An InputFormat to work with splittable multi-line JSON

Motivation

Currently there don't seem to be any JSON InputFormat classes that can support multi-line JSON.

License

Apache licensed.

Usage

To get started, simply:

1. Download, and run ant
2. Include the dist/lib/json-mapreduce-1.0.jar in your environment
3. Utilize the MultiLineJsonInputFormat class as your Mapper InputFormat

Assume you have some JSON that looks like this:

{"menu": {
    "header": "SVG Viewer",
    "items": [
        {"id": "Open"},
        {"id": "OpenNew", "label": "Open New"},
        null,
        {"id": "ZoomIn", "label": "Zoom In"},
        {"id": "ZoomOut", "label": "Zoom Out"},
        {"id": "OriginalView", "label": "Original View"},
        null,
        {"id": "Quality"},
        {"id": "Pause"},
        {"id": "Mute"},
        null,
        {"id": "Find", "label": "Find..."},
        {"id": "FindAgain", "label": "Find Again"},
        {"id": "Copy"},
        {"id": "CopyAgain", "label": "Copy Again"},
        {"id": "CopySVG", "label": "Copy SVG"},
        {"id": "ViewSVG", "label": "View SVG"},
        {"id": "ViewSource", "label": "View Source"},
        {"id": "SaveAs", "label": "Save As"},
        null,
        {"id": "Help"},
        {"id": "About", "label": "About Adobe CVG Viewer..."}
    ]
}}

With the MultiLineJsonInputFormat you must indicate the member name which it will use to determine the encapsulating object to return to your Mapper. If for example we wanted all the objects that contained "id", then we would do the following:

Configuration conf = new Configuration();
Job job = new Job(conf);
job.setMapperClass(...);
job.setReducerClass(...);
job.setInputFormatClass(MultiLineJsonInputFormat.class);
MultiLineJsonInputFormat.setInputJsonMember(job, "id");

The MultiLineJsonInputFormat supplies the Mapper with the JSON object in string form:

public static class Map extends Mapper {

  @Override
  protected void map(LongWritable key, Text value, Context context)
                     throws IOException, InterruptedException {
    context.write(key, value);
  }
}

You can run a simple example that's bundled with the project as follows, where the two arguments are an input directury ("in" in the example below) where a sample JSON file is written, and the job output directory.

$ hadoop jar json-mapreduce-1.0.jar com.alexholmes.json.mapreduce.ExampleJob in out

After the job has completed you can examine the contents of the output directory in HDFS.

$ hadoop fs -cat out/part*

Got JSON: '{             "colorName":"red",             "hexValue":"#f00"         }'
Got JSON: '{             "colorName":"green",             "hexValue":"#0f0"         }'
Got JSON: '{             "colorName":"cyan",             "hexValue":"#0ff"         }'
...