#symbolic #sentry #cache #python #memory #lookup #symbols #c #file #fast


An optimizied cache file for fast and memory efficient lookup of symbols and stack frames in debugging information

61 stable releases (7 major)

8.0.5 Mar 2, 2021
8.0.2 Jan 6, 2021
8.0.0 Nov 30, 2020
7.5.0 Aug 21, 2020
1.1.5 Dec 2, 2017

#85 in Caching

Download history 3/week @ 2020-12-18 1/week @ 2020-12-25 34/week @ 2021-01-01 21/week @ 2021-01-08 71/week @ 2021-01-15 6/week @ 2021-01-22 19/week @ 2021-01-29 57/week @ 2021-02-05 208/week @ 2021-02-12 83/week @ 2021-02-19 26/week @ 2021-02-26 43/week @ 2021-03-05 2/week @ 2021-03-12 148/week @ 2021-03-19 128/week @ 2021-03-26 129/week @ 2021-04-02

198 downloads per month
Used in 2 crates

MIT license



Symbolic is a library written in Rust which is used at Sentry to implement symbolication of native stack traces, sourcemap handling for minified JavaScript and more. It consists of multiple largely independent crates which are bundled together into a C and Python library so it can be used independently of Rust.

What's in the package

Symbolic provides the following functionality:

  • Symbolication based on custom cache files (symcache)
  • Symbol cache file generators from:
    • Mach, ELF and PE symbol tables
    • Mach and ELF embedded DWARF data
    • PDB CodeView debug information
    • Breakpad symbol files
  • Demangling support
    • C++ (GCC, clang and MSVC)
    • Objective C / Objective C++
    • Rust
    • Swift
  • JavaScript sourcemap expansion
    • Basic token mapping
    • Heuristics to find original function names based on minified sources
    • Indexed sourcemap to sourcemap merging
  • Proguard function mappings
  • Minidump / Breakpad processing
    • Generate Breakpad symbol files from Mach, ELF and PDBs
    • Process Minidumps to retrieve stack traces
  • Convenient C and Python library
  • Processing of Unreal Engine 4 native crash reports
    • Extract and process minidumps
    • Expose logs and UE4 context information

Rust Usage

The Rust crates are published to Crates.io and documentation is available on docs.rs.

Python Usage

Symbolic is hosted on PyPI. It comes as a library with prebuilt wheels for linux and macOS. On other operating systems or when using as rust library, you need to build symbolic manually. It should be compatible with both Python 2 and Python 3.

The python library ships all of the above features in a flat module:

from symbolic import Archive

fat = Archive.open('/path/to/object')
obj = fat.get_object(arch = 'x86_64')
print 'object debug id: {}' % obj.debug_id

C Bindings

Symbolic also offers C bindings, which allow for FFI into arbitrary languages. Have a look at the the Symbolic C-ABI readme for more information.

Source Crates

A lot of functionality exposed by this library come from independent Rust crates for better use:

Additionally we use the following C++ libraries to fill in gaps:

Building and Development

To build the Rust crate, we require the latest stable Rust, as well as a C++11 compiler. The crate is split into a workspace with multiple features, so when running building or running tests always make sure to pass the --all and --all-features flags.

# Check whether the crate compiles
cargo check --all --all-features

# Run Rust tests
cargo test --all --all-features

We use rustfmt and clippy from the latest stable channel for code formatting and linting. To make sure that these tools are set up correctly and running with the right configuration, use the following make targets:

# Format the entire codebase
make format

# Run clippy on the entire codebase
make lint

Most likely, new functionality also needs to be added to the Python package. This first requires to expose new functions in the C ABI. For this, refer to the Symbolic C-ABI readme.

We highly recommend to develop and test the python package in a virtual environment. Once the ABI has been updated and tested, ensure the virtualenv is active and install the package, which builds the native library. There are two ways to install this:

# Install the release build, recommended:
pip install --editable ./py

# Install the debug build, faster installation but much slower runtime:
SYMBOLIC_DEBUG=1 pip install --editable ./py

For testing, we use ubiquitous pytest. Again, ensure that your virtualenv is active and the latest version of the native library has been installed. Then, run:

# Run tests manually
pytest ./py/tests

# Creates a new virtualenv, installs the release build and runs tests:
make pytest


The repository contains a few examples that show how to use symbolic to work with debug files and minidumps. Most of these examples can also be used to extract information from such files or verify their integrity:

  • dump_cfi: Writes call frame information from an object file to standard out. The output format is Breakpad's STACK records.

  • dump_sources: Creates a source archive from all files referenced by an object file. This is now integrated into sentry-cli difutil bundle-sources.

  • minidump_stackwalk: Extracts stack traces from a minidump and symbolicates them. A path to a directory containing debug information files can be specified.

  • object_debug: Prints basic information about the contents of an object file.

  • symcache_debug: Converts an object file into a symcache and prints its contents. Optionally, this can be used to symbolicate a relative address.

  • unreal_engine_crash: Lists files contained within an Unreal Engine 4 crash archive.

To run these examples, use the run script. For example:

./run minidump_stackwalk mini.dmp /path/to/files


Symbolic is licensed under the MIT license. It uses some Apache2 licensed code from Apple for the Swift demangling.


~235K SLoC