Spark 2.3.1 insertinto table (s3) with parititions runs many queries before the actual write












0















I have a very simple Spark job that writes to S3.
The table has 3 different partition keys and many values (some of them is getting bigger every hour).



I am using the following code:



dataframe.select(reorderFields:_*).write.mode(SaveMode.Overwrite).insertInto(tableName)


At the beginning this code was pretty efficient. But after the table got bigger, it became slower and slower.



When opened a debug log I found many reads to hive before it even starts the calculation of the dataframe.



LOGS:



2019-01-03 16:50:58 [main] DataNucleus.Datastore:58 [DEBUG]: Closing PreparedStatement "com.jolbox.bonecp.PreparedStatementHandle@5470ec7e"
2019-01-03 16:50:58 [main] DataNucleus.Datastore.Native:58 [DEBUG]: SELECT `A0`.`COLUMN_NAME`,`A0`.`ORDER`,`A0`.`INTEGER_IDX` AS NUCORDER0 FROM `SORT_COLS` `A0` WHERE `A0`.`SD_ID` = <297323> AND `A0`.`INTEGER_IDX` >= 0 ORDER BY NUCORDER0
2019-01-03 16:50:58 [main] DataNucleus.Datastore.Retrieve:58 [DEBUG]: Execution Time = 1 ms
2019-01-03 16:50:58 [main] DataNucleus.Datastore:58 [DEBUG]: Closing PreparedStatement "org.datanucleus.store.rdbms.ParamLoggingPreparedStatement@325b1c61"
2019-01-03 16:50:58 [main] DataNucleus.Persistence:58 [DEBUG]: Object "org.apache.hadoop.hive.metastore.model.MStorageDescriptor@6328ec75" field "parameters" is replaced by a SCO wrapper of type "org.datanucleus.store.types.backed.Map" [cache-values=true, lazy-loading=true, queued-operations=false, allow-nulls=true]
2019-01-03 16:50:58 [main] DataNucleus.Persistence:58 [DEBUG]: Object "org.apache.hadoop.hive.metastore.model.MStorageDescriptor@6328ec75" field "parameters" loading contents to SCO wrapper from the datastore
2019-01-03 16:50:58 [main] DataNucleus.Connection:58 [DEBUG]: Connection found in the pool : org.datanucleus.store.rdbms.ConnectionFactoryImpl$ManagedConnectionImpl@236ec794 [conn=com.jolbox.bonecp.ConnectionHandle@712106b5, commitOnRelease=false, closeOnRelease=false, closeOnTxnEnd=true] for key=org.datanucleus.ExecutionContextThreadedImpl@132e3594 in factory=ConnectionFactory:tx[org.datanucleus.store.rdbms.ConnectionFactoryImpl@72c9ebfa]
2019-01-03 16:50:58 [main] DataNucleus.Datastore:58 [DEBUG]: Closing PreparedStatement "com.jolbox.bonecp.PreparedStatementHandle@250ebae4"
2019-01-03 16:50:58 [main] DataNucleus.Datastore.Native:58 [DEBUG]: SELECT `A0`.`PARAM_KEY`,`A0`.`PARAM_VALUE` FROM `SD_PARAMS` `A0` WHERE `A0`.`SD_ID` = <297323> AND `A0`.`PARAM_KEY` IS NOT NULL
2019-01-03 16:50:58 [main] DataNucleus.Datastore.Retrieve:58 [DEBUG]: Execution Time = 1 ms
2019-01-03 16:50:58 [main] DataNucleus.Datastore:58 [DEBUG]: Closing PreparedStatement "org.datanucleus.store.rdbms.ParamLoggingPreparedStatement@798a320"
2019-01-03 16:50:58 [main] DataNucleus.Persistence:58 [DEBUG]: Object "org.apache.hadoop.hive.metastore.model.MStorageDescriptor@6328ec75" field "skewedColNames" is replaced by a SCO wrapper of type "org.datanucleus.store.types.backed.List" [cache-values=true, lazy-loading=true, queued-operations=false, allow-nulls=true]
2019-01-03 16:50:58 [main] DataNucleus.Persistence:58 [DEBUG]: Object "org.apache.hadoop.hive.metastore.model.MStorageDescriptor@6328ec75" field "skewedColNames" loading contents to SCO wrapper from the datastore
2019-01-03 16:50:58 [main] DataNucleus.Connection:58 [DEBUG]: Connection found in the pool : org.datanucleus.store.rdbms.ConnectionFactoryImpl$ManagedConnectionImpl@236ec794 [conn=com.jolbox.bonecp.ConnectionHandle@712106b5, commitOnRelease=false, closeOnRelease=false, closeOnTxnEnd=true] for key=org.datanucleus.ExecutionContextThreadedImpl@132e3594 in factory=ConnectionFactory:tx[org.datanucleus.store.rdbms.ConnectionFactoryImpl@72c9ebfa]
2019-01-03 16:50:58 [main] DataNucleus.Datastore:58 [DEBUG]: Closing PreparedStatement "com.jolbox.bonecp.PreparedStatementHandle@540637b0"


