Scala macros for compile-time generation of ultra-fast JSON codecs
Clone or download

Jsoniter Scala

AppVeyor build TravisCI build code coverage Gitter chat Scaladex

Scala macros that generate codecs for case classes, standard types and collections to get maximum performance of JSON parsing and serialization.

Latest results of benchmarks which compare parsing and serialization performance of Jsoniter Scala with Circe, Play-JSON, Jackson, uPickle, dsl-json (Java API only) and AVSystem's scala-commons libraries using different JDK and GraalVM versions on the following environment: Intel® Core™ i7-7700 CPU @ 3.6GHz (max 4.2GHz), RAM 16Gb DDR4-2400, Ubuntu 18.04, latest versions of Open JDK 8, Oracle JDK 8, Open JDK 11, Oracle JDK 11, Open JDK 11 + Graal JIT, Oracle JDK 11 + Graal JIT, and GraalVM CE/EE


This library started from macros that reused Jsoniter Java reader & writer and generated codecs for them but then evolved to have own core of mechanics for parsing and serialization.

Idea to generate codecs by Scala macros and main details was borrowed from Kryo Macros and adapted for needs of the JSON domain.

Other Scala macros features were peeped in AVSystem Commons Library for Scala


Initially, this library was developed for requirements of real-time bidding in ad-tech and goals are simple:

  • do parsing and serialization of JSON directly from UTF-8 bytes to your case classes and Scala collections and back but do it crazily fast without runtime-reflection, intermediate ASTs, strings or hash maps, with minimum allocations and copying
  • do validation of UTF-8 encoding, JSON format and mapped values efficiently (fail fast approach) with clear reporting, do not replace illegally encoded characters of string values by placeholder characters
  • define classes, that will be instantiated during parsing, in compile-time to minimize a probability of runtime issues, generated sources can be inspected to prove that there are no security vulnerabilities during parsing

It targets JDK 8+ without any platform restrictions.

Support of Scala.js and Scala Native is not a goal for the moment.

Features and limitations

  • JSON parsing from Array[Byte], or java.nio.ByteBuffer
  • JSON serialization to Array[Byte], or java.nio.ByteBuffer
  • Support parsing from or writing to part of Array[Byte] or java.nio.ByteBuffer by specifying of position and limit
  • Parsing of streaming JSON values and JSON arrays from without the need of holding all parsed values in the memory
  • Support of UTF-8 encoding only
  • Parsing of strings with escaped characters for JSON keys and string values
  • Codecs can be generated for primitives, boxed primitives, enums, tuples, String, BigInt, BigDecimal, Option, Either, java.util.UUID, java.time.*, Scala collections, arrays, module classes, value classes and case classes with values/fields having any of types listed here
  • Classes should be defined with a primary constructor that has one list of arguments for all non-transient fields
  • Non-case Scala classes also supported but they should have getter accessors for all arguments of a primary constructor
  • Types that supported as map keys are primitives, boxed primitives, enums, String, BigInt, BigDecimal, java.util.UUID, java.time.*, and value classes for any of them
  • Parsing of escaped characters are not supported for strings which are mapped to numeric and data/time types
  • Support of first-order and higher-kinded types
  • Support of 2 representations of ADTs with a sealed trait or a Scala class as base type and non-abstract Scala classes or objects as leaf classes: 1st representation uses discriminator field with string type of value, 2nd one uses string values for objects and a wrapper JSON object with a discriminator key for case class instances
  • Implicitly resolvable value codecs for JSON values and key codecs for JSON object keys that are mapped to maps allows to inject your custom codecs for adding support of other types or for altering representation in JSON for already supported classes
  • Support only acyclic graphs of class instances
  • Fields with default values that defined in the constructor are optional, other fields are required (no special annotation required)
  • Fields with values that are equals to default values, or are empty options/collections/arrays are not serialized to provide a sparse output
  • Any values that used directly or as part of default values of the constructor parameters should have right implementations of the equals method (it mostly concerns non-case classes or other types that have custom codecs)
  • Fields can be annotated as transient or just not defined in the constructor to avoid parsing and serializing at all
  • Field names can be overridden for serialization/parsing by field annotation in the primary constructor of classes
  • Parsing exception always reports a hexadecimal offset of Array[Byte] or InputStream where it occurs and an optional hex dump affected by error part of an internal byte buffer
  • Configurable by field annotation ability to read/write numeric fields from/to string values
  • Both key and value codecs are specialized to be work with primitives efficiently without boxing/unboxing
  • No extra buffering is required when parsing from InputStream or serializing to OutputStream
  • No dependencies on extra libraries in runtime excluding Scala's scala-library
  • Support of compilation to a native image by GraalVM

There are configurable options that can be set in compile-time:

  • Ability to read/write numbers of containers from/to string values
  • Skipping of unexpected fields or throwing of parse exceptions
  • Skipping of serialization of field values that matched with defaults which is defined in the primary constructor can be turned off to force serialization of that values
  • Mapping function for names between classes and JSON, including predefined functions which enforce snake_case, kebab-case or camelCase names for all fields
  • An optional name of the discriminator field for ADTs
  • Mapping function for values of a discriminator field that is used for distinguishing classes of ADTs

List of options that change parsing and serialization in runtime:

  • Serialization of strings with escaped Unicode characters to be ASCII compatible
  • Indenting of output and its step
  • Throwing of stack-less parsing exceptions by default to greatly reduce impact on performance, while stack traces can be turned on in development for debugging
  • Turning off hex dumping affected by error part of an internal byte buffer to reduce the impact on performance
  • A preferred size of internal buffers when parsing from InputStream or serializing to OutputStream

For upcoming features and fixes see Commits and Issues page.

