This is a short summary of my efforts profiling and benchmarking Jobim, the actors library for Clojure I’ve been working for the last few months. I have very little experience profiling Clojure applications, so I thought a brief summary of the process may be interesting for other people in the same situation.

How to measure performance?

The first problem I found wast to make up a good way of testing the actual performance of the library. Performance of complex systems is really hard to test. The number of moving parts make difficult to spot the bottlenecks and wrong conclusions can be drawn easily.

Fortunately, in my case I found an easy solution for the problem. I used the same test suite built to benchmark Termite Scheme. Termite is a variant of Scheme built for distributed computing and modelled after Erlang programming model on the Gambit Scheme system.

The Termite paper has a full section discussing different performance tests and showing the results for Termite and Erlang. Furthermore, the implementation of the tests can be found in Termite’s source code.

Because of the similarities between the projects, implementing these benchmark tests in Clojure/Jobim provide a good starting point to test the performance of the library.

Finding tools

To measure performance, System/nanoTime can be good enough. On the other hand, to profile the code when performance is bad, you really need a good profiler tool. Unfortunately, there is no a good Clojure profiler tool as fas as I know. Commercial Java profilers like Yourkit are a good option but they can also be expensive. A good free alternative to these profilers is VisualVM. The profiler is included in the Java distribution and it’s really ease to use. Using VisualVM with Clojure is just a matter of selecting the REPL process from the profiler user interface and start gathering data.

Adding type hints

One first step to increase the performance of a Clojure application is to add type hints to the code of your functions. Clojure is a dynamic language, but that’s not the case of Java, the host-language for Clojure. In order to invoke Java methods, Clojure needs to find the type of the java object passed as arguments to these methods or returned from them. Java reflection is the mechanism that makes possible to look up for this informatipn at run-time. The use of reflection has a double impact on performance, it adds processing time and it consumes additional memory creating reflection objects. The use of reflection is usually not a problem in regular applications, memory is freed by the garbage collector and the performance penalty is not significative. Nevertheless, in some other cases the use of the reflection mechanism can become a problem.

The following images show a profiler session for the execution of the Termite ring test with a ring of 40k actors. The non-deterministic execution of the garbage collector has been disabled with the -Xincgc JVM parameter.

The screenshots show how the execution of the test allocates a huge amount of memory as java.lang.reflect.Method objects. These objects are released by the GC once it is manually executed but in a regular execution, the execution of the GC thread might have impacted very negatively the outcome of the test.

In order to avoid these situations, Clojure can notify us when it will use reflection to resolve a method call if we set up the *warn-on-reflection* var to true . With this modification, the list of conflicting calls will be shown when the source code is being compiled. Using information we can add type hints with the correct type for the call to the metadata of the problematic symbols. Clojure will use this information instead of using reflection to discover the right type for the objects.

The following figures show another run of the same test after having added the required type hints. We can see how the use of memory has decreased and the java.lang.reflect.Method objects have dissapeared.

In the case of Jobim, the number of type hints required to resolve all the problematic calls was really small. Around 15 type hints were enough. Nevertheless, some calls involving protocols were harder to solve. The use of VisualVM was a great help to check that no reflection objects were being created.

Object serialization

One of the main performance problems in tests involving the exchange of messages among actors in different nodes is the serialization and deserialization of messages. Using standard Java serialization the amount of measured time for some tests spent in the serialization of messages could reach 20% of the total. Besides, serialization of some Clojure objects like lambda functions is not supported.

Lacking an unique solution for the problem. Different serialization services have been implemented as plugins for Jobim. The following table shows some performance results for a test consisting of encoding and decoding a java HasMap with long lists of integers as values:

Serialization mechanism Time encoding+decoding JSON serialization 5.566 ms Java serialization 4.419 ms JBoss serialization 1.437 ms Kryo serialization 0.573 ms

The result shows how the standard Java serialization is almost as slow as JSON serialization. Kryo offers a very good performance with the drawback of not supporting Clojure core data types. Finally JBoss serialization library offers a much better performance than the standard Java serialization with a compatible interface and the same degree of support for Java types, including Clojure core data types.

The lack of support for clojure data structures makes Kryo a valid option only for certain cases where actors restrict the kind of messages they exchange so they can be serialized by Kryo. JBoss serialization is a good default option and has been used in the rest of the tests. Jobim data types used in the exchange of messages between nodes by the library have restricted to standard Java types so they can be used with any serialization mechanism.

Results

The following table shows the results I have obtained profiling the current version of Jobim. The results for Erlang have been measured using the same hardware where the Clojure version was tested.

test Erlang Jobim threaded Jobim evented fib(534) 585 ms 378 ms – Spawn 6.92 us 49 us 27 us Send 0.2 us 120 us 16 us ring 40k/0 3 us – 83 us ring 40k/4 5 us – 166 us ping-pong internal (0) 0.7 us 148 us 25 us ping-pong internal (1000) 16 us 152 us 29 us ping-pong same node (0) 58 us 14333 us 5027 us ping-pong same node (1000) 182 us 25921 us 6303 us ping-pong remote (0) 3267 us 18481 us 7022 us ping-pong remote (1000) 16556 us 38730 us 8400 us

Ping-Pong tests where executed using a Jobim configuration consisting of the ZooKeeper plugin for the coordination service, the TCP plugin for the messaging service and the JBoss plugin for the serialization service. The rest of the tests where run with the localnode plugin for coordination, service and serialization.

Some conclusions:

Clojure is faster than Erlang in the base case chosen, computing fibonacci of a small number with a naive implementation.

Actor models primitives: spawn, send, receive are orders of magnitude faster in Erlang that implemented as library functions in Jobim.

The number of processes/actors that can be created in a single node is far inferior in Jobim than in Erlang. In a machine where the Erlang version of the ring test was running without problems with 1M processes, the JVM was struggling with ~50k evented actors. Threaded actors are even less scalable.

Erlang seems to have some issues with the size of network messages, Jobim faces less variation in the response times when the size of the messages varies

Evented actors offer far superior performance than the threaded implementation in the tests.

In tests involving actual network communication, Jobim gets closer to Erlang and it even outperforms Erlang in one case.

With these results, it is probably not a good idea to write Erlang-in-Clojure applications using Jobim. Nevertheless, the library starts to be performant enough to be used as a distribution mechanism for Clojure applications using actors semantics and some of Erlang features like actors linking or behaviours.