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From Steve Loughran <ste...@hortonworks.com>
Subject Re: SPARK Issue in Standalone cluster
Date Wed, 02 Aug 2017 15:59:04 GMT

On 2 Aug 2017, at 14:25, Gourav Sengupta <gourav.sengupta@gmail.com<mailto:gourav.sengupta@gmail.com>>
wrote:

Hi,

I am definitely sure that at this point of time everyone who has kindly cared to respond to
my query do need to go and check this link https://spark.apache.org/docs/2.2.0/spark-standalone.html#spark-standalone-mode.

I see. Well, we shall have to edit that document to make clear something which had been omitted:

in order for multiple spark workers to process data, they must have a shared store for that
data, one with read/write access for all workers. This is must be provided by a shared filesystem:
HDFS, network-mounted NFS, Glusterfs, through an object store (S3, Azure WASB, ...), or through
alternative datastores implementing the Hadoop Filesystem API (example: Apache Cassandra).

n your case, for a small cluster of 1-3 machines, especially if you are just learning to play
with spark, I'd start with an NFS mounted disk accessible on the same path on all machines.
If you aren't willing to set that up, stick to spark standalone on a single machine first.
You don't need a shared cluster to use spark standalone.

Personally, I'd recommend downloading apache zeppelin and running it locally as the simplest
out-the-box experience.


It does mention that SPARK standalone cluster can have multiple machines running as slaves.


Clearly it omits the small detail about the requirement for a shared store.

The general idea of writing to the user group is that people who know should answer, and not
those who do not know.

Agreed, but if the answer doesn't appear to be correct to you, do consider that there may
be some detail that hasn't been mentioned, rather than immediately concluding that the person
replying is wrong.

-Steve





Regards,
Gourav Sengupta

On Tue, Aug 1, 2017 at 4:50 AM, Mahesh Sawaiker <mahesh_sawaiker@persistent.com<mailto:mahesh_sawaiker@persistent.com>>
wrote:
Gourav,
Riccardo’s answer is spot on.
What is happening is one node of spark is writing to its own directory and telling a slave
to read the data from there, when the slave goes to read it, the part is not found.

Check the folder Users/gouravsengupta/Development/spark/sparkdata/test1/part-00001-e79273b5-9b4e-4037-92f3-2e52f523dfdf-c000.snappy.parquet
on the slave.
The reason it ran on spark 1.5 may have been because the executor ran on the driver itself.
There is not much use to a set up where you don’t have some kind of distributed file system,
so I would encourage you to use hdfs, or a mounted file system shared by all nodes.

Regards,
Mahesh


From: Gourav Sengupta [mailto:gourav.sengupta@gmail.com<mailto:gourav.sengupta@gmail.com>]
Sent: Monday, July 31, 2017 9:54 PM
To: Riccardo Ferrari
Cc: user
Subject: Re: SPARK Issue in Standalone cluster

Hi Riccardo,

I am grateful for your kind response.

Also I am sure that your answer is completely wrong and errorneous. SPARK must be having a
method so that different executors do not pick up the same files to process. You also did
not answer the question why was the processing successful in SPARK 1.5 and not in SPARK 2.2.

Also the exact same directory is is present across in both the nodes.

I feel quite facinated when individuals respond before even understanding the issue, or trying
out the code.

It will be of great help if someone could kindly read my email and help me figure out the
issue.


Regards,
Gourav Sengupta



On Mon, Jul 31, 2017 at 9:27 AM, Riccardo Ferrari <ferrarir@gmail.com<mailto:ferrarir@gmail.com>>
wrote:
Hi Gourav,

The issue here is the location where you're trying to write/read from :/Users/gouravsengupta/Development/spark/sparkdata/test1/p...
When dealing with clusters all the paths and resources should be available to all executors
(and driver), and that is reason why you generally use HDFS, S3, NFS or any shared file system.

Spark assumes your data is generally available to all nodes and does not tries to pick up
the data from a selected node, it rather tries to write/read in parallel from the executor
nodes. Also given its control logic there is no way (read. you should not care) to know what
executor is doing what task.

