The MooBench micro-benchmarks can be used to quantify the performance overhead caused by observability framework components and different observability frameworks. Observability is achieved through its three pillars:
- Logs, i.e., timestamped information about system events,
- Metrics, i.e., numerical measurements of system behaviour, and
- Traces, i.e., representations of request, transaction or operation executions. MooBench can measure the overhead that is created by obtaining any of these three pillars of observability from program execution.
Continuous measurement results are available here:
- Kiel University Server (Intel Xeon CPU E5620 @ 2.40 GHz, Debian 12): https://kieker-monitoring.net/performance-benchmarks/
- GH Actions Runner (Ubuntu 22.041): https://kieker-monitoring.github.io/moobench/dev/bench/
Currenly (fully) supported observability frameworks are:
- Kieker with Java (http://kieker-monitoring.net)
- OpenTelemetry with Java (https://opentelemetry.io/)
- inspectIT with Java (https://inspectit.rocks/) For all combinations of supported observability frameworks $FRAMEWORK and languages $LANGUAGE, the folder frameworks contains a folder $FRAMEWORK-$LANGUAGE.
MooBenchs measures the overhead of gathering observability data by executing an example workload using different configurations, including no instrumentation (and hence no data gathering) at all, full distributed tracing and data serialization via binary writer. The example workload consists of $RECURSION_DEPTH recursive calls of a function to itself. For example, the following graph shows the execution of MooBench in the no instrumentation configuration:
graph TD;
BenchmarkingThreadNano.run-->MonitoredClassSimple.monitoredMethod;
MonitoredClassSimple.monitoredMethod-->MonitoredClassSimple.monitoredMethod;
MonitoredClassSimple.monitoredMethod-->id["Busy Wait"]
The binary writer configuration on the other hand includes the probe code, that is injected by the observability tool before and after the operation. For the Kieker monitoring framework, the probe inserts records into the WriterController.writerQueue, and these are then processed for finally writing binary data to the hard disk.
flowchart TD;
instrumentedCall["`probeBeforeOperation()
MonitoredClassSimple.monitoredMethod
probeAfterOperation`"]
BenchmarkingThreadNano.run-->instrumentedCall;
instrumentedCall-->instrumentedCall;
instrumentedCall-->id["Busy Wait"];
subgraph Kieker
direction LR
WriterController.writerQueue-->FileWriter.writeMonitoringRecord;
FileWriter.writeMonitoringRecord-->BinaryLogStreamHandler.serialize;
end
instrumentedCall-->Kieker;
Initially, the following steps are required:
- Make sure, that you've installed R (http://www.r-project.org/) to generate
the results , awk to install intermediate results and curl to download
processing tools (Ubuntu:
sudo apt install r-base gawk curl). - Compile the application and install it in the repository root directory.
This can be done automatically be calling
./setup.sh
All experiments are started with the provided "External Controller" scripts. The following scripts are available for every supported framework ($FRAMEWORK) and language ($LANGUAGE):
- In
frameworks/$FRAMEWORK-$LANGUAGE/benchmark.sha script is provided for regular execution (with default parameters) - In
frameworks/$FRAMEWORK-$LANGUAGE/runExponentialSizes.sha script is provided for execution with different call tree depth sizes (exponentially growing from 2)
Each scripts will start different factorial experiments (started $NUM_OF_LOOPS
times for repeatability), which will be:
- baseline execution
- execution with instrumentation but without processing or serialization
- execution with serialization to hard disc (currently not available for inspectIT)
- execution with serialization to tcp receiver, which might be a simple receiver (Kieker), or Zikpin and Prometheus (OpenTelemetry and inspectIT)
All scripts have been tested on Ubuntu and Raspbian.
The execution may be parameterized by the following environment variables:
- SLEEP_TIME between executions (default 30 seconds)
- NUM_OF_LOOPS number of repetitions (default 10)
- THREADS concurrent benchmarking threads (default 1)
- RECURSION_DEPTH recursion up to this depth (default 10)
- TOTAL_NUM_OF_CALLS the duration of the benchmark (deafult 2,000,000 calls)
- METHOD_TIME the time per monitored call (default 0 ns or 500 us)
If they are unset, the values are set via frameworks/common-function.sh.
Typical call (using Ubuntu):
export SLEEP_TIME=1
./gradlew assemble
cd frameworks/OpenTelemetry-java/
./benchmark.sh
- analysis = analysis scripts
- benchmark = moobench code
- continuous-integration = obsolete
- docker = Dockerfile to be used in automated runs on our intrestructure
- frameworks = benchmark setups for the different frameworks for the respective language
- Kieker-java
- Kieker-python
- OpenTelementry-java
- SPASSmeter (currently not maintained)
- inspectIT-java
- gradle = build system, gradle libraries
- tools = tooling used to support benchmarks and process results
- compile-results = adds new results to a result log and computes partial views of the results for presentation in websites
- getConfidenceIntervalTable = compute the confidence interval table
- receiver = receiver for Kieker TCP probe output
Each benchmark execution calls an R script providing mean, standard deviation
and confidence intervals for the benchmark variants. If you want to get these
values again, switch to frameworks and call runR.sh $FRAMEWORK, where
framework is the folder name of the framework (e.g. Kieker).
If you got data from a run with exponential growing call tree depth, unzip them
first (for file in *.zip; do unzip $file; done), copy all results-$framework
folder to a common folder and run ./getExponential.sh in analysis. This will
create a graph for each framework and an overview graph for external processing
of the traces (zipkin for OpenTelemetry and inspectIT, TCP for Kieker).
In the folder /bin/r are some R scripts provided to generate graphs to visualize the results. In the top the files, one can configure the required paths and the configuration used to analyze the data.
We also use MooBench as a performance regression test which is run periodically when new features are added to Kieker.