[jira] [Assigned] (SPARK-20937) Describe spark.sql.parquet.writeLegacyFormat property in Spark SQL, DataFrames and Datasets Guide

2018-09-26 Thread Hyukjin Kwon (JIRA)


 [ 
https://issues.apache.org/jira/browse/SPARK-20937?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Hyukjin Kwon reassigned SPARK-20937:


Assignee: Chenxiao Mao

> Describe spark.sql.parquet.writeLegacyFormat property in Spark SQL, 
> DataFrames and Datasets Guide
> -
>
> Key: SPARK-20937
> URL: https://issues.apache.org/jira/browse/SPARK-20937
> Project: Spark
>  Issue Type: Improvement
>  Components: Documentation, SQL
>Affects Versions: 2.3.0
>Reporter: Jacek Laskowski
>Assignee: Chenxiao Mao
>Priority: Trivial
> Fix For: 2.5.0, 2.4.1
>
>
> As a follow-up to SPARK-20297 (and SPARK-10400) in which 
> {{spark.sql.parquet.writeLegacyFormat}} property was recommended for Impala 
> and Hive, Spark SQL docs for [Parquet 
> Files|https://spark.apache.org/docs/latest/sql-programming-guide.html#configuration]
>  should have it documented.
> p.s. It was asked about in [Why can't Impala read parquet files after Spark 
> SQL's write?|https://stackoverflow.com/q/44279870/1305344] on StackOverflow 
> today.
> p.s. It's also covered in [~holden.ka...@gmail.com]'s "High Performance 
> Spark: Best Practices for Scaling and Optimizing Apache Spark" book (in Table 
> 3-10. Parquet data source options) that gives the option some wider publicity.



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[jira] [Assigned] (SPARK-20937) Describe spark.sql.parquet.writeLegacyFormat property in Spark SQL, DataFrames and Datasets Guide

2018-09-21 Thread Apache Spark (JIRA)


 [ 
https://issues.apache.org/jira/browse/SPARK-20937?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Apache Spark reassigned SPARK-20937:


Assignee: Apache Spark

> Describe spark.sql.parquet.writeLegacyFormat property in Spark SQL, 
> DataFrames and Datasets Guide
> -
>
> Key: SPARK-20937
> URL: https://issues.apache.org/jira/browse/SPARK-20937
> Project: Spark
>  Issue Type: Improvement
>  Components: Documentation, SQL
>Affects Versions: 2.3.0
>Reporter: Jacek Laskowski
>Assignee: Apache Spark
>Priority: Trivial
>
> As a follow-up to SPARK-20297 (and SPARK-10400) in which 
> {{spark.sql.parquet.writeLegacyFormat}} property was recommended for Impala 
> and Hive, Spark SQL docs for [Parquet 
> Files|https://spark.apache.org/docs/latest/sql-programming-guide.html#configuration]
>  should have it documented.
> p.s. It was asked about in [Why can't Impala read parquet files after Spark 
> SQL's write?|https://stackoverflow.com/q/44279870/1305344] on StackOverflow 
> today.
> p.s. It's also covered in [~holden.ka...@gmail.com]'s "High Performance 
> Spark: Best Practices for Scaling and Optimizing Apache Spark" book (in Table 
> 3-10. Parquet data source options) that gives the option some wider publicity.



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[jira] [Assigned] (SPARK-20937) Describe spark.sql.parquet.writeLegacyFormat property in Spark SQL, DataFrames and Datasets Guide

2018-09-21 Thread Apache Spark (JIRA)


 [ 
https://issues.apache.org/jira/browse/SPARK-20937?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Apache Spark reassigned SPARK-20937:


Assignee: (was: Apache Spark)

> Describe spark.sql.parquet.writeLegacyFormat property in Spark SQL, 
> DataFrames and Datasets Guide
> -
>
> Key: SPARK-20937
> URL: https://issues.apache.org/jira/browse/SPARK-20937
> Project: Spark
>  Issue Type: Improvement
>  Components: Documentation, SQL
>Affects Versions: 2.3.0
>Reporter: Jacek Laskowski
>Priority: Trivial
>
> As a follow-up to SPARK-20297 (and SPARK-10400) in which 
> {{spark.sql.parquet.writeLegacyFormat}} property was recommended for Impala 
> and Hive, Spark SQL docs for [Parquet 
> Files|https://spark.apache.org/docs/latest/sql-programming-guide.html#configuration]
>  should have it documented.
> p.s. It was asked about in [Why can't Impala read parquet files after Spark 
> SQL's write?|https://stackoverflow.com/q/44279870/1305344] on StackOverflow 
> today.
> p.s. It's also covered in [~holden.ka...@gmail.com]'s "High Performance 
> Spark: Best Practices for Scaling and Optimizing Apache Spark" book (in Table 
> 3-10. Parquet data source options) that gives the option some wider publicity.



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