iced VS Druid

iced-rs/iced for OS-native GUI app development

Rewrite Muscurdi - Password Manager in Rust with [iced](https://github.com/iced-rs/iced, dioxus and/or tauri

Based on [1] it looks like System76 does actually think about accessibility, but it's not upstreamed yet.

[1]: https://github.com/iced-rs/iced/issues/552#issuecomment-2180...

AFAICT the "big deal" here is using a rust-native toolkit, iced[0], rather than just slapping together another reskinned gtk-based solution like most similar submissions do.

So this is potentially a really cool thing, not just somebody tinkering with UI elements.

[0] https://github.com/iced-rs/iced

In the last part of the series, we will build a simple UI for Zerocalc. We will use the Iced GUI library as it has a very nice API and very poor documentation which is a great opportunity to dig into its internals...

It's not actually a limitation, the ecosystem is just young:

https://github.com/iced-rs/iced

My general point though is that there's still some ecosystem/legacy reasons to stick with C++, but Rust very much fills the same niches as C++.

Hi all!

Castiron is a super simple podcast player/manager I am building to learn Rust. It uses the iced [https://iced.rs/] GUI library.

Currently the feature set is pretty slim, but you can:

Apache Druid

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.