greptimedb
julia
greptimedb | julia | |
---|---|---|
16 | 350 | |
3,781 | 44,534 | |
4.8% | 0.4% | |
9.9 | 10.0 | |
about 13 hours ago | about 12 hours ago | |
Rust | Julia | |
Apache License 2.0 | MIT License |
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.
greptimedb
- GreptimeDB: A fast and cost-effective alternative to InfluxDB
- Another distributed time-series database written in Rust
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GreptimeAI + Xinference - Efficient Deployment and Monitoring of Your LLM Applications
GreptimeAI, built upon the open-source time-series database GreptimeDB, offers an observability solution for Large Language Model (LLM) applications, currently supporting both LangChain and OpenAI's ecosystem. GreptimeAI enables you to understand cost, performance, traffic and security aspects in real-time, helping teams enhance the reliability of LLM applications.
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What's everyone working on this week (49/2023)?
Continuing to work hard on a new MetricEngine in GreptimeDB. BTW, If you have a keen interest in Rust or database development, GreptimeDB might be a good starting point. Check it out for some good first issues here.
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Practical Tips for Refactoring Release CI using GitHub Actions
Since the very first day of GreptimeDB going open-source, it embraced the automated software building process with GitHub Actions, and leading to the inaugural Release Pipeline.
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GreptimeCloud - A Fully Managed Serverless Prometheus Backend
Born from the open-source project GreptimeDB, GreptimeCloud serves as a fully-managed, serverless cloud backend for Prometheus, offering integrated support for remote read/write protocols and PromQL as one of our primary query languages.
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Bridging Async and Sync Rust Code - A lesson learned while working with Tokio
Recently, while working on our GreptimeDB project, we encountered an issue with calling asynchronous Rust code in a synchronous context.
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A Deep Dive into PromQL — Promql Parser v0.1.0 Written in Rust is Now Available
To explore data stored in GreptimeDB through PromQL, GreptimeDB needs to provide the ability to parse the query into AST (abstract syntax tree), and retrieve data from memory or disk via logical and physical plans. Since there is no ready-to-use PromQL Rust Parser, our team decides to develop it by ourselves. We’re glad to announce that promql-parser v0.1.0 is now available.
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Extending Python with Rust
This is truly a fantastic combination -- implement the logic in Rust and use it in Python. GreptimeDB also implements a similar functionality that allows writing Python script to do post-process of SQL query results, with the help of RustPython and Arrow. Maybe this combination can bring a sweet point between performance and efficiency.
docs: https://docs.greptime.com/user-guide/coprocessor-and-scripti...
code: https://github.com/GreptimeTeam/greptimedb/tree/develop/src/...
julia
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Top Paying Programming Technologies 2024
34. Julia - $74,963
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Optimize sgemm on RISC-V platform
I don't believe there is any official documentation on this, but https://github.com/JuliaLang/julia/pull/49430 for example added prefetching to the marking phase of a GC which saw speedups on x86, but not on M1.
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Dart 3.3
3. dispatch on all the arguments
the first solution is clean, but people really like dispatch.
the second makes calling functions in the function call syntax weird, because the first argument is privileged semantically but not syntactically.
the third makes calling functions in the method call syntax weird because the first argument is privileged syntactically but not semantically.
the closest things to this i can think of off the top of my head in remotely popular programming languages are: nim, lisp dialects, and julia.
nim navigates the dispatch conundrum by providing different ways to define free functions for different dispatch-ness. the tutorial gives a good overview: https://nim-lang.org/docs/tut2.html
lisps of course lack UFCS.
see here for a discussion on the lack of UFCS in julia: https://github.com/JuliaLang/julia/issues/31779
so to sum up the answer to the original question: because it's only obvious how to make it nice and tidy like you're wanting if you sacrifice function dispatch, which is ubiquitous for good reason!
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Julia 1.10 Highlights
https://github.com/JuliaLang/julia/blob/release-1.10/NEWS.md
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Best Programming languages for Data Analysis📊
Visit official site: https://julialang.org/
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Potential of the Julia programming language for high energy physics computing
No. It runs natively on ARM.
julia> versioninfo() Julia Version 1.9.3 Commit bed2cd540a1 (2023-08-24 14:43 UTC) Build Info: Official https://julialang.org/ release
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Rust std:fs slower than Python
https://github.com/JuliaLang/julia/issues/51086#issuecomment...
