[ How can I read in a binary file from hdfs into a Spark dataframe? ]

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.

Answer 1

Edit: Please review the use of sc.binaryFiles as mentioned here: http://stackoverflow.com/a/28753276/5088142

try using:

hdfs://machine_host_name:8020/user/bin_file1.bin

you the host-name in fs.defaultFS in core-site.xml

Answer 2

So for anyone that starts with Spark as me and stumbles uopn 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.