The Fastest Ruby Profiler is a Java Profiler

Recently we announced that JXInsight can execute 1 billion instrumented Java method calls per second once the multi-resource (hotspot) strategy based metering runtime determines a particular method is a non-hotspot. So I was curious to see how our JRuby-to-Ruby cross language profiling extension faired with such optimizations as our probes technology is not in anyway tied to metering just named Java code execution constructs. The good news is that our technology can drop the overhead so low that it even out-performs no-op instrumentation calls in Ruby itself which means we are faster than any Ruby language based profiling or benchmarking solution (naturally when executed on the Java platform – JRuby VM).

Here is the Ruby micro-benchmark I used to determine the above.

Here is a chart showing the average time reported by multiple executions of the run method without and with our probes based instrumentation. Our instrumentation overhead is just over 15% but that is 15% on a method, call, that does nothing. Effectively our overhead is just under 15 nanoseconds for non-hotspots.

The overhead incurred in the instrumentation and measurement of fine grain method executions, which dominate the execution profile of most web applications, is a key performance indicator (KPI) for any enterprise performance management and monitoring solution.

Here is a partial screenshot of the resulting resource metering in our application management console. Note the strike-through on the call method indicating the associated instrumentation has been disabled following the execution of 1000 probe firings and the determination by the hotspot metering strategy that the named probe, Object.call, is a non-hotspot.

To determine the best (if not impossible) time a Ruby based profiling solution could do I added both a before and after method call to the call method which is typical of how most dynamic instrumentation agents work across various languages and runtimes. Both methods do nothing.

Here is a revised chart with the average execution time for the above manual instrumentation added. JXInsight is 3 times faster than a Ruby solution that does nothing.

Note: Disabling our hotspot metering strategy and comparing the performance with a version having a callout to Benchmark.measure() JXInsight is more than 15 times more efficient. Moving to JRuby has many advantages for production applications both in terms of the raw speed of the runtime itself and the supporting management and monitoring tools.

Hardware
Model Name: MacBook Pro
Model Identifier: MacBookPro5,1
Processor Name Intel Core 2 Duo
Processor Speed: 2.53 GHz
Number Of Processors: 1
Total Number Of Cores: 2
L2 Cache: 6 MB
Memory: 4 GB
Bus Speed: 1.07 GHz

JRuby
jruby 1.3.1 (ruby 1.8.6p287) (2009-06-15 2fd6c3d)

Runtime
java version "1.6.0_15"
Java(TM) SE Runtime Environment (build 1.6.0_15-b03-223)
Java HotSpot(TM) 64-Bit Server VM (build 14.1-b02-92, mixed mode)