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Posted to issues@spark.apache.org by "Nicholas Chammas (JIRA)" <ji...@apache.org> on 2016/01/14 17:20:39 UTC
[jira] [Commented] (SPARK-12824) Failure to maintain consistent RDD
references in pyspark
[ https://issues.apache.org/jira/browse/SPARK-12824?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15098336#comment-15098336 ]
Nicholas Chammas commented on SPARK-12824:
------------------------------------------
I can reproduce this issue. Here's a more concise reproduction:
{code}
from __future__ import print_function
rdd = sc.parallelize([
{'color':'red','size':3},
{'color':'red', 'size':7},
{'color':'red', 'size':8},
{'color':'red', 'size':10},
{'color':'green', 'size':9},
{'color':'green', 'size':5},
{'color':'green', 'size':50},
{'color':'blue', 'size':4},
{'color':'purple', 'size':6}])
colors = ['purple', 'red', 'green', 'blue']
# Defer collect() till print
color_rdds = {
color: rdd.filter(lambda x: x['color'] == color)
for color in colors
}
for k, v in color_rdds.items():
print(k, v.collect())
# collect() upfront
color_rdds = {
color: rdd.filter(lambda x: x['color'] == color).collect()
for color in colors
}
for k, v in color_rdds.items():
print(k, v)
{code}
Output:
{code}
# Defer collect() till print
purple [{'color': 'blue', 'size': 4}]
blue [{'color': 'blue', 'size': 4}]
green [{'color': 'blue', 'size': 4}]
red [{'color': 'blue', 'size': 4}]
---
# collect() upfront
purple [{'color': 'purple', 'size': 6}]
blue [{'color': 'blue', 'size': 4}]
green [{'color': 'green', 'size': 9}, {'color': 'green', 'size': 5}, {'color': 'green', 'size': 50}]
red [{'color': 'red', 'size': 3}, {'color': 'red', 'size': 7}, {'color': 'red', 'size': 8}, {'color': 'red', 'size': 10}]
{code}
Observations:
* The color that gets repeated in the first block of output is always the last color in {{colors}}.
* This happens on Python 2 and 3, and with both {{items()}} and {{iteritems()}}.
This smells like an RDD naming issue, or something related to lazy evaluation. The filtered RDDs that get generated in the first block under {{color_rdds}} don't have names. Then, when they all get {{collect()}}-ed at once, they all evaluate to the last filtered RDD.
cc [~davies] / [~joshrosen]
> Failure to maintain consistent RDD references in pyspark
> --------------------------------------------------------
>
> Key: SPARK-12824
> URL: https://issues.apache.org/jira/browse/SPARK-12824
> Project: Spark
> Issue Type: Bug
> Components: PySpark
> Affects Versions: 1.5.2
> Environment: Spark 1.5.2, Python 2.7.10, and IPython 4.0.0.
> Reporter: Paul Shearer
>
> Below is a simple {{pyspark}} script that tries to split an RDD into a dictionary containing several RDDs.
> As the *sample run* shows, the script only works if we do a {{collect()}} on the intermediate RDDs as they are created. Of course I would not want to do that in practice, since it doesn't scale.
> What's really strange is, I'm not assigning the intermediate {{collect()}} results to any variable. So the difference in behavior is due solely to a hidden side-effect of the computation triggered by the {{collect()}} call.
> Spark is supposed to be a very functional framework with minimal side effects. Why is it only possible to get the desired behavior by triggering some mysterious side effect using {{collect()}}?
> It seems that all the keys in the dictionary are referencing the same object even though in the code they are clearly supposed to be different objects.
> The run below is with Spark 1.5.2, Python 2.7.10, and IPython 4.0.0.
> h3. spark_script.py
> {noformat}
> from pprint import PrettyPrinter
> pp = PrettyPrinter(indent=4).pprint
> logger = sc._jvm.org.apache.log4j
> logger.LogManager.getLogger("org"). setLevel( logger.Level.ERROR )
> logger.LogManager.getLogger("akka").setLevel( logger.Level.ERROR )
>
> def split_RDD_by_key(rdd, key_field, key_values, collect_in_loop=False):
> d = dict()
> for key_value in key_values:
> d[key_value] = rdd.filter(lambda row: row[key_field] == key_value)
> if collect_in_loop:
> d[key_value].collect()
> return d
> def print_results(d):
> for k in d:
> print k
> pp(d[k].collect())
>
> rdd = sc.parallelize([
> {'color':'red','size':3},
> {'color':'red', 'size':7},
> {'color':'red', 'size':8},
> {'color':'red', 'size':10},
> {'color':'green', 'size':9},
> {'color':'green', 'size':5},
> {'color':'green', 'size':50},
> {'color':'blue', 'size':4},
> {'color':'purple', 'size':6}])
> key_field = 'color'
> key_values = ['red', 'green', 'blue', 'purple']
>
> print '### run WITH collect in loop: '
> d = split_RDD_by_key(rdd, key_field, key_values, collect_in_loop=True)
> print_results(d)
> print '### run WITHOUT collect in loop: '
> d = split_RDD_by_key(rdd, key_field, key_values, collect_in_loop=False)
> print_results(d)
> {noformat}
> h3. Sample run in IPython shell
> {noformat}
> In [1]: execfile('spark_script.py')
> ### run WITH collect in loop:
> blue
> [{ 'color': 'blue', 'size': 4}]
> purple
> [{ 'color': 'purple', 'size': 6}]
> green
> [ { 'color': 'green', 'size': 9},
> { 'color': 'green', 'size': 5},
> { 'color': 'green', 'size': 50}]
> red
> [ { 'color': 'red', 'size': 3},
> { 'color': 'red', 'size': 7},
> { 'color': 'red', 'size': 8},
> { 'color': 'red', 'size': 10}]
> ### run WITHOUT collect in loop:
> blue
> [{ 'color': 'purple', 'size': 6}]
> purple
> [{ 'color': 'purple', 'size': 6}]
> green
> [{ 'color': 'purple', 'size': 6}]
> red
> [{ 'color': 'purple', 'size': 6}]
> {noformat}
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