I tried to reconfigure my hive with the following parameters:



sparkConf.set("hive.auto.convert.join.noconditionaltask.size","200M")
sparkConf.set("hive.auto.convert.join.noconditionaltask","true")
sparkConf.set("hive.optimize.sort.dynamic.partition","false")
sparkConf.set("spark.sql.hive.convertMetastoreParquet.mergeSchema","false")
sparkConf.set("parquet.enable.summary-metadata","false")


Also added to hive.xml



  <property>
<name>hive.stats.autogather</name>
<value>false</value>
</property>


But it still acts the same.



I am not working with HDFS.



I will appreciate any suggestion ??










share|improve this question

























  • Problem is with Read or S3 Write?

    – Kaushal
    Jan 3 at 18:42











  • @ Kaushal With write using insertInto

    – Ehud Lev
    Jan 3 at 18:43
















0















I have a very simple Spark job that writes to S3.
The table has 3 different partition keys and many values (some of them is getting bigger every hour).



I am using the following code:



dataframe.select(reorderFields:_*).write.mode(SaveMode.Overwrite).insertInto(tableName)


At the beginning this code was pretty efficient. But after the table got bigger, it became slower and slower.



When opened a debug log I found many reads to hive before it even starts the calculation of the dataframe.



LOGS:



2019-01-03 16:50:58 [main] DataNucleus.Datastore:58 [DEBUG]: Closing PreparedStatement "com.jolbox.bonecp.PreparedStatementHandle@5470ec7e"
2019-01-03 16:50:58 [main] DataNucleus.Datastore.Native:58 [DEBUG]: SELECT `A0`.`COLUMN_NAME`,`A0`.`ORDER`,`A0`.`INTEGER_IDX` AS NUCORDER0 FROM `SORT_COLS` `A0` WHERE `A0`.`SD_ID` = <297323> AND `A0`.`INTEGER_IDX` >= 0 ORDER BY NUCORDER0
2019-01-03 16:50:58 [main] DataNucleus.Datastore.Retrieve:58 [DEBUG]: Execution Time = 1 ms
2019-01-03 16:50:58 [main] DataNucleus.Datastore:58 [DEBUG]: Closing PreparedStatement "org.datanucleus.store.rdbms.ParamLoggingPreparedStatement@325b1c61"
2019-01-03 16:50:58 [main] DataNucleus.Persistence:58 [DEBUG]: Object "org.apache.hadoop.hive.metastore.model.MStorageDescriptor@6328ec75" field "parameters" is replaced by a SCO wrapper of type "org.datanucleus.store.types.backed.Map" [cache-values=true, lazy-loading=true, queued-operations=false, allow-nulls=true]
2019-01-03 16:50:58 [main] DataNucleus.Persistence:58 [DEBUG]: Object "org.apache.hadoop.hive.metastore.model.MStorageDescriptor@6328ec75" field "parameters" loading contents to SCO wrapper from the datastore
2019-01-03 16:50:58 [main] DataNucleus.Connection:58 [DEBUG]: Connection found in the pool : org.datanucleus.store.rdbms.ConnectionFactoryImpl$ManagedConnectionImpl@236ec794 [conn=com.jolbox.bonecp.ConnectionHandle@712106b5, commitOnRelease=false, closeOnRelease=false, closeOnTxnEnd=true] for key=org.datanucleus.ExecutionContextThreadedImpl@132e3594 in factory=ConnectionFactory:tx[org.datanucleus.store.rdbms.ConnectionFactoryImpl@72c9ebfa]
