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272 lines
10 KiB
Markdown
272 lines
10 KiB
Markdown
# enry [](https://godoc.org/github.com/src-d/enry) [](https://travis-ci.com/src-d/enry) [](https://codecov.io/gh/src-d/enry)
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File programming language detector and toolbox to ignore binary or vendored files. *enry*, started as a port to _Go_ of the original [linguist](https://github.com/github/linguist) _Ruby_ library, that has an improved *2x performance*.
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Installation
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------------
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The recommended way to install enry is
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```
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go get github.com/src-d/enry/cmd/enry
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```
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To build enry's CLI you must run
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make build
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this will generate a binary in the project's root directory called `enry`. You can then move this binary to anywhere in your `PATH`.
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This project is now part of [source{d} Engine](https://sourced.tech/engine),
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which provides the simplest way to get started with a single command.
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Visit [sourced.tech/engine](https://sourced.tech/engine) for more information.
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### Faster regexp engine (optional)
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[Oniguruma](https://github.com/kkos/oniguruma) is CRuby's regular expression engine.
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It is very fast and performs better than the one built into Go runtime. *enry* supports swapping
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between those two engines thanks to [rubex](https://github.com/moovweb/rubex) project.
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The typical overall speedup from using Oniguruma is 1.5-2x. However, it requires CGo and the external shared library.
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On macOS with brew, it is
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```
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brew install oniguruma
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```
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On Ubuntu, it is
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```
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sudo apt install libonig-dev
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```
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To build enry with Oniguruma regexps use the `oniguruma` build tag
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```
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go get -v -t --tags oniguruma ./...
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```
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and then rebuild the project.
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Examples
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------------
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```go
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lang, safe := enry.GetLanguageByExtension("foo.go")
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fmt.Println(lang, safe)
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// result: Go true
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lang, safe := enry.GetLanguageByContent("foo.m", []byte("<matlab-code>"))
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fmt.Println(lang, safe)
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// result: Matlab true
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lang, safe := enry.GetLanguageByContent("bar.m", []byte("<objective-c-code>"))
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fmt.Println(lang, safe)
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// result: Objective-C true
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// all strategies together
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lang := enry.GetLanguage("foo.cpp", []byte("<cpp-code>"))
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// result: C++ true
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```
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Note that the returned boolean value `safe` is set either to `true`, if there is only one possible language detected, or to `false` otherwise.
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To get a list of possible languages for a given file, you can use the plural version of the detecting functions.
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```go
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langs := enry.GetLanguages("foo.h", []byte("<cpp-code>"))
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// result: []string{"C", "C++", "Objective-C}
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langs := enry.GetLanguagesByExtension("foo.asc", []byte("<content>"), nil)
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// result: []string{"AGS Script", "AsciiDoc", "Public Key"}
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langs := enry.GetLanguagesByFilename("Gemfile", []byte("<content>"), []string{})
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// result: []string{"Ruby"}
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```
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CLI
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------------
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You can use enry as a command,
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```bash
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$ enry --help
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enry v1.5.0 build: 10-02-2017_14_01_07 commit: 95ef0a6cf3, based on linguist commit: 37979b2
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enry, A simple (and faster) implementation of github/linguist
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usage: enry <path>
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enry [-json] [-breakdown] <path>
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enry [-json] [-breakdown]
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enry [-version]
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```
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and it'll return an output similar to *linguist*'s output,
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```bash
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$ enry
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55.56% Shell
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22.22% Ruby
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11.11% Gnuplot
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11.11% Go
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```
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but not only the output; its flags are also the same as *linguist*'s ones,
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```bash
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$ enry --breakdown
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55.56% Shell
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22.22% Ruby
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11.11% Gnuplot
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11.11% Go
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Gnuplot
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plot-histogram.gp
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Ruby
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linguist-samples.rb
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linguist-total.rb
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Shell
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parse.sh
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plot-histogram.sh
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run-benchmark.sh
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run-slow-benchmark.sh
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run.sh
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Go
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parser/main.go
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```
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even the JSON flag,
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```bash
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$ enry --json
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{"Gnuplot":["plot-histogram.gp"],"Go":["parser/main.go"],"Ruby":["linguist-samples.rb","linguist-total.rb"],"Shell":["parse.sh","plot-histogram.sh","run-benchmark.sh","run-slow-benchmark.sh","run.sh"]}
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```
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Note that even if enry's CLI is compatible with linguist's, its main point is that **_enry doesn't need a git repository to work!_**
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Java bindings
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------------
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Generated Java bindings using a C-shared library and JNI are located under [`java`](https://github.com/src-d/enry/blob/master/java)
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Development
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------------
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*enry* re-uses parts of original [linguist](https://github.com/github/linguist) to generate internal data structures. In order to update to the latest upstream and generate all the necessary code you must run:
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git clone https://github.com/github/linguist.git .linguist
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# update commit in generator_test.go (to re-generate .gold fixtures)
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# https://github.com/src-d/enry/blob/13d3d66d37a87f23a013246a1b0678c9ee3d524b/internal/code-generator/generator/generator_test.go#L18
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go generate
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We update enry when changes are done in linguist's master branch on the following files:
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* [languages.yml](https://github.com/github/linguist/blob/master/lib/linguist/languages.yml)
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* [heuristics.yml](https://github.com/github/linguist/blob/master/lib/linguist/heuristics.yml)
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* [vendor.yml](https://github.com/github/linguist/blob/master/lib/linguist/vendor.yml)
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* [documentation.yml](https://github.com/github/linguist/blob/master/lib/linguist/documentation.yml)
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Currently we don't have any procedure established to automatically detect changes in the linguist project and regenerate the code.
