[
https://issues.apache.org/jira/browse/SPARK-12824?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Paul Shearer updated SPARK-12824:
---------------------------------
Description:
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()`?
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}
was:
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()`?
The run below is with Spark 1.5.2, Python 2.7.10, and IPython 4.0.0.
spark_script.py
```
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)
```
Sample run in IPython shell
```
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}]
```
> 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
> 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()`?
> 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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