CODEWITHSUNDEEP
pythonapache-spark
Baradwaj Aryasomayajula
Baradwaj Aryasomayajula

Reputation: 1204

How to resolve : Very large size tasks in spark

Here I am pasting my python code which I am running on spark in order to perform some analysis on data. I am able to run the following program on small amount of data-set. But when coming large data-set, it is saying "Stage 1 contains a task of very large size (17693 KB). The maximum recommended task size is 100 KB". 

import os
import sys
import unicodedata
from operator import add

try:
    from pyspark import SparkConf
    from pyspark import SparkContext
except ImportError as e:
    print ("Error importing Spark Modules", e)
    sys.exit(1)

def tokenize(text):
    resultDict = {}
    text = unicodedata.normalize('NFKD', text).encode('ascii','ignore')

    str1= text[1]
    str2= text[0]

    arrText= text.split(str1)

    ss1 = arrText[0].split("/")

    docID = ss1[0].strip()

    docName = ss[1].strip()

    resultDict[docID+"_"+docName] = 1

    return resultDict.iteritems()

sc=SparkContext('local')
textfile = sc.textFile("path to my data")
fileContent = textfile.flatMap(tokenize)
rdd = sc.parallelize(fileContent.collect())
rdd= rdd.map(lambda x: (x[0], x[1])).reduceByKey(add)
print rdd.collect()
#reduceByKey(lambda a,b: a+b)
rdd.coalesce(1).saveAsTextFile("path to result")

Here I am posting a little more warning: The job is not running after this. Can some one help me with this.

16/06/10 19:19:58 WARN TaskSetManager: Stage 1 contains a task of very large size (17693 KB). The maximum recommended task size is 100 KB.
16/06/10 19:19:58 INFO TaskSetManager: Starting task 0.0 in stage 1.0 (TID 5314, localhost, partition 0,PROCESS_LOCAL, 18118332 bytes)
16/06/10 19:19:58 INFO Executor: Running task 0.0 in stage 1.0 (TID 5314)
16/06/10 19:43:00 INFO BlockManagerInfo: Removed broadcast_1_piece0 on localhost:43480 in memory (size: 3.9 KB, free: 511.1 MB)
16/06/10 19:43:00 INFO ContextCleaner: Cleaned accumulator 2

Upvotes: 7

Views: 8375

Answers (1)

Sim
Sim

Reputation: 13538

When Spark serializes tasks, it recursively serializes the full closure context. In this case, the logical culprit seems to be unicodedata which you use in tokenize. I may be wrong but I don't see any other heavy data structures in the code. (Caveat, I typically use Spark with Scala and my Python is rusty.) I wonder whether the library is backed by heavy data structures that are not available on the executor nodes.

The typical patterns for dealing with these types of problems are:

  1. Make sure all libraries are available on the executor nodes.

  2. Use broadcast variables to distribute heavy data structures to executors.

Unrelated, unless you are using this as a debugging tool, you are doing much unnecessary collection of all the data back to the driver with collect. Transformation can be chained:

sc.textFile(...).flatMap(...).map(...).reduceByKey(add).coalesce(1).saveAsTextFile(...)

Upvotes: 3

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