[jira] [Updated] (SPARK-24316) Spark sql queries stall for column width more than 6k for parquet based table
[
https://issues.apache.org/jira/browse/SPARK-24316?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Dongjoon Hyun updated SPARK-24316:
--
Affects Version/s: (was: 3.0.0)
3.1.0
> Spark sql queries stall for column width more than 6k for parquet based table
> --
>
> Key: SPARK-24316
> URL: https://issues.apache.org/jira/browse/SPARK-24316
> Project: Spark
> Issue Type: Improvement
> Components: SQL
>Affects Versions: 3.1.0
>Reporter: Bimalendu Choudhary
>Priority: Major
>
> When we create a table from a data frame using spark sql with columns around
> 6k or more, even simple queries of fetching 70k rows takes 20 minutes, while
> the same table if we create through Hive with same data , the same query just
> takes 5 minutes.
>
> Instrumenting the code we see that the executors are looping in the while
> loop of the function initializeInternal(). The majority of time is getting
> spent in the for loop in below code looping through the columns and the
> executor appears to be stalled for long time .
>
> {code:java|title=spark/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/VectorizedParquetRecordReader.java|borderStyle=solid}
> private void initializeInternal() ..
> ..
> for (int i = 0; i < requestedSchema.getFieldCount(); ++i)
> { ... }
> }
> {code:java}
> {code}
>
> When spark sql is creating table, it also stores the metadata in the
> TBLPROPERTIES in json format. We see that if we remove this metadata from the
> table the queries become fast , which is the case when we create the same
> table through Hive. The exact same table takes 5 times more time with the
> Json meta data as compared to without the json metadata.
>
> So looks like as the number of columns are growing bigger than 5 to 6k, the
> processing of the metadata and comparing it becomes more and more expensive
> and the performance degrades drastically.
> To recreate the problem simply run the following query:
> import org.apache.spark.sql.SparkSession
> val resp_data = spark.sql("SELECT * FROM duplicatefgv limit 7")
> resp_data.write.format("csv").save("/tmp/filename")
>
> The table should be created by spark sql from dataframe so that the Json meta
> data is stored. For ex:-
> val dff = spark.read.format("csv").load("hdfs:///tmp/test.csv")
> dff.createOrReplaceTempView("my_temp_table")
> val tmp = spark.sql("Create table tableName stored as parquet as select *
> from my_temp_table")
>
>
> from pyspark.sql import SQL
> Context
> sqlContext = SQLContext(sc)
> resp_data = spark.sql( " select * from test").limit(2000)
> print resp_data_fgv_1k.count()
> (resp_data_fgv_1k.write.option('header',
> False).mode('overwrite').csv('/tmp/2.csv') )
>
>
> The performance seems to be even slow in the loop if the schema does not
> match or the fields are empty and the code goes into the if condition where
> the missing column is marked true:
> missingColumns[i] = true;
>
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[jira] [Updated] (SPARK-24316) Spark sql queries stall for column width more than 6k for parquet based table
[
https://issues.apache.org/jira/browse/SPARK-24316?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Dongjoon Hyun updated SPARK-24316:
--
Affects Version/s: (was: 2.4.0)
(was: 2.2.1)
(was: 2.3.0)
(was: 2.2.0)
3.0.0
> Spark sql queries stall for column width more than 6k for parquet based table
> --
>
> Key: SPARK-24316
> URL: https://issues.apache.org/jira/browse/SPARK-24316
> Project: Spark
> Issue Type: Improvement
> Components: SQL
>Affects Versions: 3.0.0
>Reporter: Bimalendu Choudhary
>Priority: Major
>
> When we create a table from a data frame using spark sql with columns around
> 6k or more, even simple queries of fetching 70k rows takes 20 minutes, while
> the same table if we create through Hive with same data , the same query just
> takes 5 minutes.
>
> Instrumenting the code we see that the executors are looping in the while
> loop of the function initializeInternal(). The majority of time is getting
> spent in the for loop in below code looping through the columns and the
> executor appears to be stalled for long time .
>
> {code:java|title=spark/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/VectorizedParquetRecordReader.java|borderStyle=solid}
> private void initializeInternal() ..
> ..
> for (int i = 0; i < requestedSchema.getFieldCount(); ++i)
> { ... }
> }
> {code:java}
> {code}
>
> When spark sql is creating table, it also stores the metadata in the
> TBLPROPERTIES in json format. We see that if we remove this metadata from the
> table the queries become fast , which is the case when we create the same
> table through Hive. The exact same table takes 5 times more time with the
> Json meta data as compared to without the json metadata.
