Applied ELK Stack: Data Insights and Business Metrics With Collective Capability of ElasticSearch, Logstash and Kibana by Gurpreet Sachdeva

Author:Gurpreet Sachdeva [Sachdeva, Gurpreet]
Language: eng
Format: epub
Publisher: UNKNOWN
Published: 2017-04-17T07:00:00+00:00

Figure 7-2. Mean Price of Phone Makes with Standard Error

n Tip Microsoft-Excel has been used to make the histogram based on the raw data.

Time Series Aggregations

Search is the most popular activity in Elasticsearch and date histograms comes next in popularity. Use of date histograms serves many purposes. If your data has a timestamp then it can benefit tremendously from data histogram. It does not matter what kind of data you have - server logs, alarms, account transactions, etc. If you have data which consists of timestamp, it is natural to build metrics based on time:

· Number of phones sold each month in this year.

· Number of requests processed in last 1 hour by trading system.

· Average latency per week for Ecommerce site.

Data histograms are represented slightly different from regular histograms. Data histograms are best depicted by line graphs representing time series. One of the most popular uses of Elasticsearch is to plot analytics data over a time period. The date_histogram buckets is pretty similar to the regular histogram. Instead of building buckets based on a numeric field corresponding to numeric ranges, it builds buckets on the basis of time ranges. This necessitates each bucket to be of a certain calendar size (for e.g., 1 week or 2 months).

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