[
https://issues.apache.org/jira/browse/SPARK-22809?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
holdenk reopened SPARK-22809:
-----------------------------
Assignee: holdenk
After further investigation this turns out to be an issue maye have been fixed
upstream in cloud pickle (see 0.4.1 in
https://github.com/cloudpipe/cloudpickle/blob/fb3a80f4aa8e76098b4cebd0dc8ff2331424e53d/CHANGES.md
). This issue only presents when serializing from an IPython/Jupyter/Zeppelin
notebook as the import ends up being in the module space where as in regular
console this is not the case.
We can verify this is purely a cloudpickle/pickling/serialization issue by
serializing a simple function with the version of cloudpickle PySpark ships
with, like the one shown above, opening a new python shell and doing pickle
loads (after importing pickle & cloudpickle).
The solution would be to update cloud pickle, or stop copying cloud pickles
source code and just use it as a regular pypi dependency. Since we're at the
start of a release I think a copy update is probably the best path forward to
try and get this in for Spark 2.3.
Thanks [~CricketScience] for the pair programming/debugging session on this.
cc [~rgbkrk]
> pyspark is sensitive to imports with dots
> -----------------------------------------
>
> Key: SPARK-22809
> URL: https://issues.apache.org/jira/browse/SPARK-22809
> Project: Spark
> Issue Type: Bug
> Components: PySpark
> Affects Versions: 2.2.0, 2.2.1
> Reporter: Cricket Temple
> Assignee: holdenk
>
> User code can fail with dotted imports. Here's a repro script.
> {noformat}
> import numpy as np
> import pandas as pd
> import pyspark
> import scipy.interpolate
> import scipy.interpolate as scipy_interpolate
> import py4j
> scipy_interpolate2 = scipy.interpolate
> sc = pyspark.SparkContext()
> spark_session = pyspark.SQLContext(sc)
> #######################################################
> # The details of this dataset are irrelevant #
> # Sorry if you'd have preferred something more boring #
> #######################################################
> x__ = np.linspace(0,10,1000)
> freq__ = np.arange(1,5)
> x_, freq_ = np.ix_(x__, freq__)
> y = np.sin(x_ * freq_).ravel()
> x = (x_ * np.ones(freq_.shape)).ravel()
> freq = (np.ones(x_.shape) * freq_).ravel()
> df_pd = pd.DataFrame(np.stack([x,y,freq]).T, columns=['x','y','freq'])
> df_sk = spark_session.createDataFrame(df_pd)
> assert(df_sk.toPandas() == df_pd).all().all()
> try:
> import matplotlib.pyplot as plt
> for f, data in df_pd.groupby("freq"):
> plt.plot(*data[['x','y']].values.T)
> plt.show()
> except:
> print("I guess we can't plot anything")
> def mymap(x, interp_fn):
> df = pd.DataFrame.from_records([row.asDict() for row in list(x)])
> return interp_fn(df.x.values, df.y.values)(np.pi)
> df_by_freq = df_sk.rdd.keyBy(lambda x: x.freq).groupByKey()
> result = df_by_freq.mapValues(lambda x: mymap(x,
> scipy_interpolate.interp1d)).collect()
> assert(np.allclose(np.array(zip(*result)[1]), np.zeros(len(freq__)),
> atol=1e-6))
> try:
> result = df_by_freq.mapValues(lambda x: mymap(x,
> scipy.interpolate.interp1d)).collect()
> raise Excpetion("Not going to reach this line")
> except py4j.protocol.Py4JJavaError, e:
> print("See?")
> result = df_by_freq.mapValues(lambda x: mymap(x,
> scipy_interpolate2.interp1d)).collect()
> assert(np.allclose(np.array(zip(*result)[1]), np.zeros(len(freq__)),
> atol=1e-6))
> # But now it works!
> result = df_by_freq.mapValues(lambda x: mymap(x,
> scipy.interpolate.interp1d)).collect()
> assert(np.allclose(np.array(zip(*result)[1]), np.zeros(len(freq__)),
> atol=1e-6))
> {noformat}
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