spark-submit : Spark Application

Spark Python Application – Example

Apache Spark provides APIs for many popular programming languages. Python is on of them. One can write a python script for Apache Spark and run it using spark-submit command line interface.

In this tutorial, we shall learn to write a Spark Application in Python Programming Language and submit the application to run in Spark with local input and minimal (no) options. The step by step process of creating and running Spark Python Application is demonstrated using Word-Count Example.

Prepare Input

For Word-Count Example, we shall provide a text file as input. Input file contains multiple lines and each line has multiple words separated by white space.

Input File is located at : /home/input.txt

Spark Application – Python Program

Following is Python program that does word count in Apache Spark.

wordcount.py

import sys

from pyspark import SparkContext, SparkConf

if __name__ == "__main__":

  # create Spark context with Spark configuration
  conf = SparkConf().setAppName("Word Count - Python").set("spark.hadoop.yarn.resourcemanager.address", "192.168.0.104:8032")
  sc = SparkContext(conf=conf)

  # read in text file and split each document into words
  words = sc.textFile("/home/arjun/input.txt").flatMap(lambda line: line.split(" "))

  # count the occurrence of each word
  wordCounts = words.map(lambda word: (word, 1)).reduceByKey(lambda a,b:a +b)

  wordCounts.saveAsTextFile("/home/arjun/output/")

Submit Python Application to Spark

To submit the above Spark Application to Spark for running, Open a Terminal or Command Prompt from the location of wordcount.py, and run the following command :