Hope it helps,
Riccardo

On Mon, Jul 31, 2017 at 2:14 AM, Gourav Sengupta <gourav.sengupta@gmail.com<mailto:gourav.sengupta@gmail.com>>
wrote:
Hi,

I am working by creating a native SPARK standalone cluster (https://spark.apache.org/docs/2.2.0/spark-standalone.html)

Therefore I  do not have a HDFS.


EXERCISE:
Its the most fundamental and simple exercise. Create a sample SPARK dataframe and then write
it to a location and then read it back.

SETTINGS:
So after I have installed SPARK in two physical systems with the same:
1. SPARK version,
2. JAVA version,
3. PYTHON_PATH
4. SPARK_HOME
5. PYSPARK_PYTHON
the user in both the systems is the root user therefore there are no permission issues anywhere.

I am able to start:
1. ./spark-2.2.0-bin-hadoop2.7/sbin/start-master.sh
2. ./spark-2.2.0-bin-hadoop2.7/sbin/start-slave.sh (from two separate computers)

After that I can see in the spark UI (at port 8080) two workers.


CODE:
Then I run the following code:

======================================================
import findspark
import os
os.environ["SPARK_HOME"] = '/Users/gouravsengupta/Development/spark/spark/'
findspark.init()
import pyspark
from pyspark.sql import SparkSession
spark = (SparkSession.builder
        .master("spark://mastersystem.local:7077")
        .appName("gouravtest")
        .enableHiveSupport()
        .getOrCreate())
import pandas, numpy
testdf = spark.createDataFrame(pandas.DataFrame(numpy.random.randn(10000, 4), columns=list('ABCD')))
testdf.cache()
testdf.count()
testdf.write.save("/Users/gouravsengupta/Development/spark/sparkdata/test2")
spark.read.load("/Users/gouravsengupta/Development/spark/sparkdata/test2").count()
======================================================


ERROR I (in above code):
ERROR in line: testdf.write.save("/Users/gouravsengupta/Development/spark/sparkdata/test2")
This line does not fail or report any error. But when I am looking at the stage in spark Application
UI the error reported for one of the slave node which is not in the same system as the master
node is mentioned below. The writing on the slave node which is in the same physical system
as the Master happens correctly. (NOTE: slave node basically the worker and master node the
driver)
----------------------------------------------------------------------------------------------------------------------------------

0 (TID 41). 2060 bytes result sent to driver

17/07/31 00:19:29 INFO FileOutputCommitter: Saved output of task 'attempt_20170731001928_0002_m_000006_0'
to file:/Users/gouravsengupta/Development/spark/sparkdata/test1/_temporary/0/task_20170731001928_0002_m_000006

17/07/31 00:19:29 INFO SparkHadoopMapRedUtil: attempt_20170731001928_0002_m_000006_0: Committed

17/07/31 00:19:29 INFO Executor: Finished task 31.0 in stage 2.0 (TID 64). 2060 bytes result
sent to driver

17/07/31 00:19:29 INFO FileOutputCommitter: Saved output of task 'attempt_20170731001928_0002_m_000028_0'
to file:/Users/gouravsengupta/Development/spark/sparkdata/test1/_temporary/0/task_20170731001928_0002_m_000028

17/07/31 00:19:29 INFO SparkHadoopMapRedUtil: attempt_20170731001928_0002_m_000028_0: Committed

17/07/31 00:19:29 INFO FileOutputCommitter: Saved output of task 'attempt_20170731001928_0002_m_000021_0'
to file:/Users/gouravsengupta/Development/spark/sparkdata/test1/_temporary/0/task_20170731001928_0002_m_000021

17/07/31 00:19:29 INFO SparkHadoopMapRedUtil: attempt_20170731001928_0002_m_000021_0: Committed

17/07/31 00:19:29 INFO Executor: Finished task 12.0 in stage 2.0 (TID 45). 2103 bytes result
sent to driver

17/07/31 00:19:29 INFO Executor: Finished task 4.0 in stage 2.0 (TID 37). 2060 bytes result
sent to driver

17/07/31 00:19:29 INFO Executor: Finished task 6.0 in stage 2.0 (TID 39). 2060 bytes result
sent to driver

17/07/31 00:19:29 INFO FileOutputCommitter: Saved output of task 'attempt_20170731001928_0002_m_000018_0'
to file:/Users/gouravsengupta/Development/spark/sparkdata/test1/_temporary/0/task_20170731001928_0002_m_000018

