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https://issues.apache.org/jira/browse/SPARK-22809?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16293209#comment-16293209
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Cricket Temple commented on SPARK-22809:
----------------------------------------
Outputs: When I run it, it plots a picture and prints "See?"
This is certainly "unexpected behavior" for me.
{noformat}
> import a.b
> import a.b as a_b
> a_b2 = a.b
> your_function(a_b)
Yay!
>your_function(a.b)
Boo!
>your_function(a_b2)
Yay!
>your_function(a.b)
Yay!
{noformat}
The problem is that when people port code to pyspark they're going to have
errors until they go through and update imports to avoid this. If it's
possible to trigger this from a library (which I don't know if it is), that
might be hard to work around.
> 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
> Reporter: Cricket Temple
>
> 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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