In an era where "cloud-native," "distributed," and "infinitely scalable" are the reigning buzzwords, proposing a simple, file-based database might seem like a step backward. But we believe that for a significant class of modern applications, radical simplicity is the key to unlocking performance and developer productivity. This is the story of why we created Gagatek.

The Problem with Complexity

Modern database systems are engineering marvels. They can handle petabytes of data, replicate across continents, and guarantee transactional consistency under extreme load. However, this power comes at a cost:

  • Configuration Hell: Setting up, tuning, and maintaining a distributed database is a full-time job.
  • Opaque Performance: When a query is slow, debugging it can feel like a journey into a black box.
  • High Overhead: For many read-heavy applications, the overhead of a full-fledged database is simply unnecessary.
"We noticed a growing trend: applications with read-heavy workloads were drowning in the complexity of systems designed for write-heavy, transactional scenarios."

The Gagatek Philosophy

We decided to build a database with a different set of priorities, focusing on the needs of analytics dashboards, serverless functions, mobile apps with offline capabilities, and edge computing.

Our core principles are:

  1. Speed Through Simplicity: By removing layers of abstraction and focusing on a read-only, columnar format, we can optimize directly for modern hardware (multi-core CPUs, fast SSDs).
  2. Data Portability: Your entire dataset is a single .sldb file. Move it, copy it, version it with Git. It's just a file.
  3. Zero-Configuration: No servers to manage. No network latency. Just a library that reads a file.

An Example in Practice

Imagine you have a dataset of user events. With a traditional database, you might run a query like this:

SELECT
    event_type,
    COUNT(*) AS event_count
FROM
    user_events
WHERE
    timestamp >= '2025-07-01'
GROUP BY
    event_type
ORDER BY
    event_count DESC;

With Gagatek, the equivalent operation is performed by a highly optimized, native library function that scans the relevant columns directly from the file, providing results with minimal overhead. It's not just a query; it's a direct conversation with your data.

We invite you to join us on this journey. Check out the project on GitHub and see how radical simplicity can change the way you think about data.