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?
-
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.