Embedded databases provide many benefits compared to traditional databases. They are simpler to set up and manage, require fewer resources, and deliver superior performance for applications that demand quick data processing and low latency.
As a counterpoint, it is often argued that embedded databases donāt scale but ā for most folks out there ā this is simply not true anymore.
In this article, I’ll highlight the top embedded database options for various scenarios, helping you identify the ideal database for your specific requirements.
Ready to dive in? Let’s get started.
1. SQLite:
SQLite is the de-facto-embedded relational database. So much so that itās become the most used database in the world. It runs as a simple library within an application, providing a robust solution without requiring a separate process. SQLite supports full SQL syntax and is incredibly compact, making it an ideal choice for applications across various platforms including mobile devices, desktop applications, and large-scale websites and web applications.
Its simplicity and the ability to operate without configuration while maintaining a small footprint make SQLite a popular choice among developers for applications where database requirements are modest but demands for reliability and efficiency are high.
2. DuckDB
DuckDB, an in-process SQL OLAP database, is designed specifically for on-the-fly data analysis and is intended to be easy to integrate into other projects. It operates directly on blobs of data and excels in executing complex analytical SQL queries efficiently. DuckDB’s design focuses on vectorized query execution, making it particularly well-suited for analytic workloads where performance and speed are critical.
Its ability to run embedded within other applications without any external dependencies makes it a powerful tool for developers looking to embed data analysis capabilities directly into their applications.
Support us ā
This is not an embedded database, but still relevant if you need to do embedded analytics. We know that building it from scratch can be difficult. That is why we created Latitude. We’re developing it as an open-source project. If youād like to try, here’s a quick start guide available on our repository. If you like it, consider showing your support by giving us a star, itād mean a lot! ā
https://github.com/latitude-dev/latitude
3. RocksDB
RocksDB, developed by Facebook, is a key-value store optimized for fast reads and writes at a high volume, suitable for applications that demand efficient data persistence.
RocksDB’s use of a log-structured merge-tree (LSM tree) is a key aspect of its design, allowing it to achieve high write throughput while maintaining the ability to support reads. This makes it a valuable tool for applications that need to handle large streams of incoming data with minimal latency.
4. Chroma
Chroma is a vector database designed to handle high-throughput and multi-dimensional data with ease. It become widely popular with the advent of genAI, which introduced the need to use vector stores in order to provide AI agents with long term āmemoryā. Itās main use case is to implement RAG for AI agents.
Chroma utilizes a unique storage architecture that optimizes data retrieval processes, significantly reducing query times and enhancing overall application performance. Its ability to efficiently manage large-scale data makes it an excellent choice for developers working on enterprise-level solutions that require robust, scalable, and highly available data management capabilities.
5. KĆ¹zu
KĆ¹zu is an embedded graph database, tailored for edge computing environments and mobile platforms. Think Neo4j but embedded. Graph DBs excel at handling data models that have a lot of many-to-many relationships, like social networks. On top of that, Kuzu offers offline capabilities and synchronization with cloud services and is built to support SQL and NoSQL data models, making it highly adaptable to various data structures and application requirements.
6. Faiss
Faiss is the other big vector database in the AI space, currently. Developed by Facebook AI Research (FAIR), Faiss is designed to handle billions of vectors in high-dimensional spaces with astonishing speed and accuracy. It is particularly useful in applications that involve large-scale machine learning computations, such as recommendation systems, image retrieval, and clustering large datasets.
Faiss can be used standalone or in conjunction with other databases as a backend for handling complex vectorized data. Its robust performance metrics make Faiss a preferred choice among AI researchers and developers working on sophisticated AI-driven applications.
This overview should help you choose the most appropriate embedded database for your specific needs, enabling your applications to perform efficiently while maintaining simplicity in management and deployment.
Each database is suited to specific types of tasks and workflows, ensuring there’s a tool for every requirement.
I hope this was useful, let me know if Iām missing something in the comments.
If you liked the article, you can support us in doing more stuff like this by giving us a star on GitHub! ā
https://github.com/latitude-dev/latitude
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