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Revision as of 09:38, 2 May 2014


Gnocchi is the project name of a TDBaaS (Time Series Database as a Service) project started under the Ceilometer program umbrella.


From the beginning of the Ceilometer project, a large part of the goal was to store time series data that were collected. In the early stages of the project, it wasn't really clear what and how these time series were going to be handled, manipulated and queried, so the data model used by Ceilometer was very flexible. That ended up being really powerful and handy, but the performance behind have been terrible, to a point where storing a large amount of metrics on several weeks is really hard to achieve without having the data storage backend collapsing.

Having such a flexible data model and query system is great, but in the end users are doing the same request over and over and the use cases that need to be addressed are a subset of that data model. On the other hand, some queries and use cases are not solved by the current data model, either because they are not easy to be expressed either because they are just damn too slow to run.

Lately, during the Icehouse Design Summit in Hong-Kong, developers and users showed interest in having Ceilometer doing metric data aggregation, in order to keep data in a more long running fashion. No work has been done during the Icehouse cycle on that, probably due to the lack of manpower around the idea, even if the idea and motivation was validated by the core team back then.

Considering the amount of data and metrics Ceilometer generates and have to store, a new strategy and a rethinking of the problem was needed, so Gnocchi is a try on that.

Rethinking the problem

Ceilometer is nowadays trying to achieve two different things:

  • Store metrics, that is a list of (timestamp, value) for a given entity, this entity being anything from the temperature in your datacenter to the CPU usage of a VM.
  • Store events, that is a list of things that happens in your OpenStack installation: an API request has been received, a VM has been started, an image has been uploaded, a server fell of the roof, whatever

These two things are both very useful for all the use cases Ceilometer tries to achieve. Metrics are useful for monitoring, billing and alarming, where events are useful to do audit, performance analysis, debugging, etc.

However, while the event collection of Ceilometer is pretty solid and ok (but still needs to be working on), the metrics part suffers terrible design and performance issues.

Having the so called free form metadata associated with each metric generated by Ceilometer is the most problematic design we have. It stores a lot of redundant information that it is hard to query in a efficient manner. On the other hand, systems like RRD have existed for a while, storing a large amount of (aggregated) metrics without much problem. The metadata associated to these metrics being another issue.

So that let us with 2 different problem to solve: store metrics and store information (the so called metadata) about resources.

Prototype implementation

jd has started a prototype of that solution with the Gnocchi project. It provides a time series storage and a resource indexer, which are both fast and scalable.