heaptrack - a heap memory profiler for Linux
Heaptrack traces all memory allocations and annotates these events with stack traces. Dedicated analysis tools then allow you to interpret the heap memory profile to:
- find hotspots that need to be optimized to reduce the memory footprint of your application
- find memory leaks, i.e. locations that allocate memory which is never deallocated
- find allocation hotspots, i.e. code locations that trigger a lot of memory allocation calls
- find temporary allocations, which are allocations that are directly followed by their deallocation
The recommended way is to launch your application and start tracing from the beginning:
heaptrack <your application and its parameters> heaptrack output will be written to "/tmp/heaptrack.APP.PID.gz" starting application, this might take some time... ... heaptrack stats: allocations: 65 leaked allocations: 60 temporary allocations: 1 Heaptrack finished! Now run the following to investigate the data: heaptrack_gui "/tmp/heaptrack.APP.PID.gz"
Alternatively, you can attach to an already running process:
heaptrack --pid $(pidof <your application>) heaptrack output will be written to "/tmp/heaptrack.APP.PID.gz" injecting heaptrack into application via GDB, this might take some time... injection finished ... Heaptrack finished! Now run the following to investigate the data: heaptrack_gui "/tmp/heaptrack.APP.PID.gz"
Heaptrack is split into two parts: The data collector, i.e.
heaptrack itself, and the
analyzer GUI called
heaptrack_gui. The following summarizes the dependencies for these
two parts as they can be build independently. You will find corresponding development
packages on all major distributions for these dependencies.
On an embedded device or older Linux distribution, you will only want to build
The data can then be analyzed on a different machine with a more modern Linux distribution
that has access to the required GUI dependencies.
If you need help with building, deploying or using heaptrack, you can contact KDAB for commercial support: https://www.kdab.com/software-services/workshops/profiling-workshops/
Both parts require the following tools and libraries:
- cmake 2.8.9 or higher
- a C++11 enabled compiler like g++ or clang++
- optionally: zstd for faster (de)compression
The heaptrack data collector and the simplistic
heaptrack_print analyzer depend on the
- boost 1.41 or higher: iostreams, program_options
- elfutils: libdwarf
For runtime-attaching, you will need
The graphical user interface to interpret and analyze the data collected by heaptrack depends on Qt 5 and some KDE libraries:
- Qt 5.2 or higher: Core, Widgets
- KDE Frameworks 5: CoreAddons, I18n, ItemModels, ThreadWeaver, ConfigWidgets, KIO
When any of these dependencies is missing,
heaptrack_gui will not be build.
Optionally, install the following dependencies to get additional features in
- KDiagram: KChart (for chart visualizations)
Run the following commands to compile heaptrack. Do pay attention to the output of the CMake command, as it will tell you about missing dependencies!
cd heaptrack # i.e. the source folder mkdir build cd build cmake -DCMAKE_BUILD_TYPE=Release .. # look for messages about missing dependencies! make -j$(nproc)
heaptrack_gui on macOS using homebrew
heaptrack_gui can be built on platforms other than Linux, using the dependencies mentioned above.
On macOS the dependencies can be installed easily using homebrew and the KDE homebrew tap.
# prepare tap brew tap kde-mac/kde "$(brew --repo kde-mac/kde)/tools/do-caveats.sh" # install dependencies brew install kde-mac/kde/kf5-extra-cmake-modules kde-mac/kde/kf5-kcoreaddons kde-mac/kde/kf5-ki18n \ kde-mac/kde/kf5-kitemmodels kde-mac/kde/kf5-threadweaver kde-mac/kde/kf5-kconfigwidgets \ kde-mac/kde/kf5-kio kde-mac/kde/kdiagram \ boost zstd gettext # run manual steps as printed by brew ln -sfv "$(brew --prefix)/share/kf5" "$HOME/Library/Application Support" ln -sfv "$(brew --prefix)/share/knotifications5" "$HOME/Library/Application Support" ln -sfv "$(brew --prefix)/share/kservices5" "$HOME/Library/Application Support" ln -sfv "$(brew --prefix)/share/kservicetypes5" "$HOME/Library/Application Support"
To compile make sure to use Qt from homebrew and to have gettext in the path:
cd heaptrack # i.e. the source folder mkdir build cd build CMAKE_PREFIX_PATH=/usr/local/opt/qt PATH=$PATH:/usr/local/opt/gettext/bin cmake .. cmake -DCMAKE_BUILD_TYPE=Release .. # look for messages about missing dependencies! make heaptrack_gui heaptrack_print
Interpreting the heap profile
Heaptrack generates data files that are impossible to analyze for a human. Instead, you need
to use either
heaptrack_gui to interpret the results.
