By Ted Dunning
Time sequence facts is of transforming into significance, in particular with the swift growth of the net of items. This concise advisor exhibits you powerful how one can gather, persist, and entry large-scale time sequence info for research. you will discover the idea at the back of time sequence databases and research useful tools for enforcing them. Authors Ted Dunning and Ellen Friedman supply an in depth exam of open resource instruments akin to OpenTSDB and new ameliorations that drastically accelerate info ingestion.
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Extra info for Time Series Databases: New Ways to Store and Access Data
Likewise, when the partition time is long with respect to the average query, the fraction of usable data decreases again since most of the data in a file is outside the time range of interest. Efforts to remedy these problems typically lead to other problems. Using lots of files to keep the number of series per file small multiplies the number of files. Like‐ wise, shortening the partition time will multiply the number of files as well. When storing data on a system such as Apache Hadoop using HDFS, having a large number of files can cause serious stability prob‐ lems.
The degree of mismatch to the model expectations can be used to trigger an alert that signals apparent faults or discrepancies as they occur. Sensor data is a natural fit to be collected and stored as a time series database. Sensors on equipment or system logs for servers can generate an enormous amount of time-based data, and with new tech‐ nologies such as the Apache Hadoop–based NoSQL systems described in this book, it is now feasible to save months or even years of such data in time series databases.
How does the direct blob approach get this bump in performance? The essential difference is that the blob maker has been moved into the data flow between the catcher and the NoSQL time series database. This way, the blob maker can use incoming data from a memory cache rather than extracting its input from wide table rows already stored in the storage tier. The basic idea is that data is kept in memory as samples arrive. These samples are also written to log files. These log files are the “restart logs” shown in Figure 3-6 and are flat files that are stored on the Hadoop system but not as part of the storage tier itself.