superduperdb
gRPC
superduperdb | gRPC | |
---|---|---|
24 | 11 | |
4,390 | 11,178 | |
3.2% | 0.6% | |
9.9 | 9.6 | |
5 days ago | 6 days ago | |
Python | Java | |
Apache License 2.0 | Apache License 2.0 |
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.
superduperdb
- FLaNK Stack Weekly 12 February 2024
- FLaNK Stack Weekly 11 Dec 2023
- Trending on GitHub top 10 globally for the 4th day in a row: Open-source framework for integrating OpenAI with major databases
- Trending on GitHub top 10 for the 4th day in a row: Open-source framework for integrating AI models and APIs directly with all major SQL databases
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Trending on GitHub top 10 for the 4th day in a row and official technology partner of MongoDB: Open-source framework for integrating AI with MongoDB and MongoDB Atlas
Definitely check it out: https://github.com/SuperDuperDB/superduperdb and find it here: https://cloud.mongodb.com/ecosystem/
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Trending on GitHub top 10 globally for the 4th day in a row: Open-source framework for integrating OpenAI and GPT with major databases
Build a chatbot with OpenAI: https://github.com/SuperDuperDB/superduperdb/blob/main/examples/question_the_docs.ipynb
- SuperDuperDB - how to use it to talk to your documents locally using llama 7B or Mistral 7B?
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Trending on GitHub globally 3 days in a row: SuperDuperDB, a framework for integrating AI with major databases (making them super-duper)
It is for building AI (into your) apps easily without complex pipelines and make your database intelligent (including vector search), definitely check it out: https://github.com/SuperDuperDB/superduperdb
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🔮 SuperDuperDB is #3 on GitHub Trending globally! 🥉
VentureBeat already covered the launch This is our website This is our main GitHub repository
gRPC
- FLaNK Stack Weekly 12 February 2024
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Reference Count, Don't Garbage Collect
That's not true at all. Case in point In general, this is not a problem that AGC can solve. The language can help (something Java is admittedly particularly bad at) but even so, there'll always be avenues for leaks. That's just the nature of shared things. Interestingly, in the linked grpc case, the leaked memory is only half the problem -- AGC doesn't help at all with the leaked HTTP2 connection.
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Distroless Alpine
I've trialled my new image with an existing project via JLink that's heavy on Netty and gRPC the image works great (with a small tweak to exclude grpc-netty-shaded due to grpc-java#9083).
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What are the user agents?
When developing an application, the vast majority of code is written by other people. We import that code and make use of it to get whatever we need done. In this case, the developer of an various android applications are using grpc-java.
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Buf raises $93M to deprecate REST/JSON
`proto_library` for building the `.bin` file from protos works great. Generating stubs/messages for "all" languages does not. Each language does not want to implement gRPC rules, the gRPC team does not want to implement rules for each language. Sort of a deadlock situation. For example:
- C++: https://github.com/grpc/grpc/blob/master/bazel/cc_grpc_libra...
- Python: https://github.com/grpc/grpc/blob/master/bazel/python_rules....
- ObjC: https://github.com/grpc/grpc/blob/master/bazel/objc_grpc_lib...
- Java: https://github.com/grpc/grpc-java/blob/master/java_grpc_libr...
- Go (different semantics than all of the other): https://github.com/bazelbuild/rules_go/blob/master/proto/def...
But there's also no real cohesion within the community. The biggest effort to date has been in https://github.com/stackb/rules_proto which integrates with gazelle.
tl;dr: Low alignment results in diverging implementations that are complicated to understand for newcomers. Buff's approach is much more appealing as it's a "this is the one way to do the right thing" and having it just work by detecting `proto_library` and doing all of the linting/registry stuff automagically in CI would be fantastic.
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grpc_bench: open-source, objective gRPC benchmark
Small clarification (to my understanding, I'm not a Java Guru) on why Java got on top - those Java implementations use something called Direct Executor. It's super performant when there's no chance of a blocking operation. But if you are to do anything more than echo service, you might be in trouble. Other implementations probably don't suffer from the same constraint. The related discussion can be found in this PR.
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Android Java GRPC Tutorial
clone https://github.com/grpc/grpc-java
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GRPC
If you do streaming then the best option would be to use a so called manual flow control. You can find an example here.
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High performing APIs with gRPC
Another interesting link is their official grpc-java benchmarks project, which is also used in the benchmark I've posted you.
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Java 16 EA Alpine & JLink vs Graal
Both JLink (gRPC#3522) and Graal have some issues; I'm especially concerned about the Serial GC in Graal so will be putting that under some stress soon to see if that confirms my suspicions. I'll also be good when some Java 16 JRE Alpine images appear as the JDK is too bloaty.
What are some alternatives?
ds2 - Easiest way to use AI models without coding (Web UI & API support)
Dubbo - The java implementation of Apache Dubbo. An RPC and microservice framework.
best-of-ml-python - 🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.
Netty - Netty project - an event-driven asynchronous network application framework
metaflow - :rocket: Build and manage real-life ML, AI, and data science projects with ease!
Finagle - A fault tolerant, protocol-agnostic RPC system
nyc_traffic_flask - Flask App with leaflet.js that can perform NYC Traffic Prediction
OkHttp - Square’s meticulous HTTP client for the JVM, Android, and GraalVM.
Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials - A comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials - rapidly expanding into areas of AI/Deep Learning / Machine Vision / NLP and industry specific areas such as Climate / Energy, Automotives, Retail, Pharma, Medicine, Healthcare, Policy, Ethics and more.
Undertow - High performance non-blocking webserver
mlops-python-package - Kickstart your MLOps initiative with a flexible, robust, and productive Python package.
KryoNet - TCP/UDP client/server library for Java, based on Kryo