Disruptor 2.0 Released

Significantly improved performance and a cleaner API are the key takeaways for the Disruptor 2.0 concurrent programming framework for Java.  This release is the result of all the great feedback we have received from the community.  Feedback is very welcome and really improves the end product so please keep it coming.

You can find the Disruptor project here, plus we have a wiki with links to detailed blogs describing how things work.

Naming & API

Over the lifetime of the Disruptor naming has been a challenge.  The funny thing is that with the 2.0 release we have come almost full circle.  Originally we considered the Disruptor as an event processing framework that often got used as a queue replacement.  To make it understandable to queue users we adopted the nomenclature of producers and consumers.  However the consumers are not true consumers.  With this release the consensus is to return to the event processing roots and adopt the following naming changes.

Producer -> Publisher
Events are claimed in strict sequence and published to the RingBuffer.

Entry -> Event
Events represent the currency of data exchange through the dependency graph of EventProcessors.

Consumer -> EventProcessor
Events are processed by EventProcessors.  The processing of an event can be read only, but can also involve mutations on which other EventProcessors depend.

ConsumerBarrier -> DependencyBarrier
Complex graphs of dependent EventProcessors can be constructed for the processing of an Event.  The DependencyBarriers are assembled to represent the dependency graph.  This topic is the real value of the Disruptor and often misunderstood.  A fun example can be seen playing FizzBuzz in our performance tests.

The ProducerBarrier was always a one-to-one relationship with the RingBuffer so for ease of use its behaviour has been merged into the RingBuffer.  This allows direct publishing into the RingBuffer.

DSL Wizard

The most complex part of using the Disruptor is the setting up of the dependency graph of EventProcessors.   To simplify this for the most common cases we have integrated the DisruptorWizard project which provides a DSL as a fluent API for assembling the graph and assigning threads.

Performance

Significant performance tuning effort has gone into this release.  This effort has resulted in a ~2-3X improvement in throughput depending on CPU architecture.  For most use cases it is now an order of magnitude better than queue based approaches. On Sandybridge processors I've seen over 50 million events processed per second.

Sequence tracking has been completely rewritten to reduce the usage of hardware memory barriers, indirection layers, and megamorphic method calls resulting in a much more data and instruction cache friendly design.  New techniques have been employed to prevent false sharing because the previous ones got optimised out by the Oracle Java 7 JVM.

The one area not seeing a significant performance increase is the sequencer pattern.  The Disruptor is still much faster than queue based approaches for this pattern but a limitation of Java hits us hard here.   Java on x86/x64 is using LOCK CMPXCHG for CAS operations to implement the AtomicLong incrementAndGet() method which, based on my measurements, is ~2-10X slower than using LOCK XADD as contention increases.  Hopefully Oracle will see the error of SUNs ways on this and embrace x86/x64 to take advantage of such instructions.  Dave Dice at Oracle has blogged on the subject so I live in hope.

Memory Barriers

Of special note for this release is the elimination of hardware memory barriers on x86/x64 for Sequence tracking.  The beauty in the Disruptor design is that on CPU architectures that have a memory model [1] whereby:

• “loads are not reordered with older loads”, and
• “stores are not reordered with older stores”;

it is then possible to take advantage of the semantics provided by AtomicLong to avoid the use of the Java volatile keyword, and thus hardware fences on x86/x64.  The one sticky rule for concurrent algorithms, such as Dekker [2] and Peterson [3] locks, on x86/x64 is “loads can be re-ordered with older stores”.  This is not an issue given the design of the Disruptor.  The issue relates to the snooping of CPU local store buffers for older writes.  I’m likely to blog in more detail about why this is the case at a later date.  The code should be safe on other CPU architectures if the JVM implementers get the semantics of AtomicLong and Unsafe correct, however your mileage may vary for performance on other architectures compared to x64.

Roadmap

With this latest release it is becoming increasingly obvious how sensitive some CPU architectures are to processor affinity for threads.  When an EventProcessor gets rescheduled on a different core, after its time-slice is exhausted or it yields, the resulting cache pollution really hits performance.  For those who require more extreme and predictable performance I plan to release an Executor service with the Disruptor to allow the pinning of threads to CPU cores.

