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jsoniter-scala

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Scala macros for compile-time generation of safe and ultra-fast JSON codecs.

image

Latest results of benchmarks on JVMs that compare parsing and serialization performance of jsoniter-scala with: borer, circe, circe with jsoniter-scala booster, jackson-module-scala, json4s-jackson, json4s-native, play-json, play-json with jsoniter-scala booster, smithy4s-json, spray-json, uPickle, weePickle, zio-json libraries using different JDK and GraalVM versions on the following environment: Intel® Core™ i9-13900K CPU @ 3.0GHz (max 5.8GHz, performance-cores only), RAM 64Gb DDR5-4800, Ubuntu 24.04 (Linux 6.8), and latest versions of JDK 17/21/24-ea, GraalVM Community JDK 17/21/24-ea, and GraalVM JDK 17/21/24-ea*.

Latest results of benchmarks on browsers that compare performance of jsoniter-scala with: circe, circe with jsoniter-scala booster, play-json, play-json with jsoniter-scala booster, smithy4s-json, uPickle, zio-json compiled by Scala.js 1.17.0 to ES 2015 with GCC v20220202 optimizations applied on Intel® Core™ i7-11800H CPU @ 2.3GHz (max 4.6GHz), RAM 64Gb DDR4-3200, Ubuntu 23.10 (Linux 6.8).

Contents

Acknowledgments

This library had started from macros that reused jsoniter (json-iterator) for Java reader and writer but then the library evolved to have its own core of mechanics for parsing and serialization.

The idea to generate codecs by Scala macros and main details were borrowed from Kryo Macros (originally developed by Alexander Nemish) and adapted for the needs of the JSON domain.

Other Scala macros features were peeped in AVSystem Commons and magnolia libraries.

Ideas for the most efficient parsing and serialization of java.time.* values were inspired by DSL-JSON's implementation for java.time.OffsetDateTime.

Other projects and a blog post that have helped deliver unparalleled safety and performance characteristics for parsing and serialization of numbers:

  • Schubfach - the most efficient and concise way to serialize doubles and floats to the textual representation
  • rust-lexical - the most efficient way to parse floats and doubles from the textual representation precisely
  • big-math - parsing of BigInt and BigDecimal values with the O(n^1.5) complexity instead of O(n^2) using Java's implementations where n is a number of digits
  • James Anhalt's algorithm - the ingenious algorithm for printing integers into decimal strings

A bunch of SWAR technique tricks for JVM platform are based on following projects and a blog post:

  • borer - the fast parsing of JSON strings by 8-byte words
  • simdjson - the fast checking of string for digits by 8-byte words
  • FastDoubleParser - the fast parsing of numbers by 8-byte words
  • Johnny Lee's article - the fast time string to seconds conversion

Big kudos to all contributors:

GitHub contributors

Goals

  1. Safety: validate parsed values safely with the fail-fast approach and clear reporting, provide configurable limits for suboptimal data structures with safe defaults to be resilient for DoS attacks, generate codecs that create instances of a fixed set of classes during parsing to avoid RCE attacks
  2. Correctness: support the latest JSON format (RFC-8259), do not replace illegally encoded characters of string values by placeholder characters, parse numbers with limited binary representation doing half even rounding for too long JSON numbers, serialize floats and doubles to the shortest textual representation without loosing of precision
  3. Speed: do parsing and serialization of JSON directly from UTF-8 bytes to your data structures and back, do it crazily fast without using of runtime reflection or runtime code generation, intermediate ASTs, hash maps, but with minimum allocations and copying
  4. Productivity: derive codecs recursively for complex types using one line macro, do it in compile-time to minimize the probability of run-time issues, optionally print generated sources as compiler output to be inspected for proving safety and correctness or to be reused as a starting point for the implementation of custom codecs, prohibit serializing of null Scala values and parsing immediately to them in generated codecs
  5. Ergonomics: have preconfigured defaults for the safest and common usage that can be easily altered by compile- and run-time configuration instances, combined with compile-time annotations and implicits, embrace the textual representation of JSON providing a pretty printing option, provide a hex dump in the error message to speed up the view of an error context

