Revision as of 01:22, 27 September 2017

Installing

Docker

Easiest solution: use Docker.

Use the Jupyter PySpark notebook Docker container: https://hub.docker.com/r/jupyter/pyspark-notebook/

This comes bundled with Apache Mesos, which is a cluster resource management framework. This enables you to connect to a Mesos-managed cluster and use compute resources on that cluster.

This Docker image is provided courtesy of the Jupyter project on Github: https://github.com/jupyter/docker-stacks

Nice explanation of how to set it up with either a standalone (single node) or Mesos cluster in the PySpark notebook image's README: https://github.com/jupyter/docker-stacks/tree/master/pyspark-notebook

Basically, here are the first few lines of a standalone notebook:

import pyspark
sc = pyspark.SparkContext('local[*]')

# do something to prove it works
rdd = sc.parallelize(range(1000))
rdd.takeSample(False, 5)

Mac

Ensure you have the following software installed:

Python 3.x distribution
Jupyter notebook
Java 8 JDK (link: https://www.java.com/en/download/faq/java_mac.xml)

Installing with Homebrew

Install Apache Spark using Homebrew:

$ brew install apache-spark

This should put pyspark on your path:

$ which pyspark
/usr/local/bin/pyspark

I was still getting problems importing pyspark, so I also ended up running a

$ pip3 install pyspark

Linux

Have the following software installed:

Python 3.x distribution
Jupyter notebook
Java 8 JDK (link: http://www.oracle.com/technetwork/java/javase/downloads/jdk8-downloads-2133151.html)

Download spark from this page: http://spark.apache.org/downloads.html

Now get the Scala build tool into aptitude (see https://stackoverflow.com/questions/35529913/how-to-install-sbt-on-ubuntu-debian-with-apt-get):

$ echo "deb https://dl.bintray.com/sbt/debian /" | sudo tee -a /etc/apt/sources.list.d/sbt.list
$ sudo apt-key adv --keyserver hkp://keyserver.ubuntu.com:80 --recv 642AC823
$ sudo apt-get update
$ sudo apt-get install sbt

Now unzip the Spark source, enter the directory, and run:

$ sbt assembly

Ensure Spark was built correctly by running this command from the same directory:

$ bin/pyspark

Now set the $SPARK_HOME environment variable to wherever your Spark lives:

export SPARK_HOME="/path/to/unzipped/spark-2.2"

Testing Out Pyspark

Test it out by running the pyspark command. This should look a bit like Python, but with a Spark splash message:

$ pyspark
Python 2.7.10 (default, Feb  7 2017, 00:08:15)
[GCC 4.2.1 Compatible Apple LLVM 8.0.0 (clang-800.0.34)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
17/09/26 17:53:09 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
17/09/26 17:53:16 WARN ObjectStore: Version information not found in metastore. hive.metastore.schema.verification is not enabled so recording the schema version 1.2.0
17/09/26 17:53:16 WARN ObjectStore: Failed to get database default, returning NoSuchObjectException
17/09/26 17:53:17 WARN ObjectStore: Failed to get database global_temp, returning NoSuchObjectException
Welcome to
      ____              __
     / __/__  ___ _____/ /__
    _\ \/ _ \/ _ `/ __/  '_/
   /__ / .__/\_,_/_/ /_/\_\   version 2.2.0
      /_/

Using Python version 2.7.10 (default, Feb  7 2017 00:08:15)
SparkSession available as 'spark'.
>>>

Test that it's ok by checking if the sc variable is holding a Spark context:

>>> sc
<SparkContext master=local[*] appName=PySparkShell>

Set Up PySpark With Jupyter

To use PySpark through a Jupyter notebook, instead of through the command line, first make sure your Jupyter is up to date:

$ pip3 install --upgrade jupyter

You may also need to install pyspark using pip:

$ pip3 install --upgrade pyspark

Start up Jupyter and create a new notebook:

$ jupyter notebook

Make a cell that tests out pyspark:

import pyspark
sc = pyspark.SparkContext('local[*]')

# do something to prove it works
rdd = sc.parallelize(range(1000))
rdd.takeSample(False, 5)

Errors

When I ran the above test, I saw this error:

---------------------------------------------------------------------------
Py4JJavaError                             Traceback (most recent call last)
<ipython-input-4-0fdbaf1f4e92> in <module>()
      1 # do something to prove it works
      2 rdd = sc.parallelize(range(1000))
----> 3 rdd.takeSample(False, 5)

/usr/local/lib/python3.6/site-packages/pyspark/rdd.py in takeSample(self, withReplacement, num, seed)
    477             return []
    478 
--> 479         initialCount = self.count()
    480         if initialCount == 0:
    481             return []

/usr/local/lib/python3.6/site-packages/pyspark/rdd.py in count(self)
   1039         3
   1040         """
-> 1041         return self.mapPartitions(lambda i: [sum(1 for _ in i)]).sum()
   1042 
   1043     def stats(self):

