15a8c2cc84
Summary: Pull Request resolved: https://github.com/facebook/Haxl/pull/120 This adds tracking of memo/fetches per label by a unique id for each. Using this we can track exactly where time was spent, and where it was shared Reviewed By: simonmar Differential Revision: D20792435 fbshipit-source-id: 55c1e778d313d103a910c6dd5be512f95125acce |
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| example | ||
| Haxl | ||
| tests | ||
| .gitignore | ||
| .travis.yml | ||
| changelog.md | ||
| CODE_OF_CONDUCT.md | ||
| CONTRIBUTING.md | ||
| haxl.cabal | ||
| LICENSE | ||
| logo.png | ||
| logo.svg | ||
| readme.md | ||
| Setup.hs | ||
| stack.yaml | ||
Haxl
Haxl is a Haskell library that simplifies access to remote data, such as databases or web-based services. Haxl can automatically
- batch multiple requests to the same data source,
- request data from multiple data sources concurrently,
- cache previous requests,
- memoize computations.
Having all this handled for you behind the scenes means that your data-fetching code can be much cleaner and clearer than it would otherwise be if it had to worry about optimizing data-fetching. We'll give some examples of how this works in the pages linked below.
There are two Haskell packages here:
haxl: The core Haxl frameworkhaxl-facebook(in https://github.com/facebook/Haxl/tree/master/example/facebook): An (incomplete) example data source for accessing the Facebook Graph API
To use Haxl in your own application, you will likely need to build one or more data sources: the thin layer between Haxl and the data that you want to fetch, be it a database, a web API, a cloud service, or whatever.
There is a generic datasource in "Haxl.DataSource.ConcurrentIO" that can be used for performing arbitrary IO operations concurrently, given a bit of boilerplate to define the IO operations you want to perform.
The haxl-facebook package shows how we might build a Haxl data
source based on the existing fb package for talking to the Facebook
Graph API.
Where to go next?
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The Story of Haxl explains how Haxl came about at Facebook, and discusses our particular use case.
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An example Facebook data source walks through building an example data source that queries the Facebook Graph API concurrently.
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Fun with Haxl (part 1) Walks through using Haxl from scratch for a simple SQLite-backed blog engine.
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The N+1 Selects Problem explains how Haxl can address a common performance problem with SQL queries by automatically batching multiple queries into a single query, without the programmer having to specify this behavior.
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Haxl Documentation on Hackage.
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There is no Fork: An Abstraction for Efficient, Concurrent, and Concise Data Access, our paper on Haxl, accepted for publication at ICFP'14.