Usage of local variables in closures when accessing Spark RDDs

Question

I have a question regarding the usage of local variables in closures when accessing Spark RDDs. The problem I would like to solve looks as follows:

I have a list of textfiles that should be read into an RDD. However, first I need to add additional information to an RDD that is created from a single textfile. This additional information is extracted from the filename. Then, the RDDs are put into one big RDD using union().

from pyspark import SparkConf, SparkContext
spark_conf = SparkConf().setAppName("SparkTest")
spark_context = SparkContext(conf=spark_conf)

list_of_filenames = ['file_from_Ernie.txt', 'file_from_Bert.txt']
rdd_list = []
for filename in list_of_filenames:
    tmp_rdd = spark_context.textFile(filename)
    # extract_file_info('file_from_Owner.txt') == 'Owner'
    file_owner = extract_file_info(filename)   
    tmp_rdd = tmp_rdd.map(lambda x : (x, file_owner))
    rdd_list.append(tmp_rdd)
overall_content_rdd = spark_context.union(rdd_list)
# ...do something...
overall_content_rdd.collect()
# However, this does not work: 
# The result is that always Bert will be the owner, i.e., never Ernie.

The problem is that the map() function within the loop does not refer to the “correct” file_owner. Instead, it will refer to the latest value of file_owner. On my local machine, I managed to fix the problem by calling the cache() function for each single RDD:

# ..
tmp_rdd = tmp_rdd.map(lambda x : (x, file_owner))
tmp_rdd.cache()
# ..

My Question: Is using cache() the correct solution for my problem? Are there any alternatives?

Many Thanks!

Answer 1

This is not a Spark phenomenon, but a plain Python one.

>>> fns = []
>>> for i in range(3):
...   fns.append(lambda: i)
... 
>>> for fn in fns:
...   print fn()
... 
2
2
2

One way to avoid it is to declare functions which default arguments. The default value is evaluated at the time of declaration.

>>> fns = []
>>> for i in range(3):
...   def f(i=i):
...     return i
...   fns.append(f)
... 
>>> for fn in fns:
...   print fn()
... 
0
1
2

This comes up a lot, see these other questions:

• Lexical closures in Python
• What do (lambda) function closures capture?

Answer 2

So the cache() method that you are doing won't necessarily work 100% of the time, it works provided that no nodes fail and no partitions need to be recomputed. A simple solution would be to make a function that will "capture" the value of file_owner. Here is a quick little illustration in the pyspark shell of a potential solution:

Welcome to
      ____              __
     / __/__  ___ _____/ /__
    _\ \/ _ \/ _ `/ __/  '_/
   /__ / .__/\_,_/_/ /_/\_\   version 1.2.0-SNAPSHOT
      /_/

Using Python version 2.7.6 (default, Mar 22 2014 22:59:56)
SparkContext available as sc.
>>> hi = "hi"
>>> sc.parallelize(["panda"])
ParallelCollectionRDD[0] at parallelize at PythonRDD.scala:365
>>> r = sc.parallelize(["panda"])
>>> meeps = r.map(lambda x : x + hi)
>>> hi = "by"
>>> meeps.collect()
['pandaby']
>>> hi = "hi"
>>> def makeGreetFunction(param):
...     return (lambda x: x + param)
... 
>>> f = makeGreetFunction(hi)
>>> hi="by"
>>> meeps = r.map(f)
>>> meeps.collect()
['pandahi']
>>>