[jira] [Updated] (SPARK-6016) Cannot read the parquet table after overwriting the existing table when spark.sql.parquet.cacheMetadata=true
[
https://issues.apache.org/jira/browse/SPARK-6016?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Sean Owen updated SPARK-6016:
-
Assignee: Yin Huai
Cannot read the parquet table after overwriting the existing table when
spark.sql.parquet.cacheMetadata=true
Key: SPARK-6016
URL: https://issues.apache.org/jira/browse/SPARK-6016
Project: Spark
Issue Type: Bug
Components: SQL
Reporter: Yin Huai
Assignee: Yin Huai
Priority: Blocker
Fix For: 1.3.0
saveAsTable is fine and seems we have successfully deleted the old data and
written the new data. However, when reading the newly created table, an error
will be thrown.
{code}
Error in SQL statement: java.lang.RuntimeException:
java.lang.RuntimeException: could not merge metadata: key
org.apache.spark.sql.parquet.row.metadata has conflicting values:
at
parquet.hadoop.api.InitContext.getMergedKeyValueMetaData(InitContext.java:67)
at parquet.hadoop.api.ReadSupport.init(ReadSupport.java:84)
at
org.apache.spark.sql.parquet.FilteringParquetRowInputFormat.getSplits(ParquetTableOperations.scala:469)
at
parquet.hadoop.ParquetInputFormat.getSplits(ParquetInputFormat.java:245)
at
org.apache.spark.sql.parquet.ParquetRelation2$$anon$1.getPartitions(newParquet.scala:461)
...
{code}
If I set spark.sql.parquet.cacheMetadata to false, it's fine to query the
data.
Note: the newly created table needs to have more than one file to trigger the
bug (if there is only a single file, we will not need to merge metadata).
To reproduce it, try...
{code}
import org.apache.spark.sql.SaveMode
import sqlContext._
sql(drop table if exists test)
val df1 = sqlContext.jsonRDD(sc.parallelize((1 to 10).map(i =
s{a:$i}), 2)) // we will save to 2 parquet files.
df1.saveAsTable(test, parquet, SaveMode.Overwrite)
sql(select * from test).collect.foreach(println) // Warm the
FilteringParquetRowInputFormat.footerCache
val df2 = sqlContext.jsonRDD(sc.parallelize((1 to 10).map(i =
s{b:$i}), 4)) // we will save to 4 parquet files.
df2.saveAsTable(test, parquet, SaveMode.Overwrite)
sql(select * from test).collect.foreach(println)
{code}
For this example, we have two outdated footers for df1 in footerCache and
since we have four parquet files for the new test table, we picked up 2 new
footers for df2. Then, we hit the bug.
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[jira] [Updated] (SPARK-6016) Cannot read the parquet table after overwriting the existing table when spark.sql.parquet.cacheMetadata=true
[
https://issues.apache.org/jira/browse/SPARK-6016?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Yin Huai updated SPARK-6016:
Description:
saveAsTable is fine and seems we have successfully deleted the old data and
written the new data. However, when reading the newly created table, an error
will be thrown.
{code}
Error in SQL statement: java.lang.RuntimeException: java.lang.RuntimeException:
could not merge metadata: key org.apache.spark.sql.parquet.row.metadata has
conflicting values:
at parquet.hadoop.api.InitContext.getMergedKeyValueMetaData(InitContext.java:67)
at parquet.hadoop.api.ReadSupport.init(ReadSupport.java:84)
at
org.apache.spark.sql.parquet.FilteringParquetRowInputFormat.getSplits(ParquetTableOperations.scala:469)
at
parquet.hadoop.ParquetInputFormat.getSplits(ParquetInputFormat.java:245)
at
org.apache.spark.sql.parquet.ParquetRelation2$$anon$1.getPartitions(newParquet.scala:461)
...
{code}
If I set spark.sql.parquet.cacheMetadata to false, it's fine to query the data.
Note: the newly created table needs to have more than one file to trigger the
bug (if there is only a single file, we will not need to merge metadata).
was:
saveAsTable is fine and seems we have successfully deleted the old data and
written the new data. However, when reading the newly created table, an error
will be thrown.
