SPARK Throwing error while using pyspark on sql context

[#1]

Welcome to
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Using Python version 2.7.14 (default, Dec 7 2017 17:05:42)
SparkSession available as 'spark'.
>>>
>>> df=spark.sql('select * from sws_dev.vw_dlx_rpr_ordr_dtl_base limit 1').show()
[Stage 0:=====================> (18 + 28) / 46]20/03/03 07:01:08 ERROR DiskBlockObjectWriter: Uncaught exception while reverting partial writes to file /tmp/blockmgr-c5bcbbe3-8da0-44a0-8025-1b183c81d532/03/temp_shuffle_280c5065-f954-4ec8-b3d0-7c1f5c18b581
java.io.FileNotFoundException: /tmp/blockmgr-c5bcbbe3-8da0-44a0-8025-1b183c81d532/03/temp_shuffle_280c5065-f954-4ec8-b3d0-7c1f5c18b581 (Too many open files)
at java.io.FileOutputStream.open0(Native Method)
at java.io.FileOutputStream.open(FileOutputStream.java:270)
at java.io.FileOutputStream.<init>(FileOutputStream.java:213)
at org.apache.spark.storage.DiskBlockObjectWriter$$anonfun$revertPartialWritesAndClose$2.apply$mcV$sp(DiskBlockObjectWriter.scala:217)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1386)
at org.apache.spark.storage.DiskBlockObjectWriter.revertPartialWritesAndClose(DiskBlockObjectWriter.scala:214)
at org.apache.spark.shuffle.sort.BypassMergeSortShuffleWriter.stop(BypassMergeSortShuffleWriter.java:237)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:102)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:53)
at org.apache.spark.scheduler.Task.run(Task.scala:109)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:345)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
20/03/03 07:01:08 ERROR Executor: Exception in task 3.0 in stage 0.0 (TID 3)
java.io.FileNotFoundException: /tmp/blockmgr-c5bcbbe3-8da0-44a0-8025-1b183c81d532/3c/temp_shuffle_8450fcd1-d97c-4c34-ac52-196e03030bf9 (Too many open files)
at java.io.FileOutputStream.open0(Native Method)
at java.io.FileOutputStream.open(FileOutputStream.java:270)
at java.io.FileOutputStream.<init>(FileOutputStream.java:213)
at org.apache.spark.storage.DiskBlockObjectWriter.initialize(DiskBlockObjectWriter.scala:103)
at org.apache.spark.storage.DiskBlockObjectWriter.open(DiskBlockObjectWriter.scala:116)
at org.apache.spark.storage.DiskBlockObjectWriter.write(DiskBlockObjectWriter.scala:237)
at org.apache.spark.shuffle.sort.BypassMergeSortShuffleWriter.write(BypassMergeSortShuffleWriter.java:151)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:96)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:53)
at org.apache.spark.scheduler.Task.run(Task.scala:109)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:345)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
20/03/03 07:01:08 ERROR Executor: Exception in task 9.0 in stage 0.0 (TID 9)
java.io.FileNotFoundException: /tmp/blockmgr-c5bcbbe3-8da0-44a0-8025-1b183c81d532/21/temp_shuffle_19e93f90-4de2-43c9-a715-c8668e96d793 (Too many open files)

[#2]

Tried verbose mode and still finding this issues !! 

[#3 · Accepted solution]

Fixed:

This is what i infered, while running spark the mode is made as client as you see below:

Parsed arguments:
master local[*]
deployMode null
executorMemory null
executorCores null
totalExecutorCores null
propertiesFile /usr/hdp/current/spark2-client/conf/spark-defaults.conf
driverMemory 4g
driverCores null
driverExtraClassPath null
driverExtraLibraryPath /usr/hdp/current/hadoop-client/lib/native:/usr/hdp/current/hadoop-client/lib/native/Linux-amd64-64
driverExtraJavaOptions null
supervise false
queue default
numExecutors null
files null
pyFiles null
archives null
mainClass null
primaryResource pyspark-shell
name PySparkShell
childArgs []
jars null
packages null
packagesExclusions null
repositories null
verbose true

When we use --master yarn this gets success !! .