This section describes running a Spark wordcount application in Python.
Upload the to-be-processed file to COS first. If the file is in your local storage, upload it directly via the COS console; if it is in the EMR cluster, upload it by running the following Hadoop command:
[hadoop@10 hadoop]$ hadoop fs -put $testfile cosn:// $bucketname/
Here, $testfile is the full path with file name and $bucketname is your bucket name. After the upload is completed, you can check whether the file is available in COS.
First, log in to any node (preferably a master one) in the EMR cluster. For information about how to log in to EMR, see Logging in to Linux Instance Using Standard Login Method. Here, you can use WebShell to log in. Click Login on the right of the desired CVM instance to go to the login page. The default username is
root, and the password is the one you set when creating the EMR cluster. Once your credentials are validated, you can enter the command line interface.
Run the following command on the EMR command-line interface to switch to the Hadoop user and go to the Spark installation directory
[root@172 ~]# su hadoop [hadoop@172 root]$ cd /usr/local/service/spark
Create a Python file named wordcount.py and add the following code:
from __future__ import print_function import sys from operator import add from pyspark.sql import SparkSession if __name__ == "__main__": if len(sys.argv) != 3: print("Usage: wordcount <file>", file=sys.stderr) exit(-1) spark = SparkSession\ .builder\ .appName("PythonWordCount")\ .getOrCreate() sc = spark.sparkContext lines = spark.read.text(sys.argv).rdd.map(lambda r: r) counts = lines.flatMap(lambda x: x.split(' ')) \ .map(lambda x: (x, 1)) \ .reduceByKey(add) output = counts.collect() counts.saveAsTextFile(sys.argv) spark.stop()
Submit the task by running the following command:
[hadoop@10 spark]$ ./bin/spark-submit --master yarn ./wordcount.py cosn://$bucketname/$yourtestfile cosn:// $bucketname/$output
Here, $bucketname is your COS bucket name, $yourtestfile is the full path with test file name in the bucket, and $output is your output folder. If the $output folder already exists before the command is executed, the program will fail.
After the program is running automatically, you can find the output file in the destination bucket:
[hadoop@172 spark]$ hadoop fs -ls cosn:// $bucketname/$output Found 2 items -rw-rw-rw- 1 hadoop Hadoop 0 2018-06-29 15:35 cosn:// $bucketname/$output /_SUCCESS -rw-rw-rw- 1 hadoop Hadoop 2102 2018-06-29 15:34 cosn:// $bucketname/$output /part-00000
You can also look up the the result by running the following command:
[hadoop@172 spark]$ hadoop fs -cat cosn:// $bucketname/$output /part-00000 (u'', 27) (u'code', 1) (u'both', 1) (u'Hadoop', 1) (u'Bureau', 1) (u'Department', 1)
You can also output the result to HDFS by changing the output location in the command as follows:
[hadoop@10spark]$ ./bin/spark-submit ./wordcount.py cosn://$bucketname/$yourtestfile /user/hadoop/$output
/user/hadoop/ is the path in HDFS. If this path does not exist, you can create one.
After the task is completed, you can view the Spark execution log by running the following command:
[hadoop@10 spark]$ /usr/local/service/hadoop/bin/yarn logs -applicationId $yourId
Here, $yourId should be replaced with your task ID, which can be viewed in Yarn's WebUI.