GZIP compressed files are usually considered to be non-splitable. This makes them a rather impractical input format for distributed processing / Hadoop, which stores its files in a distributed filesystem and exploits data locality to gain efficiency. However, a GZIP file may be comprised of multiple concatenated GZIP records, which constitutes a valid GZIP file again. Such compressed files can be split by identifying the start of a new record and this way, can be used by Hadoop much more efficiently.

One application that heavily uses concatenated GZIP files is Web archives. The standardized data format WARC is typically stored in GZIP files with every record being a separate GZIP record.

We provide both, one general input format for concatenated GZIP files (ConcatGzipInputFormat) as well as a specialized one for *.warc.gz files, i.e., WARC records stored in concatenated GZIP files (WarcGzInputFormat). The WarcGzInputFormat also serves as an example to show how to easily implement custom input formats based on concatenated GZIP files.

If configured as input format in your Hadoop job, the key in your mapper will be of Text and the value of type FileBackedBytesWritable, which is based on Google's com.google.common.io.FileBackedOutputStream. The key contains the filname as well as the position of the current value in the form filename:position. The value contains the current GZIP record as bytes, which can be accessed by calling value.getBytes() as com.google.common.io.ByteSource. To decompress the contents you can read the bytes through input stream:

ByteArrayInputStream in = value.getBytes().openBufferedStream();
GZIPInputStream decompressed = new GZIPInputStream(in);
...
decompressed.read();
...
decompressed.close();

This input format gives you only a value of type WarcWritable, with the key being of type NullWritable. WarcWritable provides access to the raw (compressed) bytes, the filename, the offset as well as a wrapped WarcRecord with convenient getter methods to read headers, contents and parsed HTTP responses.

...
String file = value.getFilename();
long offset = value.getOffset();
WarcRecord warc = value.getRecord();
...
ArchiveRecord record = warc.getRecord();
ArchiveRecordHeader header = warc.getHeader();
if (warc.isHttp()) {
    Map<String, String> httpHeaders = warc.getHttpHeaders();
    byte[] body = warc.getHttpBody();
    String bodyString = warc.getHttpStringBody();
    String mime = warc.getHttpMimeType();
    ...
}
...

To facilitate the output with Hadoop and allow for easy creation of GZIP records and concatenated GZIP files, we provide some helper classes:

ImmediateOutput enables you to write out data directly to HDFS without using Hadoop output mechanisms. It takes care of a unique naming per task and overwrites files in case of failed and rerun tasks. This is how you configure your Hadoop job to use ImmediateOutput:

Job job = Job.getInstance(config);
ImmediateOutput.initialize(job);
ImmediateOutput.setPath(job, outPath);
ImmediateOutput.setExtension(job, ".gz");
ImmediateOutput.setReplication(job, (short) 2);

Now, to use it in your mapper / reducer, you need to create an instance as follows:

private ImmediateOutput output;

@Override
protected void setup(Context context) throws IOException, InterruptedException {
    output = new ImmediateOutput(context, true);
}

@Override
public void cleanup(Context context) throws IOException, InterruptedException {
    output.close();
}

GZipBytes lets you output any kind of data into a GZIP compressed byte array, that you can write out using your ImmediateOutput:

GZipBytes gzip = new GZipBytes();
...
OutputStream out = gzip.open();
out.write(...);
...
byte[] bytes = out.close();
output.write(bytes, "location/relative/to/outPath/raw");
...
DataOutputStream data = gzip.openData();
data.writeInt(123);
...
bytes = data.close();
output.write(bytes, "location/relative/to/outPath/data");
...
PrintStream print = gzip.openPrint();
print.println("text");
...
bytes = print.close();
output.write(bytes, "location/relative/to/outPath/text");
...

The easiest way to build a JAR file of this project that you can add to your Hadoop classpath is to use Maven:

mvn package

If you want to add this library as a dependency to your project, you can get it from the Internet Archive's build server under http://builds.archive.org/maven2/de/l3s/hadoop-concat-gz (thanks for hosting this!). The artifact is also available through Maven Central (may not be the latest release).

In order to get it from the Internet Archive build server in your Spark project, add this as a new resolver: resolvers ++= Seq("internetarchive" at "http://builds.archive.org/maven2")

Now you can add the dependency as follows (please check for the newest version):

libraryDependencies ++= Seq("com.github.helgeho" % "hadoop-concat-gz" % "1.2")

Finally, load your WARC dataset into an RDD:

val rdd = sc.newAPIHadoopFile(INPUT_PATH, classOf[ConcatGzipInputFormat], classOf[Text], classOf[FileBackedBytesWritable

The MIT License (MIT)

Copyright (c) 2016 Helge Holzmann (L3S)

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

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