17/07/31 00:19:29 INFO SparkHadoopMapRedUtil: attempt_20170731001928_0002_m_000018_0: Committed

17/07/31 00:19:29 INFO FileOutputCommitter: Saved output of task 'attempt_20170731001928_0002_m_000029_0'
to file:/Users/gouravsengupta/Development/spark/sparkdata/test1/_temporary/0/task_20170731001928_0002_m_000029

17/07/31 00:19:29 INFO SparkHadoopMapRedUtil: attempt_20170731001928_0002_m_000029_0: Committed

17/07/31 00:19:29 INFO FileOutputCommitter: Saved output of task 'attempt_20170731001928_0002_m_000027_0'
to file:/Users/gouravsengupta/Development/spark/sparkdata/test1/_temporary/0/task_20170731001928_0002_m_000027

17/07/31 00:19:29 INFO SparkHadoopMapRedUtil: attempt_20170731001928_0002_m_000027_0: Committed

17/07/31 00:19:29 INFO Executor: Finished task 21.0 in stage 2.0 (TID 54). 2060 bytes result
sent to driver

17/07/31 00:19:29 INFO FileOutputCommitter: Saved output of task 'attempt_20170731001928_0002_m_000010_0'
to file:/Users/gouravsengupta/Development/spark/sparkdata/test1/_temporary/0/task_20170731001928_0002_m_000010

17/07/31 00:19:29 INFO Executor: Finished task 19.0 in stage 2.0 (TID 52). 2060 bytes result
sent to driver

17/07/31 00:19:29 INFO SparkHadoopMapRedUtil: attempt_20170731001928_0002_m_000010_0: Committed

17/07/31 00:19:29 INFO FileOutputCommitter: Saved output of task 'attempt_20170731001928_0002_m_000030_0'
to file:/Users/gouravsengupta/Development/spark/sparkdata/test1/_temporary/0/task_20170731001928_0002_m_000030

17/07/31 00:19:29 INFO Executor: Finished task 22.0 in stage 2.0 (TID 55). 2060 bytes result
sent to driver

17/07/31 00:19:29 INFO SparkHadoopMapRedUtil: attempt_20170731001928_0002_m_000030_0: Committed

17/07/31 00:19:29 INFO Executor: Finished task 20.0 in stage 2.0 (TID 53). 2060 bytes result
sent to driver

17/07/31 00:19:29 INFO Executor: Finished task 28.0 in stage 2.0 (TID 61). 2060 bytes result
sent to driver

17/07/31 00:19:29 INFO FileOutputCommitter: Saved output of task 'attempt_20170731001928_0002_m_000016_0'
to file:/Users/gouravsengupta/Development/spark/sparkdata/test1/_temporary/0/task_20170731001928_0002_m_000016

17/07/31 00:19:29 INFO SparkHadoopMapRedUtil: attempt_20170731001928_0002_m_000016_0: Committed

17/07/31 00:19:29 INFO Executor: Finished task 26.0 in stage 2.0 (TID 59). 2060 bytes result
sent to driver

17/07/31 00:19:29 INFO Executor: Finished task 18.0 in stage 2.0 (TID 51). 2060 bytes result
sent to driver

17/07/31 00:19:29 INFO FileOutputCommitter: Saved output of task 'attempt_20170731001928_0002_m_000024_0'
to file:/Users/gouravsengupta/Development/spark/sparkdata/test1/_temporary/0/task_20170731001928_0002_m_000024

17/07/31 00:19:29 INFO SparkHadoopMapRedUtil: attempt_20170731001928_0002_m_000024_0: Committed

17/07/31 00:19:29 INFO FileOutputCommitter: Saved output of task 'attempt_20170731001928_0002_m_000023_0'
to file:/Users/gouravsengupta/Development/spark/sparkdata/test1/_temporary/0/task_20170731001928_0002_m_000023

17/07/31 00:19:29 INFO SparkHadoopMapRedUtil: attempt_20170731001928_0002_m_000023_0: Committed

17/07/31 00:19:29 INFO Executor: Finished task 29.0 in stage 2.0 (TID 62). 2103 bytes result
sent to driver

