The gProfiler

Production Profiling Made Easy


gProfiler combines multiple sampling profilers to produce unified visualization of what your CPU is spending time on, displaying stack traces of your processes across native programs1 (includes Golang), Java and Python runtimes, and kernel routines.

gProfiler can upload its results to the Granulate Performance Studio, which aggregates the results from different instances over different periods of time and can give you a holistic view of what is happening on your entire cluster. To upload results, you will have to register and generate a token on the website.

gProfiler runs on Linux (on x86_64 and Aarch64; Aarch64 support is not complete yet and not all runtime profilers are supported, see architecture support).

Granulate Performance Studio example view


This section describes the possible options to control gProfiler's output, and the various execution modes (as a container, as an executable, etc...)

Output options

gProfiler can produce output in two ways:

  • Create an aggregated, collapsed stack samples file (profile_<timestamp>.col) and a flamegraph file (profile_<timestamp>.html). Two symbolic links (last_profile.col and last_flamegraph.html) always point to the last output files.

    Use the --output-dir/-o option to specify the output directory.

    If --rotating-output is given, only the last results are kept (available via last_profle.col and last_flamegraph.html). This can be used to avoid increasing gProfiler's disk usage over time. Useful in conjunction with --upload-results (explained ahead) - historical results are available in the Granulate Performance Studio, and the very latest results are available locally.

    --no-flamegraph can be given to avoid generation of the profile_<timestamp>.html file - only the collapsed stack samples file will be created.

  • Send the results to the Granulate Performance Studio for viewing online with filtering, insights, and more.

    Use the --upload-results/-u flag. Pass the --token option to specify the token provided by Granulate Performance Studio, and the --service-name option to specify an identifier for the collected profiles, as will be viewed in the Granulate Performance Studio. Profiles sent from numerous gProfilers using the same service name will be aggregated together.

Note: both flags can be used simultaneously, in which case gProfiler will create the local files and upload the results.

Profiling options

  • --profiling-frequency: The sampling frequency of the profiling, in hertz.

  • --profiling-duration: The duration of the each profiling session, in seconds.

The default profiling frequency is 11 hertz. Using higher frequency will lead to more accurate results, but will create greater overhead on the profiled system & programs.

For each profiling session (each profiling duration), gProfiler produces outputs (writing local files and/or uploading the results to the Granulate Performance Studio).

Java profiling options

  • --no-java or --java-mode disabled: Disable profilers for Java.

  • --no-java-async-profiler-buildids: Disable embedding of buildid+offset in async-profiler native frames (used when debug symbols are unavailable).

Python profiling options

  • --no-python: Alias of --python-mode disabled.

  • --python-mode: Controls which profiler is used for Python.

    • auto - (default) try with PyPerf (eBPF), fall back to py-spy.

    • pyperf - Use PyPerf with no py-spy fallback.

    • pyspy - Use py-spy.

    • disabled - Disable profilers for Python.

Profiling using eBPF incurs lower overhead & provides kernel stacks. This (currently) requires kernel headers to be installed.

PHP profiling options

  • --no-php or --php-mode disabled: Disable profilers for PHP.

  • --php-proc-filter: Process filter (pgrep) to select PHP processes for profiling (this is phpspy's -P option)

Ruby profiling options

  • --no-ruby or --ruby-mode disabled: Disable profilers for Ruby.

NodeJS profiling options

  • --nodejs-mode: Controls which profiler is used for NodeJS.

    • none - (default) no profiler is used.

    • perf - augment the system profiler (perf) results with jitdump files generated by NodeJS. This requires running your node processes with --perf-prof (and for Node >= 10, with --interpreted-frames-native-stack). See this NodeJS page for more information.

System profiling options

  • --perf-mode: Controls the global perf strategy. Must be one of the following options:

    • fp - Use Frame Pointers for the call graph

    • dwarf - Use DWARF for the call graph (adds the --call-graph dwarf argument to the perf command)

    • smart - Run both fp and dwarf, then choose the result with the highest average of stack frames count, per process.

    • disabled - Avoids running perf at all. See perf-less mode.

