go-py-arrow-bridge VS ClickHouse

Not only in between processes, but also in between languages in a single process. In this POC I spun up a Python interpreter in Go and can pass the Arrow data buffer between processes in constant time. https://github.com/nickpoorman/go-py-arrow-bridge

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

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;

Recently, there were similar attempts (two) of supply chain attacks on the ClickHouse repository, but: - it didn't do anything because CI does not run without approval; - the user's account magically disappeared from GitHub with all pull requests within a day.

Also worth reading a similar example: https://blog.cloudflare.com/cloudflares-handling-of-an-rce-v...

Also, let me recommend our bug bounty program: https://github.com/ClickHouse/ClickHouse/issues/38986 It sounds easy - pick your favorite fuzzer, find a segfault (it should be easy because C++ isn't a memory-safe language), and get your paycheck.

It is the case when you use a default malloc, default memcpy, or default string functions from libc.

In ClickHouse, we use jemalloc as a memory allocator and custom memcpy: https://github.com/ClickHouse/ClickHouse/blob/master/base/gl...

So, the Musl build does not imply performance degradations. But the usage of Musl is not related to Docker, because ClickHouse is a single self-contained binary anyway, and it is easy to use without Docker.