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gimli 0.14.0

A blazing fast DWARF debugging format parser.
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# `gimli`

[![](http://meritbadge.herokuapp.com/gimli) ![](https://img.shields.io/crates/d/gimli.png)](https://crates.io/crates/gimli) [![](https://docs.rs/gimli/badge.svg)](https://docs.rs/gimli/) [![Build Status](https://travis-ci.org/gimli-rs/gimli.png?branch=master)](https://travis-ci.org/gimli-rs/gimli) [![Coverage Status](https://coveralls.io/repos/github/gimli-rs/gimli/badge.svg?branch=master)](https://coveralls.io/github/gimli-rs/gimli?branch=master)

A lazy, zero-copy parser for the [DWARF debugging format](http://dwarfstd.org/).

* **Zero copy:** everything is just a reference to the original input buffer. No
  copies of the input data get made.

* **Lazy:** you can iterate compilation units without parsing their
  contents. Parse only as many debugging information entry (DIE) trees as you
  iterate over. `gimli` also uses `DW_AT_sibling` references to avoid parsing a
  DIE's children to find its next sibling, when possible.

* **Cross-platform:** `gimli` makes no assumptions about what kind of object
  file you're working with. The flipside to that is that it's up to you to
  provide an ELF loader on Linux or Mach-O loader on OSX.

  * Unsure which object file parser to use? Try the cross-platform
  [`object`](https://github.com/gimli-rs/object) crate. See the
  [`examples/`](./examples) directory for usage with `gimli`.

## Install

Add this to your `Cargo.toml`:

```toml
[dependencies]
gimli = "0.14.0"
```

## Documentation

* [Documentation on docs.rs](https://docs.rs/gimli/)

* Example programs:

  * [A `dwarfdump` clone](./examples/dwarfdump.rs)

  * [An `addr2line` clone](https://github.com/gimli-rs/addr2line)

## License

Licensed under either of

  * Apache License, Version 2.0 ([`LICENSE-APACHE`](./LICENSE-APACHE) or http://www.apache.org/licenses/LICENSE-2.0)
  * MIT license ([`LICENSE-MIT`](./LICENSE-MIT) or http://opensource.org/licenses/MIT)

at your option.

## Contribution

See [CONTRIBUTING.md](./CONTRIBUTING.md) for hacking.

Unless you explicitly state otherwise, any contribution intentionally submitted
for inclusion in the work by you, as defined in the Apache-2.0 license, shall be
dual licensed as above, without any additional terms or conditions.