histogram

== Building Bar Charts

One of the exciting aspects of aggregations are how easily they are converted into charts and graphs.((("bar charts, building from aggregations", id="ix_barcharts", range="startofrange")))((("aggregations", "building bar charts from"))) In this chapter, we are focusing on various analytics that we can wring out of our example dataset. We will also demonstrate the types of charts aggregations can power.

The ++histogram++ bucket is particularly useful.((("buckets", "histogram")))((("histogram bucket")))((("histograms"))) Histograms are essentially bar charts, and if you've ever built a report or analytics dashboard, you undoubtedly had a few bar charts in it. The histogram works by specifying an interval. If we were histogramming sale prices, you might specify an interval of 20,000. This would create a new bucket every $20,000. Documents are then sorted into buckets.

For our dashboard, we want to know how many cars sold in each price range. We would also like to know the total revenue generated by that price bracket. This is calculated by summing the price of each car sold in that interval.

To do this, we use a histogram and a nested sum metric:

[source,js]

GET /cars/transactions/_search?search_type=count { "aggs":{ "price":{ "histogram":{ <1> "field": "price", "interval": 20000 }, "aggs":{ "revenue": { "sum": { <2> "field" : "price" } } } } }

}

// SENSE: 300_Aggregations/30_histogram.json

<1> The histogram bucket requires two parameters: a numeric field, and an interval that defines the bucket size. // Mention use of "size" to get back just the top result?

<2> A sum metric is nested inside each price range, which will show us the total revenue for that bracket

As you can see, our query is built around the price aggregation, which contains a histogram bucket. This bucket requires a numeric field to calculate buckets on, and an interval size. The interval defines how "wide" each bucket is. An interval of 20000 means we will have the ranges [0-19999, 20000-39999, ...].

Next, we define a nested metric inside the histogram. This is a sum metric, which will sum up the price field from each document landing in that price range. This gives us the revenue for each price range, so we can see if our business makes more money from commodity or luxury cars.

And here is the response:

[source,js]

{ ... "aggregations": { "price": { "buckets": [ { "key": 0, "doc_count": 3, "revenue": { "value": 37000 } }, { "key": 20000, "doc_count": 4, "revenue": { "value": 95000 } }, { "key": 80000, "doc_count": 1, "revenue": { "value": 80000 } } ] } }

}

The response is fairly self-explanatory, but it should be noted that the histogram keys correspond to the lower boundary of the interval. The key 0 means 0-19,999, the key 20000 means 20,000-39,999, and so forth.

[NOTE]

You'll notice that empty intervals, such as $40,000-60,000, is missing in the response. The histogram bucket omits these by default, since it could lead to the unintended generation of potentially enormous output.

We'll discuss how to include empty buckets in the next section, <<_returning_empty_buckets>>.

Graphically, you could represent the preceding data in the histogram shown in <>.

[[barcharts-histo1]] .Sales and Revenue per price bracket image::images/elas_28in01.png["Sales and Revenue per price bracket"]

Of course, you can build bar charts with any aggregation that emits categories and statistics, not just the histogram bucket. Let's build a bar chart of popular makes, and their average price, and then calculate the standard error to add error bars on our chart. This will use the terms bucket and an extended_stats ((("extended_stats metric")))metric:

[source,js]

GET /cars/transactions/_search?search_type=count { "aggs": { "makes": { "terms": { "field": "make", "size": 10 }, "aggs": { "stats": { "extended_stats": { "field": "price" } } } } }

}

This will return a list of makes (sorted by popularity) and a variety of statistics about each. In particular, we are interested in stats.avg, stats.count, and stats.std_deviation. Using((("standard error, calculating"))) this information, we can calculate the standard error:

................................ std_err = std_deviation / count ................................

This will allow us to build a chart like <>.

[[barcharts-bar1]] .Average price of all makes, with error bars image::images/elas_28in02.png["Average price of all makes, with error bars"]

((("bar charts, building from aggregations", range="endofrange", startref="ix_barcharts")))