SlothDB
An experimental embedded SQL engine in C++20. Query Parquet, CSV, JSON, Arrow, Avro, SQLite, and Excel files directly with SQL, in-process. Early-stage.
SlothDB is an open-source database project on GitHub under the MIT license (permissive), with 412 stars. It is built with LLVM project and GCC. Its last push was 5 months ago.
- Stars
- 412
- License
- MIT
- Activity
- Slowing
- Latest release
- v0.2.7
Judgement
- What it is
- Building block
- What it does
- An embedded SQL engine in C++20 that queries Parquet, CSV, JSON, Arrow, Avro, SQLite and Excel files in-process, in the style of DuckDB. It has Python and WebAssembly packages and an optional local model for turning questions into SQL.
- Adopt it when
- You want to study or experiment with a small embedded analytical engine and can tolerate early-stage behavior.
- Watch out
- The author calls it a learning exercise and tells readers to treat its performance numbers as anecdotal, and the last commit was in June.
- Ready for
- Experimental
- Runs as
- Library and command line
- Built with
- C++, Python and WebAssembly
- How established
- Young: started 6 months ago, latest release v0.2.7.
- What the license allows
- You can build it into a product you sell, as long as you keep its copyright notice.
Judged on 2026-11-06 from the project's README and GitHub data. The license and age lines are worked out directly.
Built from
Follow it down: SlothDB → LLVM project → runs on a microchip
Open-source alternatives to SlothDB
-
1
Redis redis
For developers, who are building real-time data-driven applications, Redis is the preferred, fastest, and most feature-rich cache, data structure server, and document and vector query engine.
Building block Production-ready You need very fast caching, counters, queues or pub/sub next to your main database.
★ 77k See repository C Active, pushed today
-
2
Prometheus prometheus
The Prometheus monitoring system and time series database.
Complete system Production-ready You run services or Kubernetes and need metrics and alerting with the de facto standard toolset.
★ 67k Apache-2.0 Go Active, pushed today
-
3
Etcd etcd-io
Distributed reliable key-value store for the most critical data of a distributed system
Building block Production-ready You need a small amount of critical coordination data kept consistent across a cluster.
★ 52k Apache-2.0 Go Active, pushed today
-
4
ClickHouse ClickHouse
ClickHouse® is a real-time analytics database management system
Building block Production-ready You need fast analytics on large append-heavy data such as events, logs or metrics.
★ 51k Apache-2.0 C++ Active, pushed 3 days ago
-
5
Milvus milvus-io
Milvus is a high-performance, cloud-native vector database built for scalable vector ANN search
Building block Production-ready You need vector search over hundreds of millions of embeddings or more and have the people to run a distributed system.
★ 47k Apache-2.0 Go Active, pushed 2 days ago
-
6
DuckDB duckdb
DuckDB is a high-performance analytical database system
Building block Production-ready You analyse data that fits on one machine and want fast SQL without running a server.
★ 43k MIT C++ Active, pushed 6 days ago
-
7
TiDB pingcap
TiDB is built for agentic workloads that grow unpredictably, with ACID guarantees and native support for transactions, analytics, and vector search. No data silos. No noisy neighbors. No infrastructure ceiling.
Building block Production-ready You run MySQL workloads that have outgrown one server and need to scale out without manual sharding.
★ 41k Apache-2.0 Go Active, pushed today
-
8
Qdrant qdrant
Qdrant - High-performance, massive-scale Vector Database and Vector Search Engine for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
Building block Production-ready You are building semantic search, RAG or recommendations and want a dedicated vector store that is simple to self-host.
★ 35k Apache-2.0 Rust Active, pushed 3 days ago
Commercial databases
-
1
Snowflake Snowflake Inc.
Snowflake powers AI, data engineering, applications, and analytics on a trusted, scalable AI Data Cloud—eliminating silos and accelerating innovation.
Complete system Production-ready A company has many data sources to combine for analytics and wants a managed warehouse that scales without tuning.
6.5k readers a month Since 2012 Commercial
-
2
MongoDB Atlas MongoDB, Inc.
Build AI applications and agents on MongoDB Atlas platform, the #1 modern, run anywhere platform that brings together real-time data, intelligent retrieval, and governed action.
Building block Production-ready Your app suits a JSON document model and you want MongoDB without running it yourself.
4.1k readers a month Since 2009 Commercial -
3
Oracle Database Oracle Corporation
Proprietary database management system
Building block Production-ready A large enterprise runs software that requires Oracle, or needs its high-end availability features.
2.2k readers a month Since 1979 Commercial -
4
Microsoft SQL Server Microsoft
SQL Server 2025 redefines what's possible for enterprise data. With developer-first features and integration with analytics and AI models, SQL Server 2025 accelerates AI innovation using the data you already have.
Building block Production-ready Your business runs .NET applications or vendor software that requires SQL Server.
945 readers a month Commercial -
5
Google BigQuery Google
BigQuery is the autonomous data and AI platform, automating the entire data life cycle so you can go from data to AI to action faster.
Complete system Production-ready You are on Google Cloud or export GA4 data and want SQL analytics without managing servers.
313 readers a month Since 2010 Commercial -
6
InfluxDB Cloud InfluxData
InfluxDB 3 is the leading time series database to collect, organize, and act on massive volumes of high-resolution data in real-time.
Building block Production-ready You collect high-volume IoT or monitoring metrics and want a purpose-built time-series store you do not run yourself.
298 readers a month Since 2013 Commercial
Stars, license and activity come from GitHub and refresh on a rolling hourly schedule. The project's own description is quoted as written.