Enabling the Datadog Profiler for Linux

The Datadog Profiler for Linux is in public beta. Datadog recommends evaluating the profiler in a non-sensitive environment before deploying in production.

The Datadog Profiler for Linux lets you collect profile data for applications running on Linux. It is ideally suited for applications written in compiled languages, such as C, C++, or Rust. It can also profile services such as nginx, Redis, or Postgres, as it does not require access to source code.


The Datadog Profiler for Linux is compatible with Linux v4.17+ on amd64 or arm64 processors. It is not supported on serverless platforms, such as AWS Lambda, or on operating systems other than Linux, such as Windows, BSD, and MacOS.

Standalone installation

The profiler can be used either as a standalone executable or as a library. Skip to library installation instructions if you want to use it as a library. Otherwise, to begin profiling applications with the standalone executable:

  1. Configure the perf_event_paranoid kernel setting to be at most 2. On many distributions, you can check this parameter by running cat /proc/sys/kernel/perf_event_paranoid or sysctl kernel.perf_event_paranoid. You can set this value until the next reboot by running echo 2 | sudo tee /proc/sys/kernel/perf_event_paranoid and set it on every system startup with sudo sysctl -w kernel.perf_event_paranoid=2. These commands can’t usually be run from a container, as the perf_event_paranoid setting is an operating system parameter. These commands may not work for all configurations. For alternatives, see Troubleshooting.

  2. Ensure you are running the Datadog Agent version 7.20.2+ or 6.20.2+.

  3. Download the appropriate ddprof release for your Linux distribution. For example, here is one way to pull the latest release for an amd64 platform:

    curl -L -O https://github.com/DataDog/ddprof/releases/download/v0.8.1/ddprof-x86_64-linux-gnu.tar.xz
    tar xvf ddprof-x86_64-linux-gnu.tar.xz
    mv ddprof/bin/ddprof INSTALLATION_TARGET

    Where INSTALLATION_TARGET specifies the location you’d like to store the ddprof binary. The examples that follow assume INSTALLATION_TARGET is set to ./ddprof.

  4. Modify your service invocation to include the profiler. Your usual command is passed as the last arguments to the ddprof executable.

    export DD_ENV=prod
    export DD_SERVICE=my-web-app
    export DD_VERSION=1.0.3
    ./ddprof myapp --arg1 --arg2

    Note: If you usually launch your application using a shell builtin, for example:

    exec myapp --arg1 --arg2

    Then you must invoke ddprof with that builtin instead:

    export DD_ENV=prod
    export DD_SERVICE=my-web-app
    export DD_VERSION=1.0.3
    exec ./ddprof myapp --arg1 --arg2
    ./ddprof --environment prod --service my-web-app --service_version 1.0.3 myapp --arg1 --arg2

    Note: If you usually launch your application using a shell builtin, for example:

    exec myapp --arg1

    Then you must invoke ddprof with that builtin instead:

    exec ./ddprof --environment prod --service my-web-app --service_version 1.0.3 myapp --arg1 --arg2

  5. A minute or two after starting your application, your profiles appear on the Datadog APM > Profiler page.

Library installation

Alternatively, you can use a library instead of a standalone executable. The profiler is available as both shared and static libraries. There are a few notable similarities and differences between the library and standalone versions:

  • The profiling library is available only for Linux v4.17+ on amd64 or arm64.
  • The profiling library has the same requirements (items 1 and 2 in the Standalone installation section) as the standalone executable.
  • The behavior of the library is nearly identical to the standalone executable, including the sandboxing features that isolate the profiler from your application.
  • The profiling library interfaces check environment variables, but they do not have other means of configuration-passing.

For generality, the library exposes a C API. Here is an example of incorporating the library into a C application:

  1. Download a release of ddprof with library support (v0.8.0 or later) and extract the tarball. For example:

    curl -L -o ddprof-x86_64-linux-gnu.tar.xz https://github.com/DataDog/ddprof/releases/download/v0.8.1/ddprof-0.8.1-x86_64-unknown-linux-gnu.tar.xz
    tar xvf ddprof-x86_64-linux-gnu.tar.xz --directory /tmp
  2. In your code, start the profiler using the ddprof_start_profiling() interface, defined in the _dd_profiling.h_ header provided by the release. The profiler stops automatically when your program closes. To stop the profiler manually, use ddprof_stop_profiling(ms) with the ms parameter indicating the maximum block time of the function in milliseconds. Here is a standalone example (profiler_demo.c) in C:

