In a previous tutorial, we discussed the structure of Elasticsearch pipeline aggregations and walked you through setting up several common pipelines such as derivatives, cumulative sums, and avg bucket aggregations.

In this article, we’ll continue with the analysis of Elasticsearch pipeline aggregations, focusing on such pipelines as stats, moving averages and moving functions, percentiles, bucket sorts, and bucket scripts, among others. Some of the pipeline aggregations discussed in the article such as moving averages are supported in Kibana, so we’ll show you how to visualize them as well. Let’s get started!

Keep reading

As you might already know from the previous Elasticsearch aggregation series, both metrics and buckets aggregations work on the numeric fields in the document set directly.

In contrast to this, pipeline aggregations, which we discuss in this article, work on the output produced by other aggregations transforming the values already computed by them. A pipeline aggregation, hence, works on the intermediary values not present in the original document set. This makes pipeline aggregation very useful for calculating complex statistical and mathematical measures like cumulative sum, derivatives, and moving averages among others.

In the first part of this series, we’ll discuss two basic types of pipeline aggregations and show examples of such common Elasticsearch pipelines as a sum and cumulative sum, min and max, avg bucket, and derivative pipeline aggregations. Let’s get started!

Keep reading