winery: A compact, well-typed seralisation format for Haskell values

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Versions 0, 0.1, 0.1.1, 0.1.2, 0.2, 0.2.1, 0.3, 0.3.1, 1, 1.0.1, 1.1, 1.1.1, 1.1.1, 1.1.2, 1.1.3, 1.2, 1.3, 1.3.1, 1.3.2
Change log
Dependencies aeson, base (>=4.7 && <5), bytestring, containers, cpu, fast-builder, hashable, HUnit, megaparsec (>=6.0.0), mtl, prettyprinter, prettyprinter-ansi-terminal, QuickCheck, scientific, semigroups, text, time, transformers, unordered-containers, vector, winery [details]
License BSD-3-Clause
Copyright Copyright (c) 2019 Fumiaki Kinoshita
Author Fumiaki Kinoshita
Category Data, Codec, Parsing, Serialization
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Source repo head: git clone
Uploaded by FumiakiKinoshita at 2019-04-22T05:54:20Z




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Readme for winery-1.1.1

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winery is a serialisation library focusing on performance, compactness and compatibility. The primary feature is that metadata (types, field names, etc) are packed into one schema.

A number of formats, like JSON and CBOR, attach metadata for each value:

[{"id": 0, "name": "Alice"}, {"id": 1, "name": "Bob"}]

In contrast, winery stores them separately, eliminating redundancy while guaranteeing well-typedness:

0402 0402 0269 6410 046e 616d 6514  [{ id :: Integer, name :: Text }]
0200 0541 6c69 6365 0103 426f 62    [(0, "Alice"), (1, "Bob")]

Unlike binary or cereal which doesn't preserve metadata at all, winery also allows readers to decode values regardless of the current implementation.


The interface is simple; serialise encodes a value with its schema, and deserialise decodes a ByteString using the schema in it.

class Serialise a where
  schema :: Serialise a => proxy a -> Schema

serialise :: Serialise a => a -> B.ByteString
deserialise :: Serialise a => B.ByteString -> Either WineryException a

It's also possible to serialise schemata and data separately. serialiseSchema encodes a schema and its version number into a ByteString, and serialiseOnly serialises a value without a schema.

serialiseSchema :: Schema -> B.ByteString
serialiseOnly :: Serialise a => a -> B.ByteString

In order to decode data generated this way, pass the result of deserialiseSchema to getDecoder. Finally run evalDecoder to deserialise them.

deserialiseSchema :: B.ByteString -> Either WineryException Schema
getDecoder :: Serialise a => Schema -> Either WineryException (Decoder a)
evalDecoder :: Decoder a -> B.ByteString -> a

Deriving an instance

The recommended way to create an instance of Serialise is to use DerivingVia.

  deriving Generic
  deriving Serialise via WineryRecord Foo

for single-constructor records, or just

  deriving Generic
  deriving Serialise via WineryVariant Foo

for any ADT. The former explicitly describes field names in the schema, and the latter does constructor names.

Backward compatibility

If the representation is not the same as the current version (i.e. the schema is different), the data cannot be decoded directly. This is where extractors come in.

Extractor parses a schema and returns a function which gives a value back from a Term.

If having default values for missing fields is sufficient, you can pass a default value to gextractorRecord:

  extractor = gextractorRecord $ Just $ Foo "" 42 0

You can also build an extractor using combinators such as extractField, extractConstructor, etc.

  $ ("None", \() -> Nothing)
  `extractConstructor` ("Some", Just)
  `extractConstructor` extractVoid
  :: Extractor (Maybe a)

Extractor is Alternative, meaning that multiple extractors (such as a default generic implementation and fallback plans) can be combined into one.


Term can be deserialised from any winery data. It can be pretty-printed using the Pretty instance:

{ bar: "hello"
, baz: 3.141592653589793
, foo: Just 42

You can use the winery command-line tool to inspect values.

$ winery '.[:10] | .first_name .last_name' benchmarks/data.winery
Shane Plett
Mata Snead
Levon Sammes
Irina Gourlay
Brooks Titlow
Antons Culleton
Regine Emerton
Starlin Laying
Orv Kempshall
Elizabeth Joseff
Cathee Eberz

At the moment, the following queries are supported:

* F | G compose queries (left to right)


A useful library should also be fast. Benchmarking encoding/decoding of the following datatype.

data Gender = Male | Female

data TestRec = TestRec
  { id_ :: !Int
  , first_name :: !Text
  , last_name :: !Text
  , email :: !Text
  , gender :: !Gender
  , num :: !Int
  , latitude :: !Double
  , longitude :: !Double

Here's the result:

encode 1 encode 1000 decode length
winery 0.28 μs 0.26 ms 0.81 ms 58662
cereal 0.82 μs 0.78 ms 0.90 ms 91709
binary 1.7 μs 1.7 ms 2.0 ms 125709
serialise 0.61 μs 0.50 ms 1.4 ms 65437
aeson 9.9 μs 9.7 ms 17 ms 160558