2019-01-03 16:50:58 [main] DataNucleus.Datastore:58 [DEBUG]: Closing PreparedStatement "com.jolbox.bonecp.PreparedStatementHandle@250ebae4"
2019-01-03 16:50:58 [main] DataNucleus.Datastore.Native:58 [DEBUG]: SELECT `A0`.`PARAM_KEY`,`A0`.`PARAM_VALUE` FROM `SD_PARAMS` `A0` WHERE `A0`.`SD_ID` = <297323> AND `A0`.`PARAM_KEY` IS NOT NULL
2019-01-03 16:50:58 [main] DataNucleus.Datastore.Retrieve:58 [DEBUG]: Execution Time = 1 ms
2019-01-03 16:50:58 [main] DataNucleus.Datastore:58 [DEBUG]: Closing PreparedStatement "org.datanucleus.store.rdbms.ParamLoggingPreparedStatement@798a320"
2019-01-03 16:50:58 [main] DataNucleus.Persistence:58 [DEBUG]: Object "org.apache.hadoop.hive.metastore.model.MStorageDescriptor@6328ec75" field "skewedColNames" is replaced by a SCO wrapper of type "org.datanucleus.store.types.backed.List" [cache-values=true, lazy-loading=true, queued-operations=false, allow-nulls=true]
2019-01-03 16:50:58 [main] DataNucleus.Persistence:58 [DEBUG]: Object "org.apache.hadoop.hive.metastore.model.MStorageDescriptor@6328ec75" field "skewedColNames" loading contents to SCO wrapper from the datastore
2019-01-03 16:50:58 [main] DataNucleus.Connection:58 [DEBUG]: Connection found in the pool : org.datanucleus.store.rdbms.ConnectionFactoryImpl$ManagedConnectionImpl@236ec794 [conn=com.jolbox.bonecp.ConnectionHandle@712106b5, commitOnRelease=false, closeOnRelease=false, closeOnTxnEnd=true] for key=org.datanucleus.ExecutionContextThreadedImpl@132e3594 in factory=ConnectionFactory:tx[org.datanucleus.store.rdbms.ConnectionFactoryImpl@72c9ebfa]
2019-01-03 16:50:58 [main] DataNucleus.Datastore:58 [DEBUG]: Closing PreparedStatement "com.jolbox.bonecp.PreparedStatementHandle@540637b0"


I tried to reconfigure my hive with the following parameters:



sparkConf.set("hive.auto.convert.join.noconditionaltask.size","200M")
sparkConf.set("hive.auto.convert.join.noconditionaltask","true")
sparkConf.set("hive.optimize.sort.dynamic.partition","false")
sparkConf.set("spark.sql.hive.convertMetastoreParquet.mergeSchema","false")
sparkConf.set("parquet.enable.summary-metadata","false")


Also added to hive.xml



  <property>
<name>hive.stats.autogather</name>
<value>false</value>
</property>


But it still acts the same.



I am not working with HDFS.



I will appreciate any suggestion ??










share|improve this question

























  • Problem is with Read or S3 Write?

    – Kaushal
    Jan 3 at 18:42











  • @ Kaushal With write using insertInto

    – Ehud Lev
    Jan 3 at 18:43














0












0








0








I have a very simple Spark job that writes to S3.
The table has 3 different partition keys and many values (some of them is getting bigger every hour).



I am using the following code:



dataframe.select(reorderFields:_*).write.mode(SaveMode.Overwrite).insertInto(tableName)


At the beginning this code was pretty efficient. But after the table got bigger, it became slower and slower.



