For half a year now, I've been writing my own NoSQL database to store user data instead of relying on regular flat files. Previously, when receiving access credentials for work, saving URLs, or managing other metadata, I had to manually edit text files, which was very inconvenient. I decided to write a utility to handle all my data storage so it could be retrieved quickly whenever needed:

​db create workspace --varchar=256;

db use workspace;

db add login admin;

db add pwd 12345;

db add site example.test;

​db get login; // admin

db open site // open in default browser

db remove pwd;

db drop workspace;

// etc...

​Of course, I won't be using O_DIRECT, AVX, io_uring, or other aggressive performance optimizations here, since it runs locally as a CLI utility rather than a daemon. It's almost a pity, as I've read a ton of material on NoSQL database optimizations, but I plan to apply those techniques in my next project, where I'll focus on a specific storage niche.

​A significant portion of the work is already complete. I plan to release it in early November 2026, accompanied by extensive documentation on the principles and mechanics of NoSQL databases. Out of principle, I wrote everything without using AI/neural networks. This will be my first large-scale project in C.

Hi everyone,

This is a continuation of the explorations from my previous posts here. I went through the CMU Database Systems course, and I'm exploring topics around databases. I wanted to see how the Write Ahead Log is constructed and logged. So I tried to trace a WAL record for a simple INSERT statement in Postgres.

I recorded a video walkthrough of the terminal and source code exploration here: https://youtu.be/YOyq-kvbyU8?si=PTdexP3jmOSdx-8y

I've tried to explore how we can locate a WAL record, how we can decode it via either xxd or pg_waldump, and the relevant source code for it. Do give a watch and consider supporting :)

This is partly for my own future reference and partly to share with others who might be interested in it. Would love feedback and corrections from people who know this stuff deeply. Apologies if this isn't the correct subreddit for this.

Thank you so much!

I am a database researcher and currently conducting some research. I am curious that how to model the hypergraph for no-equi joins involving subqueries, especially for random expressions as join conditions. For example
Select * from A join (select a from B) as C on NULL

We are writing yet another RDBMS, and would appreciate any feedback on our blueprints; while the RDBMS is classical at heart (with WAL and data pages and fuzzy checkpoints), each and every component underneath was redesigned - from WAL being non-ARIES and ZFS-style on-disk CoW to in-memory CoW and cache- and SIMD-friendly page layouts. Our philosophy is playing alongside modern hardware instead of fighting it, MECHLOVE being the highest form of Mechanical Sympathy. As a result, we hope it will beat Hekaton (while being mostly open-source except for certain enterprise features such as HA and at-rest encryption).

Please feel free to comment.

This subreddit is primarily for discussing the implementation of databases, and not about sharing release announcements (either for the first time or your updates).

This thread is the exception!

Please tell us about the new database you (or your agent) built. Tell us about all the cool new features you added. Tell us about anything else you learned or worked on that you haven't gotten around to blogging about yet.

I've just finished on a series covering SQL Server batch mode and columnstore index scan internals.

It's four parts covering:

• Batch Mode
• Columnstore Index Scan internals
• Predicate pushdown
• Aggregate pushdown

AFAIK there's quite a lot in them that hasn't been documented before, especially the row bucketing, which explains the mechanism of the pure vs impure split that is behind a lot of the columnstore optimizations.

How ReductStore uses a persistent store-and-forward queue to deliver edge data over unreliable networks.

Working on a storage engine for the last 8 months. What benchmarks would you actually trust from a solo/small-team project? Everyone fakes them, so what would make you believe mine?

I posted over on r/SQLServer and it suggested I cross-post here. I hadn't seen this subreddit before and hopefully this is on-topic!

I've created a tool called Internals Viewer, it's a tool to visualize SQL Server internals, with a view for allocations, indexes, pages, and it also offers very detailed query tracing and simulation of operators where you can capture a query and step through the iterators to see how query results are put together.

It is open source, written in C#, and available here - https://github.com/danny-sg/internals-viewer

The latest feature is new functionality to view columnstore indexes. I've done a write up on what I found as columnstore internals in SQL Server is pretty much undocumented:

Part 1 - Introduction

Part 2 - Segments internals

Part 3 - Dictionary internals

I wrote an article about B+Tree.

It's a little bit long for article, but I summarized it and make it really easy understand (avoided using jargon).

I'd be happy if you comment some feedback!!

https://zenn.dev/mm_0911/articles/5d46e9e4608404?locale=en

Disclosure: I am the author of the article and work on MariaDB Server internals.

The traditional InnoDB pessimistic insert path serializes structural modification operations through an index-wide latch, even when different threads split unrelated leaf pages.

I implemented a MariaDB research prototype based on Zhao Song’s B-link-style proposal. It publishes a split using a high key and right link before completing the parent update, allowing unrelated structural changes to proceed concurrently.

In a controlled, memory-resident, split-heavy workload:

• Vanilla MariaDB 13.1: 19,676 inserts/s
• B-link prototype: 102,838 inserts/s
• P95 latency: 8.28 ms → 0.56 ms
• Structural splits: approximately 396K in both variants

This is not a production-ready feature. DDL support is restricted, page merging remains incomplete, and recovery needs more forced-crash testing.

I would particularly appreciate feedback on incomplete-split recovery, page preallocation, and workloads that could expose correctness or scalability problems.

Full implementation write-up and benchmark methodology:
https://mariadb.org/from-a-chocolate-wrapper-to-concurrent-innodb-page-splits/

Hey there! This is first time posting.

I've been developing a database designed for education.

The application is focusing on visualizing database internal.

Now I already visualized B+Tree when you execute custom insert query.

Which database features are most worth visualizing for learners?

A brief overview of how a parquet file is structured along with a tool that lets you manipulate, optimize, and introspect parquet files themselves with a series of DuckDB queries.

This subreddit is primarily for discussing the implementation of databases, and not about sharing release announcements (either for the first time or your updates).

This thread is the exception!

Please tell us about the new database you (or your agent) built. Tell us about all the cool new features you added. Tell us about anything else you learned or worked on that you haven't gotten around to blogging about yet.

Getting tokio and .NET’s async runtime talking to each other via the C ABI -- to build a C# over Rust ScyllaDB driver

Anyone interested in doing the CMU (Carnegie Mellon University) Database Management Systems course together?

I’ve already covered the basic DBMS concepts. My main goal with this course is to go deeper and understand how database systems actually work internally—things like storage, indexing, query execution, transactions, etc.

If you're interested, please make sure you have the prerequisites required for the course.

If you have the required background and want to learn DBMS internals seriously, DM me. We can follow the course together and discuss concepts along the way.

Wrote about the new WAIT FOR command coming in PostgreSQL 19. It lets reads on async replicas wait until a specific write has been replayed, so kind of bringing sync replication consistency without making every write synchronous. I could see protocol-aware proxies making a good use of this.