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This artifact contains lots of goodies I've accumulated over the years while hacking Hadoop. Highlights include:

  • Hadoop Writable datatypes (including primitive collections)
  • Frequency distributions and conditional frequency distributions
  • Classes to track of top k items

Hadoop Writable datatypes

This artifact contains lots of Hadoop Writable datatypes for building arbitrarily complex structures to use as keys and values in your mappers and reducers. Of course, using these building blocks won't be as fast as implementing your own custom Writable objects, but they will be faster than serializing to string (stuffing inside a Text object) and then parsing the string to reconstruct the data.

It's perhaps easiest to illustrate by examples:

PairOfWritables<PairOfStringInt, IntArrayWritable> data =
    new PairOfWritables<PairOfStringInt, IntArrayWritable>(
        new PairOfStringInt("foo", 42),
        new IntArrayWritable(new int[] {1, 2, 3, 4, 5}));

This creates the following data structure:

((foo, 42), [1, 2, 3, 4, 5])

Try this:

ArrayListWritable<PairOfInts> data = new ArrayListWritable<PairOfInts>();
data.add(new PairOfInts(1, 2));
data.add(new PairOfInts(3, 4));
data.add(new PairOfInts(5, 6));
data.add(new PairOfInts(7, 8));

Which corresponds to:

[(1, 2), (3, 4), (5, 6), (7, 8)]


HMapIVW<PairOfStrings> data = new HMapIVW<PairOfStrings>();
data.put(1, new PairOfStrings("a", "b"));
data.put(2, new PairOfStrings("foo", "bar"));
data.put(4, new PairOfStrings("alpha", "beta"));
data.put(42, new PairOfStrings("four", "two"));


{1=(a, b), 2=(foo, bar), 4=(alpha, beta), 42=(four, two)}

Note that these are all Writable objects, so they are Hadoop-compatible "out of the box".

The following provides more detail on each of the datatype packages, but the general theme of the design is to create custom classes for each primitive type. This means avoiding things like List<Integer> and Map<String,Integer>, which incur memory overhead due to a proliferation of Java objects and latency overhead for boxing/unboxing.

Summary of classes:

  • {Double,Float,Int,Long,Short}ArrayWritable: these classes are Writable objects that wrap primitive arrays. Use this if you have static fixed-length arrays. Otherwise, use the other classes below.
  • ArrayListOf{Double,Float,IntLong,Short}: these are implementations of ArrayList customized for each primitive type. Like a normal ArrayList, the backing array grows dynamically.
  • ArrayListOf{Double,Float,IntLong,Short}Writable: the above classes, except as Writable objects.
  • ArrayListWritable and ArrayListWritableComparable: as the names suggest, holds arbitrary Writable and WritableComparable objects.

This package contains specialized primitive maps. Understanding the naming convention: HMap is the base hash map.

  • D for double
  • F for float
  • I for integer
  • S for short
  • K for generic key
  • V for generic value
  • W for a Writable

So, for example, HMapIIW is a Writable hash map that maps from integers to integers and HMapKFW is a Writable hash map that maps from arbitrary (WritableComparable) keys to float values.

This contains Writable objects representing pairs. Naming convention is PairOfFooBar with Foo and Bar as the respective types.

Frequency distributions

This package contains classes for frequency distributions and conditional frequency distributions. Basically, they're built on hash maps, but with convenience methods for incrementing, decrementing, enumerating top entries, etc.

Classes to track of top k items

This package contains subclasses of Hadoop's PriorityQueue for keeping track of top k items in terms of score.

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