When opened a debug log I found many reads to hive before it even starts the calculation of the dataframe.



LOGS:



2019-01-03 16:50:58 [main] DataNucleus.Datastore:58 [DEBUG]: Closing PreparedStatement "com.jolbox.bonecp.PreparedStatementHandle@5470ec7e"
2019-01-03 16:50:58 [main] DataNucleus.Datastore.Native:58 [DEBUG]: SELECT `A0`.`COLUMN_NAME`,`A0`.`ORDER`,`A0`.`INTEGER_IDX` AS NUCORDER0 FROM `SORT_COLS` `A0` WHERE `A0`.`SD_ID` = <297323> AND `A0`.`INTEGER_IDX` >= 0 ORDER BY NUCORDER0
2019-01-03 16:50:58 [main] DataNucleus.Datastore.Retrieve:58 [DEBUG]: Execution Time = 1 ms
2019-01-03 16:50:58 [main] DataNucleus.Datastore:58 [DEBUG]: Closing PreparedStatement "org.datanucleus.store.rdbms.ParamLoggingPreparedStatement@325b1c61"
2019-01-03 16:50:58 [main] DataNucleus.Persistence:58 [DEBUG]: Object "org.apache.hadoop.hive.metastore.model.MStorageDescriptor@6328ec75" field "parameters" is replaced by a SCO wrapper of type "org.datanucleus.store.types.backed.Map" [cache-values=true, lazy-loading=true, queued-operations=false, allow-nulls=true]
2019-01-03 16:50:58 [main] DataNucleus.Persistence:58 [DEBUG]: Object "org.apache.hadoop.hive.metastore.model.MStorageDescriptor@6328ec75" field "parameters" loading contents to SCO wrapper from the datastore
2019-01-03 16:50:58 [main] DataNucleus.Connection:58 [DEBUG]: Connection found in the pool : org.datanucleus.store.rdbms.ConnectionFactoryImpl$ManagedConnectionImpl@236ec794 [conn=com.jolbox.bonecp.ConnectionHandle@712106b5, commitOnRelease=false, closeOnRelease=false, closeOnTxnEnd=true] for key=org.datanucleus.ExecutionContextThreadedImpl@132e3594 in factory=ConnectionFactory:tx[org.datanucleus.store.rdbms.ConnectionFactoryImpl@72c9ebfa]
2019-01-03 16:50:58 [main] DataNucleus.Datastore:58 [DEBUG]: Closing PreparedStatement "com.jolbox.bonecp.PreparedStatementHandle@250ebae4"
2019-01-03 16:50:58 [main] DataNucleus.Datastore.Native:58 [DEBUG]: SELECT `A0`.`PARAM_KEY`,`A0`.`PARAM_VALUE` FROM `SD_PARAMS` `A0` WHERE `A0`.`SD_ID` = <297323> AND `A0`.`PARAM_KEY` IS NOT NULL
2019-01-03 16:50:58 [main] DataNucleus.Datastore.Retrieve:58 [DEBUG]: Execution Time = 1 ms
2019-01-03 16:50:58 [main] DataNucleus.Datastore:58 [DEBUG]: Closing PreparedStatement "org.datanucleus.store.rdbms.ParamLoggingPreparedStatement@798a320"
2019-01-03 16:50:58 [main] DataNucleus.Persistence:58 [DEBUG]: Object "org.apache.hadoop.hive.metastore.model.MStorageDescriptor@6328ec75" field "skewedColNames" is replaced by a SCO wrapper of type "org.datanucleus.store.types.backed.List" [cache-values=true, lazy-loading=true, queued-operations=false, allow-nulls=true]
2019-01-03 16:50:58 [main] DataNucleus.Persistence:58 [DEBUG]: Object "org.apache.hadoop.hive.metastore.model.MStorageDescriptor@6328ec75" field "skewedColNames" loading contents to SCO wrapper from the datastore
2019-01-03 16:50:58 [main] DataNucleus.Connection:58 [DEBUG]: Connection found in the pool : org.datanucleus.store.rdbms.ConnectionFactoryImpl$ManagedConnectionImpl@236ec794 [conn=com.jolbox.bonecp.ConnectionHandle@712106b5, commitOnRelease=false, closeOnRelease=false, closeOnTxnEnd=true] for key=org.datanucleus.ExecutionContextThreadedImpl@132e3594 in factory=ConnectionFactory:tx[org.datanucleus.store.rdbms.ConnectionFactoryImpl@72c9ebfa]
2019-01-03 16:50:58 [main] DataNucleus.Datastore:58 [DEBUG]: Closing PreparedStatement "com.jolbox.bonecp.PreparedStatementHandle@540637b0"


