![]() Having one data point per second makes it very clear and easy to read. To illustrate following designs, I use random generated data, basically one point/second. If you’re unfamiliar with Table Service on Azure, I highly recommend you to read the Introduction then the Design Guide after reading both articles, I hope you will understand why storing time series is not so easy and needs this kind of article. Data is aggregated depending on the selected time range (every second for last hour, every minute for last day, every 5 minutes for last 48 hours, …) to keep it easy to understand. ![]() ![]() In this example, the end user can view response time for several time ranges. Here is an example on availability on Application Insight. Time series are very frequently plotted via –beautiful- charts. Time series data often arise when monitoring an application or tracking business metric, but occur naturally in many application areas like finance, economics, medicine … As for most of NoSQL databases, the design is very important and you have to mainly think about your access patterns.Ī time series is a sequence of data points, typically consisting of successive measurements made over a time interval. In a recent project, I needed to store time series into Microsoft Azure Table Storage.
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