Features and limitations

  • JSON parsing from String, Array[Byte], java.nio.ByteBuffer, java.io.InputStream/java.io.FileInputStream
  • JSON serialization to String, Array[Byte], java.nio.ByteBuffer, java.io.OutputStream/java.io.FileOutputStream
  • Support of 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 java.io.InputStream/java.io.FileInputStream without the need of holding all input and parsed values in the memory
  • Only UTF-8 encoding is supported when working with buffered bytes directly but there is a fallback to parse and serialize JSON from/to String (while this is much less efficient)
  • 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.* (to/from ISO-8601 representation only), Scala collections, arrays (including immutable arrays in Scala 3), module classes, literal types, value classes, and case classes with values/fields having any of types listed here
  • Classes should be defined with a primary constructor that hasn't defined default values in non-first parameter lists
  • 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.*, literal types, and value classes for any of them
  • Codecs for sorted maps and sets can be customized by implicit Ordering[K] instances for keys that are available at the scope of the make macro call
  • Core module support reading and writing byte arrays from/to Base16 and Base64 representations (RFC 4648) for using in custom codecs
  • Parsing of escaped characters is not supported for strings which are mapped to byte arrays, numeric and java.time.* types
  • Support of first-order and higher-kind types
  • Support of 2 representations of ADTs with a sealed trait or a Scala class as a 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
  • Type aliases are supported for all types mentioned above
  • Only acyclic graphs of class instances are supported by generated codecs
  • Order of instance fields is preserved during serialization for generated codecs
  • Throws a parsing exception if duplicated keys were detected for a class instance (except maps)
  • Serialization of null values is prohibited by throwing of NullPointerException errors
  • Parsing of null values allowed only for optional or collection types (that means the None value or an empty collection accordingly) and for fields which have defined non-null default values
  • 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
  • Reading and writing of any arbitrary bytes or raw values are possible by using custom codecs
  • Parsing exception always reports a hexadecimal offset of Array[Byte], java.nio.ByteBuffer, java.io.InputStream/ java.io.FileInputStream 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 work with primitives efficiently without boxing/unboxing
  • No extra buffering is required when parsing from java.io.InputStream/java.io.FileInputStream or serializing to java.io.OutputStream/java.io.FileOuputStream
  • Using black box macros only for codec generation ensures that your types will never be changed
  • Ability to print generated code for codecs using an implicit val of CodecMakerConfig.PrintCodec type in a scope of codec derivation
  • No dependencies on extra libraries in runtime excluding Scala's scala-library (all platforms) and scala-java-time (replacement of JDKs java.time._ types for Scala.js and Scala Native)
  • On Scala.js and Scala Native platforms, if you need support for timezones besides UTC then you should follow the scala-java-time documentation for adding a time zone database to your application
  • Codecs and runtime configurations implement java.io.Serializable for easier usage in distributive computing
  • Support of shading to another package for locking on a particular released version
  • Patch versions are backward and forward compatible, minor versions are backward compatible
  • Integration with circe for faster parsing/serialization and decoding/encoding to/from circe AST
  • Releases for different Scala versions: 2.12, 2.13, and 3.3
  • Support of JVMs for Java 11+ versions
  • Support of compilation to a native image by GraalVM
  • Support of Scala.js 1.0+ for all supported Scala versions
  • Support of Scala Native 0.5+ for all supported Scala versions
  • Suppressing of all WartRemover warnings of generated codecs for Scala 2.12 and 2.13