I tried to reconfigure my hive with the following parameters:



sparkConf.set("hive.auto.convert.join.noconditionaltask.size","200M")
sparkConf.set("hive.auto.convert.join.noconditionaltask","true")
sparkConf.set("hive.optimize.sort.dynamic.partition","false")
sparkConf.set("spark.sql.hive.convertMetastoreParquet.mergeSchema","false")
sparkConf.set("parquet.enable.summary-metadata","false")


Also added to hive.xml



  <property>
<name>hive.stats.autogather</name>
<value>false</value>
</property>


But it still acts the same.



I am not working with HDFS.



I will appreciate any suggestion ??










share|improve this question
















I have a very simple Spark job that writes to S3.
The table has 3 different partition keys and many values (some of them is getting bigger every hour).



I am using the following code:



dataframe.select(reorderFields:_*).write.mode(SaveMode.Overwrite).insertInto(tableName)


At the beginning this code was pretty efficient. But after the table got bigger, it became slower and slower.



When opened a debug log I found many reads to hive before it even starts the calculation of the dataframe.



LOGS:



2019-01-03 16:50:58 [main] DataNucleus.Datastore:58 [DEBUG]: Closing PreparedStatement "com.jolbox.bonecp.PreparedStatementHandle@5470ec7e"
2019-01-03 16:50:58 [main] DataNucleus.Datastore.Native:58 [DEBUG]: SELECT `A0`.`COLUMN_NAME`,`A0`.`ORDER`,`A0`.`INTEGER_IDX` AS NUCORDER0 FROM `SORT_COLS` `A0` WHERE `A0`.`SD_ID` = <297323> AND `A0`.`INTEGER_IDX` >= 0 ORDER BY NUCORDER0
2019-01-03 16:50:58 [main] DataNucleus.Datastore.Retrieve:58 [DEBUG]: Execution Time = 1 ms
2019-01-03 16:50:58 [main] DataNucleus.Datastore:58 [DEBUG]: Closing PreparedStatement "org.datanucleus.store.rdbms.ParamLoggingPreparedStatement@325b1c61"
2019-01-03 16:50:58 [main] DataNucleus.Persistence:58 [DEBUG]: Object "org.apache.hadoop.hive.metastore.model.MStorageDescriptor@6328ec75" field "parameters" is replaced by a SCO wrapper of type "org.datanucleus.store.types.backed.Map" [cache-values=true, lazy-loading=true, queued-operations=false, allow-nulls=true]
2019-01-03 16:50:58 [main] DataNucleus.Persistence:58 [DEBUG]: Object "org.apache.hadoop.hive.metastore.model.MStorageDescriptor@6328ec75" field "parameters" loading contents to SCO wrapper from the datastore
2019-01-03 16:50:58 [main] DataNucleus.Connection:58 [DEBUG]: Connection found in the pool : org.datanucleus.store.rdbms.ConnectionFactoryImpl$ManagedConnectionImpl@236ec794 [conn=com.jolbox.bonecp.ConnectionHandle@712106b5, commitOnRelease=false, closeOnRelease=false, closeOnTxnEnd=true] for key=org.datanucleus.ExecutionContextThreadedImpl@132e3594 in factory=ConnectionFactory:tx[org.datanucleus.store.rdbms.ConnectionFactoryImpl@72c9ebfa]
2019-01-03 16:50:58 [main] DataNucleus.Datastore:58 [DEBUG]: Closing PreparedStatement "com.jolbox.bonecp.PreparedStatementHandle@250ebae4"
2019-01-03 16:50:58 [main] DataNucleus.Datastore.Native:58 [DEBUG]: SELECT `A0`.`PARAM_KEY`,`A0`.`PARAM_VALUE` FROM `SD_PARAMS` `A0` WHERE `A0`.`SD_ID` = <297323> AND `A0`.`PARAM_KEY` IS NOT NULL