{code}
Error in SQL statement: java.lang.RuntimeException: java.lang.RuntimeException:
could not merge metadata: key org.apache.spark.sql.parquet.row.metadata has
conflicting values:
at parquet.hadoop.api.InitContext.getMergedKeyValueMetaData(InitContext.java:67)
at parquet.hadoop.api.ReadSupport.init(ReadSupport.java:84)
at
org.apache.spark.sql.parquet.FilteringParquetRowInputFormat.getSplits(ParquetTableOperations.scala:469)
at
parquet.hadoop.ParquetInputFormat.getSplits(ParquetInputFormat.java:245)
at
org.apache.spark.sql.parquet.ParquetRelation2$$anon$1.getPartitions(newParquet.scala:461)
...
{code}
If I set spark.sql.parquet.cacheMetadata to false, it's fine to query the data.
Cannot read the parquet table after overwriting the existing table when
spark.sql.parquet.cacheMetadata=true
Key: SPARK-6016
URL: https://issues.apache.org/jira/browse/SPARK-6016
Project: Spark
Issue Type: Bug
Components: SQL
Reporter: Yin Huai
Priority: Blocker
saveAsTable is fine and seems we have successfully deleted the old data and
written the new data. However, when reading the newly created table, an error
will be thrown.
{code}
Error in SQL statement: java.lang.RuntimeException:
java.lang.RuntimeException: could not merge metadata: key
org.apache.spark.sql.parquet.row.metadata has conflicting values:
at
parquet.hadoop.api.InitContext.getMergedKeyValueMetaData(InitContext.java:67)
at parquet.hadoop.api.ReadSupport.init(ReadSupport.java:84)
at
org.apache.spark.sql.parquet.FilteringParquetRowInputFormat.getSplits(ParquetTableOperations.scala:469)
at
parquet.hadoop.ParquetInputFormat.getSplits(ParquetInputFormat.java:245)
at
org.apache.spark.sql.parquet.ParquetRelation2$$anon$1.getPartitions(newParquet.scala:461)
...
{code}
If I set spark.sql.parquet.cacheMetadata to false, it's fine to query the
data.
Note: the newly created table needs to have more than one file to trigger the
bug (if there is only a single file, we will not need to merge metadata).
--
This message was sent by Atlassian JIRA
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-
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
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[jira] [Updated] (SPARK-6016) Cannot read the parquet table after overwriting the existing table when spark.sql.parquet.cacheMetadata=true
[
https://issues.apache.org/jira/browse/SPARK-6016?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Yin Huai updated SPARK-6016:
Description:
saveAsTable is fine and seems we have successfully deleted the old data and
written the new data. However, when reading the newly created table, an error
will be thrown.
{code}
Error in SQL statement: java.lang.RuntimeException: java.lang.RuntimeException:
could not merge metadata: key org.apache.spark.sql.parquet.row.metadata has
conflicting values:
at parquet.hadoop.api.InitContext.getMergedKeyValueMetaData(InitContext.java:67)
at parquet.hadoop.api.ReadSupport.init(ReadSupport.java:84)
at
org.apache.spark.sql.parquet.FilteringParquetRowInputFormat.getSplits(ParquetTableOperations.scala:469)
at
parquet.hadoop.ParquetInputFormat.getSplits(ParquetInputFormat.java:245)
at
org.apache.spark.sql.parquet.ParquetRelation2$$anon$1.getPartitions(newParquet.scala:461)
...
{code}
If I set spark.sql.parquet.cacheMetadata to false, it's fine to query the data.
Note: the newly created table needs to have more than one file to trigger the
bug (if there is only a single file, we will not need to merge metadata).
To reproduce it, try...
{code}
import org.apache.spark.sql.SaveMode
import sqlContext._
sql(drop table if exists test)
val df1 = sqlContext.jsonRDD(sc.parallelize((1 to 10).map(i =
s{a:$i}), 2)) // we will save to 2 parquet files.
df1.saveAsTable(test, parquet, SaveMode.Overwrite)
sql(select * from test).collect.foreach(println) // Warm the
FilteringParquetRowInputFormat.footerCache
val df2 = sqlContext.jsonRDD(sc.parallelize((1 to 10).map(i =
s{b:$i}), 4)) // we will save to 4 parquet files.
df2.saveAsTable(test, parquet, SaveMode.Overwrite)
sql(select * from test).collect.foreach(println)
{code}
was:
saveAsTable is fine and seems we have successfully deleted the old data and
written the new data. However, when reading the newly created table, an error
will be thrown.
