9744e9e122
Summary: The value was too sensitive, it was different in the dbg way, and even when running individual tests vs. all tests. Reviewed By: codemiller Differential Revision: D3310273 fbshipit-source-id: b15f81350f389888189409e7195486dc218f2573 |
||
|---|---|---|
| example | ||
| Haxl | ||
| tests | ||
| .gitignore | ||
| .travis.yml | ||
| changelog.md | ||
| haxl.cabal | ||
| LICENSE | ||
| logo.png | ||
| logo.svg | ||
| PATENTS | ||
| readme.md | ||
| Setup.hs | ||
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.
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 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.
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?
-
The Story of Haxl explains how Haxl came about at Facebook, and discusses our particular use case.
-
An example Facebook data source walks through building an example data source that queries the Facebook Graph API concurrently.
-
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, completely invisibly to the programmer.
-
Haxl Documentation on Hackage.
-
There is no Fork: An Abstraction for Efficient, Concurrent, and Concise Data Access, our paper on Haxl, accepted for publication at ICFP'14.