QuestDB vs TDengine
Both QuestDB and TDengine are open source relational databases that are optimized for high-performance time-series data processing. Here are some detailed comparisons between the two: Data Model: QuestDB is based on the SQL language and uses a relational database model with support for standard SQL features such as joins, subqueries, and transactions. TDengine, on the other hand, uses a hybrid data model that combines the features of a relational database with a time-series database. It has its own SQL-like query language called TAOSQL that is optimized for time-series data. Scalability: Both QuestDB and TDengine are designed to scale horizontally and vertically. QuestDB can handle large volumes of data with low latency and supports sharding for horizontal scaling. TDengine supports clustering and replication for high availability and scaling out read and write operations. Performance: Both databases are optimized for fast reads and writes of time-series data. QuestDB uses a columnar storage format that is optimized for data compression and query performance. It also supports vectorized query execution, which can significantly speed up queries. TDengine uses a fixed-size data structure and memory pool, which helps it achieve high write and read speeds. Ease of Use: QuestDB has a user-friendly web-based console for managing and querying data. It also has built-in support for various data connectors, including Kafka, PostgreSQL, and MySQL. TDengine has a command-line interface for managing data and can be integrated with other applications through APIs and SDKs. Community and Support: Both QuestDB and TDengine have active open source communities and offer documentation and support through their websites, GitHub repositories, and community forums. In summary, QuestDB and TDengine are both powerful open source databases for time-series data processing. While QuestDB is more focused on a traditional relational database model, TDengine is more geared towards a hybrid data model that combines relational and time-series data. Ultimately, the choice between the two will depend on specific use cases and requirements.