1brc VS MindsDB

This would be the code to beat. Ideally with only 8 cores but any number of cores is also very interesting.

https://github.com/gunnarmorling/1brc/discussions/710

Given that 1-billion-line-file is approximately 13GB, instead of providing a fixed database, the official repository offers a script to generate synthetic data with random readings. Just follow the instructions to create your own database.

Local disk I/O is no longer the bottleneck on modern systems: https://benhoyt.com/writings/io-is-no-longer-the-bottleneck/

In addition, the official 1BRC explicitly evaluated results on a RAM disk to avoid I/O speed entirely: https://github.com/gunnarmorling/1brc?tab=readme-ov-file#eva... "Programs are run from a RAM disk (i.o. the IO overhead for loading the file from disk is not relevant)"

You may have heard of the "The One Billion Row Challenge" (1brc) and in case you don't, go checkout Gunnar Morlings's 1brc repo.

Here’s a thread on results with duckdb, I don’t mean to discourage you taking a shot at all though: https://github.com/gunnarmorling/1brc/discussions/39

If you are in “javaland” look at billion row challenge, you will learn a lot - https://github.com/gunnarmorling/1brc

Even manual vectorization is pain...writing ASM, really?

Rust has unstable portable SIMD and a few third-party crates, C++ has that as well, C# has stable portable SIMD and a very small BLAS-like library on top of it (hell it even exercises PackedSIMD when ran in a browser) and Java is getting stable Panama vectors some time in the future (though the question of codegen quality stands open given planned changes to unsafe API).

Go among these is uniquely disadvantaged. And if that's not enough, you may want to visit 1Brc's challenge discussions and see that Go struggles get anywhere close to 2s mark with both C# and C++ are blazing past it:

https://hotforknowledge.com/2024/01/13/1brc-in-dotnet-among-...

https://github.com/gunnarmorling/1brc/discussions/67

In terms of performance: I realize that this is a somewhat "toy" issue, and it's a sample size of 1, but for the currently ongoing "One Billion Row Challenge"[1] (an ongoing Java performance competition related to parsing and aggregating a 13 GB file), all of the current top-performers are using Unsafe. More specifically, the use of Unsafe appears to have been the change for a few entries that allowed getting below the 3-second barrier in the test.

1. https://github.com/gunnarmorling/1brc

Check us out on GitHub.

If your data lacks uniform time intervals between consecutive entries, QuestDB offers a solution by allowing you to sample your data. After that, MindsDB facilitates creating, training, and deploying your time-series models.

MindsDB is an open-source AI platform for developers that connects AI/ML models with real-time data. It provides tools and automation to easily build and maintain personalized AI solutions.

MindsDB combines both AI and SQL functions in one; users can create, train, optimize, and deploy ML models without the need for external tools. Data analysts can create and visualize forecasts without having to navigate the complexities of ML pipelines.MindsDB is open-source and works with well-known databases like MySQL, Postgres, Redit, Snowflakes, etc.

Mindsdb is a good example. It abstracts everything related to an AI workflow as "virtual tables". For example, you can import OpenAI API as a "virtual table":

Repo : https://github.com/mindsdb/mindsdb

MindsDB is an AI Automation platform for building AI/ML powered features and applications. It works by connecting any data source with any AI/ML model or framework and automating how real-time data flows between them. MindsDB is integrated with LlamaIndex, which makes use of its data framework for connecting custom data sources to large language models. LlamaIndex data ingestion allows you to connect to data sources like PDF’s, webpages, etc., provides data indexing and a query interface that takes input prompts from your data and provides knowledge-augmented responses, thus making it easy to Q&A over documents and webpages.

Now, imagine if you can deploy these highly trained models in your database to get insights, make predictions, understand your users, auto-generate content, and more. MindsDB makes this possible! MindsDB is an open-source AI database middleware that allows you to supercharge your databases by integrating various machine learning (ML) engines.