August 2019
Intermediate to advanced
560 pages
13h 41m
English
Content preview from Advanced Elasticsearch 7.0
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Moving average aggregation
The purpose of this aggregation is to compute a series of average values from a subset of buckets into a specified window size from the aggregation in the previous stage. There are five different models to compute the moving average. They are simple, linear, ewma, holt and holt_winter. The window parameter is the size of the subset of buckets. Another important parameter is settings, which lets us specify the parameters of the selected model. Moving average aggregation must be processed within the histogram or date_histogram aggregations. We will show different models with the moving average using a window size of 4 for the monthly closing price change from the buckets for the ACWF ETF in the cf_etf_hist_price
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.