I'm also thinking of adding a progressive back off strategy for waiting EventProcessors as a WaitStrategy.  This strategy would first busy spin, then yield, then eventually sleep in millisecond periods to conserve CPU resource for those applications that burst for a while then go quiet.

1. Memory Model: See Section 8.2 of http://www.intel.com/content/www/us/en/architecture-and-technology/64-ia-32-architectures-software-developer-vol-3a-part-1-manual.html
2. Dekker algorithm: http://en.wikipedia.org/wiki/Dekker%27s_algorithm
3. Peterson Algorithm: http://en.wikipedia.org/wiki/Peterson%27s_algorithm

Code Refurbishment

Within our industry we use a huge range of terminology.  Unfortunately we don’t all agree on what individual terms actually mean.  I so often hear people misuse the term “Refactoring” which has come to make the business in many organisations recoil in fear.  The reason for this fear I’ve observed is because of what people often mean when misusing this term.

I feel we are holding back our industry by not being disciplined in our use of terminology.  If one chemist said to another chemist “we are about to perform titration”, both would have a good idea what is involved.  I believe computing is still a very immature science.  As our subject matures hopefully we will become more precise and disciplined in our use of terminology and thus make our communication more accurate and effective.

Refactoring is a very useful technique for improving code quality and clarity.  To be precise it is a behaviour preserving change that improves a code base for future maintenance and understanding.  A good example would be extracting a method to remove code duplication and applying this method at every site of the duplication, thus removing the duplication.  Refactoring was first discussed in the early 1990s and became mainstream after Martin Fowler’s excellent “Refactoring” book in 1999.

Refactoring involves making a number of small internal changes to the code structure.  These changes will typically not have any external impact.  Well written unit tests that just assert externally observable behaviour will not change when code is refactored.  If the external behaviour of code is changing when the structure is being changed then this is not refactoring.

Now, why do our business folk recoil in fear when this simple and useful technique of “refactoring” is mentioned?  I believe this is because developers are actually talking about a much more extensive structural redevelopment technique that does not have a common term.  These structural changes are often not a complete ground-up rewrite because much of the existing code will be reused.  The reason the business folk have come to recoil is that they fear we are about to head off into uncharted waters with no idea of how long things will take and if any value will come out of the exercise.

This example of significant structural change reminds me of when a bar or restaurant gets taken over by new management.  The new management often undertake a refurbishment exercise to make the place more appealing and suitable for the customers they are targeting.  A lot of the building will be preserved and reused thus greatly reducing the costs of a complete rebuild.  In my experience when developers use the term “refactoring” what they really mean is that some module, or bounded context, in a code base is about to undergo significant refurbishment.  If we define this term, and agree the goal and value to the business, we may be able to better plan and manage our projects.

These code refurbishment exercises should have clear goals defined at the outset and all change must be tested against these goals.   For example, we may have discovered that code is not a true reflection of the business domain after new insights.  These insights may have been gleaned over a period of time and the code has grown out of step to become an approximation of what the business requires.  While performing Domain Driven Design the penny may drop with the essence of the business model becoming clear.  After this clarity of understanding the code may need a major overhaul to align it with this new understanding of the business.  Code can also drift from being a distilled model of the business domain if quick hacks are put in place to meet a deadline.  Over time these hacks can build on each other until the model no longer describes the business, it just about makes itself useful by side effect.  During this exercise our tests are likely to see significant change as we tighten up the specification for our new improved understanding of the business domain.

A code refurbishment is worthwhile to correct the core domain if it's about to undergo significant further development, or if a module is business critical and needs to be occasionally corrected under production pressure to preserve revenue generation.

I’m interested to know if other folk have observed similar developments and if you think refinement of this concept would be valuable?

