haxl: A Haskell library for efficient, concurrent, and concise data access.
Haxl is a library and EDSL for efficient scheduling of concurrent data accesses with a concise applicative API.
To use Haxl, you need to implement one or more data sources, which provide the means for accessing remote data or other I/O that you want to perform using Haxl.
Haxl provides two top-level modules:
Data-source implementations import Haxl.Core,
Client code import your data sources and Haxl.Prelude, or some other client-level API that you provide.
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| Versions [RSS] | 0.1.0.0, 0.2.0.0, 0.3.0.0, 0.3.1.0, 0.4.0.0, 0.4.0.1, 0.4.0.2, 0.5.0.0, 0.5.1.0, 2.0.0.0, 2.0.1.0, 2.0.1.1, 2.1.2.0, 2.3.0.0, 2.4.0.0, 2.5.1.0, 2.5.1.1 |
|---|---|
| Change log | changelog.md |
| Dependencies | aeson (>=0.6 && <1.5), base (>=4.7 && <5), binary (>=0.7 && <0.10), bytestring (>=0.9 && <0.11), containers (>=0.5 && <0.7), deepseq, exceptions (>=0.8 && <0.11), filepath (>=1.3 && <1.5), ghc-prim, hashable (>=1.2 && <1.3), haxl, pretty (>=1.1 && <1.2), semigroups (>=0.18.3 && <0.19), stm (>=2.4 && <2.6), text (>=1.2.1.0 && <1.3), time (>=1.4 && <1.9), transformers, unordered-containers (>=0.2 && <0.3), vector (>=0.10 && <0.13) [details] |
| Tested with | ghc ==7.8.4, ghc ==7.10.3, ghc ==8.0.2, ghc ==8.2.2, ghc ==8.4.3 |
| License | BSD-3-Clause |
| Copyright | Copyright (c) 2014-present, Facebook, Inc. |
| Author | Facebook, Inc. |
| Maintainer | The Haxl Team <haxl-team@fb.com> |
| Category | Concurrency |
| Home page | https://github.com/facebook/Haxl |
| Bug tracker | https://github.com/facebook/Haxl/issues |
| Uploaded | by SimonMarlow at 2019-06-20T07:03:20Z |
| Distributions | Stackage:2.5.1.1 |
| Reverse Dependencies | 10 direct, 0 indirect [details] |
| Executables | cachebench, monadbench |
| Downloads | 13863 total (8 in the last 30 days) |
| Rating | 2.25 (votes: 2) [estimated by Bayesian average] |
| Your Rating | |
| Status | Docs available [build log] Last success reported on 2019-06-20 [all 1 reports] |
Readme for haxl-2.1.2.0
[back to package description]
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.