How to run multiple jobs in one Sparkcontext from separate threads in PySpark?

Question

It is understood from Spark documentation about Scheduling Within an Application:

Inside a given Spark application (SparkContext instance), multiple

Answer 1

I was running into the same issue, so I created a tiny self-contained example. I create multiple threads using python's threading module and submit multiple spark jobs simultaneously.

Note that by default, spark will run the jobs in First-In First-Out (FIFO): http://spark.apache.org/docs/latest/job-scheduling.html#scheduling-within-an-application. In the example below, I change it to FAIR scheduling

# Prereqs:
# set 
# spark.dynamicAllocation.enabled         true
# spark.shuffle.service.enabled           true
  spark.scheduler.mode                    FAIR
# in spark-defaults.conf

import threading
from pyspark import SparkContext, SparkConf

def task(sc, i):
  print sc.parallelize(range(i*10000)).count()

def run_multiple_jobs():
  conf = SparkConf().setMaster('local[*]').setAppName('appname')
  # Set scheduler to FAIR: http://spark.apache.org/docs/latest/job-scheduling.html#scheduling-within-an-application
  conf.set('spark.scheduler.mode', 'FAIR')
  sc = SparkContext(conf=conf)
  for i in range(4):
    t = threading.Thread(target=task, args=(sc, i))
    t.start()
    print 'spark task', i, 'has started'


run_multiple_jobs()

Output:

spark task 0 has started
spark task 1 has started
spark task 2 has started
spark task 3 has started
30000
0 
10000
20000