Druid
rust-analyzer
Druid | rust-analyzer | |
---|---|---|
25 | 132 | |
13,204 | 13,583 | |
0.3% | 0.8% | |
9.9 | 10.0 | |
5 days ago | 1 day ago | |
Java | Rust | |
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.
Druid
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System Design: Databases and DBMS
Apache Druid
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How to choose the right type of database
Apache Druid: Focused on real-time analytics and interactive queries on large datasets. Druid is well-suited for high-performance applications in user-facing analytics, network monitoring, and business intelligence.
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Choosing Between a Streaming Database and a Stream Processing Framework in Python
Online analytical processing (OLAP) databases like Apache Druid, Apache Pinot, and ClickHouse shine in addressing user-initiated analytical queries. You might write a query to analyze historical data to find the most-clicked products over the past month efficiently using OLAP databases. When contrasting with streaming databases, they may not be optimized for incremental computation, leading to challenges in maintaining the freshness of results. The query in the streaming database focuses on recent data, making it suitable for continuous monitoring. Using streaming databases, you can run queries like finding the top 10 sold products where the “top 10 product list” might change in real-time.
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Show HN: The simplest tiny analytics tool – storywise
https://github.com/apache/druid
It's always a question of tradeoffs.
The awesome-selfhosted project has a nice list of open-source analytics projects. It's really good inspiration to dig into these projects and find out about the technology choices that other open-source tools in the space have made.
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Analysing Github Stars - Extracting and analyzing data from Github using Apache NiFi®, Apache Kafka® and Apache Druid®
Spencer Kimball (now CEO at CockroachDB) wrote an interesting article on this topic in 2021 where they created spencerkimball/stargazers based on a Python script. So I started thinking: could I create a data pipeline using Nifi and Kafka (two OSS tools often used with Druid) to get the API data into Druid - and then use SQL to do the analytics? The answer was yes! And I have documented the outcome below. Here’s my analytical pipeline for Github stars data using Nifi, Kafka and Druid.
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Apache Druid® - an enterprise architect's overview
Apache Druid is part of the modern data architecture. It uses a special data format designed for analytical workloads, using extreme parallelisation to get data in and get data out. A shared-nothing, microservices architecture helps you to build highly-available, extreme scale analytics features into your applications.
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Real Time Data Infra Stack
Apache Druid
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When you should use columnar databases and not Postgres, MySQL, or MongoDB
But then you realize there are other databases out there focused specifically on analytical use cases with lots of data and complex queries. Newcomers like ClickHouse, Pinot, and Druid (all open source) respond to a new class of problem: The need to develop applications using endpoints published on analytical queries that were previously confined only to the data warehouse and BI tools.
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Druids by Datadog
Datadog's product is a bit too close to Apache Druid to have named their design system so similarly.
From https://druid.apache.org/ :
> Druid unlocks new types of queries and workflows for clickstream, APM, supply chain, network telemetry, digital marketing, risk/fraud, and many other types of data. Druid is purpose built for rapid, ad-hoc queries on both real-time and historical data.
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Mom at 54 is thinking about coding and a complete career shift. Thoughts?
Maybe rare for someone to be seeking their first coding job at that age. But plenty of us are in our 50s or older and still coding up a storm. And not necessarily ancient tech or anything. My current project exposes analytics data from Apache Druid and Cassandra via Go microservices hosted in K8s.
rust-analyzer
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Cranelift code generation comes to Rust
go build 3.62s user 0.76s system 171% cpu 2.545 total
I was looking forward to parallel front-end[4], but I have not seen any improvement for these small changes.
[1]: https://github.com/rust-lang/rust-analyzer
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A guide on Neovim's LSP client
For example, intelephense can show diagnostics in real time, there is no need to save the file to get new diagnostics. But rust-analyzer, the language server for rust, can only update diagnostics after saving the file.
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Top 10 Rusty Repositories for you to start your Open Source Journey
6. Rust Analyzer
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The rust-analyzer vscode extension is not working at all.
The rust-analyzer readme suggests you go here for support request. But even there, you'll need to provide more details to get useful help.
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LSP could have been better
For example: https://github.com/rust-lang/rust-analyzer/blob/master/docs/...
> If you create an LSP, it will work best in VS Code.
Any editor can work just as well as (or even better than) VS Code.
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Discussion Thread
So, apparently the reason why rust-analyzer, the LSP server for Rust does not have persistent caching is because it would make "optimizing initial passes less important".
- The AI Content Flippening
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Introducing RustRover – A Standalone Rust IDE by JetBrains
All I want to know is: Will it have a build configuration pulldown?
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Mastering Emacs: What's new in Emacs 29.1
I am not a Rust dev. It surely looks great.
However, from what I understand it seems to supply just a parser separate from the Rust compiler (https://github.com/rust-lang/rust-analyzer/tree/master/crate...) trying to keep up with Rust‘s development. So, in principle, it could have been just another treesitter parser plugin, too.
So, again, the LSP framework does not directly provide any magical benefit over a static parsing framework. All the semantic analysis capabilities stem from a good parser.
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helix shows rust "language server exited"
rust-analyzer > manual > helix > binary > rustup component add rust-analyzer
What are some alternatives?
iced - A cross-platform GUI library for Rust, inspired by Elm
vscode-rust - Rust extension for Visual Studio Code
cube.js - 📊 Cube — The Semantic Layer for Building Data Applications
intellij-rust - Rust plugin for the IntelliJ Platform
Apache Cassandra - Mirror of Apache Cassandra
rustfmt - Format Rust code
Apache HBase - Apache HBase
sublime-rust - The official Sublime Text 4 package for the Rust Programming Language
egui - egui: an easy-to-use immediate mode GUI in Rust that runs on both web and native
coc-rust-analyzer - rust-analyzer extension for coc.nvim
Scylla - NoSQL data store using the seastar framework, compatible with Apache Cassandra
eglot - A client for Language Server Protocol servers