Other options

Sending logs to server

By default, gProfiler sends logs to Granulate Performance Studio (when using --upload-results/-u flag) This behavior can be disabled by passing --dont-send-logs or the setting environment variable GPROFILER_DONT_SEND_LOGS=1.

Metrics and metadata collection

By default, gProfiler agent sends system metrics (CPU and RAM usage) and metadata to the Performance Studio. The metadata includes system metadata like the kernel version and CPU count, and cloud metadata like the type of the instance you are running on. The metrics collection will not be enabled if the --upload-results/-u flag is not set. Otherwise, you can disable metrics and metadata by using the following parameters:

  • Use --disable-metrics-collection to disable metrics collection

  • Use --disable-metadata-collection to disable metadata collection

Continuous mode

gProfiler can be run in a continuous mode, profiling periodically, using the --continuous/-c flag. Note that when using --continuous with --output-dir, a new file will be created during each sampling interval. Aggregations are only available when uploading to the Granulate Performance Studio.

Running as a Docker container

Supported both for x86_64 and Aarch64.

Run the following to have gProfiler running continuously, uploading to Granulate Performance Studio:

docker pull granulate/gprofiler:latest
docker run --name granulate-gprofiler -d --restart=on-failure:10 \
--pid=host --userns=host --privileged \
-v /lib/modules:/lib/modules:ro -v /usr/src:/usr/src:ro \
-v /var/run/docker.sock:/var/run/docker.sock \
granulate/gprofiler:latest -cu --token <token> --service-name <service> [options]

For profiling with eBPF, kernel headers must be accessible from within the container at /lib/modules/$(uname -r)/build. On Ubuntu, this directory is a symlink pointing to /usr/src. The command above mounts both of these directories.

Running as an executable

Supported only on x86_64!

Run the following to have gprofiler running continuously, uploading to Granulate Performance Studio:

sudo chmod +x gprofiler
sudo ./gprofiler -cu --token <token> --service-name <service> [options]

gProfiler unpacks executables to /tmp by default; if your /tmp is marked with noexec, you can add TMPDIR=/proc/self/cwd to have everything unpacked in your current working directory.

sudo TMPDIR=/proc/self/cwd ./gprofiler -cu --token <token> --service-name <service> [options]

Executable known issues

The following platforms are currently not supported with the gProfiler executable:

  • Alpine

Remark: container-based execution works and can be used in those cases.

Running as a Kubernetes DaemonSet

See gprofiler.yaml for a basic template of a DaemonSet running gProfiler. Make sure to insert the GPROFILER_TOKEN and GPROFILER_SERVICE variables in the appropriate location!

Running as an ECS (Elastic Container Service) Daemon service

Creating the ECS Task Definition

  • Choose EC2, and click Next Step

  • Scroll to the bottom of the page, and click Configure via JSON Configure via JSON button

  • Replace the JSON contents with the contents of the gprofiler_task_definition.json file and Make sure you change the following values:

    • Replace <TOKEN> in the command line with your token you got from the gProfiler Performance Studio site

    • Replace <SERVICE NAME> in the command line with the service name you wish to use

  • Note - if you wish to see the logs from the gProfiler service, be sure to follow AWS's guide on how to auto-configure logging, or to set it up manually yourself.

  • Click Save

  • Click Create

Deploying the gProfiler service

  • Go to your ECS Clusters and enter the relevant cluster

  • Click on Services, and choose Create

  • Choose the EC2 launch type and the granulate-gprofiler task definition with the latest revision

  • Enter a service name

  • Choose the DAEMON service type

  • Click Next step until you reach the Review page, and then click Create Service

Running on an AWS Fargate service

At the time of this writing, Fargate does not support DAEMON tasks (see this tracking issue).

Furthermore, Fargate does not allow using "pidMode": "host" in the task definition (see documentation of pidMode here). Host PID is required for gProfiler to be able to profile processes running in other containers (in case of Fargate, other containers under the same containerDefinition).

