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https://issues.apache.org/jira/browse/SPARK-16449?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Jeff Levy updated SPARK-16449:
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Description:
Goal: Take the output from `describe` on a large DataFrame, then use a loop to
calculate `skewness` and `kurtosis` from pyspark.sql.functions for each column,
build them into a DataFrame of two rows, then use `unionAll` to merge them
together.
Issue: Despite having the same column names, in the same order with the same
dtypes, the `unionAll` fails with "Task not serializable". However, if I build
two test rows using dummy data then `unionAll` works fine. Also, if I collect
my results then turn them straight back into DataFrames, `unionAll` succeeds.
Step-by-step code and output with comments can be seen here:
https://github.com/UrbanInstitute/pyspark-tutorials/blob/master/unionAll%20error.ipynb
The issue appears to be in the way the loop in code block 6 is building the
rows before parallelizing, but the results look no different from the test rows
that do work. I reproduced this on multiple datasets, so downloading the
notebook and pointing it to any data of your own should replicate it.
was:
Goal: Take the output from `describe` on a large DataFrame, then use a loop to
calculate `skewness` and `kurtosis` from pyspark.sql.functions for each column,
build them into a DataFrame of two rows, then use `unionAll` to merge them
together.
Issue: Despite having the same column names, in the same order with the same
dtypes, the `unionAll` fails with "Task not serializable". However, if I build
two test rows using dummy data then `unionAll` works fine. Also, if I collect
my results then turn them straight back into DataFrames, `unionAll` succeeds.
Step-by-step code and output with comments can be seen here:
https://github.com/UrbanInstitute/pyspark-tutorials/blob/master/unionAll%20error.ipynb
The issue appears to be in the way the loop in code block 6 is building the
rows before parallelizing, but the results look no different from the test rows
that do work.
> unionAll raises "Task not serializable"
> ---------------------------------------
>
> Key: SPARK-16449
> URL: https://issues.apache.org/jira/browse/SPARK-16449
> Project: Spark
> Issue Type: Bug
> Components: PySpark
> Affects Versions: 1.6.1
> Environment: AWS EMR, Jupyter notebook
> Reporter: Jeff Levy
> Priority: Minor
>
> Goal: Take the output from `describe` on a large DataFrame, then use a loop
> to calculate `skewness` and `kurtosis` from pyspark.sql.functions for each
> column, build them into a DataFrame of two rows, then use `unionAll` to merge
> them together.
> Issue: Despite having the same column names, in the same order with the same
> dtypes, the `unionAll` fails with "Task not serializable". However, if I
> build two test rows using dummy data then `unionAll` works fine. Also, if I
> collect my results then turn them straight back into DataFrames, `unionAll`
> succeeds.
> Step-by-step code and output with comments can be seen here:
> https://github.com/UrbanInstitute/pyspark-tutorials/blob/master/unionAll%20error.ipynb
> The issue appears to be in the way the loop in code block 6 is building the
> rows before parallelizing, but the results look no different from the test
> rows that do work. I reproduced this on multiple datasets, so downloading
> the notebook and pointing it to any data of your own should replicate it.
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