The art of thread messaging

Vlad Ilyushchenko

Vlad Ilyushchenko

QuestDB Team

Inter-thread messaging is a fundamental part of any asynchronous system. It is the component responsible for transportation of data between threads. Messaging forms the infrastructure, the scaffolding of multi-threaded application and just like real-world transport infrastructure we want it to be inexpensive, fast, reliable and clean.

For QuestDB we wrote our own messaging system and this post is about how it works and how fast it is.

Architecture

Borrowing heavily from world-famous Disruptor our messaging revolves around multiple threads accessing shared circular data structure. We call it RingQueue. Semantically RingQueue provides unbounded, index-based, random access to its elements. It does not coordinate concurrent access nor does it provide guarantees on thread safety. Coordination and thread-safety is a concern of Sequences. Sequences are responsible for providing indices that can be used to access RingQueue concurrently and safely.

To help sequences do their magic they have to be shaped into a graph. We start with syntax to chain sequences together:

a.then(b).then(c).then(d)

The result is a trivial sequence graph:

a -> b -> c -> d

To branch we use helper class FanOut:

a.then(FanOut.to(b).and(c)).then(d)

The result is this sequence graph:

+--> B -->+
A -->| |--> D
+--> C -->+

These two pieces of syntax are flexible enough to create any desired flow. This example shows that FanOut can have chain of sequences and other FanOuts:

a.then(FanOut.to(FanOut.to(b).and(c)).and(d.then(e)).then(f)

It is quite a mouthful but it creates this nice little graph:

+--> B -->+
+-> | |
| +--> C -->+
A-->| |--> F
| |
+-> D -> E -->+

FanOut can also be used as a placeholder in a chain to allow threads to subscribe/unsubscribe on the fly. Dynamic subscription is then simply adding a new sequence to FanOut:

// You can add as many sequences into fan out as you like.
// Sequences can be added either up front or subscribe/unsubscribe on the fly.
FanOut fanOut = new FanOut();
// ordinary producer sequence
Sequence seqProducer = new SPSequence(queue.getCapacity());
// daisy chain producer and fan out and loop back producer
seqProducer.then(fanOut).then(seqProducer);
// meanwhile in another thread ....
...
// Add individual consumer sequences later as needed.
// This is thread safe non-blocking operation that can be performed from any thread.
// It is important to use current producer position as consumer starting point when subscribing on the fly.
Sequence consumer1 = fanOut.addAndGet(new SCSequence(seqProducer.current()));
// do something useful with consumer1 sequence
...
// remove sequence from fanOut to unsubscribe
fanOut.remove(consumer1);

Typical graph must contain single producer sequence and one or more consumer sequences. It will also have to be circular, e.g. to start and end with producer sequence. Graph has to be circular because we use circular underlying data structure, RingQueue. Without loop-back producer would be liable to overwrite queue elements before consumers had a chance to read them. Worse still, queue elements can be written to and read from concurrently. We don't want that to happen, right?

To help create practical sequence graph we implemented 4 types of sequences we can play with. These sequences are better understood as combination of their types and properties. SP - single producer, MP - multiple producer, SC - single consumer and MC - multiple consumer. Multi- sequences allow concurrent access and they guarantee that no two threads can retrieve same index. It is this property adds extra fun dimension to sequence graphs. Consider this graph:

A -> B -> A

or in Java notations:

A.then(B).then(A)

When "B" is an instance of MCSequence() we have a self-balancing worker pool. When "A" is MPSequence(), we have many-to-many pub-sub system. Cool, eh?

Single- sequences are faster but they are not thread-safe. They should be preferred for single-threaded consumer models.

Lets take a look at how threads interact with sequences. This is a typical example of publisher:

// loop until there is work to do
// consumer thread may be able to rely on producer to
// publish "special" message to indicate end of stream.
while (true) {
// Non-blocking call. Method returns immediately either with zero-based
// ring queue index or negative long indicating one of following:
// -1 = queue is empty
// -2 = there was a contest for queue index and this thread has lost
long cursor = sequence.next();
if (cursor < 0) {
// negative cursor is an error
// thread has a choice of things to do:
// - busy spin
// - yield/park
// - work on something else
LockSupport.parkNanos(1);
continue;
}
// write to queue
try {
queue.get(cursor).value;
} finally {
// releasing cursor promptly is important
sequence.done(cursor);
}
}

Sequence.next() return values are:

-1 Queue is unavailable. It is either full or empty, depending on whether it is producer or consumer sequence

-2 Temporary race condition. Sequence failed CAS and delegated decision to your application.

Consumer sequence interaction is almost identical. The only difference would be consumer reading queue item instead of writing it.

Performance of single-threaded sequences can benefit further from batching. Batching relies on receiving range of indices from sequence and calling done() at end of batch rather than for every queue item. This is what consumer code might look like (producer code is the same):

while (running) {
long cursor = sequence.next();
if (cursor < 0) {
LockSupport.parkNanos(1);
continue;
}
// get max index sequence can reach
long available = sequence.available();
// look thru queue elements without using sequence
while (cursor < available) {
queue.get(cursor++);
}
// calling done() only once per batch can yield significant performance benefit
sequence.done(available - 1);
}

Multi-threaded sequence do not support batches.

Performance

I used Shipilev's project that already had Disruptor benchmark and I added QuestDB implementation of the same pipeline.

Benchmark source on GitHub

2 CPU MBP 2015

Benchmark (slicesK) (threads) (workMult) Mode Cnt Score Error Units
Disruptor.run 500 2 10 ss 50 10.043 ± 0.158 ms/op
Disruptor.run 1000 2 10 ss 50 19.944 ± 0.285 ms/op
Disruptor.run 5000 2 10 ss 50 133.082 ± 6.032 ms/op
QuestdbFanOut.run 500 2 10 ss 50 13.027 ± 0.180 ms/op
QuestdbFanOut.run 1000 2 10 ss 50 26.329 ± 0.327 ms/op
QuestdbFanOut.run 5000 2 10 ss 50 141.686 ± 4.129 ms/op
QuestdbWorker.run 500 2 10 ss 50 29.470 ± 0.976 ms/op
QuestdbWorker.run 1000 2 10 ss 50 62.205 ± 3.278 ms/op
QuestdbWorker.run 5000 2 10 ss 50 321.697 ± 12.031 ms/op

4 CPU x5960 @ 4.2Ghz

Benchmark (slicesK) (threads) (workMult) Mode Cnt Score Error Units
Disruptor.run 500 4 10 ss 50 6.892 ± 0.654 ms/op
Disruptor.run 1000 4 10 ss 50 10.143 ± 0.623 ms/op
Disruptor.run 5000 4 10 ss 50 54.084 ± 4.164 ms/op
QuestdbFanOut.run 500 4 10 ss 50 6.364 ± 0.197 ms/op
QuestdbFanOut.run 1000 4 10 ss 50 11.454 ± 0.754 ms/op
QuestdbFanOut.run 5000 4 10 ss 50 50.928 ± 3.264 ms/op
QuestdbWorker.run 500 4 10 ss 50 14.240 ± 1.341 ms/op
QuestdbWorker.run 1000 4 10 ss 50 27.246 ± 2.777 ms/op
QuestdbWorker.run 5000 4 10 ss 50 142.207 ± 15.157 ms/op

Disruptor and QuestDB perform essentially the same.

How to get it

Our messaging system is on Maven central as a part of QuestDB. Don't worry about package size though, QuestDB jar is around 3.6MB and has no dependencies. Jump to the GitHub release page for version reference.