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Building a Custom Aggregate Operation

One of the most important kinds of processing Jet does is aggregation. In general it is a transformation of a set of input values into a single output value. The function that does this transformation is called the aggregate function. A basic example is sum applied to a set of integer numbers, but the result can also be a complex value, for example a list of all the input items.

The Jet API contains a range of predefined aggregate functions, but it also exposes an abstraction, called AggregateOperation, that allows you to plug in your own. Since Hazelcast does the aggregation in a parallelized and distributed way, you can’t simply supply a piece of Java code that implements the aggregate function; we need you to break it down into several smaller pieces that fit into the Jet processing engine.

Characteristics of Distributed Aggregation

The ability to compute the aggregate function in parallel comes at a cost: Hazelcast must be able to give a slice of the total data set to each processing unit and then combine the partial results from all the units. The combining step is crucial: it will only make sense if we’re combining the partial results of a commutative associative function (CA for short). On the example of sum this is trivial: we know from elementary school that + is a CA operation. If you have a stream of numbers: {17, 37, 5, 11, 42}, you can sum up {17, 5} separately from {42, 11, 37} and then combine the partial sums (also note the reordering of the elements).

If you need something more complex, like average, it doesn’t by itself have this property; however if you add one more ingredient, the finish function, you can express it easily. Hazelcast allows you to first compute some CA function, whose partial results can be combined, and then at the very end apply the finish function on the fully combined result. To compute the average, your CA function will output the pair (sum, count). Two such pairs are trivial to combine by summing each component. The finish function will be sum / count.

In addition to the mathematical side, there is also the practical one: you have to provide Hazelcast with a specific mutable object, called the accumulator, which will keep the running score of the operation in progress. For the average example, it would be something like

public class AvgAccumulator {

  private long sum;
  private long count;

  public void accumulate(long value) {
    sum += value;

  public void combine(AvgAccumulator that) {
    this.sum += that.sum;
    this.count += that.sum;

  public double finish() {
    return (double) sum / count;

This object will also have to be serializable, and preferably with Hazelcast’s serialization instead of Java’s because in a group-and-aggregate operation there’s one accumulator per each key and all them have to be sent across the network to be combined and finished.

The Building Blocks

Instead of requiring you to write a complete class from scratch, Hazelcast separates the concern of holding the accumulated state from that of the computation performed on it. This means that you just need one accumulator class for each kind of structure that holds the accumulated data, as opposed to one for each aggregate operation. The Jet API offers in the com.hazelcast.jet.accumulator package several such classes, one of them being LongLongAccumulator, which is a match for our average function. You’ll just have to supply the logic on top of it.

Specifically, you have to provide a set of six functions (we call them “primitives”):

  • create a new accumulator object.

  • accumulate the data of an item by mutating the accumulator’s state.

  • combine the contents of the right-hand accumulator into the left-hand one.

  • deduct the contents of the right-hand accumulator from the left-hand one (undo the effects of combine).

  • finish accumulation by transforming the accumulator object into the final result.

  • export the result of aggregation in a way that’s not destructive for the accumulator (used in rolling aggregations).

We already mentioned most of these above. The deduct primitive is optional and Hazelcast can manage without it, but if you are computing a sliding window over an infinite stream, this primitive can give a significant performance boost because it allows Hazelcast to reuse the results of the previous calculations.

In a similar fashion Hazelcast discerns between the export and finish primitives for optimization purposes. Every function that works as the export primitive will also work as finish, but you can specify a different finish that reuses the state already allocated in the accumulator. Hazelcast applies finish only if it will never again use that accumulator.

If you happen to have a deeper familiarity with JDK’s API, you’ll find AggregateOperation quite similar to, which is also a holder of several functional primitives. The definitions in the Jet API are slightly different, though, and there are the additional optimizing primitives we just mentioned.

