If you maintain a data system that only speaks Java, you will eventually hear from someone who doesn't. A Python team building a feature store. A C++ service that needs sub-millisecond writes. An AI agent that wants to call your system through a tool binding. They all need the same capabilities (writes, reads, lookups) and none of them want to spin up a JVM to get them.
Apache Fluss, streaming storage for real-time analytics and AI, hit this exact inflection point. The Java client works well for Flink-based compute, where the JVM is already the world you live in. But outside that world, asking consumers to run a JVM sidecar just to write a record or look up a key creates friction that compounds across every service, every pipeline, every agent in the stack.
We could have written a separate client for each language. Maintain five copies of the wire protocol, five implementations of the batching logic, five sets of retry semantics and idempotence tracking. That path scales linearly with languages and ends predictably: the Java client gets features first, the Python client gets them six months later with slightly different edge-case behavior, and the C++ client is perpetually "almost done."
We took a different path and tried to leverage the lessons of the great.
