Error when importing csv into pyspark dataframe

Question

I'm running python code via ssh/PyCharm on a remote host, using a conda environment.
When trying to import a csv file into a PySpark data frame, like this

from pyspark.sql import SparkSession
url = "https://github.com/BigDaMa/COCOA/raw/master/dataset/movie.csv"
self.spark = SparkSession.builder.getOrCreate() 
df = self.spark.read.format("csv").load(url)

I get the following error message:

Traceback (most recent call last):
  File "/home/meike/anaconda3/envs/py3/lib/python3.9/site-packages/pyspark/sql/utils.py", line 111, in deco
    return f(*a, **kw)
  File "/home/meike/anaconda3/envs/py3/lib/python3.9/site-packages/py4j/protocol.py", line 326, in get_return_value
    raise Py4JJavaError(
py4j.protocol.Py4JJavaError: An error occurred while calling o28.load.
: java.lang.UnsupportedOperationException
    at org.apache.hadoop.fs.http.AbstractHttpFileSystem.listStatus(AbstractHttpFileSystem.java:91)
    at org.apache.hadoop.fs.http.HttpsFileSystem.listStatus(HttpsFileSystem.java:23)
    at org.apache.spark.util.HadoopFSUtils$.listLeafFiles(HadoopFSUtils.scala:225)
    at org.apache.spark.util.HadoopFSUtils$.$anonfun$parallelListLeafFilesInternal$1(HadoopFSUtils.scala:95)
    at scala.collection.TraversableLike.$anonfun$map$1(TraversableLike.scala:238)
    at scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)
    at scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)
    at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)
    at scala.collection.TraversableLike.map(TraversableLike.scala:238)
    at scala.collection.TraversableLike.map$(TraversableLike.scala:231)
    at scala.collection.AbstractTraversable.map(Traversable.scala:108)
    at org.apache.spark.util.HadoopFSUtils$.parallelListLeafFilesInternal(HadoopFSUtils.scala:85)
    at org.apache.spark.util.HadoopFSUtils$.parallelListLeafFiles(HadoopFSUtils.scala:69)
    at org.apache.spark.sql.execution.datasources.InMemoryFileIndex$.bulkListLeafFiles(InMemoryFileIndex.scala:158)
    at org.apache.spark.sql.execution.datasources.InMemoryFileIndex.listLeafFiles(InMemoryFileIndex.scala:131)
    at org.apache.spark.sql.execution.datasources.InMemoryFileIndex.refresh0(InMemoryFileIndex.scala:94)
    at org.apache.spark.sql.execution.datasources.InMemoryFileIndex.<init>(InMemoryFileIndex.scala:66)
    at org.apache.spark.sql.execution.datasources.DataSource.createInMemoryFileIndex(DataSource.scala:581)
    at org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:417)
    at org.apache.spark.sql.DataFrameReader.loadV1Source(DataFrameReader.scala:325)
    at org.apache.spark.sql.DataFrameReader.$anonfun$load$3(DataFrameReader.scala:307)
    at scala.Option.getOrElse(Option.scala:189)
    at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:307)
    at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:239)
    at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
    at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.base/java.lang.reflect.Method.invoke(Method.java:566)
    at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
    at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
    at py4j.Gateway.invoke(Gateway.java:282)
    at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
    at py4j.commands.CallCommand.execute(CallCommand.java:79)
    at py4j.GatewayConnection.run(GatewayConnection.java:238)
    at java.base/java.lang.Thread.run(Thread.java:829)

I have successfully imported the same csv into a pandas data frame, no problem here.
I am also able to create an empty data frame and fill it manually.

I have found this on StackOverflow, but as one of the commentators, I need to be able to use PySpark for debugging. I cannot simply run the code in the Terminal, using spark-submit.

I've also tried importing findspark and adding a MySQL package, but this doesn't solve the problem.

any ideas? if any more information is necessary, I'll be happy to add it!

PS: These are some warnings I'm getting, but they don't prevent my code from running so far.

Connected to pydev debugger (build 212.4746.96)
WARNING: An illegal reflective access operation has occurred
WARNING: Illegal reflective access by org.apache.spark.unsafe.Platform (file:/home/meike/anaconda3/envs/py3/lib/python3.9/site-packages/pyspark/jars/spark-unsafe_2.12-3.1.2.jar) to constructor java.nio.DirectByteBuffer(long,int)
WARNING: Please consider reporting this to the maintainers of org.apache.spark.unsafe.Platform
WARNING: Use --illegal-access=warn to enable warnings of further illegal reflective access operations
WARNING: All illegal access operations will be denied in a future release
21/08/23 23:07:06 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Using Spark´s default log4j profile: org/apache/spark/log4j-defaults.properties
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).

PPS: I've also managed to import the csv by copying it into the same directory as the main.py and reading it 'from local'. But the Script is meant to be executed with a URL given as input. Why isn't that working??

Answer 1

You can't load csv directly into pyspark from url. Try this:

url = "https://github.com/BigDaMa/COCOA/raw/master/dataset/movie.csv"
from pyspark import SparkFiles
spark.sparkContext.addFile(url)
df = spark.read.csv("file://"+SparkFiles.get("movie.csv"), header=True, inferSchema= True)

Other approach would be to read from url via pandas and then create spark dataframe:

import pandas as pd
df = spark.createDataFrame(pd.read_csv(url)))