[CASSANDRA-10528] Proposal: Integrate RxJava

• Type: Improvement
• Status: Resolved
• Priority: Normal
• Resolution: Duplicate
• Assignee: T Jake Luciani
• Reporter: T Jake Luciani

Authors

T Jake Luciani

• Created: 14/Oct/15 20:10
• Updated: 16/Apr/19 09:30
• Resolved: 11/Aug/16 14:54

Description

The purpose of this ticket is to discuss the merits of integrating the RxJava framework into C*. Enabling us to incrementally make the internals of C* async and move away from SEDA to a more modern thread per core architecture.

Related tickets:

• CASSANDRA-8520
• CASSANDRA-8457
• CASSANDRA-5239
• CASSANDRA-7040
• CASSANDRA-5863
• CASSANDRA-6696
• CASSANDRA-7392

My primary goals in raising this issue are to provide a way of:

• Incrementally making the backend async
• Avoiding code complexity/readability issues
• Avoiding NIH where possible
• Building on an extendable library

My non-goals in raising this issue are:

• Rewrite the entire database in one big bang
• Write our own async api/framework

-------------------------------------------------------------------------------------

I've attempted to integrate RxJava a while back and found it not ready mainly due to our lack of lambda support. Now with Java 8 I've found it very enjoyable and have not hit any performance issues. A gentle introduction to RxJava is here as well as their wiki. The primary concept of RX is the Obervable which is essentially a stream of stuff you can subscribe to and act on, chain, etc. This is quite similar to Java 8 streams api (or I should say streams api is similar to it). The difference is java 8 streams can't be used for asynchronous events while RxJava can.

Another improvement since I last tried integrating RxJava is the completion of CASSANDRA-8099 which provides is a very iterable/incremental approach to our storage engine. Iterators and Observables are well paired conceptually so morphing our current Storage engine to be async is much simpler now.

In an effort to show how one can incrementally change our backend I've done a quick POC with RxJava and replaced our non-paging read requests to become non-blocking.

https://github.com/apache/cassandra/compare/trunk...tjake:rxjava-3.0

As you can probably see the code is straight-forward and sometimes quite nice!

Old

private static PartitionIterator fetchRows(List<SinglePartitionReadCommand<?>> commands, ConsistencyLevel consistencyLevel)
    throws UnavailableException, ReadFailureException, ReadTimeoutException
    {
        int cmdCount = commands.size();

        SinglePartitionReadLifecycle[] reads = new SinglePartitionReadLifecycle[cmdCount];
        for (int i = 0; i < cmdCount; i++)
            reads[i] = new SinglePartitionReadLifecycle(commands.get(i), consistencyLevel);

        for (int i = 0; i < cmdCount; i++)
            reads[i].doInitialQueries();

        for (int i = 0; i < cmdCount; i++)
            reads[i].maybeTryAdditionalReplicas();

        for (int i = 0; i < cmdCount; i++)
            reads[i].awaitRes
ultsAndRetryOnDigestMismatch();

        for (int i = 0; i < cmdCount; i++)
            if (!reads[i].isDone())
                reads[i].maybeAwaitFullDataRead();

        List<PartitionIterator> results = new ArrayList<>(cmdCount);
        for (int i = 0; i < cmdCount; i++)
        {
            assert reads[i].isDone();
            results.add(reads[i].getResult());
        }

        return PartitionIterators.concat(results);
    }

New

private static Observable<PartitionIterator> fetchRows(List<SinglePartitionReadCommand<?>> commands, ConsistencyLevel consistencyLevel)
    throws UnavailableException, ReadFailureException, ReadTimeoutException
    {
        return Observable.from(commands)
                         .map(command -> new SinglePartitionReadLifecycle(command, consistencyLevel))
                         .flatMap(read -> read.getPartitionIterator())
                         .toList()
                         .map(results -> PartitionIterators.concat(results));

    }

Since the read call is now non blocking (no more future.get()) we can remove one thread pool hop from the native netty request pool which yields a non-trivial improvement to read performance.

http://cstar.datastax.com/tests/id/ae648c12-729a-11e5-8625-0256e416528f

At the same time the current Iterator based api still works by calling .toBlocking() on the observable. So for example the existing thrift read call requires little modification

On the async side we get the added benefits of RxJava:

• Customizable backpressure strategies (for dealing with streams that can't be processed quickly enough)
• Cancelling of work due to timeouts is a 1 line change
• When a Subscriber disconnects from the stream they Observable stops as well
• Batching/windowing of work can be added in one line
• Observers and Subscribers can do work across any thread at any stage of the pipeline
• Observables can be debugged and tested

Another plus is the community surrounding RxJava specifically our good friends at netflix have authored and used it extensively. Docs and examples are good.

In order to get the most out of this we will need to take this api further into the code. MessagingService, Disk Access/Page, Cache, Thread per core... but again I want to hammer home this will be able to be achieved incrementally.

On the bad side this is:

• Locking into a "framework"
• Will inevitably hit bugs / performance issues we need fixed upstream
• Some of the more advanced API uses look pretty mentally taxing/hard to grasp

Which brings us to the Alternatives, primarily being to just use CompletableFutures.

We certainly could but if you look at the code changes I had to make to make the SP calls asynchronous I think you will realize you would need to pass
all kinds of state around to get the read command callback to start the netty write. Vs observables which make that pipeline declarative. Also more advanced things like backpressure and message passing between N:M producers and consumers becomes complex. This isn't to say we can't use both if Observables are overkill.

I hope this ticket sparks some good discussion!