My understanding is that it just gives you a bit of extra room if you have a small to mild timeseries problem. That's still a lot of use cases, but you're right it will never work for larger use cases.
They said they didn't want to reinvent a database engine to solve the timeseries problem, so you have what you pay for.
We are actually doing a fair bit to address larger use cases. We also had customers who are doing 100s of billions of points successfully, so I guess it depends on what you mean by larger.
As our CEO mentioned in a sibling, we are working on a horizontal/scale-out solution for even higher ingest rates, as well as sharding. We're also doing some work for better compression to reduce our disk footprint.
Also since 1.2, we have support for automatic retention policies that help keep the disk usage in check. Yesterday we released 1.3, which contains our first iteration of continuous aggregations that let's you materialize aggregates over the raw data for faster querying. In a future iteration, we'll also allow you to remove the underlying/raw data but keep the aggregates -- another way to improve the disk usage of your data.
All that is to say we do consider ourselves useful for larger use cases, and have a lot of features coming down the pipe to make it even better.
Comments
My understanding is that it just gives you a bit of extra room if you have a small to mild timeseries problem. That's still a lot of use cases, but you're right it will never work for larger use cases.
They said they didn't want to reinvent a database engine to solve the timeseries problem, so you have what you pay for.
We are actually doing a fair bit to address larger use cases. We also had customers who are doing 100s of billions of points successfully, so I guess it depends on what you mean by larger.
As our CEO mentioned in a sibling, we are working on a horizontal/scale-out solution for even higher ingest rates, as well as sharding. We're also doing some work for better compression to reduce our disk footprint.
Also since 1.2, we have support for automatic retention policies that help keep the disk usage in check. Yesterday we released 1.3, which contains our first iteration of continuous aggregations that let's you materialize aggregates over the raw data for faster querying. In a future iteration, we'll also allow you to remove the underlying/raw data but keep the aggregates -- another way to improve the disk usage of your data.
All that is to say we do consider ourselves useful for larger use cases, and have a lot of features coming down the pipe to make it even better.