    #include <stdlib.h>
    #include "dd_profiling.h"
    int foo(void) {
      int n = 0;
      for (int i = 0; i < 1000; i++) {
        n += 1;
      return n;
    int main(void) {
      // Initialize and start the Datadog profiler. Uses agent defaults if not
      // specified
      setenv("DD_ENV", "prod", 1);
      setenv("DD_SERVICE", "c_testservice", 1);
      setenv("DD_VERSION", "1.0.3", 1);
      // Do some work
      for (int i = 0; i < 1e6; i++) {
      return 0;
  3. Pass the include and lib subdirectories of the extracted directory to your build system and link against libdd_profiling. For the above example:

    gcc -I/tmp/ddprof/include -L/tmp/ddprof/lib profiler_demo.c -o profiler_demo -ldd_profiling

Deploying the shared library

The shared library must be present in the system’s library search path. Otherwise, the application will fail to start. Using the example from before:

./profiler_demo: error while loading shared libraries: libdd_profiling.so: cannot open shared object file: No such file or directory

Avoid this by linking against the static library.

Installing the library

Add the library to the search path by copying it to any existing search directory. To find out what your search directories are, on Linux systems, run:

ld --verbose | grep SEARCH_DIR | tr -s ' ;' \\n

Appending a search directory

Use the LD_LIBRARY_PATH environment variable to add additional search paths to the runtime linker. For example, using the directory layout from before:

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/tmp/ddprof/lib


Configuration for the profiler can be set by command line parameters, environment variables, or a combination of both. Whenever both are provided for a given setting, the command line parameter is used.

Environment variableLong nameShort nameDefaultDescription
DD_ENVenvironmentEThe environment name, for example, production.
DD_SERVICEserviceSmyserviceThe service name, for example, web-backend.
DD_VERSIONservice_versionVThe version of your service.
DD_AGENT_HOSThostHlocalhostThe hostname for the Datadog agent.
DD_TRACE_AGENT_PORTportP8126The Datadog agent listening port.
DD_TRACE_AGENT_URLurlUhttps://<hostname>:<port> overrides other agent host/port settings.
DD_TAGStagsTTags to apply to an uploaded profile. Must be a list of <key>:<value> pairs separated by commas, such as: layer:api,team:intake.
DD_PROFILING_NATIVE_NICEniceiSets the nice level of the profiler without affecting the instrumented processes.
DD_PROFILING_NATIVE_SHOW_CONFIGshow_configanoWhether to log profiler configuration parameters.
DD_PROFILING_NATIVE_LOG_MODElog_modeostdoutHow to emit profiler logs. See the section on logging for details.
DD_PROFILING_NATIVE_LOG_LEVELlog_levellwarnDetermines log verbosity.
DD_PROFILING_NATIVE_TARGET_PIDpidpEngages pidmode. See the section on pidmode for details.
DD_PROFILING_NATIVE_GLOBALglobalgnoEngages globalmode. See the section on globalmode for details. Overrides –pid.

When passing command line arguments, precede long names with two dashes and short names with a single dash. For example, --service myservice and -S myservice.

The environment, service, and service_version settings are recommended, as they are used by the Profiling UI.

Note: Parameters must be set with a value. For example, to log profiler configuration, you must either set DD_PROFILING_NATIVE_SHOW_CONFIG=yes or pass --show_config yes, rather than --show_config alone. For such arguments, yes, true, and enable may be used interchangeably to enable the setting and no, false, and disable may be used to disable it.


You can configure logging to one of several endpoints:

  • stdout prints the logs to standard output stream (the default).
  • stderr prints the logs to the standard error stream.
  • syslog publishes the logs to syslog, attempting to adhere to the specification in RFC 3164.
  • disable disables the logs entirely.
  • Any other value is treated as a file path, with a leading / designating an absolute path.


When a target PID is given, instrument that PID. When this mode is engaged, any parameter after typical profiler argument processing is ignored. If the owning UID of the target PID and the profiler differ, do one of the following:

  • Run the profiler as the root process instead.
  • Grant CAP_PERFMON to the profiler (Linux v5.8 or later).
  • Grant CAP_SYS_ADMIN to the profiler.
  • Decrease perf_event_paranoid to -1 (consult your distro documentation or system administrator for details).

When a PID is specified in this way, only child processes (both forks and threads) spawned after instrumentation will be followed.


When globalmode is engaged, the profiler attempts to profile as many processes as possible. Usually, this requires elevated permissions (for example, running as root or granting CAP_PERFMON) or setting perf_event_paranoid to -1.

When the profiler has non-root UID, usually only processes owned by the matching UID are instrumented.

When the profiler has root UID, all visible processes are instrumented. For most configurations, this consists of all processes visible within the profiler’s PID namespace, as well as some other processes (such as the container runtime–these external processes are listed as “PID 0” in the UI).

Not sure what to do next?

The Getting Started with Profiler guide takes a sample service with a performance problem and shows you how to use Continuous Profiler to understand and fix the problem.

Further Reading