Apache Arrow VS ClickHouse

Apache Arrow : It contains a set of technologies that enable big data systems to process and move data fast

One of the main selling points of Polars over similar solutions such as Pandas is performance. Polars is written in highly optimized Rust and uses the Apache Arrow container format.

Kotlin DataFrame v0.14 comes with improvements for reading Apache Arrow format, especially loading a DataFrame from any ArrowReader. This improvement can be used to easily load results from analytical databases (such as DuckDB, ClickHouse) directly into Kotlin DataFrame.

Apache Arrow

It's this kind of certainty that underscores the vital role of the Apache Software Foundation (ASF). Many first encounter Apache through its pioneering project, the open-source web server framework that remains ubiquitous in web operations today. The ASF was initially created to hold the intellectual property and assets of the Apache project, and it has since evolved into a cornerstone for open-source projects worldwide. The ASF enforces strict standards for diverse contributions, independence, and activity in its projects, ensuring they can withstand the test of time as standards in software development. Many open-source projects strive to become Apache projects to gain the community credibility necessary for adoption as standard software building blocks, such as Apache Tomcat for Java web applications, Apache Arrow for in-memory data representation, and Apache Parquet for data file formatting, among others.

Apache Doris 2.1 has a data transmission channel built on Arrow Flight SQL. (Apache Arrow is a software development platform designed for high data movement efficiency across systems and languages, and the Arrow format aims for high-performance, lossless data exchange.) It allows high-speed, large-scale data reading from Doris via SQL in various mainstream programming languages. For target clients that also support the Arrow format, the whole process will be free of serialization/deserialization, thus no performance loss. Another upside is, Arrow Flight can make full use of multi-node and multi-core architecture and implement parallel data transfer, which is another enabler of high data throughput.

In comes Polars: a brand new dataframe library, or how the author Ritchie Vink describes it... a query engine with a dataframe frontend. Polars is built on top of the Arrow memory format and is written in Rust, which is a modern performant and memory-safe systems programming language similar to C/C++.

I learned yesterday about GoLang's assembler https://go.dev/doc/asm - after browsing how arrow is implemented for different languages (my experience is mainly C/C++) - https://github.com/apache/arrow/tree/main/go/arrow/math - there are bunch of .S ("asm" files) and I'm still not able to comprehend how these work exactly (I guess it'll take more reading) - it seems very peculiar.

The last time I've used inlined assembly was back in Turbo/Borland Pascal, then bit in Visual Studio (32-bit), until they got disabled. Then did very little gcc with their more strict specification (while the former you had to know how the ABI worked, the latter too - but it was specced out).

Anyway - I wasn't expecting to find this in "Go" :) But I guess you can always start with .go code then produce assembly (-S) then optimize it, or find/hire someone to do it.

Clickhouse also has managed service (https://clickhouse.com/)

ClickHouse is rapidly gaining traction for its unmatched speed and efficiency in processing big data. Cloudflare, for example, uses ClickHouse to process millions of rows per second and reduce memory usage by over four times, making it a key player in large-scale analytics. With its advanced features and real-time query performance, ClickHouse is becoming a go-to choice for companies handling massive datasets. In this article, we'll explore why ClickHouse is increasingly favored for analytics, its key features, and how to deploy it on Kubernetes. We'll also cover some best practices for scaling ClickHouse to handle growing workloads and maximize performance.

I have this dataset being updated in ClickHouse in real-time: https://play.clickhouse.com/play?user=play#U0VMRUNUIG1heCh0a...

I also provide a way to export it or attach it to clickhouse-local and analyze it locally: https://github.com/ClickHouse/ClickHouse/issues/29693#issuec...

And you can try it right now.

Install ClickHouse:

    curl https://clickhouse.com/ | sh

ClickHouse is a high-performance, column-oriented SQL database management system (DBMS) designed for online analytical processing (OLAP). ClickHouse allows using Arrow Stream as an output format.

We started to look for alternatives and quickly landed at Clickhouse.

In today’s story, we will discuss CVE-2024-22412 which affected ClickHouse a popular open-source column-oriented database management system typically used for online analytical processing (OLAP) in real-time. You can find the full write-up of the vulnerability here.

This is really great!

I can suggest more ideas that will be easy to add:

- a spark line or heat map of upvotes for every thread: https://github.com/ClickHouse/ClickHouse/issues/59020

- a built-in SQL editor for custom queries;

If you need help in supporting or hosting it, write to milovidov at clickhouse.com