Enabling the Native Profiler for Compiled Languages

Docs > Continuous Profiler > Enabling the Profiler > Enabling the Native Profiler for Compiled Languages

ddprof is in beta. Datadog recommends evaluating the profiler in a non-sensitive environment before deploying in production.

The native profiler for compiled languages (ddprof) uses OS level APIs to collect profiling data. It is ideally suited for applications written in compiled languages, such as C, C++, or Rust. Profiles sent from ddprof show up under the native runtime in the Datadog web app.

Requirements

For a summary of the minimum and recommended runtime and tracer versions across all languages, read Supported Language and Tracer Versions.

Supported operating systems: Linux (glibc or musl)
Supported architecture: amd64 or arm64 processors
Serverless: ddprof is not supported on serverless platforms, such as AWS Lambda.
OS Settings: perf_event_paranoid kernel setting is 2 or less (see Troubleshooting)
Debugging information: Symbols should be available. The profiler cannot provide human-readable function names if the symbol table is stripped.

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.

Standalone

Download the latest ddprof release. For example, here is one way to pull the latest release for an amd64 (also known as x86_64) platform:
```
curl -Lo ddprof-linux.tar.xz https://github.com/DataDog/ddprof/releases/latest/download/ddprof-amd64-linux.tar.xz
tar xvf ddprof-linux.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.
Use arm64 instead of amd64 for aarch64 platform.

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

A few minutes after starting your application, your profiles appear on the Datadog APM > Profiler page.

Library

The library exposes a C API.

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

curl -Lo ddprof-linux.tar.xz https://github.com/DataDog/ddprof/releases/latest/download/ddprof-amd64-linux.tar.xz
tar xvf ddprof-linux.tar.xz --directory /tmp

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);
  ddprof_start_profiling();

  // Do some work
  for (int i = 0; i < 1e6; i++) {
    foo();
  }
  return 0;
}

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
./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

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

See the full list of parameters or use the command line.

ddprof --help

Logging

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.

Global

If you want to instrument all running process, you can try out the --global option. Global mode is intended for debug purposes. This requires elevated permissions. Depending on your setup, this can mean running as root, granting CAP_PERFMON, CAP_SYSADMIN, or setting perf_event_paranoid to -1.

./ddprof --environment staging --global --service_version full-host-profile

For most configurations, this consists of all processes visible within the profiler’s PID namespace.

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.