QMetaObject crate for Rust VS Druid

You can use Rust with QML[1].

QML is actually pretty amazing. I've been building my block editor[2] view entirely in QML while the model is in C++. This separation of logic and presentation works great. And yes, there are some crashes sometimes (that I find quite easy to debug thanks to the built-in debugger), but take for example a similar app that's built with Rust and Dart[3], in my testing there were still memory leaks that caused my computer to hang. It's better to know you have a bug than for it to be hidden from you.

I agree with parent commenter, saying these cross-platform frameworks will end up supporting the least common denominator set of features. But I found with external open source libraries, the community is catching up very fast. For example, you want the awesome translucency macOS apps have for your Qt app? Here you go[4]. Many such cases. It's also pretty straightforward to add your own custom OS-dependent code, especially so, if someone already open sourced his approach. I recently wanted to move the traffic light buttons on macOS for my app, but couldn't figure the Objective-C code for that. I ended up looking at either Tauri or Electron source code and found my answer.

[1] https://github.com/woboq/qmetaobject-rs

[2] https://www.get-plume.com/

[3] https://www.appflowy.io/

[4] https://github.com/stdware/qwindowkit

I just used qmetaobject-rs and my experience with Qt/QML. There's the QML book from Qt if you're just starting, which is pretty nice https://www.qt.io/product/qt6/qml-book

qmetaobject is probably the most mature Qt Rust binding at the moment. It uses the cpp crate to embed C++ inline in Rust to create its bindings. There are some people using it and it does get some maintenance, but it's not under active development since the original author is now working on Slint.

The bindings currently use the git version of qttypes since I had to merge some upstream changes that are needed for these bindings. So they are not ready for prime time just yet.

There are a bunch of bindings with different language, but even the ones that are officially supported like PySide will still be second class citizen and awkward to use.

Automated binding generation will never give you idiomatic API in whatever language. And if you want an idiomatic library that wraps Qt, it's going to take a huge amount of work.

Which is why I think restricting to QML makes sense because that's a much smaller API surface. That was the ambition behind my previous crate that exposes QML to rust: https://github.com/woboq/qmetaobject-rs/

But now I've moved on to another GUI project: Slint https://github.com/slint-ui/slint

Whisperfish does this with Qt: https://github.com/woboq/qmetaobject-rs/issues/102

What do you want to know? It's pretty easy thanks to the amazing work of guys behind qmetaobject-rs.

This is probably the portion that I found the most difficult. The README of qmetaobject-rs gives us a basic idea of the build script, so I started with that. Here is my starting script

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.

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.

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.

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.

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.

Apache Druid

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.

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.

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.

Fortunately, it is super easy to build your own leveraging the binary distribution and existing docker.sh.