How to run PySpark as a Spark Standalone job¶
Overview¶
This is a minimal Spark script that imports PySpark, initializes a SparkContext and performs a distributed calculation on a Spark cluster in standalone mode.
Who is this for?¶
This how-to is for users of a Spark cluster that has been configured in standalone mode who wish to run a PySpark job.
Spark Standalone Summary¶
Before you start¶
To execute this example download the spark-basic.py example script
to the cluster node where you submit Spark jobs.
For this example you will need Spark running with the standalone scheduler. You can install Spark using an enterprise Hadoop distribution such as Cloudera CDH or Hortonworks HDP. Some additional configuration might be necessary to use Spark in standalone mode.
Modifying the script¶
After downloading the
spark-basic.py example script
, open
the file in a text editor on your cluster. Replace HEAD_NODE_HOSTNAME
with
the hostname or IP address of the Spark master as defined in your Hadoop
configuration.
# spark-basic.py
from pyspark import SparkConf
from pyspark import SparkContext
conf = SparkConf()
conf.setMaster('spark://HEAD_NODE_HOSTNAME:7077')
conf.setAppName('spark-basic')
sc = SparkContext(conf=conf)
def mod(x):
import numpy as np
return (x, np.mod(x, 2))
rdd = sc.parallelize(range(1000)).map(mod).take(10)
print rdd
Let’s analyze the contents of the spark-basic.rst
example script.
The first code block contains imports from PySpark.
The second code block initializes the SparkContext and sets the application name.
The third code block contains the analysis code that calculates the modulus of a range of numbers up to 1000 using the NumPy package and returns and prints the first 10 results.
The fourth code block runs the calculation on the Spark cluster using the NumPy library from Anaconda on the Spark workers and prints the results of the calculation.
NOTE: You may need to install NumPy on the cluster nodes using
adam scale -n my-cluster conda install numpy
.
Running the job¶
You can run this script by submitting it to your cluster for execution using spark-submit or by running this command:
$ python spark-basic.py
The output from the above command shows the first ten values that were returned from the spark-basic.py
script.
16/05/05 22:26:53 INFO spark.SparkContext: Running Spark version 1.6.0
[...]
16/05/05 22:27:03 INFO scheduler.TaskSetManager: Starting task 0.0 in stage 0.0 (TID 0, localhost, partition 0,PROCESS_LOCAL, 3242 bytes)
16/05/05 22:27:04 INFO storage.BlockManagerInfo: Added broadcast_0_piece0 in memory on localhost:46587 (size: 2.6 KB, free: 530.3 MB)
16/05/05 22:27:04 INFO scheduler.TaskSetManager: Finished task 0.0 in stage 0.0 (TID 0) in 652 ms on localhost (1/1)
16/05/05 22:27:04 INFO cluster.YarnScheduler: Removed TaskSet 0.0, whose tasks have all completed, from pool
16/05/05 22:27:04 INFO scheduler.DAGScheduler: ResultStage 0 (runJob at PythonRDD.scala:393) finished in 4.558 s
16/05/05 22:27:04 INFO scheduler.DAGScheduler: Job 0 finished: runJob at PythonRDD.scala:393, took 4.951328 s
[(0, 0), (1, 1), (2, 0), (3, 1), (4, 0), (5, 1), (6, 0), (7, 1), (8, 0), (9, 1)]
Troubleshooting¶
If something goes wrong, consult the Help and Support page.