automq
fury-benchmarks
| automq | fury-benchmarks | |
|---|---|---|
| 8 | 4 | |
| 1,421 | 2 | |
| 50.4% | - | |
| 9.9 | 5.9 | |
| 3 days ago | 9 days ago | |
| Java | Java | |
| GNU General Public License v3.0 or later | - |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
automq
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Tiered storage won't fix Kafka
I agree with your viewpoint. The crux of the matter is not whether to use tiered storage or not, but what trade-offs have been made in the specific storage architecture and what benefits have been gained. Here(https://github.com/AutoMQ/automq?tab=readme-ov-file#-automq-...) is a qualitative comparison chart of streaming systems including kafka/confluent/redpanda/warpstream/automq. This comparison chart does not have specific numerical comparisons, but purely based on their trade-offs at the storage level, I think this will be of some use to you.
- Streaming Platform Comparision:Kafka/Confluent/Pulsar/AutoMQ/Redpanda/Warpstream
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Show HN: AutoMQ – A Cost-Effective Kafka distro that can autoscale in seconds
Yes, thank you for the clarification. AutoMQ has replaced the topic-partition storage with cloud-native S3Stream (https://github.com/AutoMQ/automq/tree/main/s3stream) library, thereby harnessing the benefits of cloud EBS and S3.
- FLaNK Stack Weekly for 20 Nov 2023
fury-benchmarks
- FLaNK Stack Weekly for 20 Nov 2023
- FLaNK Stack Weekly for 30 Oct 2023
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Fury: 170x faster than JDK, fast serialization powered by JIT and Zero-copy
1) Fury is 41.6x faster than jackson for Struct serialization 2) Fury is 65.6x faster than jackson for Struct deserialization 3) Fury is 9.4x faster than jackson for MediaContent serialization 4) Fury is 9.6x faster than jackson for MediaContent deserialization
see https://github.com/chaokunyang/fury-benchmarks for detailed benchmark code.
What are some alternatives?
TinyLlama - The TinyLlama project is an open endeavor to pretrain a 1.1B Llama model on 3 trillion tokens.
jvm-serializers - Benchmark comparing serialization libraries on the JVM
memq - MemQ is an efficient, scalable cloud native PubSub system
MemoryPack - Zero encoding extreme performance binary serializer for C# and Unity.
depthai-python - DepthAI Python Library
MessagePack for C# (.NET, .NET Core, Unity, Xamarin) - Extremely Fast MessagePack Serializer for C#(.NET, .NET Core, Unity, Xamarin). / msgpack.org[C#]
FLaNK-SaoPauloBrazil - FLaNK-SaoPauloBrazil
grpc-dotnet - gRPC for .NET
trip - Elegant middleware functions for your HTTP clients.
incubator-fury - A blazingly fast multi-language serialization framework powered by JIT and zero-copy.
ML-For-Beginners - 12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
orbital - Orbital automates integration between data sources (APIs, Databases, Queues and Functions). BFF's, API Composition and ETL pipelines that adapt as your specs change.