>
> So looks like as the number of columns are growing bigger than 5 to 6k, the
> processing of the metadata and comparing it becomes more and more expensive
> and the performance degrades drastically.
> To recreate the problem simply run the following query:
> import org.apache.spark.sql.SparkSession
> val resp_data = spark.sql("SELECT * FROM duplicatefgv limit 7")
> resp_data.write.format("csv").save("/tmp/filename")
>
> The table should be created by spark sql from dataframe so that the Json meta
> data is stored. For ex:-
> val dff = spark.read.format("csv").load("hdfs:///tmp/test.csv")
> dff.createOrReplaceTempView("my_temp_table")
> val tmp = spark.sql("Create table tableName stored as parquet as select *
> from my_temp_table")
>
>
> from pyspark.sql import SQL
> Context
> sqlContext = SQLContext(sc)
> resp_data = spark.sql( " select * from test").limit(2000)
> print resp_data_fgv_1k.count()
> (resp_data_fgv_1k.write.option('header',
> False).mode('overwrite').csv('/tmp/2.csv') )
>
>
> The performance seems to be even slow in the loop if the schema does not
> match or the fields are empty and the code goes into the if condition where
> the missing column is marked true:
> missingColumns[i] = true;
>
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[jira] [Updated] (SPARK-24316) Spark sql queries stall for column width more than 6k for parquet based table
[
https://issues.apache.org/jira/browse/SPARK-24316?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Bimalendu Choudhary updated SPARK-24316:
Summary: Spark sql queries stall for column width more than 6k for parquet
based table (was: Spark sql queries stall for column width more 6k for
parquet based table)
> Spark sql queries stall for column width more than 6k for parquet based table
> --
>
> Key: SPARK-24316
> URL: https://issues.apache.org/jira/browse/SPARK-24316
> Project: Spark
> Issue Type: Improvement
> Components: SQL
>Affects Versions: 2.2.0, 2.2.1, 2.3.0, 2.4.0
>Reporter: Bimalendu Choudhary
>Priority: Major
>
> When we create a table from a data frame using spark sql with columns around
> 6k or more, even simple queries of fetching 70k rows takes 20 minutes, while
> the same table if we create through Hive with same data , the same query just
> takes 5 minutes.
>
> Instrumenting the code we see that the executors are looping in the while
> loop of the function initializeInternal(). The majority of time is getting
> spent in the for loop in below code looping through the columns and the
> executor appears to be stalled for long time .
>
> {code:java|title=spark/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/VectorizedParquetRecordReader.java|borderStyle=solid}
> private void initializeInternal() ..
> ..
> for (int i = 0; i < requestedSchema.getFieldCount(); ++i)
> { ... }
> }
> {code:java}
> {code}
>
> When spark sql is creating table, it also stores the metadata in the
> TBLPROPERTIES in json format. We see that if we remove this metadata from the
> table the queries become fast , which is the case when we create the same
> table through Hive. The exact same table takes 5 times more time with the
> Json meta data as compared to without the json metadata.
>
> So looks like as the number of columns are growing bigger than 5 to 6k, the
> processing of the metadata and comparing it becomes more and more expensive
> and the performance degrades drastically.
> To recreate the problem simply run the following query:
> import org.apache.spark.sql.SparkSession
> val resp_data = spark.sql("SELECT * FROM duplicatefgv limit 7")
> resp_data.write.format("csv").save("/tmp/filename")
>
> The table should be created by spark sql from dataframe so that the Json meta
> data is stored. For ex:-
> val dff = spark.read.format("csv").load("hdfs:///tmp/test.csv")
> dff.createOrReplaceTempView("my_temp_table")
> val tmp = spark.sql("Create table tableName stored as parquet as select *
> from my_temp_table")
>
>
> from pyspark.sql import SQL
> Context
> sqlContext = SQLContext(sc)
> resp_data = spark.sql( " select * from test").limit(2000)
> print resp_data_fgv_1k.count()
> (resp_data_fgv_1k.write.option('header',
> False).mode('overwrite').csv('/tmp/2.csv') )
>
>
> The performance seems to be even slow in the loop if the schema does not
> match or the fields are empty and the code goes into the if condition where
> the missing column is marked true:
> missingColumns[i] = true;
>
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