Status

Current state: Under Discussion

Discussion thread: here

JIRA: here

Please keep the discussion on the mailing list rather than commenting on the wiki (wiki discussions get unwieldy fast).

Motivation

Some algorithms are best expressed recursively, which would involve piping the output of a pipeline back to an earlier point in the same pipeline. An example of this is graph/tree-traversal, where it can be useful to recursively traverse up a tree as new leaf nodes arrive.

This document introduces the concept of streaming recursion, a new DSL operator to succinctly express it, and optimizations that Kafka Streams can make to recursive algorithms that aren't currently possible.

Public Interfaces

The following new methods will be introduced:

interface KStream<K, V> {
	KStream<K, V> recursively(UnaryOperator<KStream<K, V>> op);
	KStream<K, V> recursively(UnaryOperator<KStream<K, V>> op, Produced<K, V> produced);
}

Note: UnaryOperator is java.util.function.UnaryOperator

Proposed Changes

The new recursively method enables users to express recursive algorithms. Consider an example where we count all descendants of each node in a graph:

// <NodeId, ParentId>
KStream<String, String> nodes = builder.stream("nodes");

// <NodeId, ParentId>
KTable<String, String> parents = nodes.toTable();

// count descendants by recursively producing parent records
// 1L is used as a dummy value below, since we will be discarding values when we count the records by key
KTable<String, Long> descendants = nodes
	.map((child, parent) -> { KeyValue(parent, 1L) }   // emit "update" for parent of new node
	.recursively((updates) -> {                        // recursively emit "updates" for each ancestor of the parent
		// emit a new update for the parent of this node
	 	// the root node has no parent, so recursion terminates at the root node
		updates
			.join(parents, (count, parent) -> { parent })
			.map((child, parent) -> { KeyValue(parent, 1L) })
	})
	.groupByKey()
	.count()

Note: for simplicity, this example assumes that graph nodes arrive and are processed in-order; i.e. parent nodes are always processed before children.

The recursively method applies input records to its op argument. The results are then both emitted as a result of recursively and also fed back in to the op KStream.

Restrictions:

• op cannot be UnaryOperator.identity, or an equivalent function that simply returns its argument unmodified - this would produce an infinite recursive loop, since there's no opportunity refine the output to break out of the loop.
• op MUST "terminate"; that is, it must have some condition which eventually prevents further recursion of a record. In our example here, the terminating condition is the join, since the root node of our graph will have no parent, so the join will produce no output for the root node.
  • We can attempt to detect "definitely non-terminating" arguments by failing to detect operations that can cause the stream to terminate (e.g. filter, join, flatMap, etc.) in the process graph produced by the function.
  • We cannot guarantee that a function that includes terminating operations (filter, join, flatMap, etc.) actually terminates.

Automatic Repartitioning

If the op argument applies a key-changing operation (as it does in our example above), a repartition  topic may be automatically created. The optional Produced argument can be provided to customize repartitioning behaviour. This argument is ignored if a repartition  topic is not necessary.

• We use Produced  instead of Repartitioned, because when operating recursively, it would be an error to modify the number of partitions, since the topic MUST have the same number of partitions as the current Task.

Repartitioning is required only when the op KStream requires repartitioning and the current KStream does not require repartitioning. The reason for this is that if both streams require repartitioning, then the current stream is guaranteed to automatically repartition records before any operation that requires repartitioning. In the above example, recursively would not include a repartition topic, because join already includes one.

Implementation

In KStreamImpl, implementation is fairly simple:

1. We call op, passing our current KStream as its argument. This produces our output KStream.
2. We determine if repartitioning is required on the op stream, and if it is, we automatically include a repartition node, equivalent to adding .repartition()  to the end of the op stream.
3. We wire up the graphNode  from the output KStream  as a parent of the current KStream. This takes care of the recursion.
4. Finally, we return the output  KStream. This enables users to operate on the records that are being recursively produced, as above.

Compatibility, Deprecation, and Migration Plan

• No backwards incompatible changes are introduced.

Test Plan

The following tests will be added:

• Streaming recursion:
  • No repartitioning required
  • Repartitioning handled by main stream.
  • Repartitioning handled by op argument.

Rejected Alternatives

It's currently possible to implement streaming recursion via explicit topics, albeit with a number of disadvantages:

1. The explicit topic is entirely internal to the Topology, yet it has to be managed explicitly by the user.
   1. This is functionally a repartition  topic, however, because it's explicitly managed, Streams can't automatically delete consumed records.
   2. Consequently, to prevent the topic growing unbounded, users would need to set retention criteria, which risks possible data loss.
2. In scenarios where repartitioning is not required, the explicit recursive topic adds overhead.
3. It also adds some complexity to the user's program, making it more difficult to reason about than it needs to be.