Data processing in Gnocchi is strongly related to the index information. One of such valuable assets are metrics and resources, covered just below.
The specificity of Gnocchi is the precomputation of the measures. It doesn't allow ad-hoc queries but in the other side provides pretty good reading performance. However, as new time series points are coming, the old ones aren't kept with them.
In the recent posts about Gnocchi we could often meet the concept of archive policy. However, as one of the main points in this system, it merits its own explanation.
Gnocchi writes data partitioned by split key. But often such splitted data must be merged back for reading operations. This post focuses on "how" and "when" of this process.
To facilitate parallel processing Apache Spark and Apache Kafka have their concept of partitions, Apache Beam works with bundles and Gnocchi deals with sacks. Despite the different naming, the sacks are the same for Gnocchi as the partitions for Spark or Kafka - the unit of work parallelization.
Even though carbonara is mostly known as an Italian pasta dish, in the context of Gnocchi it means completely different thing. Carbonara is the name of time points storage format in Gnocchi.
One of the reasons behind the choice of Gnocchi as time series database to study was its naturally provided horizontal scalability. At the moment of making that choice I was relying only on the official documentation. Now it's a good moment to come back and analyze the horizontal scalability by myself.
Understanding the architecture is the key of working properly with any distributed system. It's why the series of post about Gnocchi starts by exploring its components.
In order to learn a new thing, nothing better than try it. However in some cases the choice of the tool to study is not easy. It's especially true in the context of data storage and though also in the context of time-series databases introduced in one of previous posts.
Temporal data is a little bit particular. It can be generated very frequently, as for instance every 500 ms or less. It's then important to store it efficiently and to allow quick and flexible reads. It's also important to know the specificities of time-series as a popular case of temporal data.