$ spark-submit wordcount.py

arjun@tutorialkart:~/workspace/spark$ spark-submit wordcount.py
17/11/14 10:54:57 INFO spark.SparkContext: Running Spark version 2.2.0
17/11/14 10:54:57 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
17/11/14 10:54:57 INFO spark.SparkContext: Submitted application: Word Count - Python
17/11/14 10:54:57 INFO spark.SecurityManager: Changing view acls to: arjun
17/11/14 10:54:57 INFO spark.SecurityManager: Changing modify acls to: arjun
17/11/14 10:54:57 INFO spark.SecurityManager: Changing view acls groups to: 
17/11/14 10:54:57 INFO spark.SecurityManager: Changing modify acls groups to: 
17/11/14 10:54:57 INFO spark.SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users  with view permissions: Set(arjun); groups with view permissions: Set(); users  with modify permissions: Set(arjun); groups with modify permissions: Set()
17/11/14 10:54:58 INFO util.Utils: Successfully started service 'sparkDriver' on port 38850.
17/11/14 10:54:58 INFO spark.SparkEnv: Registering MapOutputTracker
17/11/14 10:54:58 INFO spark.SparkEnv: Registering BlockManagerMaster
17/11/14 10:54:58 INFO storage.BlockManagerMasterEndpoint: Using org.apache.spark.storage.DefaultTopologyMapper for getting topology information
17/11/14 10:54:58 INFO storage.BlockManagerMasterEndpoint: BlockManagerMasterEndpoint up
17/11/14 10:54:58 INFO storage.DiskBlockManager: Created local directory at /tmp/blockmgr-c896b1d3-faab-463b-a00f-695f108c515e
17/11/14 10:54:58 INFO memory.MemoryStore: MemoryStore started with capacity 366.3 MB
17/11/14 10:54:58 INFO spark.SparkEnv: Registering OutputCommitCoordinator
17/11/14 10:54:58 INFO util.log: Logging initialized @2864ms
17/11/14 10:54:58 INFO server.Server: jetty-9.3.z-SNAPSHOT
17/11/14 10:54:58 INFO server.Server: Started @2997ms
17/11/14 10:54:58 WARN util.Utils: Service 'SparkUI' could not bind on port 4040. Attempting port 4041.
17/11/14 10:54:58 INFO server.AbstractConnector: Started ServerConnector@127b57de{HTTP/1.1,[http/1.1]}{0.0.0.0:4041}
17/11/14 10:54:58 INFO util.Utils: Successfully started service 'SparkUI' on port 4041.
17/11/14 10:54:58 INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@71fa1670{/jobs,null,AVAILABLE,@Spark}
17/11/14 10:54:58 INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@4ee9fdba{/jobs/json,null,AVAILABLE,@Spark}
17/11/14 10:54:58 INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@1ff54937{/jobs/job,null,AVAILABLE,@Spark}
17/11/14 10:54:58 INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@173df742{/jobs/job/json,null,AVAILABLE,@Spark}
17/11/14 10:54:58 INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@18a2ad0f{/stages,null,AVAILABLE,@Spark}
17/11/14 10:54:58 INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@72942f18{/stages/json,null,AVAILABLE,@Spark}
17/11/14 10:54:58 INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@78a3e7ef{/stages/stage,null,AVAILABLE,@Spark}
17/11/14 10:54:58 INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@4e295bb8{/stages/stage/json,null,AVAILABLE,@Spark}
17/11/14 10:54:58 INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@658a8f39{/stages/pool,null,AVAILABLE,@Spark}
17/11/14 10:54:58 INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@5a7c87c5{/stages/pool/json,null,AVAILABLE,@Spark}
17/11/14 10:54:58 INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@7b22142b{/storage,null,AVAILABLE,@Spark}
17/11/14 10:54:58 INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@7c723018{/storage/json,null,AVAILABLE,@Spark}
17/11/14 10:54:58 INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@58fd3f7b{/storage/rdd,null,AVAILABLE,@Spark}
17/11/14 10:54:58 INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@1f151ef{/storage/rdd/json,null,AVAILABLE,@Spark}
17/11/14 10:54:58 INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@2cfc831c{/environment,null,AVAILABLE,@Spark}
17/11/14 10:54:58 INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@390dc31e{/environment/json,null,AVAILABLE,@Spark}
17/11/14 10:54:58 INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@37a527a1{/executors,null,AVAILABLE,@Spark}