False Sharing && Java 7

In my previous post on False Sharing I suggested it can be avoided by padding the cache line with unused long fields.  It seems Java 7 got clever and eliminated or re-ordered the unused fields, thus re-introducing false sharing.  I've experimented with a number of techniques on different platforms and found the following code to be the most reliable.

import java.util.concurrent.atomic.AtomicLong;

public final class FalseSharing
    implements Runnable
{
    public final static int NUM_THREADS = 4; // change
    public final static long ITERATIONS = 500L * 1000L * 1000L;
    private final int arrayIndex;

    private static PaddedAtomicLong[] longs = new PaddedAtomicLong[NUM_THREADS];
    static
    {
        for (int i = 0; i < longs.length; i++)
        {
            longs[i] = new PaddedAtomicLong();
        }
    }

    public FalseSharing(final int arrayIndex)
    {
        this.arrayIndex = arrayIndex;
    }

    public static void main(final String[] args) throws Exception
    {
        final long start = System.nanoTime();
        runTest();
        System.out.println("duration = " + (System.nanoTime() - start));
    }

    private static void runTest() throws InterruptedException
    {
        Thread[] threads = new Thread[NUM_THREADS];

        for (int i = 0; i < threads.length; i++)
        {
            threads[i] = new Thread(new FalseSharing(i));
        }

        for (Thread t : threads)
        {
            t.start();
        }

        for (Thread t : threads)
        {
            t.join();
        }
    }

    public void run()
    {
        long i = ITERATIONS + 1;
        while (0 != --i)
        {
            longs[arrayIndex].set(i);
        }
    }

    public static long sumPaddingToPreventOptimisation(final int index)
    {
        PaddedAtomicLong v = longs[index];
        return v.p1 + v.p2 + v.p3 + v.p4 + v.p5 + v.p6;
    }

    public static class PaddedAtomicLong extends AtomicLong
    {
        public volatile long p1, p2, p3, p4, p5, p6 = 7L;
    }
}

With this code I get similar performance results to those stated in the previous False Sharing article.  The padding in PaddedAtomicLong above can be commented out to see the false sharing effect.

I think we should all lobby the powers that be inside Oracle to have intrinsics added to the language so we can have cache line aligned and padded atomic classes.  This and some other low-level changes would help make Java a real concurrent programming language.  We keep hearing them say multi-core is coming.   I say it is here and Java needs to catch up.

Inter Thread Latency

Message rates between threads are fundamentally determined by the latency of memory exchange between CPU cores.   The minimum unit of transfer will be a cache line exchanged via shared caches or socket interconnects.  In a previous article I explained Memory Barriers and why they are important to concurrent programming between threads.  These are the instructions that cause a CPU to make memory visible to other cores in an ordered and timely manner.

Lately I’ve been asked a lot about how much faster the Disruptor would be if C++ was used instead of Java.  For sure C++ would give more control for memory alignment and potential access to underlying CPU instructions such as memory barriers and lock instructions.  In this article I’ll directly compare C++ and Java to measure the cost of signalling a change between threads.

For the test we'll use two counters each updated by their own thread.  A simple ping-pong algorithm will be used to signal from one to the other and back again.  The exchange will be repeated millions of times to measure the average latency between cores.  This measurement will give us the latency of exchanging a cache line between cores in a serial manner.

For Java we’ll use volatile counters which the JVM will kindly insert a lock instruction for the update giving us an effective memory barrier.

public final class InterThreadLatency
    implements Runnable
{
    public static final long ITERATIONS = 500L * 1000L * 1000L;

    public static volatile long s1;
    public static volatile long s2;

    public static void main(final String[] args)
    {
        Thread t = new Thread(new InterThreadLatency());
        t.setDaemon(true);
        t.start();

        long start = System.nanoTime();

        long value = s1;
        while (s1 < ITERATIONS)
        {
            while (s2 != value)
            {
                // busy spin
            }
            value = ++s1;
        }

        long duration = System.nanoTime() - start;

        System.out.println("duration = " + duration);
        System.out.println("ns per op = " + duration / (ITERATIONS * 2));
        System.out.println("op/sec = " +  
            (ITERATIONS * 2L * 1000L * 1000L * 1000L) / duration);
        System.out.println("s1 = " + s1 + ", s2 = " + s2);
    }

    public void run()
    {
        long value = s2;
        while (true)
        {
            while (value == s1)
            {
                // busy spin
            }
            value = ++s2;
        }
    }
}

For C++ we’ll use the GNU Atomic Builtins which give us a similar lock instruction insertion to that which the JVM uses.