17/07/31 00:19:29 INFO Executor: Finished task 10.0 in stage 2.0 (TID 43). 2060 bytes result
sent to driver

17/07/31 00:19:29 INFO Executor: Finished task 16.0 in stage 2.0 (TID 49). 2060 bytes result
sent to driver

17/07/31 00:19:29 INFO Executor: Finished task 27.0 in stage 2.0 (TID 60). 2060 bytes result
sent to driver

17/07/31 00:19:29 INFO Executor: Finished task 30.0 in stage 2.0 (TID 63). 2103 bytes result
sent to driver

17/07/31 00:19:29 INFO Executor: Finished task 23.0 in stage 2.0 (TID 56). 2060 bytes result
sent to driver

17/07/31 00:19:29 INFO Executor: Finished task 24.0 in stage 2.0 (TID 57). 2060 bytes result
sent to driver

17/07/31 00:20:23 INFO CoarseGrainedExecutorBackend: Got assigned task 65

17/07/31 00:20:23 INFO Executor: Running task 0.0 in stage 3.0 (TID 65)

17/07/31 00:20:23 INFO TorrentBroadcast: Started reading broadcast variable 3

17/07/31 00:20:23 INFO MemoryStore: Block broadcast_3_piece0 stored as bytes in memory (estimated
size 24.9 KB, free 365.9 MB)

17/07/31 00:20:23 INFO TorrentBroadcast: Reading broadcast variable 3 took 10 ms

17/07/31 00:20:23 INFO MemoryStore: Block broadcast_3 stored as values in memory (estimated
size 70.3 KB, free 365.9 MB)

17/07/31 00:20:23 ERROR Executor: Exception in task 0.0 in stage 3.0 (TID 65)

java.io.FileNotFoundException: File file:/Users/gouravsengupta/Development/spark/sparkdata/test1/part-00001-e79273b5-9b4e-4037-92f3-2e52f523dfdf-c000.snappy.parquet
does not exist

          at org.apache.hadoop.fs.RawLocalFileSystem.deprecatedGetFileStatus(RawLocalFileSystem.java:611)

          at org.apache.hadoop.fs.RawLocalFileSystem.getFileLinkStatusInternal(RawLocalFileSystem.java:824)

          at org.apache.hadoop.fs.RawLocalFileSystem.getFileStatus(RawLocalFileSystem.java:601)

          at org.apache.hadoop.fs.FilterFileSystem.getFileStatus(FilterFileSystem.java:421)

          at org.apache.hadoop.fs.ChecksumFileSystem$ChecksumFSInputChecker.<init>(ChecksumFileSystem.java:142)

          at org.apache.hadoop.fs.ChecksumFileSystem.open(ChecksumFileSystem.java:346)

          at org.apache.hadoop.fs.FileSystem.open(FileSystem.java:769)

          at org.apache.parquet.hadoop.util.HadoopInputFile.newStream(HadoopInputFile.java:65)

          at org.apache.parquet.hadoop.ParquetFileReader.readFooter(ParquetFileReader.java:443)

          at org.apache.parquet.hadoop.ParquetFileReader.readFooter(ParquetFileReader.java:421)

          at org.apache.spark.sql.execution.datasources.parquet.ParquetFileFormat$$anonfun$readParquetFootersInParallel$1.apply(ParquetFileFormat.scala:491)

          at org.apache.spark.sql.execution.datasources.parquet.ParquetFileFormat$$anonfun$readParquetFootersInParallel$1.apply(ParquetFileFormat.scala:485)

          at scala.collection.parallel.AugmentedIterableIterator$class.flatmap2combiner(RemainsIterator.scala:132)

          at scala.collection.parallel.immutable.ParVector$ParVectorIterator.flatmap2combiner(ParVector.scala:62)

          at scala.collection.parallel.ParIterableLike$FlatMap.leaf(ParIterableLike.scala:1072)

          at scala.collection.parallel.Task$$anonfun$tryLeaf$1.apply$mcV$sp(Tasks.scala:49)

          at scala.collection.parallel.Task$$anonfun$tryLeaf$1.apply(Tasks.scala:48)

          at scala.collection.parallel.Task$$anonfun$tryLeaf$1.apply(Tasks.scala:48)