17/11/14 10:54:58 INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@16fdd972{/executors/json,null,AVAILABLE,@Spark}
17/11/14 10:54:58 INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@3ab9cfcc{/executors/threadDump,null,AVAILABLE,@Spark}
17/11/14 10:54:58 INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@7ccd147f{/executors/threadDump/json,null,AVAILABLE,@Spark}
17/11/14 10:54:58 INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@57f8eaed{/static,null,AVAILABLE,@Spark}
17/11/14 10:54:58 INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@5c542cff{/,null,AVAILABLE,@Spark}
17/11/14 10:54:58 INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@625a6ecc{/api,null,AVAILABLE,@Spark}
17/11/14 10:54:58 INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@7843ba8c{/jobs/job/kill,null,AVAILABLE,@Spark}
17/11/14 10:54:58 INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@20d38629{/stages/stage/kill,null,AVAILABLE,@Spark}
17/11/14 10:54:58 INFO ui.SparkUI: Bound SparkUI to 192.168.0.104, and started at http://192.168.0.104:4041
17/11/14 10:54:59 INFO spark.SparkContext: Added file file:/home/arjun/workspace/spark/wordcount.py at file:/home/arjun/workspace/spark/wordcount.py with timestamp 1510637099122
17/11/14 10:54:59 INFO util.Utils: Copying /home/arjun/workspace/spark/wordcount.py to /tmp/spark-39c98eb0-0434-40db-aa7c-aa4a5327a41c/userFiles-469dc820-60ab-43c3-bcd6-8c5133cb4470/wordcount.py
17/11/14 10:54:59 INFO executor.Executor: Starting executor ID driver on host localhost
17/11/14 10:54:59 INFO util.Utils: Successfully started service 'org.apache.spark.network.netty.NettyBlockTransferService' on port 39082.
17/11/14 10:54:59 INFO netty.NettyBlockTransferService: Server created on 192.168.0.104:39082
17/11/14 10:54:59 INFO storage.BlockManager: Using org.apache.spark.storage.RandomBlockReplicationPolicy for block replication policy
17/11/14 10:54:59 INFO storage.BlockManagerMaster: Registering BlockManager BlockManagerId(driver, 192.168.0.104, 39082, None)
17/11/14 10:54:59 INFO storage.BlockManagerMasterEndpoint: Registering block manager 192.168.0.104:39082 with 366.3 MB RAM, BlockManagerId(driver, 192.168.0.104, 39082, None)
17/11/14 10:54:59 INFO storage.BlockManagerMaster: Registered BlockManager BlockManagerId(driver, 192.168.0.104, 39082, None)
17/11/14 10:54:59 INFO storage.BlockManager: Initialized BlockManager: BlockManagerId(driver, 192.168.0.104, 39082, None)
17/11/14 10:54:59 INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@72050d48{/metrics/json,null,AVAILABLE,@Spark}
17/11/14 10:55:00 INFO memory.MemoryStore: Block broadcast_0 stored as values in memory (estimated size 238.2 KB, free 366.1 MB)
17/11/14 10:55:00 INFO memory.MemoryStore: Block broadcast_0_piece0 stored as bytes in memory (estimated size 22.9 KB, free 366.0 MB)
17/11/14 10:55:00 INFO storage.BlockManagerInfo: Added broadcast_0_piece0 in memory on 192.168.0.104:39082 (size: 22.9 KB, free: 366.3 MB)
17/11/14 10:55:00 INFO spark.SparkContext: Created broadcast 0 from textFile at NativeMethodAccessorImpl.java:0
17/11/14 10:55:00 INFO mapred.FileInputFormat: Total input paths to process : 1
17/11/14 10:55:00 INFO output.FileOutputCommitter: File Output Committer Algorithm version is 1
17/11/14 10:55:00 INFO spark.SparkContext: Starting job: saveAsTextFile at NativeMethodAccessorImpl.java:0
17/11/14 10:55:00 INFO scheduler.DAGScheduler: Registering RDD 3 (reduceByKey at /home/arjun/workspace/spark/wordcount.py:15)
17/11/14 10:55:00 INFO scheduler.DAGScheduler: Got job 0 (saveAsTextFile at NativeMethodAccessorImpl.java:0) with 2 output partitions
17/11/14 10:55:00 INFO scheduler.DAGScheduler: Final stage: ResultStage 1 (saveAsTextFile at NativeMethodAccessorImpl.java:0)
17/11/14 10:55:00 INFO scheduler.DAGScheduler: Parents of final stage: List(ShuffleMapStage 0)
17/11/14 10:55:00 INFO scheduler.DAGScheduler: Missing parents: List(ShuffleMapStage 0)
17/11/14 10:55:00 INFO scheduler.DAGScheduler: Submitting ShuffleMapStage 0 (PairwiseRDD[3] at reduceByKey at /home/arjun/workspace/spark/wordcount.py:15), which has no missing parents
17/11/14 10:55:01 INFO memory.MemoryStore: Block broadcast_1 stored as values in memory (estimated size 9.3 KB, free 366.0 MB)
