DolphinDB vs QuestDB

DolphinDB is a real-time analytics and stream-processing platform built around a high-performance time-series database. Its storage engines cover time-series databases, OLAP and in-memory OLTP workloads, with more than 1,500 built-in analytical functions and more than 10 streaming engines. Batch and stream processing can use the same metric expression. Programming options include imperative, vectorized, functional and SQL paradigms. The distributed system supports horizontal scaling, load balancing, fault tolerance, distributed transactions and high availability across data nodes, controllers, streaming and API clients. Deployment options include cloud, on-premise and hybrid environments. Listed use cases span finance, energy, power, transportation, industrial manufacturing and scientific research. The Community Edition has a free perpetual license, limited to two nodes, each with two CPU cores and 8 GB RAM. A free Enterprise Trial supports high-availability clusters and includes expert technical support. Related components include Swordfish for embedded use, Shark for GPU workloads, Starfish for quantitative research workflows and Octopus for complex event processing. The product is available through API, extension, Linux, self-hosted, web and Windows options.

QuestDB is a time-series database for environments where low latency matters, including mission-critical workloads. It supports standard SQL through a vectorized execution engine, along with time-series extensions such as SAMPLE BY, ASOF, and HORIZON JOIN. QuestDB advertises ingestion speeds of 8M+ rows per second. The Open Source edition is self-managed and available as binaries, containers, or source code; it is single-instance and supports Parquet storage and PostgreSQL Wire Protocol compatibility. Ingestion options include QuestDB Wire Protocol, InfluxDB Line Protocol, REST API, native client libraries, SQL INSERT, Kafka connector, and CSV import. Listed integrations include Grafana, Power BI, Apache Kafka, Telegraf, Redpanda, Apache Flink, Pandas, and Apache Spark. Open Source costs nothing and includes community support. Enterprise adds security controls, replication, automatic failover, object-storage tables, and dedicated support with response-time SLAs.