I am trying to port some code from pandas to (py)Spark. Unfortunately I am already failing with the input part, where I want to read in binary data and put it in a Spark Dataframe.
So far I am using fromfile
from numpy:
dt = np.dtype([('val1', '<i4'),('val2','<i4'),('val3','<i4'),('val4','f8')]) data = np.fromfile('binary_file.bin', dtype=dt) data=data[1:] #throw away header df_bin = pd.DataFrame(data, columns=data.dtype.names)
But for Spark I couldn’t find how to do it. My workaround so far was to use csv-Files instead of the binary file, but that is not an ideal solution. I am aware that I shouldn’t use numpy’s fromfile
with spark.
How can I read in a binary file that is already loaded into hdfs?
I tried something like
fileRDD=sc.parallelize(['hdfs:///user/bin_file1.bin','hdfs:///user/bin_file2.bin]) fileRDD.map(lambda x: ???)
But it is giving me a No such file or directory
error.
I have seen this question: spark in python: creating an rdd by loading binary data with numpy.fromfile but that only works if I have the files stored in the home of the driver node.
Advertisement
Answer
So, for anyone that starts with Spark as me and stumbles upon binary files. Here is how I solved it:
dt=np.dtype([('idx_metric','>i4'),('idx_resource','>i4'),('date','>i4'), ('value','>f8'),('pollID','>i2')]) schema=StructType([StructField('idx_metric',IntegerType(),False), StructField('idx_resource',IntegerType(),False), StructField('date',IntegerType),False), StructField('value',DoubleType(),False), StructField('pollID',IntegerType(),False)]) filenameRdd=sc.binaryFiles('hdfs://nameservice1:8020/user/*.binary') def read_array(rdd): #output=zlib.decompress((bytes(rdd[1])),15+32) # in case also zipped array=np.frombuffer(bytes(rdd[1])[20:],dtype=dt) # remove Header (20 bytes) array=array.newbyteorder().byteswap() # big Endian return array.tolist() unzipped=filenameRdd.flatMap(read_array) bin_df=sqlContext.createDataFrame(unzipped,schema)
And now you can do whatever fancy stuff you want in Spark with your dataframe.