How to use

Add the core library with a "compile" scope and the macros library with a "provided" scope to your dependencies list:

libraryDependencies ++= Seq(
  "com.github.plokhotnyuk.jsoniter-scala" %% "jsoniter-scala-core" % "0.36.2" % Compile, 
  "com.github.plokhotnyuk.jsoniter-scala" %% "jsoniter-scala-macros" % "0.36.2" % Provided // required only in compile-time

Generate codecs for your Scala classes and collections:

import com.github.plokhotnyuk.jsoniter_scala.macros._
import com.github.plokhotnyuk.jsoniter_scala.core._

case class Device(id: Int, model: String)

case class User(name: String, devices: Seq[Device])

implicit val codec: JsonValueCodec[User] = JsonCodecMaker.make[User](CodecMakerConfig())

That's it! You have generated an instance of com.github.plokhotnyuk.jsoniter_scala.core.JsonValueCodec.

Now you can use it for parsing and serialization:

val user = readFromArray("""{"name":"John","devices":[{"id":1,"model":"HTC One X"}]}""".getBytes("UTF-8"))
val json = writeToArray(User(name = "John", devices = Seq(Device(id = 2, model = "iPhone X"))))

If you don't know how make your data structures from scratch but have a JSON sample then use on-line services 1 2 to generate an initial version of them.

To see generated code for codecs add the following line to your sbt build file

scalacOptions ++= Seq("-Xmacro-settings:print-codecs")

Full code see in the examples directory

For more use cases, please, check out tests:

Known issues

  1. Scalac has a bug that affects case classes which have 2 fields where name of one is a prefix for the another name that contains a character which should be encoded immediately after the prefix (like o and o-o). You will got compilation or runtime error, depending on the version of the compiler, see details here:

The workaround is to move a definition of the field with encoded chars (o-o in our case) to be after the field that is affected by the exception (after the o field)

  1. Scalac can fail to compile the make macro call with a stacktrace like this (for more details see:
[error] java.lang.AssertionError: assertion failed: List(package com, package com)
[error] 	at scala.reflect.internal.SymbolTable.throwAssertionError(SymbolTable.scala:163)
[error] 	at scala.reflect.internal.Symbols$Symbol.suchThat(Symbols.scala:1974)
[error] 	at scala.reflect.macros.contexts.Evals.eval(Evals.scala:19)
[error] 	at scala.reflect.macros.contexts.Evals.eval$(Evals.scala:14)
[error] 	at scala.reflect.macros.contexts.Context.eval(Context.scala:6)
[error] 	at com.github.plokhotnyuk.jsoniter_scala.macros.JsonCodecMaker$Impl$.com$github$plokhotnyuk$jsoniter_scala$macros$JsonCodecMaker$Impl$$eval$1(JsonCodecMaker.scala:227)
[error] 	at com.github.plokhotnyuk.jsoniter_scala.macros.JsonCodecMaker$Impl$.make(JsonCodecMaker.scala:229)

Workarounds are:

  • isolate the make macro call(s) in the separated object, like in this PR
  • move jsoniter-scala imports to be local, like here and here
  1. Scalac can throw the following stack overflow exception on make call for ADTs with objects if the call and the ADT definition are enclosed in the definition of some outer class (for more details see:

Workarounds are:

  • don't enclose ADTs with object into outer classes
  • use outer object (not a class) instead

How to develop

Feel free to ask questions in chat, open issues, or contribute by creating pull requests (fixes and improvements to docs, code, and tests are highly appreciated)

Run tests, check coverage and binary compatibility

sbt clean coverage test coverageReport
sbt -J-XX:MaxMetaspaceSize=512m clean +test +mimaReportBinaryIssues

BEWARE that jsoniter-scala is included into Scala Community Build for 2.11.x, 2.12.x, and 2.13.x versions of Scala.

Run benchmarks

Sbt plugin for JMH tool is used for benchmarking, to see all their features and options please check Sbt-JMH docs and JMH tool docs.

Learn how to write benchmarks in JMH samples and JMH articles posted in Aleksey Shipilёv’s and Nitsan Wakart’s blogs.