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

  • Ability to read/write numbers from/to string values
  • Ability to read/write maps as JSON arrays
  • Skipping of unexpected fields or throwing of parse exceptions
  • Skipping of serialization of fields that have empty collection values can be turned off to force serialization of them
  • Skipping of serialization of fields that have empty optional values can be turned off to force serialization of them
  • Skipping of serialization of fields which values are matched with defaults that are defined in the primary constructor can be turned off to force serialization of that values
  • Ability to require collection fields in the JSON input
  • Ability to require fields with default values in the JSON input
  • Ability to override names of classes of ADTs and fields using a compile-time annotation
  • Mapping functions from names of classes and their fields to JSON keys or from names of Java enumeration values to JSON strings and back, including predefined functions which enforce snake_case, kebab-case, camelCase or PascalCase names for all fields in the generated codec
  • 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
  • Ability to set a precision, a scale limit, and the max number of significant digits when parsing BigDecimal values
  • Ability to set the max number of significant digits when parsing BigInt values
  • Ability to set the max allowed value when parsing bit sets
  • Ability to set the limit for the number of inserts when parsing sets or maps
  • Throwing of a compilation error for recursive data structures can be turned off
  • Throwing of a runtime error when the discriminator is not the first field can be turned off
  • Ability to parse/serialize Scala enumeration from/to id numbers
  • Ability to derive codecs that can distinguish null field values and missing fields as Some(None) and None values of Option[Option[_]]
  • Ability to turn on circe-like encoding of Scala objects in ADTs
  • Ability to disable generation of implementation for decoding or encoding
  • Ability to require fields that have defined default values
  • Ability to generate smaller and more efficient codecs for classes when checking of field duplication is not needed
  • Ability to inline non value classes which have the primary constructor with just one argument

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 the 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
  • Size of the hex dump can be adjusted for bigger or smaller number of 16-byte lines
  • Max size of internal input buffers when parsing from java.io.InputStream or java.nio.DirectByteBuffer
  • Preferred size of internal input buffers when parsing from java.io.InputStream or java.nio.DirectByteBuffer
  • Preferred size of internal output buffers when serializing to java.io.OutputStream or java.nio.DirectByteBuffer
  • Max size of char buffers when parsing string values
  • Preferred size of char buffers when parsing string values

The v2.13.5.2 release is the last version that supports JDK 8+ and native image compilation with earlier versions of GraalVM.

The v2.13.3.2 release is the last version that supports Scala 2.11.

The v2.30.2 release is the last version that supports Scala Native 0.4+.

For upcoming features and fixes see Commits and Issues page.

How to use

Let's assume that you have the following data structures:

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

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

Add the core library with a "compile" scope and the macros library with "compile-internal" or "provided" scopes to your list of sbt dependencies:

libraryDependencies ++= Seq(
  // Use the %%% operator instead of %% for Scala.js and Scala Native 
  "com.github.plokhotnyuk.jsoniter-scala" %% "jsoniter-scala-core" % "2.31.3",
  // Use the "provided" scope instead when the "compile-internal" scope is not supported  
  "com.github.plokhotnyuk.jsoniter-scala" %% "jsoniter-scala-macros" % "2.31.3" % "compile-internal"
)

In the beginning of Scala CLI script use "dep" scope for the core library or "compileOnly.dep" scope for the macros libary:

//> using dep "com.github.plokhotnyuk.jsoniter-scala::jsoniter-scala-core::2.31.3"
//> using compileOnly.dep "com.github.plokhotnyuk.jsoniter-scala::jsoniter-scala-macros::2.31.3"

Derive a codec for the top-level type that need to be parsed or serialized:

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

given userCodec: JsonValueCodec[User] = JsonCodecMaker.make

That's it! You have generated an instance of com.github.plokhotnyuk.jsoniter_scala.core.JsonValueCodec for the whole nested data structure. No need to derive intermediate codecs for inner nested classes like Device if you are not going to parse/serialize them from/to JSON in isolation (not as a part of User) and use the default or the same derivation configuration for their codecs.