2019-01-03 16:50:58 [main] DataNucleus.Datastore.Retrieve:58 [DEBUG]: Execution Time = 1 ms
2019-01-03 16:50:58 [main] DataNucleus.Datastore:58 [DEBUG]: Closing PreparedStatement "org.datanucleus.store.rdbms.ParamLoggingPreparedStatement@798a320"
2019-01-03 16:50:58 [main] DataNucleus.Persistence:58 [DEBUG]: Object "org.apache.hadoop.hive.metastore.model.MStorageDescriptor@6328ec75" field "skewedColNames" is replaced by a SCO wrapper of type "org.datanucleus.store.types.backed.List" [cache-values=true, lazy-loading=true, queued-operations=false, allow-nulls=true]
2019-01-03 16:50:58 [main] DataNucleus.Persistence:58 [DEBUG]: Object "org.apache.hadoop.hive.metastore.model.MStorageDescriptor@6328ec75" field "skewedColNames" loading contents to SCO wrapper from the datastore
2019-01-03 16:50:58 [main] DataNucleus.Connection:58 [DEBUG]: Connection found in the pool : org.datanucleus.store.rdbms.ConnectionFactoryImpl$ManagedConnectionImpl@236ec794 [conn=com.jolbox.bonecp.ConnectionHandle@712106b5, commitOnRelease=false, closeOnRelease=false, closeOnTxnEnd=true] for key=org.datanucleus.ExecutionContextThreadedImpl@132e3594 in factory=ConnectionFactory:tx[org.datanucleus.store.rdbms.ConnectionFactoryImpl@72c9ebfa]
2019-01-03 16:50:58 [main] DataNucleus.Datastore:58 [DEBUG]: Closing PreparedStatement "com.jolbox.bonecp.PreparedStatementHandle@540637b0"


I tried to reconfigure my hive with the following parameters:



sparkConf.set("hive.auto.convert.join.noconditionaltask.size","200M")
sparkConf.set("hive.auto.convert.join.noconditionaltask","true")
sparkConf.set("hive.optimize.sort.dynamic.partition","false")
sparkConf.set("spark.sql.hive.convertMetastoreParquet.mergeSchema","false")
sparkConf.set("parquet.enable.summary-metadata","false")


Also added to hive.xml



  <property>
<name>hive.stats.autogather</name>
<value>false</value>
</property>


But it still acts the same.



I am not working with HDFS.



I will appreciate any suggestion ??







scala apache-spark hive






share|improve this question















share|improve this question













share|improve this question




share|improve this question








edited Jan 3 at 18:02









thebluephantom

3,23231033




3,23231033










asked Jan 3 at 16:55









Ehud LevEhud Lev

8381017




8381017













  • Problem is with Read or S3 Write?

    – Kaushal
    Jan 3 at 18:42











  • @ Kaushal With write using insertInto

    – Ehud Lev
    Jan 3 at 18:43



















  • Problem is with Read or S3 Write?

    – Kaushal
    Jan 3 at 18:42











  • @ Kaushal With write using insertInto

    – Ehud Lev
    Jan 3 at 18:43

















Problem is with Read or S3 Write?

– Kaushal
Jan 3 at 18:42





Problem is with Read or S3 Write?

– Kaushal
Jan 3 at 18:42













@ Kaushal With write using insertInto

– Ehud Lev
Jan 3 at 18:43





@ Kaushal With write using insertInto

– Ehud Lev
Jan 3 at 18:43












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