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Posted to user@spark.apache.org by Anubhav Agarwal <an...@gmail.com> on 2015/10/23 17:25:07 UTC
Improve parquet write speed to HDFS and spark.sql.execution.id is
already set ERROR
I have a spark job that creates 6 million rows in RDDs. I convert the RDD
into Data-frame and write it to HDFS. Currently it takes 3 minutes to write
it to HDFS.
Here is the snippet:-
RDDList.parallelStream().forEach(mapJavaRDD -> {
if (mapJavaRDD != null) {
JavaRDD<Row> rowRDD =
mapJavaRDD.mapPartitionsWithIndex((integer, v2) -> {
<logical operation>
return new ArrayList<Row>(1).iterator();
}, false);
DataFrame dF = sqlContext.createDataFrame(rowRDD,
schema).coalesce(3);
synchronized (finalLock) {
dF.write().mode(SaveMode.Append).parquet("hdfs
location");
}
});
After looking into the logs I know the following is the reason for the job
taking too long:-
*dF.write().mode(SaveMode.Append).parquet("hdfs
location");*
I also get the following errors due to it:-
15/10/21 21:12:30 WARN scheduler.TaskSetManager: Stage 31 contains a task
of very large size (378 KB). The maximum recommended task size is 100 KB.4
of these kind of warnings appeared.
java.lang.IllegalArgumentException: java.lang.IllegalArgumentException:
spark.sql.execution.id is already set
Re: Improve parquet write speed to HDFS and spark.sql.execution.id is
already set ERROR
Posted by Anubhav Agarwal <an...@gmail.com>.
I was getting the following error without it:-
org.apache.hadoop.ipc.RemoteException(org.apache.hadoop.hdfs.server.namenode.LeaseExpiredException):
No lease on /.gz.parquet (inode ): File does not exist. [Lease. Holder:
DFSClient_NONMAPREDUCE_, pendingcreates: 1]
I think that is due to deadlock.
On Tue, Nov 3, 2015 at 7:48 AM, Ted Yu <yu...@gmail.com> wrote:
> I am a bit curious: why is the synchronization on finalLock is needed ?
>
> Thanks
>
> On Oct 23, 2015, at 8:25 AM, Anubhav Agarwal <an...@gmail.com> wrote:
>
> I have a spark job that creates 6 million rows in RDDs. I convert the RDD
> into Data-frame and write it to HDFS. Currently it takes 3 minutes to write
> it to HDFS.
>
> Here is the snippet:-
> RDDList.parallelStream().forEach(mapJavaRDD -> {
> if (mapJavaRDD != null) {
> JavaRDD<Row> rowRDD =
> mapJavaRDD.mapPartitionsWithIndex((integer, v2) -> {
> <logical operation>
> return new ArrayList<Row>(1).iterator();
> }, false);
>
> DataFrame dF = sqlContext.createDataFrame(rowRDD,
> schema).coalesce(3);
> synchronized (finalLock) {
> dF.write().mode(SaveMode.Append).parquet("hdfs
> location");
> }
>
> });
>
> After looking into the logs I know the following is the reason for the job
> taking too long:-
> *dF.write().mode(SaveMode.Append).parquet("hdfs
> location");*
>
> I also get the following errors due to it:-
> 15/10/21 21:12:30 WARN scheduler.TaskSetManager: Stage 31 contains a task
> of very large size (378 KB). The maximum recommended task size is 100 KB.4
> of these kind of warnings appeared.
>
> java.lang.IllegalArgumentException: java.lang.IllegalArgumentException:
> spark.sql.execution.id is already set
>
>
Re: Improve parquet write speed to HDFS and spark.sql.execution.id is already set ERROR
Posted by Ted Yu <yu...@gmail.com>.
I am a bit curious: why is the synchronization on finalLock is needed ?
Thanks
> On Oct 23, 2015, at 8:25 AM, Anubhav Agarwal <an...@gmail.com> wrote:
>
> I have a spark job that creates 6 million rows in RDDs. I convert the RDD into Data-frame and write it to HDFS. Currently it takes 3 minutes to write it to HDFS.
>
> Here is the snippet:-
> RDDList.parallelStream().forEach(mapJavaRDD -> {
> if (mapJavaRDD != null) {
> JavaRDD<Row> rowRDD = mapJavaRDD.mapPartitionsWithIndex((integer, v2) -> {
> <logical operation>
> return new ArrayList<Row>(1).iterator();
> }, false);
>
> DataFrame dF = sqlContext.createDataFrame(rowRDD, schema).coalesce(3);
> synchronized (finalLock) {
> dF.write().mode(SaveMode.Append).parquet("hdfs location");
> }
>
> });
>
> After looking into the logs I know the following is the reason for the job taking too long:-
> dF.write().mode(SaveMode.Append).parquet("hdfs location");
>
> I also get the following errors due to it:-
> 15/10/21 21:12:30 WARN scheduler.TaskSetManager: Stage 31 contains a task of very large size (378 KB). The maximum recommended task size is 100 KB.4 of these kind of warnings appeared.
>
> java.lang.IllegalArgumentException: java.lang.IllegalArgumentException: spark.sql.execution.id is already set