noisepage VS ClickHouse

> Carne

Okay, so, noisepage appears to be open source https://github.com/cmu-db/noisepage/

But I can't find the Ottertune Github page

Is any part of Ottertune open source?

I was one of the main authors of a research project called Peloton (https://github.com/cmu-db/peloton) which was later rebranded to NoisePage (https://github.com/cmu-db/noisepage). The initial version of RisingWave actually borrowed a lot from Peloton (fun fact: that's also how DuckDB https://duckdb.org/ started!), but we decided to rewrite in Rust due to development cost and security (e.g., memory leakage) considerations (more info: https://www.risingwave-labs.com/blog/building-a-cloud-database-from-scratch-why-we-moved-from-cpp-to-rust/).

> If I may, can you please shed light on why Peloton had to be archived and in essence re-done with OtterTune. Interested in your team's learnings from it from a software engineering point of view.

Peloton and OtterTune are completely different projects. Peloton was abandoned and rewritten as NoisePage (https://noise.page). OtterTune has always been OtterTune.

See this recent interview where I discuss why we gave up on Peloton:

https://www.ibm.com/cloud/blog/database-deep-dives-with-andy...

> - How did the team ensure this project doesn't suffer from the same disadvantages as its predecessor?

Again, different projects. OtterTune is all about not having to modify the internals of Postgres, MySQL, and any other DBMS. This is why we were able to support Oracle in the academic version in a short amount of time:

https://ottertune.com/blog/vldb-autonomous-database-tuning-i...

> - What would you advise other teams undertaking a rewrite to pay off their tech debts?

It is hard for to provide general advice for this question because every situation is different.

> How does this project compare to / contrast with Google's and SingleStore's efforts in this space?

I am not familiar with Google or SingleStore using ML in the manner that we are with OtterTune to tune configuration knobs. Or at least I have not seen anything public about it.

These days Oracle is the most aggressive with pushing automated tuning capabilities (Oracle's autonomous DBaaS, AutoPilot for MySQL Heatwave). The difference with these approaches and OtterTune is that right now we are focused on configuration tuning (to avoid data privacy issues) and our core approach is platform/DBMS agnostic.

> Any chance we see you do a Peter Bailis and Sisu Data this? (:

I don't know what you mean by this? Peter Bailis is the Ryan Gosling of databases.

The state of the art for this sort of thing is (Leanstore/Umbra - https://umbra-db.com/) or the new NoisePage database (https://github.com/cmu-db/noisepage/tree/master/src/storage). There is also the HyRise database, but that one focuses more on datasets that fit entirely in memory (https://hpi.de/plattner/projects/hyrise.html)

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;