Chapter 3. Storing and Processing Time Series Data

As we mentioned in previous chapters, a time series is a sequence of values, each with a time value indicating when the value was recorded. Time series data entries are rarely amended, and time series data is often retrieved by reading a contiguous sequence of samples, possibly after summarizing or aggregating the retrieved samples as they are retrieved. A time series database is a way to store multiple time series such that queries to retrieve data from one or a few time series for a particular time range are particularly efficient. As such, applications for which time range queries predominate are often good candidates for implementation using a time series database. As previously explained, the main topic of this book is the storage and processing of large-scale time series data, and for this purpose, the preferred technologies are NoSQL non-relational databases such as Apache HBase or MapR-DB.

Pragmatic advice for practical implementations of large-scale time series databases is the goal of this book, so we need to focus in on some basic steps that simplify and strengthen the process for real-world applications. We will look briefly at approaches that may be useful for small or medium-sized datasets and then delve more deeply into our main concern: how to implement large-scale TSDBs.

To get to a solid implementation, there are a number of design decisions to make. The drivers for these decisions are the parameters that define ...

Get Time Series Databases: New Ways to Store and Access Data now with O’Reilly online learning.

O’Reilly members experience live online training, plus books, videos, and digital content from 200+ publishers.