{code}
Error in SQL statement: java.lang.RuntimeException: java.lang.RuntimeException:
could not merge metadata: key org.apache.spark.sql.parquet.row.metadata has
conflicting values:
at parquet.hadoop.api.InitContext.getMergedKeyValueMetaData(InitContext.java:67)
at parquet.hadoop.api.ReadSupport.init(ReadSupport.java:84)
at
org.apache.spark.sql.parquet.FilteringParquetRowInputFormat.getSplits(ParquetTableOperations.scala:469)
at
parquet.hadoop.ParquetInputFormat.getSplits(ParquetInputFormat.java:245)
at
org.apache.spark.sql.parquet.ParquetRelation2$$anon$1.getPartitions(newParquet.scala:461)
...
{code}
If I set spark.sql.parquet.cacheMetadata to false, it's fine to query the data.
Note: the newly created table needs to have more than one file to trigger the
bug (if there is only a single file, we will not need to merge metadata).
Cannot read the parquet table after overwriting the existing table when
spark.sql.parquet.cacheMetadata=true
Key: SPARK-6016
URL: https://issues.apache.org/jira/browse/SPARK-6016
Project: Spark
Issue Type: Bug
Components: SQL
Reporter: Yin Huai
Priority: Blocker
saveAsTable is fine and seems we have successfully deleted the old data and
written the new data. However, when reading the newly created table, an error
will be thrown.
{code}
Error in SQL statement: java.lang.RuntimeException:
java.lang.RuntimeException: could not merge metadata: key
org.apache.spark.sql.parquet.row.metadata has conflicting values:
at
parquet.hadoop.api.InitContext.getMergedKeyValueMetaData(InitContext.java:67)
at parquet.hadoop.api.ReadSupport.init(ReadSupport.java:84)
at
org.apache.spark.sql.parquet.FilteringParquetRowInputFormat.getSplits(ParquetTableOperations.scala:469)
at
parquet.hadoop.ParquetInputFormat.getSplits(ParquetInputFormat.java:245)
at
org.apache.spark.sql.parquet.ParquetRelation2$$anon$1.getPartitions(newParquet.scala:461)
...
{code}
If I set spark.sql.parquet.cacheMetadata to false, it's fine to query the
data.
Note: the newly created table needs to have more than one file to trigger the
bug (if there is only a single file, we will not need to merge metadata).
To reproduce it, try...
{code}
import org.apache.spark.sql.SaveMode
import sqlContext._
sql(drop table if exists test)
val df1 = sqlContext.jsonRDD(sc.parallelize((1 to 10).map(i =
s{a:$i}), 2)) // we will save to 2 parquet files.
df1.saveAsTable(test, parquet, SaveMode.Overwrite)
sql(select * from test).collect.foreach(println) // Warm the
FilteringParquetRowInputFormat.footerCache
val df2 = sqlContext.jsonRDD(sc.parallelize((1 to 10).map(i =
s{b:$i}), 4)) // we will save
[jira] [Updated] (SPARK-6016) Cannot read the parquet table after overwriting the existing table when spark.sql.parquet.cacheMetadata=true
[
https://issues.apache.org/jira/browse/SPARK-6016?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Yin Huai updated SPARK-6016:
Description:
saveAsTable is fine and seems we have successfully deleted the old data and
written the new data. However, when reading the newly created table, an error
will be thrown.
{code}
Error in SQL statement: java.lang.RuntimeException: java.lang.RuntimeException:
could not merge metadata: key org.apache.spark.sql.parquet.row.metadata has
conflicting values:
at parquet.hadoop.api.InitContext.getMergedKeyValueMetaData(InitContext.java:67)
at parquet.hadoop.api.ReadSupport.init(ReadSupport.java:84)
at
org.apache.spark.sql.parquet.FilteringParquetRowInputFormat.getSplits(ParquetTableOperations.scala:469)
at
parquet.hadoop.ParquetInputFormat.getSplits(ParquetInputFormat.java:245)
at
org.apache.spark.sql.parquet.ParquetRelation2$$anon$1.getPartitions(newParquet.scala:461)
...