#include <time.h>
#include <pthread.h>
#include <stdio.h>

typedef unsigned long long uint64;
const uint64 ITERATIONS = 500LL * 1000LL * 1000LL;

volatile uint64 s1 = 0;
volatile uint64 s2 = 0;

void* run(void*)
{
    register uint64 value = s2;
    while (true)
    {
        while (value == s1)
        {
            // busy spin
        }
        value = __sync_add_and_fetch(&s2, 1);
    }
}

int main (int argc, char *argv[])
{
    pthread_t threads[1];
    pthread_create(&threads[0], NULL, run, NULL);

    timespec ts_start;
    timespec ts_finish;
    clock_gettime(CLOCK_MONOTONIC, &ts_start);

    register uint64 value = s1;
    while (s1 < ITERATIONS)
    {
        while (s2 != value)
        {
            // busy spin
        }
        value = __sync_add_and_fetch(&s1, 1);
    }

    clock_gettime(CLOCK_MONOTONIC, &ts_finish);

    uint64 start = (ts_start.tv_sec * 1000000000LL) + ts_start.tv_nsec;
    uint64 finish = (ts_finish.tv_sec * 1000000000LL) + ts_finish.tv_nsec;
    uint64 duration = finish - start;

    printf("duration = %lld\n", duration);
    printf("ns per op = %lld\n", (duration / (ITERATIONS * 2)));
    printf("op/sec = %lld\n",  
        ((ITERATIONS * 2L * 1000L * 1000L * 1000L) / duration));
    printf("s1 = %lld, s2 = %lld\n", s1, s2);

    return 0;
}

Results

$ taskset -c 2,4 /opt/jdk1.7.0/bin/java InterThreadLatency
duration = 50790271150
ns per op = 50
op/sec = 19,688,810
s1 = 500000000, s2 = 500000000

$ g++ -O3 -lpthread -lrt -o itl itl.cpp
$ taskset -c 2,4 ./itl
duration = 45087955393
ns per op = 45
op/sec = 22,178,872
s1 = 500000000, s2 = 500000000

The C++ version is slightly faster on my Intel Sandybridge laptop.  So what does this tell us?  Well, that the latency between 2 cores on a 2.2 GHz machine is ~45ns and that you can exchange 22m messages per second in a serial fashion.  On an Intel CPU this is fundamentally the cost of the lock instruction enforcing total order and forcing the store buffer and write combining buffers to drain, followed by the resulting cache coherency traffic between the cores.   Note that each core has a 96GB/s port onto the L3 cache ring bus, yet 22m * 64-bytes is only 1.4 GB/s.  This is because we have measured latency and not throughput.  We could easily fit some nice fat messages between those memory barriers as part of the exchange if the data has been written before the lock instruction was executed.

So what does this all mean for the Disruptor?  Basically, the latency of the Disruptor is about as low as we can get from Java.  It would be possible to get a ~10% latency improvement by moving to C++.  I’d expect a similar improvement in throughput for C++.  The main win with C++ would be the control, and therefore, the predictability that comes with it if used correctly.  The JVM gives us nice safety features like garbage collection in complex applications but we pay a little for that with the extra instructions it inserts that can be seen if you get Hotspot to dump the assembler instructions it is generating.

How does the Disruptor achieve more than 25m messages per second I hear you say???   Well that is one of the neat parts of its design.  The “waitFor” semantics on the SequenceBarrier enables a very efficient form of batching, which allows the BatchEventProcessor to process a series of events that occurred since it last checked in with the RingBuffer, all without incurring a memory barrier.  For real world applications this batching effect is really significant.  For micro benchmarks it only makes the results more random,  especially when there is little work done other than accepting the message.

Conclusion

So when processing events in series, the measurements tell us that the current generation of processors can do between 20-30 million exchanges per second at a latency less than 50ns.  The Disruptor design allows us to get greater throughput without explicit batching on the publisher side.  In addition the Disruptor has an explicit batching API on the publisher side that can give over 100 million messages per second.