Time series data is of growing importance, especially with the rapid expansion of the Internet of Things. This concise guide shows you effective ways to collect, persist, and access large-scale time series data for analysis. You’ll explore the theory behind time series databases and learn practical methods for implementing them. Authors Ted Dunning and Ellen Friedman provide a detailed examination of open source tools such as OpenTSDB and new modifications that greatly speed up data ingestion.
You’ll learn:
A variety of time series use cases
The advantages of NoSQL databases for large-scale time series data
NoSQL table design for high-performance time series databases
The benefits and limitations of OpenTSDB
How to access data in OpenTSDB using R, Go, and Ruby
How time series databases contribute to practical machine learning projects
How to handle the added complexity of geo-temporal data
For advice on analyzing time series data, check out Practical Machine Learning: A New Look at Anomaly Detection, also from Ted Dunning and Ellen Friedman.
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比较贴近互联网的时间序列存储实现方法的概览。不过也就一篇博客的东西，还搞了本书，也是醉了。
评分比较贴近互联网的时间序列存储实现方法的概览。不过也就一篇博客的东西，还搞了本书，也是醉了。
评分比较贴近互联网的时间序列存储实现方法的概览。不过也就一篇博客的东西，还搞了本书，也是醉了。
评分比较贴近互联网的时间序列存储实现方法的概览。不过也就一篇博客的东西，还搞了本书，也是醉了。
评分比较贴近互联网的时间序列存储实现方法的概览。不过也就一篇博客的东西，还搞了本书，也是醉了。
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