4360502913
Summary: When profiling is on (GHC's -prof flag) we can grab a call stack with GHC.Stack.currentCallStack. This diff attaches the call stack to every HaxlException when profiling is on. (this may have some runtime overhead, but it's only when profiling is on, and if it turns out to be a problem for accurate profiling then we can fix it by deferring rendering of the stack until we need to see it). Reviewed By: zilberstein Differential Revision: D2754599 fbshipit-source-id: d71f71c3f897aec7db92a3446cfc653158a1a6f4 |
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| 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?
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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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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.
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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.