Show HN: Timeplus Proton 3.0 – First vectorized streaming SQL engine(github.com)
github.com
Show HN: Timeplus Proton 3.0 – First vectorized streaming SQL engine
https://github.com/timeplus-io/proton
12 comments
Love the object storage features!
Built-in S3-based stream storage solves the problem of running separate Stream Processing and Stream Storage solutions and builds on Proton's simplicity.
S3-based state checkpoints as well, both features synergize really well with Proton's strengths.
Congrats on this release and I hope I see more from you soon!
edit: oops I've read the Timeplus release notes that Peter linked, not Proton. Which of the listed features are new in Proton 3.0?
Built-in S3-based stream storage solves the problem of running separate Stream Processing and Stream Storage solutions and builds on Proton's simplicity.
S3-based state checkpoints as well, both features synergize really well with Proton's strengths.
Congrats on this release and I hope I see more from you soon!
edit: oops I've read the Timeplus release notes that Peter linked, not Proton. Which of the listed features are new in Proton 3.0?
Hi! You can find the detailed differences here: https://docs.timeplus.com/proton-oss-vs-enterprise
In short, Proton focuses on simplicity — it’s a single-instance engine powerful enough for most common streaming and analytics workflows.
Features like clustering, mutable streams, and S3-based stream storage/state checkpoints are part of the enterprise edition, while Proton keeps the core performance and streaming capabilities in an open-source form.
In short, Proton focuses on simplicity — it’s a single-instance engine powerful enough for most common streaming and analytics workflows.
Features like clustering, mutable streams, and S3-based stream storage/state checkpoints are part of the enterprise edition, while Proton keeps the core performance and streaming capabilities in an open-source form.
Very nice, curious to see new use cases with the UDFs!
Thanks, yes, that is something I am exploring with our customers, and please share with me your idea as well.
Release notes here:
https://docs.timeplus.com/enterprise-v3.0
https://docs.timeplus.com/enterprise-v3.0
Congrats on the major release! And good to see Redpanda mentioned as a first-class citizen with a native connector!
redpanda was in our radar back to 2022, it is still the first choice to make low latency streaming processing partner with Timeplus
https://www.timeplus.com/post/realizing-low-latency-streamin...
Redpanda + Timeplus, the perfect pair for data streaming developers. No JVM, ZK ...
Probably the smallest yet most powerful binary for real-time, incremental SQL data processing, end to end!
a single binary is nice, but how to scale?
here is the overview of the Timeplus Cluster https://docs.timeplus.com/cluster#overview
basically all the cluster nodes are deployed with the same binary with extra configurations.
basically all the cluster nodes are deployed with the same binary with extra configurations.
amazing.. congrats on the release, excited to upgrade
Key features:
First vectorized streaming SQL engine in modern C++ with JIT compilation
High-throughput, low-latency, high-cardinality processing End-to-end streaming: ETL, joins, aggregation, alerts, and tasks
Native connectors: Kafka, Redpanda, Pulsar, ClickHouse, Splunk, Elastic, MongoDB, S3, Iceberg
Native Python UDF/UDAF support to support your AI/ML work loads
The same performance we've proven in large enterprise deployments is now available in the community edition.
Would love feedback from anyone working with streaming data or looking for Flink/ksqlDB alternatives.