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0.4 to 0.5

When used with the UTF-8 encoding performance improvements introduced in version of the text package, this release improves aeson's JSON encoding performance by 33% relative to aeson 0.4.

As part of achieving this improvement, an API change was necessary. The fromValue function in the Data.Aeson.Encode module now uses the text package's Builder type instead of the blaze-builder package's Builder type.

0.3 to 0.4

Ease of use

The new decode function complements the longstanding encode function, and makes the API simpler.

New examples make it easier to learn to use the package.

Generics support

aeson's support for data-type generic programming makes it possible to use JSON encodings of most data types without writing any boilerplate instances.

Thanks to Bas Van Dijk, aeson now supports the two major schemes for doing datatype-generic programming:

  • the modern mechanism, built into GHC itself

  • the older mechanism, based on SYB (aka "scrap your boilerplate")

The modern GHC-based generic mechanism is fast and terse: in fact, its performance is generally comparable in performance to hand-written and TH-derived ToJSON and FromJSON instances. To see how to use GHC generics, refer to examples/Generic.hs.

The SYB-based generics support lives in Data.Aeson.Generic, and is provided mainly for users of GHC older than 7.2. SYB is far slower (by about 10x) than the more modern generic mechanism. To see how to use SYB generics, refer to examples/GenericSYB.hs.

Improved performance

  • We switched the intermediate representation of JSON objects from Data.Map to Data.HashMap, which has improved type conversion performance.

  • Instances of ToJSON and FromJSON for tuples are between 45% and 70% faster than in 0.3.

Evaluation control

This version of aeson makes explicit the decoupling between identifying an element of a JSON document and converting it to Haskell. See the Data.Aeson.Parser documentation for details.

The normal aeson decode function performs identification strictly, but defers conversion until needed. This can result in improved performance (e.g. if the results of some conversions are never needed), but at a cost in increased memory consumption.

The new decode' function performs identification and conversion immediately. This incurs an up-front cost in CPU cycles, but reduces reduce memory consumption.