Bucket correlation aggregationedit
A sibling pipeline aggregation which executes a correlation function on the configured sibling multi-bucket aggregation.
Parametersedit
-
buckets_path -
(Required, string)
Path to the buckets that contain one set of values to correlate.
For syntax, see
buckets_pathSyntax. -
function -
(Required, object) The correlation function to execute.
Properties of
function-
count_correlation -
(Required*, object) The configuration to calculate a count correlation. This function is designed for determining the correlation of a term value and a given metric. Consequently, it needs to meet the following requirements.
-
The
buckets_pathmust point to a_countmetric. -
The total count of all the
bucket_pathcount values must be less than or equal toindicator.doc_count. -
When utilizing this function, an initial calculation to gather the required
indicatorvalues is required.
Properties of
count_correlation-
indicator -
(Required, object) The indicator with which to correlate the configured
bucket_pathvalues.Properties of
indicator-
doc_count -
(Required, integer)
The total number of documents that initially created the
expectations. It’s required to be greater than or equal to the sum of all values in thebuckets_pathas this is the originating superset of data to which the term values are correlated. -
expectations -
(Required, array)
An array of numbers with which to correlate the configured
bucket_pathvalues. The length of this value must always equal the number of buckets returned by thebucket_path. -
fractions -
(Optional, array)
An array of fractions to use when averaging and calculating variance. This should be used if the pre-calculated data and the
buckets_pathhave known gaps. The length offractions, if provided, must equalexpectations.
-
-
The
-
Syntaxedit
A bucket_correlation aggregation looks like this in isolation:
Exampleedit
The following snippet correlates the individual terms in the field version with the latency metric. Not shown
is the pre-calculation of the latency indicator values, which was done utilizing the
percentiles aggregation.
This example is only using the 10s percentiles.
POST correlate_latency/_search?size=0&filter_path=aggregations
{
"aggs": {
"buckets": {
"terms": {
"field": "version",
"size": 2
},
"aggs": {
"latency_ranges": {
"range": {
"field": "latency",
"ranges": [
{ "to": 0.0 },
{ "from": 0, "to": 105 },
{ "from": 105, "to": 225 },
{ "from": 225, "to": 445 },
{ "from": 445, "to": 665 },
{ "from": 665, "to": 885 },
{ "from": 885, "to": 1115 },
{ "from": 1115, "to": 1335 },
{ "from": 1335, "to": 1555 },
{ "from": 1555, "to": 1775 },
{ "from": 1775 }
]
}
},
"bucket_correlation": {
"bucket_correlation": {
"buckets_path": "latency_ranges>_count",
"function": {
"count_correlation": {
"indicator": {
"expectations": [0, 52.5, 165, 335, 555, 775, 1000, 1225, 1445, 1665, 1775],
"doc_count": 200
}
}
}
}
}
}
}
}
}
|
The term buckets containing a range aggregation and the bucket correlation aggregation. Both are utilized to calculate the correlation of the term values with the latency. |
|
|
The range aggregation on the latency field. The ranges were created referencing the percentiles of the latency field. |
|
|
The bucket correlation aggregation that calculates the correlation of the number of term values within each range and the previously calculated indicator values. |
And the following may be the response:
{
"aggregations" : {
"buckets" : {
"doc_count_error_upper_bound" : 0,
"sum_other_doc_count" : 0,
"buckets" : [
{
"key" : "1.0",
"doc_count" : 100,
"latency_ranges" : {
"buckets" : [
{
"key" : "*-0.0",
"to" : 0.0,
"doc_count" : 0
},
{
"key" : "0.0-105.0",
"from" : 0.0,
"to" : 105.0,
"doc_count" : 1
},
{
"key" : "105.0-225.0",
"from" : 105.0,
"to" : 225.0,
"doc_count" : 9
},
{
"key" : "225.0-445.0",
"from" : 225.0,
"to" : 445.0,
"doc_count" : 0
},
{
"key" : "445.0-665.0",
"from" : 445.0,
"to" : 665.0,
"doc_count" : 0
},
{
"key" : "665.0-885.0",
"from" : 665.0,
"to" : 885.0,
"doc_count" : 0
},
{
"key" : "885.0-1115.0",
"from" : 885.0,
"to" : 1115.0,
"doc_count" : 10
},
{
"key" : "1115.0-1335.0",
"from" : 1115.0,
"to" : 1335.0,
"doc_count" : 20
},
{
"key" : "1335.0-1555.0",
"from" : 1335.0,
"to" : 1555.0,
"doc_count" : 20
},
{
"key" : "1555.0-1775.0",
"from" : 1555.0,
"to" : 1775.0,
"doc_count" : 20
},
{
"key" : "1775.0-*",
"from" : 1775.0,
"doc_count" : 20
}
]
},
"bucket_correlation" : {
"value" : 0.8402398981360937
}
},
{
"key" : "2.0",
"doc_count" : 100,
"latency_ranges" : {
"buckets" : [
{
"key" : "*-0.0",
"to" : 0.0,
"doc_count" : 0
},
{
"key" : "0.0-105.0",
"from" : 0.0,
"to" : 105.0,
"doc_count" : 19
},
{
"key" : "105.0-225.0",
"from" : 105.0,
"to" : 225.0,
"doc_count" : 11
},
{
"key" : "225.0-445.0",
"from" : 225.0,
"to" : 445.0,
"doc_count" : 20
},
{
"key" : "445.0-665.0",
"from" : 445.0,
"to" : 665.0,
"doc_count" : 20
},
{
"key" : "665.0-885.0",
"from" : 665.0,
"to" : 885.0,
"doc_count" : 20
},
{
"key" : "885.0-1115.0",
"from" : 885.0,
"to" : 1115.0,
"doc_count" : 10
},
{
"key" : "1115.0-1335.0",
"from" : 1115.0,
"to" : 1335.0,
"doc_count" : 0
},
{
"key" : "1335.0-1555.0",
"from" : 1335.0,
"to" : 1555.0,
"doc_count" : 0
},
{
"key" : "1555.0-1775.0",
"from" : 1555.0,
"to" : 1775.0,
"doc_count" : 0
},
{
"key" : "1775.0-*",
"from" : 1775.0,
"doc_count" : 0
}
]
},
"bucket_correlation" : {
"value" : -0.5759855613334943
}
}
]
}
}
}