{code}
If I set spark.sql.parquet.cacheMetadata to false, it's fine to query the data.
Note: the newly created table needs to have more than one file to trigger the
bug (if there is only a single file, we will not need to merge metadata).
To reproduce it, try...
{code}
import org.apache.spark.sql.SaveMode
import sqlContext._
sql(drop table if exists test)
val df1 = sqlContext.jsonRDD(sc.parallelize((1 to 10).map(i =
s{a:$i}), 2)) // we will save to 2 parquet files.
df1.saveAsTable(test, parquet, SaveMode.Overwrite)
sql(select * from test).collect.foreach(println) // Warm the
FilteringParquetRowInputFormat.footerCache
val df2 = sqlContext.jsonRDD(sc.parallelize((1 to 10).map(i =
s{b:$i}), 4)) // we will save to 4 parquet files.
df2.saveAsTable(test, parquet, SaveMode.Overwrite)
sql(select * from test).collect.foreach(println)
{code}
For this example, we have two outdated footers for df1 in footerCache and since
we have four parquet files for the new test table, we picked up 2 new footers
for df2. Then, we hit the bug.
was:
saveAsTable is fine and seems we have successfully deleted the old data and
written the new data. However, when reading the newly created table, an error
will be thrown.
{code}
Error in SQL statement: java.lang.RuntimeException: java.lang.RuntimeException:
could not merge metadata: key org.apache.spark.sql.parquet.row.metadata has
conflicting values:
at parquet.hadoop.api.InitContext.getMergedKeyValueMetaData(InitContext.java:67)
at parquet.hadoop.api.ReadSupport.init(ReadSupport.java:84)
at
org.apache.spark.sql.parquet.FilteringParquetRowInputFormat.getSplits(ParquetTableOperations.scala:469)
at
parquet.hadoop.ParquetInputFormat.getSplits(ParquetInputFormat.java:245)
at
org.apache.spark.sql.parquet.ParquetRelation2$$anon$1.getPartitions(newParquet.scala:461)
...
{code}
If I set spark.sql.parquet.cacheMetadata to false, it's fine to query the data.
Note: the newly created table needs to have more than one file to trigger the
bug (if there is only a single file, we will not need to merge metadata).
To reproduce it, try...
{code}
import org.apache.spark.sql.SaveMode
import sqlContext._
sql(drop table if exists test)
val df1 = sqlContext.jsonRDD(sc.parallelize((1 to 10).map(i =
s{a:$i}), 2)) // we will save to 2 parquet files.
df1.saveAsTable(test, parquet, SaveMode.Overwrite)
sql(select * from test).collect.foreach(println) // Warm the
FilteringParquetRowInputFormat.footerCache
val df2 = sqlContext.jsonRDD(sc.parallelize((1 to 10).map(i =
s{b:$i}), 4)) // we will save to 4 parquet files.
df2.saveAsTable(test, parquet, SaveMode.Overwrite)
sql(select * from test).collect.foreach(println)
{code}
Cannot read the parquet table after overwriting the existing table when
spark.sql.parquet.cacheMetadata=true
Key: SPARK-6016
URL: https://issues.apache.org/jira/browse/SPARK-6016
Project: Spark
Issue Type: Bug
Components: SQL
Reporter: Yin Huai
Priority: Blocker
saveAsTable is fine and seems we have successfully deleted the old data and
written the new data. However, when reading the newly created table, an error
will be thrown.
{code}
Error in SQL statement: java.lang.RuntimeException:
java.lang.RuntimeException: could not merge metadata: key
org.apache.spark.sql.parquet.row.metadata has conflicting values:
at
parquet.hadoop.api.InitContext.getMergedKeyValueMetaData(InitContext.java:67)
at parquet.hadoop.api.ReadSupport.init(ReadSupport.java:84)
at
org.apache.spark.sql.parquet.FilteringParquetRowInputFormat.getSplits(ParquetTableOperations.scala:469)
at
parquet.hadoop.ParquetInputFormat.getSplits(ParquetInputFormat.java:245)
at