List of available option can be printed by:

sbt 'jsoniter-scala-benchmark/jmh:run -h'

Results of benchmark can be stored in different formats: *.csv, *.json, etc. All supported formats can be listed by:

sbt 'jsoniter-scala-benchmark/jmh:run -lrf'

JMH allows to run benchmarks with different profilers, to get a list of supported use (can require entering of user password):

sbt 'jsoniter-scala-benchmark/jmh:run -lprof'

Help for profiler options can be printed by following command (<profiler_name> should be replaced by name of the supported profiler from the command above):

sbt 'jsoniter-scala-benchmark/jmh:run -prof <profiler_name>:help'

For parametrized benchmarks the constant value(s) for parameter(s) can be set by -p option:

sbt clean 'jsoniter-scala-benchmark/jmh:run -p size=1,10,100,1000 .*ArrayOf.*'

To see throughput with allocation rate of generated codecs run benchmarks with GC profiler using the following command:

sbt clean 'jsoniter-scala-benchmark/jmh:run -prof gc -rf json -rff jdk8.json .*Benchmark.*'

Results that are stored in JSON can be easy plotted in JMH Visualizer by drugging & dropping of your file to the drop zone or using the source parameter with an HTTP link to your file in the URL like here.

On Linux the perf profiler can be used to see CPU event statistics normalized per ops:

sbt clean 'jsoniter-scala-benchmark/jmh:run -prof perfnorm .*TwitterAPI.*JsoniterScala.*'

To get a result for some benchmarks with an in-flight recording file from JFR profiler use command like this:

sbt clean 'jsoniter-scala-benchmark/jmh:run -jvmArgsAppend "-XX:+UnlockDiagnosticVMOptions -XX:+DebugNonSafepoints" -prof jmh.extras.JFR:dir=/tmp/profile-jfr;flameGraphDir=/home/andriy/Projects/com/github/brendangregg/FlameGraph;jfrFlameGraphDir=/home/andriy/Projects/com/github/chrishantha/jfr-flame-graph;verbose=true -wi 10 -i 60 .*GoogleMapsAPI.*readJsoniter.*'

Now you can open files from the /tmp/profile-jfr directory:

profile.jfr                             # JFR profile, open and analyze it using JMC
jfr-collapsed-cpu.txt                   # Data from JFR profile that are extracted for Flame Graph tool
flame-graph-cpu.svg                     # Flame graph of CPU usage 
flame-graph-cpu-reverse.svg             # Reversed flame graph of CPU usage
flame-graph-allocation-tlab.svg         # Flame graph of heap allocations in TLAB
flame-graph-allocation-tlab-reverse.svg # Reversed flame graph of heap allocations in TLAB

To run benchmarks with recordings by Async profiler, clone its repository and use command like this:

sbt -no-colors 'jsoniter-scala-benchmark/jmh:run -jvmArgsAppend "-XX:+UnlockDiagnosticVMOptions -XX:+DebugNonSafepoints" -prof jmh.extras.Async:event=cpu;dir=/tmp/profile-async;asyncProfilerDir=/home/andriy/Projects/com/github/jvm-profiling-tools/async-profiler;flameGraphDir=/home/andriy/Projects/com/github/brendangregg/FlameGraph;flameGraphOpts=--color,java;verbose=true -wi 10 -i 60 .*TwitterAPIBenchmark.readJsoniterScala.*'

To see list of available events need to start your app or benchmark, and run jps command. I will show list of PIDs and names for currently running Java processes. While your Java process still running launch the Async Profiler with the list option and ID of your process like here:

$ ~/Projects/com/github/jvm-profiling-tools/async-profiler/ list 6924
Perf events:
Java events:

Following command can be used to profile and print assembly code of hottest methods, but it requires a setup of an additional library to make PrintAssembly feature enabled:

sbt clean 'jsoniter-scala-benchmark/jmh:run -prof perfasm -wi 10 -i 10 .*Adt.*readJsoniter.*'

More info about extras, options and ability to generate flame graphs see in Sbt-JMH docs

Other benchmarks with results for jsoniter-scala:

  • comparison with best binary parsers and serializers for Scala
  • comparison with a state of the art filter that by "building structural indices converts control flow into data flow, thereby largely eliminating inherently unpredictable branches in the program and exploiting the parallelism available in modern processors"

Publish locally

Publish to local Ivy repo:

sbt publishLocal

Publish to local Maven repo:

sbt publishM2


For version numbering use Recommended Versioning Scheme that is used in the Scala ecosystem.

Double check binary and source compatibility, including behavior, and release using the following command (credentials are required):

sbt -J-XX:MaxMetaspaceSize=512m release

Do not push changes to github until promoted artifacts for the new version are not available for download on Maven Central Repository to avoid binary compatibility check failures in triggered Travis CI builds.

Create PRs with updated version of jsoniter-scala for OSS projects which depends on it in case of binary incompatible or security release:

  1. akka-http-json
  2. kafka-serialization
  3. kafka-serde-scala
  4. loco
  5. flatjoin
  6. tasks

Also half of Scala web frameworks which take part in TechEmpower benchmarks have used jsoniter-scala for serialization of JSON responses.