A boxplot
metrics aggregation that computes boxplot of numeric values extracted from the aggregated documents.
These values can be generated by a provided script or extracted from specific numeric or
histogram fields in the documents.
The boxplot
aggregation returns essential information for making a box plot: minimum, maximum
median, first quartile (25th percentile) and third quartile (75th percentile) values.
A boxplot
aggregation looks like this in isolation:
{ "boxplot": { "field": "load_time" } }
Let’s look at a boxplot representing load time:
GET latency/_search { "size": 0, "aggs" : { "load_time_boxplot" : { "boxplot" : { "field" : "load_time" } } } }
The response will look like this:
{ ... "aggregations": { "load_time_boxplot": { "min": 0.0, "max": 990.0, "q1": 165.0, "q2": 445.0, "q3": 725.0 } } }
The boxplot metric supports scripting. For example, if our load times are in milliseconds but we want values calculated in seconds, we could use a script to convert them onthefly:
GET latency/_search { "size": 0, "aggs" : { "load_time_boxplot" : { "boxplot" : { "script" : { "lang": "painless", "source": "doc['load_time'].value / params.timeUnit", "params" : { "timeUnit" : 1000 } } } } } }
The 

Scripting supports parameterized input just like any other script 
This will interpret the script
parameter as an inline
script with the painless
script language and no script parameters. To use a
stored script use the following syntax:
GET latency/_search { "size": 0, "aggs" : { "load_time_boxplot" : { "boxplot" : { "script" : { "id": "my_script", "params": { "field": "load_time" } } } } } }
The algorithm used by the boxplot
metric is called TDigest (introduced by
Ted Dunning in
Computing Accurate Quantiles using TDigests).
Boxplot as other percentile aggregations are also nondeterministic. This means you can get slightly different results using the same data.
Approximate algorithms must balance memory utilization with estimation accuracy.
This balance can be controlled using a compression
parameter:
GET latency/_search { "size": 0, "aggs" : { "load_time_boxplot" : { "boxplot" : { "field" : "load_time", "compression" : 200 } } } }
The TDigest algorithm uses a number of "nodes" to approximate percentiles — the
more nodes available, the higher the accuracy (and large memory footprint) proportional
to the volume of data. The compression
parameter limits the maximum number of
nodes to 20 * compression
.
Therefore, by increasing the compression value, you can increase the accuracy of
your percentiles at the cost of more memory. Larger compression values also
make the algorithm slower since the underlying tree data structure grows in size,
resulting in more expensive operations. The default compression value is
100
.
A "node" uses roughly 32 bytes of memory, so under worstcase scenarios (large amount of data which arrives sorted and inorder) the default settings will produce a TDigest roughly 64KB in size. In practice data tends to be more random and the TDigest will use less memory.
The missing
parameter defines how documents that are missing a value should be treated.
By default they will be ignored but it is also possible to treat them as if they
had a value.