FlintDB

FlintDB is a local, embedded database designed for storing, querying, and analyzing JSON data directly on disk without the need for a separate server, schema definitions, or ETL processes. Its architecture is built around a Rust core with mmap-backed storage, enabling synchronous operations and read times of approximately two microseconds. FlintDB is positioned for developers seeking a fast, schema-less solution for managing and analyzing structured or nested JSON data in local applications.

The database allows users to store JSON documents as-is, including API responses, logs, and nested objects, eliminating the need for migrations, schema headaches, or object-relational mapping. Queries are expressed as plain JSON objects, rather than SQL strings, simplifying the query process and removing the need for a query parser. FlintDB supports a range of query capabilities, including basic filtering, sorting, grouping (GroupBy), window functions, and analytics performed directly within the engine. Built-in analytics functions include average, standard deviation, exponential moving average (EMA), percentile, and correlation, allowing users to compute statistical metrics without exporting data or relying on external tools like Pandas.

A notable feature of FlintDB is its integrated vector search, enabling users to store vector embeddings alongside documents and perform similarity searches using cosine or L2 metrics. This unifies data and vector storage within a single database, removing the need for a separate vector database. The platform also supports advanced analytics in its Pro plan, such as financial indicators (MACD, RSI, Bollinger, Ichimoku, VWAP), statistical analysis (t-test, ANOVA, Chi-square, K-means), seasonal decomposition, anomaly detection, and linear forecasting.

FlintDB is installable via npm or pip, making it accessible for developers working in different environments. The core offering is free, while the Pro plan is available for $99 per year per module. The tool is developed by Orchardworks and is suitable for use cases that require fast, local data storage and analysis without the complexity of traditional database servers or ETL pipelines.