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So we update the generated code as needed, without any specific criteria.
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If you want to update *enry* because of changes in linguist, you can run the *go
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generate* command and do a pull request that only contains the changes in
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generated files (those files in the subdirectory [data](https://github.com/src-d/enry/blob/master/data)).
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To run the tests,
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make test
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Divergences from linguist
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------------
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`enry` [CLI tool](#cli) does *not* require a full Git repository to be present in the filesystem in order to report languages.
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Using [linguist/samples](https://github.com/github/linguist/tree/master/samples)
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as a set for the tests, the following issues were found:
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* [Heuristics for ".es" extension](https://github.com/github/linguist/blob/e761f9b013e5b61161481fcb898b59721ee40e3d/lib/linguist/heuristics.yml#L103) in JavaScript could not be parsed, due to unsupported backreference in RE2 regexp engine
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* As of (Linguist v5.3.2)[https://github.com/github/linguist/releases/tag/v5.3.2] it is using [flex-based scanner in C for tokenization](https://github.com/github/linguist/pull/3846). Enry still uses [extract_token](https://github.com/github/linguist/pull/3846/files#diff-d5179df0b71620e3fac4535cd1368d15L60) regex-based algorithm. See [#193](https://github.com/src-d/enry/issues/193).
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* Bayesian classifier can't distinguish "SQL" from "PLpgSQL. See [#194](https://github.com/src-d/enry/issues/194).
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* Detection of [generated files](https://github.com/github/linguist/blob/bf95666fc15e49d556f2def4d0a85338423c25f3/lib/linguist/generated.rb#L53) is not supported yet.
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(Thus they are not excluded from CLI output). See [#213](https://github.com/src-d/enry/issues/213).
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* XML detection strategy is not implemented. See [#192](https://github.com/src-d/enry/issues/192).
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* Overriding languages and types though `.gitattributes` is not yet supported. See [#18](https://github.com/src-d/enry/issues/18).
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* `enry` CLI output does NOT exclude `.gitignore`ed files and git submodules, as linguist does
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In all the cases above that have an issue number - we plan to update enry to match Linguist behaviour.
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Benchmarks
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------------
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Enry's language detection has been compared with Linguist's one. In order to do that, Linguist's project directory [*linguist/samples*](https://github.com/github/linguist/tree/master/samples) was used as a set of files to run benchmarks against.
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We got these results:
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The histogram represents the number of files for which spent time in language
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detection was in the range of the time interval indicated in the x axis.
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So you can see that most of the files were detected quicker in enry.
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We found some few cases where enry turns slower than linguist. This is due to
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Golang's regexp engine being slower than Ruby's, which uses the [oniguruma](https://github.com/kkos/oniguruma) library, written in C.
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You can find scripts and additional information (like software and hardware used
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and benchmarks' results per sample file) in [*benchmarks*](https://github.com/src-d/enry/blob/master/benchmarks) directory.
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### Benchmark Dependencies
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As benchmarks depend on Ruby and Github-Linguist gem make sure you have:
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- Ruby (e.g using [`rbenv`](https://github.com/rbenv/rbenv)), [`bundler`](https://bundler.io/) installed
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- Docker
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- [native dependencies](https://github.com/github/linguist/#dependencies) installed
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- Build the gem `cd .linguist && bundle install && rake build_gem && cd -`
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- Install it `gem install --no-rdoc --no-ri --local .linguist/github-linguist-*.gem`
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### How to reproduce current results
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If you want to reproduce the same benchmarks as reported above:
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- Make sure all [dependencies](#benchmark-dependencies) are installed
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- Install [gnuplot](http://gnuplot.info) (in order to plot the histogram)
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- Run `ENRY_TEST_REPO="$PWD/.linguist" benchmarks/run.sh` (takes ~15h)
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It will run the benchmarks for enry and linguist, parse the output, create csv files and plot the histogram. This takes some time.
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### Quick
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To run quicker benchmarks you can either:
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make benchmarks
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to get average times for the main detection function and strategies for the whole samples set or:
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make benchmarks-samples
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if you want to see measures per sample file.
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Why Enry?
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------------
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In the movie [My Fair Lady](https://en.wikipedia.org/wiki/My_Fair_Lady), [Professor Henry Higgins](http://www.imdb.com/character/ch0011719/?ref_=tt_cl_t2) is one of the main characters. Henry is a linguist and at the very beginning of the movie enjoys guessing the origin of people based on their accent.
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`Enry Iggins` is how [Eliza Doolittle](http://www.imdb.com/character/ch0011720/?ref_=tt_cl_t1), [pronounces](https://www.youtube.com/watch?v=pwNKyTktDIE) the name of the Professor during the first half of the movie.
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License
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------------
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Apache License, Version 2.0. See [LICENSE](LICENSE)
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