How to open/stream .zip files through Spark?

Question

I have zip files that I would like to open 'through' Spark. I can open .gzip file no problem because of Hadoops native Codec support, but am unable to do so with .zip files.

Is there an easy way to read a zip file in your Spark code? I've also searched for zip codec implementations to add to the CompressionCodecFactory, but am unsuccessful so far.

Answer 1

There was no solution with python code and I recently had to read zips in pyspark. And, while searching how to do that I came across this question. So, hopefully this'll help others.

import zipfile
import io

def zip_extract(x):
    in_memory_data = io.BytesIO(x[1])
    file_obj = zipfile.ZipFile(in_memory_data, "r")
    files = [i for i in file_obj.namelist()]
    return dict(zip(files, [file_obj.open(file).read() for file in files]))


zips = sc.binaryFiles("hdfs:/Testing/*.zip")
files_data = zips.map(zip_extract).collect()

In the above code I returned a dictionary with filename in the zip as a key and the text data in each file as the value. you can change it however you want to suit your purposes.

Answer 2

@user3591785 pointed me in the correct direction, so I marked his answer as correct.

For a bit more detail, I was able to search for ZipFileInputFormat Hadoop, and came across this link: http://cotdp.com/2012/07/hadoop-processing-zip-files-in-mapreduce/

Taking the ZipFileInputFormat and its helper ZipfileRecordReader class, I was able to get Spark to perfectly open and read the zip file.

    rdd1  = sc.newAPIHadoopFile("/Users/myname/data/compressed/target_file.ZIP", ZipFileInputFormat.class, Text.class, Text.class, new Job().getConfiguration());

The result was a map with one element. The file name as key, and the content as the value, so I needed to transform this into a JavaPairRdd. I'm sure you could probably replace Text with BytesWritable if you want, and replace the ArrayList with something else, but my goal was to first get something running.

JavaPairRDD<String, String> rdd2 = rdd1.flatMapToPair(new PairFlatMapFunction<Tuple2<Text, Text>, String, String>() {

    @Override
    public Iterable<Tuple2<String, String>> call(Tuple2<Text, Text> textTextTuple2) throws Exception {
        List<Tuple2<String,String>> newList = new ArrayList<Tuple2<String, String>>();

        InputStream is = new ByteArrayInputStream(textTextTuple2._2.getBytes());
        BufferedReader br = new BufferedReader(new InputStreamReader(is, "UTF-8"));

        String line;

        while ((line = br.readLine()) != null) {

        Tuple2 newTuple = new Tuple2(line.split("\\t")[0],line);
            newList.add(newTuple);
        }
        return newList;
    }
});

Answer 3

Please try the code below:

using API sparkContext.newAPIHadoopRDD(
    hadoopConf,
    InputFormat.class,
    ImmutableBytesWritable.class, Result.class)