          at scala.collection.parallel.Task$class.tryLeaf(Tasks.scala:51)

          at scala.collection.parallel.ParIterableLike$FlatMap.tryLeaf(ParIterableLike.scala:1068)

          at scala.collection.parallel.AdaptiveWorkStealingTasks$WrappedTask$class.compute(Tasks.scala:152)

          at scala.collection.parallel.AdaptiveWorkStealingForkJoinTasks$WrappedTask.compute(Tasks.scala:443)

          at scala.concurrent.forkjoin.RecursiveAction.exec(RecursiveAction.java:160)

          at scala.concurrent.forkjoin.ForkJoinTask.doExec(ForkJoinTask.java:260)

          at scala.concurrent.forkjoin.ForkJoinTask.doJoin(ForkJoinTask.java:341)

          at scala.concurrent.forkjoin.ForkJoinTask.join(ForkJoinTask.java:673)

          at scala.collection.parallel.ForkJoinTasks$WrappedTask$class.sync(Tasks.scala:378)

          at scala.collection.parallel.AdaptiveWorkStealingForkJoinTasks$WrappedTask.sync(Tasks.scala:443)

          at scala.collection.parallel.ForkJoinTasks$class.executeAndWaitResult(Tasks.scala:426)

          at scala.collection.parallel.ForkJoinTaskSupport.executeAndWaitResult(TaskSupport.scala:56)

          at scala.collection.parallel.ParIterableLike$ResultMapping.leaf(ParIterableLike.scala:958)

          at scala.collection.parallel.Task$$anonfun$tryLeaf$1.apply$mcV$sp(Tasks.scala:49)

          at scala.collection.parallel.Task$$anonfun$tryLeaf$1.apply(Tasks.scala:48)

          at scala.collection.parallel.Task$$anonfun$tryLeaf$1.apply(Tasks.scala:48)

          at scala.collection.parallel.Task$class.tryLeaf(Tasks.scala:51)

          at scala.collection.parallel.ParIterableLike$ResultMapping.tryLeaf(ParIterableLike.scala:953)

          at scala.collection.parallel.AdaptiveWorkStealingTasks$WrappedTask$class.compute(Tasks.scala:152)

          at scala.collection.parallel.AdaptiveWorkStealingForkJoinTasks$WrappedTask.compute(Tasks.scala:443)

          at scala.concurrent.forkjoin.RecursiveAction.exec(RecursiveAction.java:160)

          at scala.concurrent.forkjoin.ForkJoinTask.doExec(ForkJoinTask.java:260)

          at scala.concurrent.forkjoin.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:1339)

          at scala.concurrent.forkjoin.ForkJoinPool.runWorker(ForkJoinPool.java:1979)

          at scala.concurrent.forkjoin.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:107)

17/07/31 00:20:23 INFO CoarseGrainedExecutorBackend: Got assigned task 66

----------------------------------------------------------------------------------------------------------------------------------


ERROR II  (in above code):
While trying to read the file there is now a distinct error thrown which mentions the same
saying that the files do not exist.

Also why is SPARK trying to search for the same files in both the systems? If the same path
in two systems have different files should SPARK not combine and work on them?



NOW DEMONSTRATING THAT THIS IS AN ERROR IN SPARK 2.x
I started spark using the same method but now using SPARK 1.5 and this does not give any error:
======================================================
import findspark
import os
os.environ["SPARK_HOME"] = '/Users/gouravsengupta/Development/spark/spark/'
findspark.init()
import pyspark

sc = pyspark.SparkContext("spark://Gouravs-iMac.local:7077", "test")
sqlContext = pyspark.SQLContext(sc)
import pandas, numpy
testdf = sqlContext createDataFrame(pandas.DataFrame(numpy.random.randn(10000, 4), columns=list('ABCD')))
testdf.cache()
testdf.count()
testdf.write.save("/Users/gouravsengupta/Development/spark/sparkdata/test3")
spark.read.load("/Users/gouravsengupta/Development/spark/sparkdata/test3").count()
======================================================

I will be sincerely obliged if someone could kindly help me out with this issue and point
out my mistakes/ assumptions.




Regards,
Gourav Sengupta


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