DolphinDB is a real-time analytics and stream processing platform built around a high-performance time-series database. It is positioned not as a standalone database, but as an integrated data infrastructure covering storage, compute, stream processing, analytics, and industry-specific components. The official site highlights columnar storage, vectorized execution, distributed architecture, millisecond-level queries, and compute capabilities for trillion-scale datasets. Its main target scenarios are high-frequency time-series workloads in finance, energy and power, manufacturing, research, public utilities, and similar sectors.
Functionally, DolphinDB supports multiple storage engines, including TSDB, OLAP, and In-Memory OLTP, with horizontal scaling, load balancing, fault tolerance, distributed transactions, and Raft-based high availability. A key feature is unified stream and batch processing: the same factor or indicator expression can be used for both historical backtesting and real-time computation, reducing duplicate development work. On the development side, it provides SQL-compatible scripting, Python-like syntax, and multiple programming paradigms including imperative, vectorized, functional, and SQL-style programming. It also includes 1500+/2000+ built-in functions, making it suitable for pushing complex computation down into the database.
The site says DolphinDB can integrate with existing systems through a rich set of APIs, with Python, C++, and Java interfaces explicitly mentioned. Its tooling ecosystem includes VS Code Extension and DolphinDB GUI. The former supports script execution, code completion, Quick Info, multi-user connections, version control, and debugging. The product line also includes the embedded version Swordfish, GPU version Shark, quantitative research platform Starfish, and complex event processing engine Octopus. For AI/ML, the official site mentions support for loading and running inference with PyTorch and TensorFlow models, as well as integration with XGBoost and LightGBM. It also lists capabilities such as RAG, Chat-to-Query, MCP, and the DolphinMind intelligent assistant.
DolphinDB offers a perpetual Community Edition license, limited to up to 2 nodes, 2 CPU Core per node, and 8GB RAM. An Enterprise Trial is available by request, with support for high-availability clusters and expert technical support. Official enterprise pricing is not disclosed, so users need to contact sales or request a Demo. For deployment, DolphinDB supports cloud, on-premises, and hybrid setups, and downloadable versions are also available. This makes it suitable for enterprises with requirements around data security, low latency, or private-network deployment.
Its main advantage is that it combines time-series storage, real-time stream processing, batch analytics, in-database computation, and financial business components in one platform. It is especially well suited to performance-intensive scenarios such as high-frequency market data, factor computation, strategy backtesting, real-time risk control, and industrial equipment monitoring. Downsides include the lack of clear open-source information on the official site and opaque enterprise pricing. The product also covers a broad range of capabilities, which may be excessive for small teams or simple monitoring-oriented time-series use cases, and the learning curve may be steeper than with lightweight databases.
The crawled content does not provide enough information to assess access from mainland China, payment methods, or local service availability, so these are marked as unknown. Comparable alternatives include ClickHouse, TimescaleDB, InfluxDB, QuestDB, Apache Druid, Apache Flink, TDengine, and kdb+, which is commonly used in the financial sector.
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