The highly recommended way to analyze a heap profile is by using the
It depends on Qt 5 and KF 5 to graphically visualize the recorded data. It features:
- a summary page of the data
- bottom-up and top-down tree views of the code locations that allocated memory with their aggregated cost and stack traces
- flame graph visualization
- graphs of allocation costs over time
heaptrack_print tool is a command line application with minimal dependencies. It takes
the heap profile, analyzes it, and prints the results in ASCII format to the command line.
In its most simple form, you can use it like this:
heaptrack_print heaptrack.APP.PID.gz | less
By default, the report will contain three sections:
MOST CALLS TO ALLOCATION FUNCTIONS PEAK MEMORY CONSUMERS MOST TEMPORARY ALLOCATIONS
Each section then lists the top ten hotspots, i.e. code locations that triggered e.g. the most memory allocations.
Have a look at
heaptrack_print --help for changing the output format and other options.
Note that you can use this tool to convert a heaptrack data file to the Massif data format.
You can generate a collapsed stack report for consumption by
Comparison to Valgrind's massif
The idea to build heaptrack was born out of the pain in working with Valgrind's massif. Valgrind comes with a huge overhead in both memory and time, which sometimes prevent you from running it on larger real-world applications. Most of what Valgrind does is not needed for a simple heap profiler.
Advantages of heaptrack over massif
speed and memory overhead
Multi-threaded applications are not serialized when you trace them with heaptrack and even for single-threaded applications the overhead in both time and memory is significantly lower. Most notably, you only pay a price when you allocate memory -- time-intensive CPU calculations are not slowed down at all, contrary to what happens in Valgrind.
Valgrind's massif aggregates data before writing the report. This step loses a lot of useful information. Most notably, you are not longer able to find out how often memory was allocated, or where temporary allocations are triggered. Heaptrack does not aggregate the data until you interpret it, which allows for more useful insights into your allocation patterns.
Advantages of massif over heaptrack
ability to profile page allocations as heap
This allows you to heap-profile applications that use pool allocators that circumvent malloc & friends. Heaptrack can in principle also profile such applications, but it requires code changes to annotate the memory pool implementation.
ability to profile stack allocations
This is inherently impossible to implement efficiently in heaptrack as far as I know.
Contributing to heaptrack
As a FOSS project, we welcome contributions of any form. You can help improve the project by:
- submitting bug reports at https://bugs.kde.org/enter_bug.cgi?product=Heaptrack
- contributing patches via https://invent.kde.org/sdk/heaptrack
- translating the GUI with the help of https://l10n.kde.org/
- writing documentation on https://userbase.kde.org/Heaptrack
When submitting bug reports, you can anonymize your data with the
tools/anonymize heaptrack.APP.PID.gz heaptrack.bug_report_data.gz
Known bugs and limitations
Issues with old gold linker
Libunwind may produce bogus backtraces when unwinding from code linked with old versions of the gold linker. In such cases, recording with heaptrack seems to work and produces data files. But parsing these data files with heaptrack_gui will often lead to out-of-memory crashes. Looking at the data with heaptrack_print, one will see garbage backtraces that are completely broken.
If you encounter such issues, try to relink your application and also libunwind with
ld.bfd instead of
You can see if you are affected by running the libunwind unit tests via
make check. But do note that you
need to relink your application too, not only libunwind.