BTrDB vs DolphinDB

BTrDB is a free, MIT-licensed, self-hosted time-series database for dense, high-precision univariate data. It partitions data by time, supports clustered deployments, and records stream changes as new versions that queries can target. Streams have unique UUIDs and can be grouped into collections with tags and annotations. The Python btrdb package supports nanosecond-timestamp queries and insertion through Stream.insert, PyArrow tables, or pandas DataFrames. Statistical queries can return minimum, maximum, mean, count, and standard deviation without reading every underlying point. StreamSets can be filtered and converted to pandas DataFrames or CSV files. Documentation shows Dash and Plotly visualization, Ray-worker serialization, TLS on port 4411, and API-key authentication requiring that TLS port. Arrow-based tabular methods are documented for commercial customers with Arrow-enabled servers. BTrDB reports measured cluster throughput and compression figures, and describes smart-grid aggregation at city or country scale as one target use case.

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.