/usr/local/lib/python3.6/site-packages/pyspark/rdd.py in sum(self)
   1030         6.0
   1031         """
-> 1032         return self.mapPartitions(lambda x: [sum(x)]).fold(0, operator.add)
   1033 
   1034     def count(self):

/usr/local/lib/python3.6/site-packages/pyspark/rdd.py in fold(self, zeroValue, op)
    904         # zeroValue provided to each partition is unique from the one provided
    905         # to the final reduce call
--> 906         vals = self.mapPartitions(func).collect()
    907         return reduce(op, vals, zeroValue)
    908 

/usr/local/lib/python3.6/site-packages/pyspark/rdd.py in collect(self)
    807         """
    808         with SCCallSiteSync(self.context) as css:
--> 809             port = self.ctx._jvm.PythonRDD.collectAndServe(self._jrdd.rdd())
    810         return list(_load_from_socket(port, self._jrdd_deserializer))
    811 

/usr/local/lib/python3.6/site-packages/py4j/java_gateway.py in __call__(self, *args)
   1131         answer = self.gateway_client.send_command(command)
   1132         return_value = get_return_value(
-> 1133             answer, self.gateway_client, self.target_id, self.name)
   1134 
   1135         for temp_arg in temp_args:

/usr/local/lib/python3.6/site-packages/py4j/protocol.py in get_return_value(answer, gateway_client, target_id, name)
    317                 raise Py4JJavaError(
    318                     "An error occurred while calling {0}{1}{2}.\n".
--> 319                     format(target_id, ".", name), value)
    320             else:
    321                 raise Py4JError(

Py4JJavaError: An error occurred while calling z:org.apache.spark.api.python.PythonRDD.collectAndServe.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 1 in stage 1.0 failed 1 times, most recent failure: Lost task 1.0 in stage 1.0 (TID 5, localhost, executor driver): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
  File "/usr/local/lib/python3.6/site-packages/pyspark/python/lib/pyspark.zip/pyspark/worker.py", line 123, in main
    ("%d.%d" % sys.version_info[:2], version))
Exception: Python in worker has different version 2.7 than that in driver 3.6, PySpark cannot run with different minor versions.Please check environment variables PYSPARK_PYTHON and PYSPARK_DRIVER_PYTHON are correctly set.

	at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRDD.scala:193)
	at org.apache.spark.api.python.PythonRunner$$anon$1.<init>(PythonRDD.scala:234)
	at org.apache.spark.api.python.PythonRunner.compute(PythonRDD.scala:152)
	at org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:63)
	at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
	at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
	at org.apache.spark.scheduler.Task.run(Task.scala:108)
	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:335)
	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)

Driver stacktrace:
	at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1499)
	at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1487)
	at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1486)
	at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
	at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
	at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1486)
	at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:814)
	at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:814)
	at scala.Option.foreach(Option.scala:257)
	at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:814)
	at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1714)
	at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1669)
	at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1658)
	at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
	at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:630)
	at org.apache.spark.SparkContext.runJob(SparkContext.scala:2022)
	at org.apache.spark.SparkContext.runJob(SparkContext.scala:2043)
	at org.apache.spark.SparkContext.runJob(SparkContext.scala:2062)
	at org.apache.spark.SparkContext.runJob(SparkContext.scala:2087)
	at org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:936)
	at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
	at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
	at org.apache.spark.rdd.RDD.withScope(RDD.scala:362)
	at org.apache.spark.rdd.RDD.collect(RDD.scala:935)
	at org.apache.spark.api.python.PythonRDD$.collectAndServe(PythonRDD.scala:458)
	at org.apache.spark.api.python.PythonRDD.collectAndServe(PythonRDD.scala)
	at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
	at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
	at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
	at java.lang.reflect.Method.invoke(Method.java:498)
	at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
	at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
	at py4j.Gateway.invoke(Gateway.java:280)
	at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
	at py4j.commands.CallCommand.execute(CallCommand.java:79)
	at py4j.GatewayConnection.run(GatewayConnection.java:214)
	at java.lang.Thread.run(Thread.java:745)
Caused by: org.apache.spark.api.python.PythonException: Traceback (most recent call last):
  File "/usr/local/lib/python3.6/site-packages/pyspark/python/lib/pyspark.zip/pyspark/worker.py", line 123, in main
    ("%d.%d" % sys.version_info[:2], version))
Exception: Python in worker has different version 2.7 than that in driver 3.6, PySpark cannot run with different minor versions.Please check environment variables PYSPARK_PYTHON and PYSPARK_DRIVER_PYTHON are correctly set.

	at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRDD.scala:193)
	at org.apache.spark.api.python.PythonRunner$$anon$1.<init>(PythonRDD.scala:234)
	at org.apache.spark.api.python.PythonRunner.compute(PythonRDD.scala:152)
	at org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:63)
	at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
	at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
	at org.apache.spark.scheduler.Task.run(Task.scala:108)
	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:335)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
	... 1 more

I ended up running

$ export PYSPARK_PYTHON="python3" jupyter notebook

This resolved the issue. You should see:

Out[1] : [285, 777, 376, 101, 737]

Flags

PySpark: Difference between revisions

From charlesreid1