Now use it for parsing and serialization from/to String:

val user = readFromString[User]("""{"name":"John","devices":[{"id":1,"model":"HTC One X"}]}""")
val json = writeToString(User("John", Seq(Device(2, "iPhone X"))))

When your input comes from the network or disks much more efficient ways are to parse and serialize from/to:

  • byte arrays using readFromArray/writeToArray
  • byte sub-arrays using readFromSubArray/writeToSubArray
  • java.nio.ByteBuffer instances using readFromByteBuffer/writeToByteBuffer
  • java.io.InputStream/java.io.OutputStream instances using readFromStream/writeToStream

Also, parsing from bytes will check UTF-8 encoding and throw an error in case of malformed bytes.

To print generated code for codecs add the following line to the scope of the codec derivation before make call.

given CodecMakerConfig.PrintCodec with {}

For more use cases of jsoniter-scala, please, check out tests:

All Scala 3 only features are tested by specs in this directory.

NOTE: Until official docs will be published, please, use all these tests as tutorials and how-tos to help in your 
journey to become happy users. Also, they are recommended to skim through for checking of your expectation before
selection of this library among others.

You can use the following on-line services to generate an initial version of your data structures from JSON samples:

Also, if you have JSON Schema the following on-line service can generate corresponding data structures for you:

And the following library can generate JSON Schema for your existing data structures:

Samples for its integration with different web frameworks and HTTP servers:

Usages of jsoniter-scala in OSS libraries:

  • akka-http-json - integrates some of the best JSON libs in Scala with Akka HTTP
  • bootzooka - a project to quickly start developing a Scala-based microservice or web app, without the need to write login, user registration etc.
  • caliban - a purely functional library for building GraphQL servers and clients in Scala
  • dijon - support of schema-less JSON using safe and efficient AST representation
  • geo-scala - a core AST and utilities for GeoJSON (RFC 7946) and more
  • iron - a lightweight library for refined types in Scala 3
  • jsoniter-scala-circe - the circe booster for faster parsing/serialization to/form circe AST and decoding/encoding of java.time._ and BigInt types
  • kafka-serde-scala - implicitly converts typeclass encoders to kafka Serializer, Deserializer, Serde
  • logging4s - structured logging for Scala 3
  • neotype - a friendly newtype library for Scala 3
  • oath - yet another scala-jwt library which has the aim to enhance user experience
  • pekko-http-json - integrates some of the best JSON libs in Scala with Pekko HTTP
  • play-json-jsoniter - provides the fastest way to convert an instance of play.api.libs.json.JsValue to byte array (or byte buffer, or output stream) and read it back
  • scalatest-json - Scalatest matchers with appropriate equality and descriptive error messages
  • smithy4s-json - JSON protocol of Smithy tooling for Scala
  • sttp - the Scala HTTP client you always wanted!
  • sttp-oauth2 - OAuth2 client library implemented in Scala using sttp
  • tapir - Typed API descRiptions

Also, for usages in other OSS projects see the Dependents section of peoject's Scala Index page

For all dependent projects it is recommended to use sbt-updates plugin or Scala steward service to keep up with using of the latest releases.

Known issues

  1. There is no validation for the length of JSON representation during parsing.

If your system can accept too long untrusted input then check the input length before parsing with readFromStream or other read... calls.

Also, if you have an input that is an array of values or white-space separate values then consider parsing it by scanJsonArrayFromInputStream or scanJsonValuesFromInputStream instead of readFromStream.

  1. The configuration parameter for the make macro is evaluated in compile-time. It requires no dependency on other code that uses a result of the macro's call, otherwise the following compilation error will be reported:
[error] Cannot evaluate a parameter of the 'make' macro call for type 'full.name.of.YourType'. It should not depend on
        code from the same compilation module where the 'make' macro is called. Use a separated submodule of the project
        to compile all such dependencies before their usage for generation of codecs.