17/11/14 10:55:01 INFO memory.MemoryStore: Block broadcast_1_piece0 stored as bytes in memory (estimated size 5.8 KB, free 366.0 MB)
17/11/14 10:55:01 INFO storage.BlockManagerInfo: Added broadcast_1_piece0 in memory on 192.168.0.104:39082 (size: 5.8 KB, free: 366.3 MB)
17/11/14 10:55:01 INFO spark.SparkContext: Created broadcast 1 from broadcast at DAGScheduler.scala:1006
17/11/14 10:55:01 INFO scheduler.DAGScheduler: Submitting 2 missing tasks from ShuffleMapStage 0 (PairwiseRDD[3] at reduceByKey at /home/arjun/workspace/spark/wordcount.py:15) (first 15 tasks are for partitions Vector(0, 1))
17/11/14 10:55:01 INFO scheduler.TaskSchedulerImpl: Adding task set 0.0 with 2 tasks
17/11/14 10:55:01 INFO scheduler.TaskSetManager: Starting task 0.0 in stage 0.0 (TID 0, localhost, executor driver, partition 0, PROCESS_LOCAL, 4834 bytes)
17/11/14 10:55:01 INFO scheduler.TaskSetManager: Starting task 1.0 in stage 0.0 (TID 1, localhost, executor driver, partition 1, PROCESS_LOCAL, 4834 bytes)
17/11/14 10:55:01 INFO executor.Executor: Running task 0.0 in stage 0.0 (TID 0)
17/11/14 10:55:01 INFO executor.Executor: Running task 1.0 in stage 0.0 (TID 1)
17/11/14 10:55:01 INFO executor.Executor: Fetching file:/home/arjun/workspace/spark/wordcount.py with timestamp 1510637099122
17/11/14 10:55:01 INFO util.Utils: /home/arjun/workspace/spark/wordcount.py has been previously copied to /tmp/spark-39c98eb0-0434-40db-aa7c-aa4a5327a41c/userFiles-469dc820-60ab-43c3-bcd6-8c5133cb4470/wordcount.py
17/11/14 10:55:01 INFO rdd.HadoopRDD: Input split: file:/home/arjun/input.txt:0+4248
17/11/14 10:55:01 INFO rdd.HadoopRDD: Input split: file:/home/arjun/input.txt:4248+4248
17/11/14 10:55:02 INFO python.PythonRunner: Times: total = 419, boot = 347, init = 50, finish = 22
17/11/14 10:55:02 INFO python.PythonRunner: Times: total = 410, boot = 342, init = 55, finish = 13
17/11/14 10:55:02 INFO executor.Executor: Finished task 1.0 in stage 0.0 (TID 1). 1612 bytes result sent to driver
17/11/14 10:55:02 INFO executor.Executor: Finished task 0.0 in stage 0.0 (TID 0). 1612 bytes result sent to driver
17/11/14 10:55:02 INFO scheduler.TaskSetManager: Finished task 0.0 in stage 0.0 (TID 0) in 1037 ms on localhost (executor driver) (1/2)
17/11/14 10:55:02 INFO scheduler.TaskSetManager: Finished task 1.0 in stage 0.0 (TID 1) in 1013 ms on localhost (executor driver) (2/2)
17/11/14 10:55:02 INFO scheduler.TaskSchedulerImpl: Removed TaskSet 0.0, whose tasks have all completed, from pool 
17/11/14 10:55:02 INFO scheduler.DAGScheduler: ShuffleMapStage 0 (reduceByKey at /home/arjun/workspace/spark/wordcount.py:15) finished in 1.089 s
17/11/14 10:55:02 INFO scheduler.DAGScheduler: looking for newly runnable stages
17/11/14 10:55:02 INFO scheduler.DAGScheduler: running: Set()
17/11/14 10:55:02 INFO scheduler.DAGScheduler: waiting: Set(ResultStage 1)
17/11/14 10:55:02 INFO scheduler.DAGScheduler: failed: Set()
17/11/14 10:55:02 INFO scheduler.DAGScheduler: Submitting ResultStage 1 (MapPartitionsRDD[8] at saveAsTextFile at NativeMethodAccessorImpl.java:0), which has no missing parents
17/11/14 10:55:02 INFO memory.MemoryStore: Block broadcast_2 stored as values in memory (estimated size 75.7 KB, free 366.0 MB)
17/11/14 10:55:02 INFO memory.MemoryStore: Block broadcast_2_piece0 stored as bytes in memory (estimated size 28.3 KB, free 365.9 MB)
17/11/14 10:55:02 INFO storage.BlockManagerInfo: Added broadcast_2_piece0 in memory on 192.168.0.104:39082 (size: 28.3 KB, free: 366.2 MB)
17/11/14 10:55:02 INFO spark.SparkContext: Created broadcast 2 from broadcast at DAGScheduler.scala:1006
17/11/14 10:55:02 INFO scheduler.DAGScheduler: Submitting 2 missing tasks from ResultStage 1 (MapPartitionsRDD[8] at saveAsTextFile at NativeMethodAccessorImpl.java:0) (first 15 tasks are for partitions Vector(0, 1))
17/11/14 10:55:02 INFO scheduler.TaskSchedulerImpl: Adding task set 1.0 with 2 tasks
17/11/14 10:55:02 INFO scheduler.TaskSetManager: Starting task 0.0 in stage 1.0 (TID 2, localhost, executor driver, partition 0, ANY, 4621 bytes)
17/11/14 10:55:02 INFO scheduler.TaskSetManager: Starting task 1.0 in stage 1.0 (TID 3, localhost, executor driver, partition 1, ANY, 4621 bytes)