So while this "fixes" the issue, it'll introduce a confusing time delay between you freeing the memory and you observing that in `htop`.
But according to https://jemalloc.net/jemalloc.3.html you can set `opt.muzzy_decay_ms = 0` to remove the delay.
Still, the musl author has some reservations against making `jemalloc` the default:
https://www.openwall.com/lists/musl/2018/04/23/2
> It's got serious bloat problems, problems with undermining ASLR, and is optimized pretty much only for being as fast as possible without caring how much memory you use.
With the above-mentioned tunables, this should be mitigated to some extent, but the general "theme" (focusing on e.g. performance vs memory usage) will likely still mean "it's a tradeoff" or "it's no tradeoff, but only if you set tunables to what you need".
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Eleven strategies for making reproducible research the norm
I have asked about Julia's reproducibility story on the Guix mailing list in the past, and at the time Simon Tournier didn't think it was promising. I seem to recall Julia itself didnt have a reproducible build. All I know now is that github issue is still not closed.
https://github.com/JuliaLang/julia/issues/34753
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Julia as a unifying end-to-end workflow language on the Frontier exascale system
I don't really know what kind of rebuttal you're looking for, but I will link my HN comments from when this was first posted for some thoughts: https://news.ycombinator.com/item?id=31396861#31398796. As I said, in the linked post, I'm quite skeptical of the business of trying to assess relative buginess of programming in different systems, because that has strong dependencies on what you consider core vs packages and what exactly you're trying to do.
However, bugs in general suck and we've been thinking a fair bit about what additional tooling the language could provide to help people avoid the classes of bugs that Yuri encountered in the post.
The biggest class of problems in the blog post, is that it's pretty clear that `@inbounds` (and I will extend this to `@assume_effects`, even though that wasn't around when Yuri wrote his post) is problematic, because it's too hard to write. My proposal for what to do instead is at https://github.com/JuliaLang/julia/pull/50641.
Another common theme is that while Julia is great at composition, it's not clear what's expected to work and what isn't, because the interfaces are informal and not checked. This is a hard design problem, because it's quite close to the reasons why Julia works well. My current thoughts on that are here: https://github.com/Keno/InterfaceSpecs.jl but there's other proposals also.
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Getaddrinfo() on glibc calls getenv(), oh boy
Doesn't musl have the same issue? https://github.com/JuliaLang/julia/issues/34726#issuecomment...
I also wonder about OSX's libc. Newer versions seem to have some sort of locking https://github.com/apple-open-source-mirror/Libc/blob/master...
but older versions (from 10.9) don't have any lockign: https://github.com/apple-oss-distributions/Libc/blob/Libc-99...
What are some alternatives?
risingwave - Cloud-native SQL stream processing, analytics, and management. KsqlDB and Apache Flink alternative. 🚀 10x more productive. 🚀 10x more cost-efficient.
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
cnosdb - A cloud-native open source distributed time series database with high performance, high compression ratio and high availability. http://www.cnosdb.cloud
NetworkX - Network Analysis in Python
FlashDB - An ultra-lightweight database that supports key-value and time series data | 一款支持 KV 数据和时序数据的超轻量级数据库
Lua - Lua is a powerful, efficient, lightweight, embeddable scripting language. It supports procedural programming, object-oriented programming, functional programming, data-driven programming, and data description.
datafuse - An elastic and reliable Cloud Warehouse, offers Blazing Fast Query and combines Elasticity, Simplicity, Low cost of the Cloud, built to make the Data Cloud easy [Moved to: https://github.com/datafuselabs/databend]
rust-numpy - PyO3-based Rust bindings of the NumPy C-API
numexpr - Fast numerical array expression evaluator for Python, NumPy, Pandas, PyTables and more
Numba - NumPy aware dynamic Python compiler using LLVM
corrosion - Gossip-based service discovery (and more) for large distributed systems.
F# - Please file issues or pull requests here: https://github.com/dotnet/fsharp