Sometime Scala 2 compiler can fail to compile the make macro call with the same error message for the configuration that has not clear dependencies on other code. For those cases workarounds can be simpler than recommended usage of separated submodule:

  • use make or make... macro calls without parameters
  • isolate the make macro call in the separated object, like in this PR
  • move jsoniter-scala imports to be local, like here and here
  • use sbt clean compile stage or sbt clean test stage instead of just sbt clean stage, like in this repo
  • use mill clean if mill's native BSP support is used in IntelliJ IDEA
  1. Unexpected compiler errors can happen during compilation of ADT definitions or their derived codecs if they are nested in some classes or functions like here.

The workaround is the same for both cases: don't enclose ADT definitions into outer classes, traits or functions, use the outer object (not a class) instead.

  1. Compile-time configuration for make calls in Scala 3 has limited support of possible expressions for name mapping.

Please use examples of CodecMakerConfig usage from unit tests.

  1. Unexpected parsing or serialization errors can happen for nested parsing or serialization routines when the same instance of JsonReader or JsonWriter is reused:
scanJsonValuesFromStream[String](in) { s =>
  readFromString[String](s)
}

The workaround is using reentrant parsing or serialization routines for all except the most nested call. That will create a new instance of JsonReader or JsonWriter on each reentrant call:

scanJsonValuesFromStreamReentrant[String](in) { s =>
  readFromString[String](s)
}
  1. Scala.js doesn't support Java enums compiled from Java sources, so linking fails with errors like:
[error] Referring to non-existent class com.github.plokhotnyuk.jsoniter_scala.macros.Level
[error]   called from private com.github.plokhotnyuk.jsoniter_scala.macros.JsonCodecMakerSpec.$anonfun$new$24()void
[error]   called from private com.github.plokhotnyuk.jsoniter_scala.macros.JsonCodecMakerSpec.$anonfun$new$1()void
[error]   called from constructor com.github.plokhotnyuk.jsoniter_scala.macros.JsonCodecMakerSpec.<init>()void
[error]   called from static constructor com.github.plokhotnyuk.jsoniter_scala.macros.JsonCodecMakerSpec.<clinit>()void
[error]   called from core module analyzer

The workaround for Scala 2 is to split sources for JVM and other platforms and use Java enum emulation for Scala.js and Scala Native.

Code for JVM:

public enum Level {
    HIGH, LOW;
}

Code for Scala.js and Scala Native:

object Level {
  val HIGH: Level = new Level("HIGH", 0)
  val LOW: Level = new Level("LOW", 1)
  
  val values: Array[Level] = Array(HIGH, LOW)

  def valueOf(name: String): Level =
    if (HIGH.name() == name) HIGH
    else if (LOW.name() == name) LOW
    else throw new IllegalArgumentException(s"Unrecognized Level name: $name")
}

final class Level private (name: String, ordinal: Int) extends Enum[Level](name, ordinal)

For Scala 3 the workaround can be the same for all platforms:

enum Level extends Enum[Level] {
  case HIGH
  case LOW
}
  1. Scala 3 compiler cannot derive anonymous codecs for generic types with concrete type parameters:
case class DeResult[T](isSucceed: Boolean, data: T, message: String)

case class RootPathFiles(files: List[String])

given JsonValueCodec[DeResult[Option[String]]] = JsonCodecMaker.make
given JsonValueCodec[DeResult[RootPathFiles]] = JsonCodecMaker.make

Current 3.2.x versions of scalac fail with the duplicating definition error like this:

[error] 19 |      given JsonValueCodec[DeResult[RootPathFiles]] = JsonCodecMaker.make
[error]    |      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[error]    |given_JsonValueCodec_DeResult is already defined as given instance given_JsonValueCodec_DeResult

The workaround is using named instances of codecs:

given codecOfDeResult1: JsonValueCodec[DeResult[Option[String]]] = JsonCodecMaker.make
given codecOfDeResult2: JsonValueCodec[DeResult[RootPathFiles]] = JsonCodecMaker.make

or private type aliases with given definitions gathered in some trait:

trait DeResultCodecs:

  private type DeResult1 = DeResult[Option[String]]
  private type DeResult2 = DeResult[RootPathFiles]

  given JsonValueCodec[DeResult1] = JsonCodecMaker.make
  given JsonValueCodec[DeResult2] = JsonCodecMaker.make

end DeResultCodecs

object DeResultCodecs extends DeResultCodecs

import DeResultCodecs.given

  1. Currently, the JsonCodecMaker.make call cannot derive codecs for Scala 3 opaque and union types. The workaround is using a custom codec for these types defined with implicit val before the JsonCodecMaker.make call, like here and here.

