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Timescale is built on top of Postgres, which is a row oriented database. They've built a kind of columnar layer on top of it, which is quite interesting. Because it's Postgres you get their full SQL support. Meanwhile, InfluxDB IOx has a very different set of goals than Postgres. It's not an OLTP (transactional) DB and never will be. It's firmly targeted at OLAP and real-time OLAP workloads. That means we can do things like optimize for running on ephemeral storage with object storage as the persistence layer. It'll have fine grained control over replication, how data is partitioned in a cluster, and where data is indexed, queried, queued for writes and more. Push and pull replication, bulk transfer, and persistence with Parquet. This last bit means you get integration with other data processing and data warehousing tools with minimal effort. It'll also support Arrow Flight which will give it great integration into the data science ecosystems in Python and R. Right now, InfluxDB IOx is really too early to do any real comparison on actual operation. We're putting this out now so that people can see what we're doing, comment on it, and maybe even contribute. We think it's an interesting approach where no single item is completely novel, but the composition of everything together makes it an entirely unique offering in open source. Edit: one other thing I forgot to mention. InfluxDB IOx is open source, Timescale isn't. For some that matters, for many it doesn't. Depends on your use case. |
https://github.com/timescale/timescaledb