So in order to deploy gProfiler, we need to modify a container definition to include running gProfiler alongside the actual application. This can be done with the following steps:

  1. Modify the command parameter of your entry in the containerDefinitions array. The new command should include downloading of gProfiler & executing it in the background.

    For example, if your default command is ["python", "/path/to/my/"], we will now change it to: "bash", "-c", "(wget -q -O /tmp/gprofiler; chmod +x /tmp/gprofiler; /tmp/gprofiler -cu --token <TOKEN> --service-name <SERVICE NAME> --disable-pidns-check --perf-mode none) > /tmp/gprofiler_log 2>&1 & python /path/to/my/". This new command will start the downloading of gProfiler in the background, then run your application.

    --disable-pidns-check is required because, well, we won't run in init PID NS :)

    --perf-mode none is required because our container will not have permissions to run system-wide perf, so gProfiler will profile only runtime processes. See perf-less mode for more information.

    This requires your image to have wget installed - you can make sure wget is installed, or substitute it with curl or any other HTTP-downloader you wish.

  2. Add linuxParameters to the container definition (this goes directly in your entry in containerDefinitinos):

    "linuxParameters": {
    "capabilities": {
    "add": [

    SYS_PTRACE is required by various profilers, and Fargate by default denies it for containers.

Alternatively, you can download gProfiler in your Dockerfile to avoid having to download it every time in run-time. Then you just need to invoke it upon container start-up.

Running as a docker-compose service

You can run a gProfiler container with docker-compose by using the template file in docker-compose.yml. Start by replacing the <TOKEN> and <SERVICE NAME> with values in the command section -

Optionally, you can add more command line arguments to the command section. For example, if you wish to use the py-spy profiler, you could replace the command with -cu --token "<TOKEN>" --service-name "<SERVICE NAME>" --python-mode pyspy.

To run it, run the following command:

docker-compose -f /path/to/docker-compose.yml up -d

Running from source

gProfiler requires Python 3.6+ to run.

pip3 install -r requirements.txt

Then, run the following as root:

python3 -m gprofiler [options]

Theory of operation

gProfiler invokes perf in system wide mode, collecting profiling data for all running processes. Alongside perf, gProfiler invokes runtime-specific profilers for processes based on these environments:

  • Java runtimes (version 7+) based on the HotSpot JVM, including the Oracle JDK and other builds of OpenJDK like AdoptOpenJDK and Azul Zulu.

    • Uses async-profiler.

  • The CPython interpreter, versions 2.7 and 3.5-3.9.

    • eBPF profiling (based on PyPerf) requires Linux 4.14 or higher; see Python profiling options for more info.

    • If eBPF is not available for whatever reason, py-spy is used.

  • PHP (Zend Engine), versions 7.0-8.0.

  • Ruby versions (versions 1.9.1 to 3.0.1)

  • NodeJS (version >= 10 for functioning --perf-prof):

The runtime-specific profilers produce stack traces that include runtime information (i.e, stacks of Java/Python functions), unlike perf which produces native stacks of the JVM / CPython interpreter. The runtime stacks are then merged into the data collected by perf, substituting the native stacks perf has collected for those processes.

Architecture support




perf (native, Golang, ...)



Java (async-profiler)



Python (py-spy)



Python (PyPerf eBPF)


Ruby (rbspy)



PHP (phpspy)


NodeJS (perf)



perf-less mode

It is possible to run gProfiler without using perf - this is useful where perf can't be used, for whatever reason (e.g permissions). This mode is enabled by --perf-mode disabled.

In this mode, gProfiler uses runtime-specific profilers only, and their results are concatenated (instead of scaled into the results collected by perf). This means that, although the results from different profilers are viewed on the same graph, they are not necessarily of the same scale: so you can compare the samples count of Java to Java, but not Java to Python.


We welcome all feedback and suggestion through Github Issues:

Releasing a new version

  1. Update __version__ in

  2. Create a tag with the same version (after merging the __version__ update) and push it.

We recommend going through our contribution guide for more details.



1: To profile native programs that were compiled without frame pointers, make sure you use the --perf-mode smart (which is the default). Read more about it in the Profiling options section