  2. If ADT leaf classes/object contains dots in their simple names the default name mapper will strip names up to the last dot character. The workaround is to use @named annotation like here:

sealed abstract class Version(val value: String)

object Version {
  @named("8.10") case object `8.10` extends Version("8.10")

  @named("8.09") case object `8.09` extends Version("8.9")
}
  1. When parsing JSON strings to numeric or java.time.* values escaped encoding of ASCII characters is not supported. The workaround is to use custom codecs which parse those values as strings and then convert them to corresponding types, like here:
implicit val customCodecOfOffsetDateTime: JsonValueCodec[OffsetDateTime] = new JsonValueCodec[OffsetDateTime] {
  private[this] val defaultCodec: JsonValueCodec[OffsetDateTime] = JsonCodecMaker.make[OffsetDateTime]
  private[this] val maxLen = 44 // should be enough for the longest offset date time value
  private[this] val pool = new ThreadLocal[Array[Byte]] {
    override def initialValue(): Array[Byte] = new Array[Byte](maxLen + 2)
  }

  def nullValue: OffsetDateTime = null

  def decodeValue(in: JsonReader, default: OffsetDateTime): OffsetDateTime = {
    val buf = pool.get
    val s = in.readString(null)
    val len = s.length
    if (len <= maxLen && {
      buf(0) = '"'
      var bits, i = 0
      while (i < len) {
        val ch = s.charAt(i)
        buf(i + 1) = ch.toByte
        bits |= ch
        i += 1
      }
      buf(i + 1) = '"'
      bits < 0x80
    }) {
      try {
        return readFromSubArrayReentrant(buf, 0, len + 2, ReaderConfig)(defaultCodec)
      } catch {
        case NonFatal(_) => ()
      }
    }
    in.decodeError("illegal offset date time")
  }

  def encodeValue(x: OffsetDateTime, out: JsonWriter): Unit = out.writeVal(x)
}
  1. Do not use implicit def and inline given methods for generation of custom codes. Scala 3.5.0+ shows compilation time warning New anonymous class definition will be duplicated at each inline site for some inline given cases, but for other use cases the compiler will silently generate duplicated codec instances. To mitigate that convert methods of codec generation to def and explicitly derive custom codecs, like here:
object Tags {
  opaque type Tagged[+V, +T] = Any

  type @@[+V, +T] = V & Tagged[V, T]

  def tag[T]: [V] => V => V @@ Tag = [V] => (v: V) => v
}

object Graph {
  import Tags.{@@, tag}

  def tagJsonValueCodec[V, T](codec: JsonValueCodec[V]): JsonValueCodec[V @@ T] = new JsonValueCodec[V @@ T]:
    //println("+1")
    override def decodeValue(in: JsonReader, default: V @@ T): V @@ T = tag[T](codec.decodeValue(in, default: V))
    override def encodeValue(x: V @@ T, out: JsonWriter): Unit = codec.encodeValue(x, out)
    override def nullValue: V @@ T = tag[T](codec.nullValue)

  trait NodeIdTag

  type NodeId = Int @@ NodeIdTag

  case class Node(id: NodeId, name: String)
  case class Edge(node1: NodeId, node2: NodeId)

  given JsonValueCodec[Graph.NodeId] = Graph.tagJsonValueCodec(JsonCodecMaker.make)
  given JsonValueCodec[Graph.Node] = JsonCodecMaker.make
  given JsonValueCodec[Graph.Edge] = JsonCodecMaker.make
}

How to develop

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

Currently, the gh-pages branch contains a lot of historycal data of benchmark results, so to avoid cloing 10Gb of them use --single-branch branch option to fetch sources only.

