Awesome
Summary
Minimalistic library for encoding JSON directly to strict bytestring.
The library focuses on 2 aspects: simplicity and performance. The API consists of just a few functions and achieves performance that gets up to 3 times better than that of "aeson" in typical use-cases. In cases where we deal with very large documents the performance difference becomes less drastic.
Performance
Benchmarks
Following are the benchmark results comparing the performance of encoding typical documents using this library and "aeson". Every approach is measured on Twitter API data of sizes ranging from roughly 1kB to 60MB. "aeson" stands for "aeson" producing a strict bytestring, "lazy-aeson" - lazy bytestring, "lazy-aeson-untrimmed-32k" - lazy bytestring using an untrimmed builder strategy with allocation of 32k.
1kB/jsonifier mean 2.054 μs ( +- 30.83 ns )
1kB/aeson mean 6.456 μs ( +- 126.7 ns )
1kB/lazy-aeson mean 6.338 μs ( +- 169.1 ns )
1kB/lazy-aeson-untrimmed-32k mean 6.905 μs ( +- 280.2 ns )
6kB/jsonifier mean 12.80 μs ( +- 196.9 ns )
6kB/aeson mean 31.28 μs ( +- 733.2 ns )
6kB/lazy-aeson mean 30.30 μs ( +- 229.5 ns )
6kB/lazy-aeson-untrimmed-32k mean 29.17 μs ( +- 371.3 ns )
60kB/jsonifier mean 122.9 μs ( +- 1.492 μs )
60kB/aeson mean 258.4 μs ( +- 1.000 μs )
60kB/lazy-aeson mean 259.4 μs ( +- 4.494 μs )
60kB/lazy-aeson-untrimmed-32k mean 255.7 μs ( +- 3.239 μs )
600kB/jsonifier mean 1.299 ms ( +- 16.44 μs )
600kB/aeson mean 3.389 ms ( +- 106.8 μs )
600kB/lazy-aeson mean 2.520 ms ( +- 45.51 μs )
600kB/lazy-aeson-untrimmed-32k mean 2.509 ms ( +- 30.76 μs )
6MB/jsonifier mean 20.91 ms ( +- 821.7 μs )
6MB/aeson mean 30.74 ms ( +- 509.4 μs )
6MB/lazy-aeson mean 24.83 ms ( +- 184.3 μs )
6MB/lazy-aeson-untrimmed-32k mean 24.93 ms ( +- 383.2 μs )
60MB/jsonifier mean 194.8 ms ( +- 13.93 ms )
60MB/aeson mean 276.0 ms ( +- 5.194 ms )
60MB/lazy-aeson mean 246.9 ms ( +- 3.122 ms )
60MB/lazy-aeson-untrimmed-32k mean 245.1 ms ( +- 1.050 ms )
The benchmark suite is bundled with the package.
Reasoning
Such performance is achieved due to the approach taken to the process of building a bytestring. Unlike "aeson", this library doesn't use the builder distributed with the "bytestring" package, instead it uses a custom solution which produces a bytestring in two steps: first it counts how many bytes the rendering of data will occupy then it allocates a buffer of that exact size and renders directly into it. As the benchmarks show, at least for the purpose of rendering JSON this approach turns out to be faster than manipulations on temporary buffers which the builder from "bytestring" does.
This approach opens doors to optimizations otherwise inaccessible. E.g., we can efficiently count how many bytes a Text value encoded as JSON string literal will occupy, then render it into its final destination in one pass. We can efficiently count how many bytes a decimal encoding of an integer will occupy, and also render it in one pass despite the rendering of integers needing to be done in reverse direction and requiring a second pass of reversing the bytes in alternative solutions.
With all those observations some general concepts have emerged and have been extracted as the lower-level "ptr-poker" package, which focuses on the problem of populating pointers.
Quality
The quality of the library is ensured with a test property in which a random JSON tree is generated, then rendered using "jsonifier", then parsed using "aeson" and compared to the original.
Demo
Following is a complete program that shows how you can render JSON from your domain model.
{-# LANGUAGE OverloadedStrings, RecordWildCards #-}
import qualified Jsonifier as J
import qualified Data.ByteString.Char8
{-|
Outputs the following:
> {"name":"Metallica","genres":[{"name":"Metal"},{"name":"Rock"},{"name":"Blues"}]}
-}
main =
Data.ByteString.Char8.putStrLn (J.toByteString (artistJson metallica))
metallica :: Artist
metallica =
Artist "Metallica" [Genre "Metal", Genre "Rock", Genre "Blues"]
-- * Model
-------------------------
data Artist =
Artist { artistName :: Text, artistGenres :: [Genre] }
data Genre =
Genre { genreName :: Text }
-- * Encoders
-------------------------
artistJson :: Artist -> J.Json
artistJson Artist{..} =
J.object [
("name", J.textString artistName),
("genres", J.array (fmap genreJson artistGenres))
]
genreJson :: Genre -> J.Json
genreJson Genre{..} =
J.object [
("name", J.textString genreName)
]
A compilable version of this demo comes bundled with the package as the "demo" test-suite.