{"id":7896,"date":"2019-08-21T02:00:34","date_gmt":"2019-08-21T09:00:34","guid":{"rendered":"http:\/\/softwareengineeringdaily.com\/?p=7896"},"modified":"2021-10-06T07:49:24","modified_gmt":"2021-10-06T14:49:24","slug":"time-series-databases-with-rob-skillington","status":"publish","type":"post","link":"https:\/\/softwareengineeringdaily.com\/2019\/08\/21\/time-series-databases-with-rob-skillington\/","title":{"rendered":"Time Series Databases with Rob Skillington"},"content":{"rendered":"

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A time series database is optimized for the storage of high volumes of sequential data across time.<\/span><\/p>\n

Time series databases are often organized as columnar data stores that can write large volumes of data quickly. These systems can sometimes tolerate data loss, because the data they are gathering is used for monitoring and other applications that require aggregated data sets rather than highly important individual transactions.<\/span><\/p>\n

The demand for time series databases has grown over the last decade with the rise of mobile devices and the decreasing cost of cloud storage. There has been an increase in the number of systems that require monitoring, and some of those systems produce an incredibly large amount of data, requiring compression, downsampling, and garbage collection.<\/span><\/p>\n

Rob Skillington is an engineer at Uber, where he helped create M3DB, a time series database. In a previous show, Rob described the basics of M3DB and how it helps Uber with storing data from Prometheus, a monitoring system. In today\u2019s show we discuss the field of time series databases, and Rob\u2019s approach to building M3.<\/span><\/p>\n

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