If developing on a fork, make sure to download the git tags (required by the sbt build):

git remote add upstream git@github.com:plokhotnyuk/jsoniter-scala.git
git fetch --tags upstream

Prerequisites for building of Scala.js and Scala Native modules are Clang 18.x and Node.js 16.x. The following sequence of commands works for me:

sudo apt install clang libstdc++-12-dev libgc-dev 
curl https://raw.githubusercontent.com/creationix/nvm/master/install.sh | bash 
source ~/.bashrc
nvm install 16
node -v

Get report of available dependency updates

sbt ";dependencyUpdates; reload plugins; dependencyUpdates; reload return"

Run tests, check coverage and binary compatibility

sbt -java-home /usr/lib/jvm/jdk-11 ++2.13.15 clean coverage jsoniter-scala-coreJVM/test jsoniter-scala-circeJVM/test jsoniter-scala-macrosJVM/test jsoniter-scala-benchmarkJVM/test coverageReport
sbt -java-home /usr/lib/jvm/jdk-11 clean +test +mimaReportBinaryIssues

BEWARE: jsoniter-scala is included into Scala Community Build for Scala 2 and Scala Open Community Build for Scala 3.

Run JVM benchmarks

Before benchmark running check if your CPU works in performance mode (not a powersave one). On Linux use following commands to print current and set the performance mode:

cat /sys/devices/system/cpu/cpu*/cpufreq/scaling_governor
for i in $(ls /sys/devices/system/cpu/cpu*/cpufreq/scaling_governor); do echo performance | sudo tee $i; done

Then view your CPU frequency with:

cat /proc/cpuinfo | grep -i mhz

Stop un-needed applications and services. List of running services can be printed by:

sudo service --status-all | grep '\[ + \]'
sudo systemctl list-units --state running

Then clear cache memory to improve system performance. One way to clear cache memory on Linux without having to reboot the system:

sudo su
free -m -h && sync && echo 3 > /proc/sys/vm/drop_caches && free -m -h

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 options can be printed by:

sbt jsoniter-scala-benchmarkJVM/clean 'jsoniter-scala-benchmarkJVM/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-benchmarkJVM/clean 'jsoniter-scala-benchmarkJVM/jmh:run -lrf'

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

sbt jsoniter-scala-benchmarkJVM/clean 'jsoniter-scala-benchmarkJVM/jmh:run -lprof'

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

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

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

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

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

sbt jsoniter-scala-benchmarkJVM/clean 'jsoniter-scala-benchmarkJVM/jmh:run -prof gc .*Reading.*'

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 jsoniter-scala-benchmarkJVM/clean 'jsoniter-scala-benchmarkJVM/jmh:run -prof perfnorm TwitterAPIReading.jsoniterScala'

Also, it can be run with a specified list of events. Here is an example of benchmarking using 16 threads to check of CPU stalls:

sbt jsoniter-scala-benchmarkJVM/clean 'jsoniter-scala-benchmarkJVM/jmh:run -t 16 -prof "perfnorm:event=cycles,instructions,uops_executed.core,uops_executed.stall_cycles,cache-references,cache-misses,cycle_activity.stalls_total,cycle_activity.stalls_mem_any,cycle_activity.stalls_l3_miss,cycle_activity.stalls_l2_miss,cycle_activity.stalls_l1d_miss" .*'

List of available events for the perf profiler can be retrieved by the following command:

perf list

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

sbt jsoniter-scala-benchmarkJVM/clean 'jsoniter-scala-benchmarkJVM/jmh:run -prof "jfr:dir=target/jfr-reports" -wi 10 -i 60 TwitterAPIReading.jsoniterScala'

You will get the profile in the jsoniter-scala-benchmark/jvm/target/jfr-reports directory.

