Key: SPARK-23521
> URL: https://issues.apache.org/jira/browse/SPARK-23521
> Project: Spark
> Issue Type: Improvement
> Components: SQL
> Affects Versions: 2.3.0
> Reporter: Ryan Blue
> Priority: Major
> Labels: SPIP
>
> 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.
> The SPIP doc presents a small set of operations, most of which are already
> defined in SQL or implemented by some write path in Spark. This set is
> complete enough to handle all the write cases.
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