Improve parquet write speed to HDFS and spark.sql.execution.id is already set ERROR

[morfious902002]

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.
I am using spark 1.5.1 with YARN.

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



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