To run benchmarks with recordings by Async profiler, extract binaries to /opt/async-profiler directory and set the following runtime variables to capture kernel frames:

sudo sysctl kernel.perf_event_paranoid=1
sudo sysctl kernel.kptr_restrict=0

Then use command like this:

sbt -java-home /usr/lib/jvm/jdk-21 jsoniter-scala-benchmarkJVM/clean 'jsoniter-scala-benchmarkJVM/jmh:run -prof "async:dir=target/async-reports;interval=1000000;output=flamegraph;libPath=/opt/async-profiler/lib/libasyncProfiler.so" -jvmArgsAppend "-XX:+UnlockDiagnosticVMOptions -XX:+DebugNonSafepoints" --p size=128 -wi 5 -i 10 jsoniterScala'

Now you can open direct and reverse flame graphs in the jsoniter-scala-benchmark/jvmtarget/async-reports directory.

Beware that -XX:+DebugNonSafepoints can lead to incorrect report due to a bug which was fixed only for JDK 21 currently.

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/profiler.sh list 6924
Basic events:
  cpu
  alloc
  lock
  wall
  itimer
Perf events:
  page-faults
  context-switches
  cycles
  instructions
  cache-references
  cache-misses
  branches
  branch-misses
  bus-cycles
  L1-dcache-load-misses
  LLC-load-misses
  dTLB-load-misses
  mem:breakpoint
  trace:tracepoint

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

sbt jsoniter-scala-benchmarkJVM/clean 'jsoniter-scala-benchmarkJVM/jmh:run -prof perfasm -wi 10 -i 10 -p size=128 BigIntReading.jsoniterScala'

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 other JSON parsers for different programming languages and compilers
  • comparison with most performant java parsers: simdjson-java, fastjson, jackson, etc.
  • comparison with other JSON parsers for Scala mostly on samples from real APIs, but with mapping to simple types only like strings and primitives and results for GraalVM EE Java8 only
  • comparison with the best binary parsers and serializers for Scala
  • comparison with different binary and text serializers for Scala
  • comparison with JSON serializers for Scala on synthetic samples
  • comparison with JSON parsers for Scala when parsing from/to a string representation
  • 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"

Run Scala.js benchmarks

Use JDK 11+ for building of jsoniter-scala-benchmarkJS module for Scala 2.13 and Scala 3:

sbt -DassemblyJSBenchmarks -java-home /usr/lib/jvm/jdk-11 +jsoniter-scala-benchmarkJS/fullOptJS

Then open the list of benchmarks in a browser:

cd jsoniter-scala-benchmark/js
open scala-3-fullopt.html
open scala-2.13-fullopt.html

Then select the batch mode with storing results in a .zip file.

Use the following command for merging unpacked results from browsers: jq -s '[.[][]]' firefox/*.json >firefox.json

The released version of Scala.js benchmarks is available here.

Run compilation time benchmarks

Use the circe-argonaut-compile-times project to compare compilation time of jsoniter-scala for deeply nested product types with other JSON parsers like argonaut, play-json, and circe in 3 modes: auto, semi-auto, and derivation.

For Scala 3 use the scala3-compile-tests project to compare compilation time of jsoniter-scala for Scala 3 enumerations (sum types) with circe in semi-auto mode.

Please, also, see an amazing talk from Mateusz Kubuszok, co-author of Chimney, about of different approaches in type class derivation and how some secret trick from jsoniter-scala can greatly speed up the most sanely way of auto-derivation that you would like to re-use for derivation of your type-classes too.

Publish locally

Use publishing of SNAPSHOT versions to your local artifact repositories for testing with other libraries or applications.

Publish to the local Ivy repo:

sbt clean +publishLocal

Publish to the local Maven repo:

sbt clean +publishM2

Release

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 on the environment with 16+GB of RAM:

sbt -java-home /usr/lib/jvm/jdk-11 -J-Xmx12g clean release

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

The last step is updating of the tag info in a release list.