jvm-serializers
java-json-benchmark
jvm-serializers | java-json-benchmark | |
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7 | 5 | |
3,277 | 928 | |
- | - | |
4.4 | 6.0 | |
8 months ago | 9 days ago | |
Java | Java | |
- | MIT License |
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jvm-serializers
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Fury: 170x faster than JDK, fast serialization powered by JIT and Zero-copy
Compared with protobuf, fury is 3.2x faster. When comparing with avro, fury is 5.3x faster. Compared with flatbuffers, fury is 4.8x faster. See https://github.com/eishay/jvm-serializers/wiki for detailed benchmark data
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The state of Java Object Serialization libraries in Q2 2023
First, there's benchmarks here if you haven't seen it: jvm-serializers. Not terribly scientific, but it's something. To make any decision, you really need to benchmark your own object graph and it's important to configure the serializer for your particular usage. Still, it is sort of useful for comparing frameworks. It would be interesting to see how Loial performs there. Ping me if you add it.
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Up to 100x Faster FastAPI with simdjson and io_uring on Linux 5.19+
It depends. Some binary encodings such as flatbuffer are actually slower than some JSON libraries. There's a wide range of performance even in the JSON libraries themselves. Generally the faster JSON libraries are the ones that work on a predefined schema and so are able to generate code specifically for that JSON.
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Go standard library: structured, leveled logging
> I'm surprised this is up for debate.
I looked into logging in protobuf when I was seeing if there was a better binary encoding for ring-buffer logging, along the same lines as nanolog:
https://tersesystems.com/blog/2020/11/26/queryable-logging-w...
What I found was that it's typically not the binary encoding vs string encoding that makes a difference. The biggest factors are "is there a predefined schema", "is there a precompiler that will generate code for this schema", and "what is the complexity of the output format". With that in mind, if you are dealing with chaotic semi-structured data, JSON is pretty good, and actually faster than some binary encodings:
https://github.com/eishay/jvm-serializers/wiki/Newer-Results...
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Scala 3.0 serialization
You could use any of the JVM serialisers which should still work.
java-json-benchmark
- Any fresh jvm21 benchmarks ?
- The Newest Java Json Benchmark Results just dropped
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Serverless Speed: Rust vs. Go, Java, and Python in AWS Lambda Functions
https://github.com/fabienrenaud/java-json-benchmark is an older benchmark. Jsoniter / DslJson haven't really had updates in a while.
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Up to 100x Faster FastAPI with simdjson and io_uring on Linux 5.19+
It depends. Some binary encodings such as flatbuffer are actually slower than some JSON libraries. There's a wide range of performance even in the JSON libraries themselves. Generally the faster JSON libraries are the ones that work on a predefined schema and so are able to generate code specifically for that JSON.
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Is there a reason to not use Spring Data JPA and Jackson in big projects?
The org.json library is a great little library,. I use it all the time. Just make sure you don't use it for anything that you want to be high performance... We noticed a bottle neck in our application around the reading and writing of json. I did a little reading and found that it was one of the slower libraries out there. https://github.com/fabienrenaud/java-json-benchmark
What are some alternatives?
fury-benchmarks - Serialization Benchmarks for fury with other libraries
fastjson2 - 🚄 FASTJSON2 is a Java JSON library with excellent performance.
Apache Avro - Apache Avro is a data serialization system.
MyBatis - MyBatis SQL mapper framework for Java
zio-json - Fast, secure JSON library with tight ZIO integration.
fastjson - FASTJSON 2.0.x has been released, faster and more secure, recommend you upgrade.
opentelemetry-specificatio
jackson-databind - General data-binding package for Jackson (2.x): works on streaming API (core) implementation(s)
janino - Janino is a super-small, super-fast Java™ compiler.
json_benchmarks - Java Json library benchmarks
grpc-dotnet - gRPC for .NET
ustore - Multi-Modal Database replacing MongoDB, Neo4J, and Elastic with 1 faster ACID solution, with NetworkX and Pandas interfaces, and bindings for C 99, C++ 17, Python 3, Java, GoLang 🗄️