Bedrock VS ClickHouse

Also Expensify's Bedrock, which powers their famous "Scaling SQLite to 4M QPS" article:

https://bedrockdb.com/

https://use.expensify.com/blog/scaling-sqlite-to-4m-qps-on-a...

Lots of things don't need failover, but if you do, you can use Bedrock, which is built on sqlite.

At first, I thought Amazon was launching their own SQLite hosted database.

BedrockDB is a SQLite based database with MySQL compatible drivers.

https://bedrockdb.com

Vapor[0] based on Swift. Advantage of this is that you don't have to evaluate multiple frameworks for Swift and suffer paralysis by analysis. All the Swift community is behind one framework.

The next is Actix[1] based on Rust. There are many frameworks in Rust and most of them have not reached 1.0 And which framework will survive becomes a question.

Other not so well-known is Wt[2] based on C++. This actually is created for programmers who are not web developers. The development experience is similar to desktop app development like Qt.

If that is not acceptable then Django[3], based on Python, is the one that will be good for you.

For the front-end I would recommend Flutter[4]. As much as I dislike getting tied to a single company for whom the framework is not their bread-and-butter, I don't see any other viable options to Flutter that will cover all web, mobile and desktop out of the box.

For databases, I would recommend BedrockDB[5], if you are not averse to SQLite. Or FoundationDB[6], if you want NoSQL. But if you are not concerned about horizontal scalability or okay with self-managing database availability, then PostgreSQL[7] is a very good option.

For push notifications, PushPin[8] is a good option.

[0] https://vapor.codes

[1] https://actix.rs

[2] https://webtoolkit.eu

[3] https://www.djangoproject.com

[4] https://flutter.dev

[5] https://bedrockdb.com

[6] https://www.foundationdb.org

[7] https://postgresql.org

[8] https://pushpin.org

Recently I stumbled upon BedrockDB[0] from Expensify. It is based on SQLite and has very interesting idea on HA and distributed DB.

[0] https://bedrockdb.com

This is not SQLite though, also the test is trivial compared to TPC: https://github.com/Expensify/Bedrock/blob/dbarrett_perftest/...

Don’t forget BedrockDB (built on SQLite) that’s used in production at Expensify.

How it scales as well.

https://bedrockdb.com/

https://blog.expensify.com/2018/01/08/scaling-sqlite-to-4m-q...

Yes, we are working on it! :) Taking some of the learnings from current experimental JSON Object datatype, we are now working on what will become the production-ready implementation. Details here: https://github.com/ClickHouse/ClickHouse/issues/54864

Variant datatype is already available as experimental in 24.1, Dynamic datatype is WIP (PR almost ready), and JSON datatype is next up. Check out the latest comment on that issue with how the Dynamic datatype will work: https://github.com/ClickHouse/ClickHouse/issues/54864#issuec...

In our repository, I've set up a few hard limits: each translation unit cannot spend more than a certain amount of memory for compilation and a certain amount of CPU time, and the compiled binary has to be not larger than a certain size.

When these limits are reached, the CI stops working, and we have to remove the bloat: https://github.com/ClickHouse/ClickHouse/issues/61121

Although these limits are too generous as of today: for example, the maximum CPU time to compile a translation unit is set to 1000 seconds, and the memory limit is 5 GB, which is ridiculously high.

I have a project dedicated to this topic: https://github.com/ClickHouse/ClickBench

It is important to explain the limitations of a benchmark, provide a methodology, and make it reproducible. It also has to be simple enough, otherwise it will not be realistic to include a large number of participants.

I'm also collecting all database benchmarks I could find: https://github.com/ClickHouse/ClickHouse/issues/22398

ClickHouse: A fast open-source column-oriented database management system. ClickHouse is designed for real-time analytics on large datasets and excels in high-speed data insertion and querying, making it ideal for real-time monitoring and reporting.

Today we're going to create an UDF (User-defined Function) in Golang that can be run inside Clickhouse query, this function will parse uuid v1 and return timestamp of it since Clickhouse doesn't have this function for now. Inspired from the python version with TabSeparated delimiter (since it's easiest to parse), UDF in Clickhouse will read line by line (each row is each line, and each text separated with tab is each column/cell value):

For the third, examples here might be analytics plugins in specialized databases like Clickhouse, data-transformations in places like your ETL pipeline using Airflow or Fivetran, or special integrations in your authentication workflow with Auth0 hooks and rules.

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.

Proton is a lightweight streaming processing "add-on" for ClickHouse, and we are making these delta parts as standalone as possible. Meanwhile contributing back to the ClickHouse community can also help a lot.

Please check this PR from the proton team: https://github.com/ClickHouse/ClickHouse/pull/54870

curl https://clickhouse.com/ | sh

But I continue to find garbage in some of our CI scripts.

Here is an example: https://github.com/ClickHouse/ClickHouse/pull/58794/files

The right way is to:

- always pin versions of all packages;