Let’s see how this works with our average function. Using LongLongAccumulator we can express our accumulate primitive as

(acc, n) -> {
  acc.set1(acc.get1() + n);
  acc.set2(acc.get2() + 1);

The export/finish primitive will be

acc -> (double) acc.get1() / acc.get2()

Now we have to define the other three primitives to match our main logic. For create we just refer to the constructor: LongLongAccumulator::new. The combine primitive expects you to update the left-hand accumulator with the contents of the right-hand one, so:

(left, right) -> {
  left.set1(left.get1() + right.get1());
  left.set2(left.get2() + right.get2());

Deducting must undo the effect of a previous combine:

(left, right) -> {
  left.set1(left.get1() - right.get1());
  left.set2(left.get2() - right.get2());

All put together, we can define our averaging operation as follows:

AggregateOperation1<Long, LongLongAccumulator, Double> aggrOp = AggregateOperation
  .<Long>andAccumulate((acc, n) -> {
    acc.set1(acc.get1() + n);
    acc.set2(acc.get2() + 1);
  .andCombine((left, right) -> {
    left.set1(left.get1() + right.get1());
    left.set2(left.get2() + right.get2());
  .andDeduct((left, right) -> {
    left.set1(left.get1() - right.get1());
    left.set2(left.get2() - right.get2());
  .andExportFinish(acc -> (double) acc.get1() / acc.get2());

Let’s stop for a second to look at the type we got: AggregateOperation1<Long, LongLongAccumulator, Double>. Its type parameters are:

  1. Long: the type of the input item

  2. LongLongAccumulator: the type of the accumulator

  3. Double: the type of the result

Specifically note the 1 at the end of the type’s name: it signifies that it’s the specialization of the general AggregateOperation to exactly one input stream. In Hazelcast you can also perform a co-aggregating operation, aggregating several input streams together. Since the number of input types is variable, the general AggregateOperation type cannot statically capture them and we need separate subtypes. We decided to statically support up to three input types; if you need more, you’ll have to resort to the less type-safe, general AggregateOperation.

Aggregating Over Multiple Inputs

Hazelcast can join several streams and simultaneously perform aggregation on all them. You specify a separate aggregate operation for each input stream and have the opportunity to combine their results when done. You can use aggregate operations provided in the library (see the section on co-aggregating for an example).

If you cannot express your aggregation logic using this approach, you can also specify a custom multi-input aggregate operation that can combine the items into the accumulator immediately as it receives them.

We’ll present a simple example on how to build a custom multi-input aggregate operation. Note that the same logic can also be expressed using separate single-input operations; the point of the example is introducing the API.

Say we are interested in the behavior of users in an online shop application and want to gather the following statistics for each user:

  1. total load time of the visited product pages

  2. quantity of items added to the shopping cart

  3. amount paid for bought items

This data is dispersed among separate datasets: PageVisit, AddToCart and Payment. Note that in each case we’re dealing with a simple sum applied to a field in the input item. We can perform a cogroup-and-aggregate transform with the following aggregate operation:

Pipeline p = Pipeline.create();
BatchStage<PageVisit> pageVisit = p.readFrom(Sources.list("pageVisit"));
BatchStage<AddToCart> addToCart = p.readFrom(Sources.list("addToCart"));
BatchStage<Payment> payment = p.readFrom(Sources.list("payment"));

AggregateOperation3<PageVisit, AddToCart, Payment, LongAccumulator[], long[]>
  aggrOp = AggregateOperation
    .withCreate(() -> new LongAccumulator[] {
      new LongAccumulator(),
      new LongAccumulator(),
      new LongAccumulator()
    .<PageVisit>andAccumulate0((accs, pv) -> accs[0].add(pv.loadTime()))
    .<AddToCart>andAccumulate1((accs, atc) -> accs[1].add(atc.quantity()))
    .<Payment>andAccumulate2((accs, pm) -> accs[2].add(pm.amount()))
    .andCombine((accs1, accs2) -> {
    .andExportFinish(accs -> new long[] {

BatchStage<Entry<Integer, long[]>> coGrouped =

Note how we got an AggregateOperation3 and how it captured each input type. When we use it as an argument to a cogroup-and-aggregate transform, the compiler will ensure that the `ComputeStage`s we attach to it have the correct type and are in the correct order.

On the other hand, if you use the co-aggregation builder object, you’ll construct the aggregate operation by calling andAccumulate(tag, accFn) with all the tags you got from the co-aggregation builder, and the static type will be just AggregateOperation. The compiler won’t be able to match up the inputs to their treatment in the aggregate operation.