17/11/14 10:55:02 INFO executor.Executor: Running task 0.0 in stage 1.0 (TID 2)
17/11/14 10:55:02 INFO executor.Executor: Running task 1.0 in stage 1.0 (TID 3)
17/11/14 10:55:02 INFO storage.ShuffleBlockFetcherIterator: Getting 2 non-empty blocks out of 2 blocks
17/11/14 10:55:02 INFO storage.ShuffleBlockFetcherIterator: Getting 2 non-empty blocks out of 2 blocks
17/11/14 10:55:02 INFO storage.ShuffleBlockFetcherIterator: Started 0 remote fetches in 13 ms
17/11/14 10:55:02 INFO storage.ShuffleBlockFetcherIterator: Started 0 remote fetches in 12 ms
17/11/14 10:55:02 INFO output.FileOutputCommitter: File Output Committer Algorithm version is 1
17/11/14 10:55:02 INFO output.FileOutputCommitter: File Output Committer Algorithm version is 1
17/11/14 10:55:02 INFO python.PythonRunner: Times: total = 49, boot = -558, init = 600, finish = 7
17/11/14 10:55:02 INFO python.PythonRunner: Times: total = 61, boot = -560, init = 613, finish = 8
17/11/14 10:55:02 INFO output.FileOutputCommitter: Saved output of task 'attempt_20171114105500_0001_m_000001_3' to file:/home/arjun/output/_temporary/0/task_20171114105500_0001_m_000001
17/11/14 10:55:02 INFO output.FileOutputCommitter: Saved output of task 'attempt_20171114105500_0001_m_000000_2' to file:/home/arjun/output/_temporary/0/task_20171114105500_0001_m_000000
17/11/14 10:55:02 INFO mapred.SparkHadoopMapRedUtil: attempt_20171114105500_0001_m_000000_2: Committed
17/11/14 10:55:02 INFO mapred.SparkHadoopMapRedUtil: attempt_20171114105500_0001_m_000001_3: Committed
17/11/14 10:55:02 INFO executor.Executor: Finished task 0.0 in stage 1.0 (TID 2). 1638 bytes result sent to driver
17/11/14 10:55:02 INFO executor.Executor: Finished task 1.0 in stage 1.0 (TID 3). 1638 bytes result sent to driver
17/11/14 10:55:02 INFO scheduler.TaskSetManager: Finished task 0.0 in stage 1.0 (TID 2) in 264 ms on localhost (executor driver) (1/2)
17/11/14 10:55:02 INFO scheduler.TaskSetManager: Finished task 1.0 in stage 1.0 (TID 3) in 261 ms on localhost (executor driver) (2/2)
17/11/14 10:55:02 INFO scheduler.TaskSchedulerImpl: Removed TaskSet 1.0, whose tasks have all completed, from pool 
17/11/14 10:55:02 INFO scheduler.DAGScheduler: ResultStage 1 (saveAsTextFile at NativeMethodAccessorImpl.java:0) finished in 0.262 s
17/11/14 10:55:02 INFO scheduler.DAGScheduler: Job 0 finished: saveAsTextFile at NativeMethodAccessorImpl.java:0, took 1.787527 s
17/11/14 10:55:02 INFO spark.SparkContext: Invoking stop() from shutdown hook
17/11/14 10:55:02 INFO server.AbstractConnector: Stopped Spark@127b57de{HTTP/1.1,[http/1.1]}{192.168.0.104:4041}
17/11/14 10:55:02 INFO ui.SparkUI: Stopped Spark web UI at http://192.168.0.104:4041
17/11/14 10:55:02 INFO spark.MapOutputTrackerMasterEndpoint: MapOutputTrackerMasterEndpoint stopped!
17/11/14 10:55:02 INFO memory.MemoryStore: MemoryStore cleared
17/11/14 10:55:02 INFO storage.BlockManager: BlockManager stopped
17/11/14 10:55:02 INFO storage.BlockManagerMaster: BlockManagerMaster stopped
17/11/14 10:55:02 INFO scheduler.OutputCommitCoordinator$OutputCommitCoordinatorEndpoint: OutputCommitCoordinator stopped!
17/11/14 10:55:02 INFO spark.SparkContext: Successfully stopped SparkContext
17/11/14 10:55:02 INFO util.ShutdownHookManager: Shutdown hook called
17/11/14 10:55:02 INFO util.ShutdownHookManager: Deleting directory /tmp/spark-39c98eb0-0434-40db-aa7c-aa4a5327a41c/pyspark-14935fce-0b7b-4473-b000-e16d0b7005d9
17/11/14 10:55:02 INFO util.ShutdownHookManager: Deleting directory /tmp/spark-39c98eb0-0434-40db-aa7c-aa4a5327a41c

Output

The word counts are written to the output folder. Verify the counts for the correctness of the program. (We have provided the output path in wordcount.py Python script).

Output has been written to two part files. Files contain tuples of word and the corresponding number of occurrences in the input file.

Conclusion

In this Apache Spark Tutorial, Python Application for Spark, we have learnt to run a simple Spark Application written in Python Programming language.

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Spark Python Application – Example

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