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 framework
• haxl-facebook (in [https://github.com/facebook/Haxl/tree/master/example/facebook](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?

• 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.

• Fun with Haxl (part 1) Walks through using Haxl from scratch for a simple SQLite-backed blog engine.

• 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.

• 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.