[jira] [Updated] (SPARK-15420) Repartition and sort before Parquet writes
[
https://issues.apache.org/jira/browse/SPARK-15420?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Ryan Blue updated SPARK-15420:
--
Target Version/s: (was: 2.4.0)
> Repartition and sort before Parquet writes
> --
>
> Key: SPARK-15420
> URL: https://issues.apache.org/jira/browse/SPARK-15420
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Affects Versions: 1.6.1
>Reporter: Ryan Blue
>Priority: Major
>
> Parquet requires buffering data in memory before writing a group of rows
> organized by column. This causes significant memory pressure when writing
> partitioned output because each open file must buffer rows.
> Currently, Spark will sort data and spill if necessary in the
> {{WriterContainer}} to avoid keeping many files open at once. But, this isn't
> a full solution for a few reasons:
> * The final sort is always performed, even if incoming data is already sorted
> correctly. For example, a global sort will cause two sorts to happen, even if
> the global sort correctly prepares the data.
> * To prevent a large number of output small output files, users must manually
> add a repartition step. That step is also ignored by the sort within the
> writer.
> * Hive does not currently support {{DataFrameWriter#sortBy}}
> The sort in {{WriterContainer}} makes sense to prevent problems, but should
> detect if the incoming data is already sorted. The {{DataFrameWriter}} should
> also expose the ability to repartition data before the write stage, and the
> query planner should expose an option to automatically insert repartition
> operations.
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[jira] [Updated] (SPARK-15420) Repartition and sort before Parquet writes
[
https://issues.apache.org/jira/browse/SPARK-15420?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Sameer Agarwal updated SPARK-15420:
---
Target Version/s: 2.4.0 (was: 2.3.0)
> Repartition and sort before Parquet writes
> --
>
> Key: SPARK-15420
> URL: https://issues.apache.org/jira/browse/SPARK-15420
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Affects Versions: 1.6.1
>Reporter: Ryan Blue
>
> Parquet requires buffering data in memory before writing a group of rows
> organized by column. This causes significant memory pressure when writing
> partitioned output because each open file must buffer rows.
> Currently, Spark will sort data and spill if necessary in the
> {{WriterContainer}} to avoid keeping many files open at once. But, this isn't
> a full solution for a few reasons:
> * The final sort is always performed, even if incoming data is already sorted
> correctly. For example, a global sort will cause two sorts to happen, even if
> the global sort correctly prepares the data.
> * To prevent a large number of output small output files, users must manually
> add a repartition step. That step is also ignored by the sort within the
> writer.
> * Hive does not currently support {{DataFrameWriter#sortBy}}
> The sort in {{WriterContainer}} makes sense to prevent problems, but should
> detect if the incoming data is already sorted. The {{DataFrameWriter}} should
> also expose the ability to repartition data before the write stage, and the
> query planner should expose an option to automatically insert repartition
> operations.
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[jira] [Updated] (SPARK-15420) Repartition and sort before Parquet writes
[
https://issues.apache.org/jira/browse/SPARK-15420?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Michael Armbrust updated SPARK-15420:
-
Target Version/s: 2.3.0 (was: 2.2.0)
> Repartition and sort before Parquet writes
> --
>
> Key: SPARK-15420
> URL: https://issues.apache.org/jira/browse/SPARK-15420
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Affects Versions: 1.6.1
>Reporter: Ryan Blue
>
> Parquet requires buffering data in memory before writing a group of rows
> organized by column. This causes significant memory pressure when writing
> partitioned output because each open file must buffer rows.
> Currently, Spark will sort data and spill if necessary in the
> {{WriterContainer}} to avoid keeping many files open at once. But, this isn't
> a full solution for a few reasons:
> * The final sort is always performed, even if incoming data is already sorted
> correctly. For example, a global sort will cause two sorts to happen, even if
> the global sort correctly prepares the data.
> * To prevent a large number of output small output files, users must manually
> add a repartition step. That step is also ignored by the sort within the
> writer.
> * Hive does not currently support {{DataFrameWriter#sortBy}}
> The sort in {{WriterContainer}} makes sense to prevent problems, but should
> detect if the incoming data is already sorted. The {{DataFrameWriter}} should
> also expose the ability to repartition data before the write stage, and the
> query planner should expose an option to automatically insert repartition
> operations.
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[jira] [Updated] (SPARK-15420) Repartition and sort before Parquet writes
[
https://issues.apache.org/jira/browse/SPARK-15420?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Reynold Xin updated SPARK-15420:
Target Version/s: 2.2.0 (was: 2.1.0)
> Repartition and sort before Parquet writes
> --
>
> Key: SPARK-15420
> URL: https://issues.apache.org/jira/browse/SPARK-15420
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Affects Versions: 1.6.1
>Reporter: Ryan Blue
>
> Parquet requires buffering data in memory before writing a group of rows
> organized by column. This causes significant memory pressure when writing
> partitioned output because each open file must buffer rows.
> Currently, Spark will sort data and spill if necessary in the
> {{WriterContainer}} to avoid keeping many files open at once. But, this isn't
> a full solution for a few reasons:
> * The final sort is always performed, even if incoming data is already sorted
> correctly. For example, a global sort will cause two sorts to happen, even if
> the global sort correctly prepares the data.
> * To prevent a large number of output small output files, users must manually
> add a repartition step. That step is also ignored by the sort within the
> writer.
> * Hive does not currently support {{DataFrameWriter#sortBy}}
> The sort in {{WriterContainer}} makes sense to prevent problems, but should
> detect if the incoming data is already sorted. The {{DataFrameWriter}} should
> also expose the ability to repartition data before the write stage, and the
> query planner should expose an option to automatically insert repartition
> operations.
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[jira] [Updated] (SPARK-15420) Repartition and sort before Parquet writes
[
https://issues.apache.org/jira/browse/SPARK-15420?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Reynold Xin updated SPARK-15420:
Target Version/s: 2.1.0
> Repartition and sort before Parquet writes
> --
>
> Key: SPARK-15420
> URL: https://issues.apache.org/jira/browse/SPARK-15420
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Affects Versions: 1.6.1
>Reporter: Ryan Blue
>
> Parquet requires buffering data in memory before writing a group of rows
> organized by column. This causes significant memory pressure when writing
> partitioned output because each open file must buffer rows.
> Currently, Spark will sort data and spill if necessary in the
> {{WriterContainer}} to avoid keeping many files open at once. But, this isn't
> a full solution for a few reasons:
> * The final sort is always performed, even if incoming data is already sorted
> correctly. For example, a global sort will cause two sorts to happen, even if
> the global sort correctly prepares the data.
> * To prevent a large number of output small output files, users must manually
> add a repartition step. That step is also ignored by the sort within the
> writer.
> * Hive does not currently support {{DataFrameWriter#sortBy}}
> The sort in {{WriterContainer}} makes sense to prevent problems, but should
> detect if the incoming data is already sorted. The {{DataFrameWriter}} should
> also expose the ability to repartition data before the write stage, and the
> query planner should expose an option to automatically insert repartition
> operations.
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