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CONSOLEify percentile and percentile-ranks docs 

Related #18160
This commit is contained in:
Zachary Tong 2017-08-02 17:47:27 -04:00
parent 268923ebdc
commit d8414ffa29
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3 changed files with 136 additions and 59 deletions
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34
docs/build.gradle
View File

@ -31,8 +31,6 @@ buildRestTests.expectedUnconvertedCandidates = [
'reference/aggregations/bucket/significantterms-aggregation.asciidoc',
'reference/aggregations/bucket/terms-aggregation.asciidoc',
'reference/aggregations/matrix/stats-aggregation.asciidoc',
'reference/aggregations/metrics/percentile-aggregation.asciidoc',
'reference/aggregations/metrics/percentile-rank-aggregation.asciidoc',
'reference/aggregations/metrics/scripted-metric-aggregation.asciidoc',
'reference/aggregations/metrics/tophits-aggregation.asciidoc',
'reference/cluster/allocation-explain.asciidoc',
@ -476,3 +474,35 @@ buildRestTests.setups['analyze_sample'] = '''
properties:
obj1.field1:
type: text'''
// Used by percentile/percentile-rank aggregations
buildRestTests.setups['latency'] = '''
- do:
indices.create:
index: latency
body:
settings:
number_of_shards: 1
number_of_replicas: 1
mappings:
data:
properties:
load_time:
type: long
- do:
bulk:
index: latency
type: data
refresh: true
body: |'''
for (int i = 0; i < 100; i++) {
def value = i
if (i % 10) {
value = i*10
}
buildRestTests.setups['latency'] += """
{"index":{}}
{"load_time": "$value"}"""
}

80
docs/reference/aggregations/metrics/percentile-aggregation.asciidoc
View File

@ -26,7 +26,9 @@ Let's look at a range of percentiles representing load time:
[source,js]
--------------------------------------------------
GET latency/data/_search
{
"size": 0,
"aggs" : {
"load_time_outlier" : {
"percentiles" : {
@ -36,6 +38,8 @@ Let's look at a range of percentiles representing load time:
}
}
--------------------------------------------------
// CONSOLE
// TEST[setup:latency]
<1> The field `load_time` must be a numeric field
By default, the `percentile` metric will generate a range of
@ -49,18 +53,19 @@ percentiles: `[ 1, 5, 25, 50, 75, 95, 99 ]`. The response will look like this:
"aggregations": {
"load_time_outlier": {
"values" : {
"1.0": 15,
"5.0": 20,
"25.0": 23,
"50.0": 25,
"75.0": 29,
"95.0": 60,
"99.0": 150
"1.0": 9.9,
"5.0": 29.500000000000004,
"25.0": 167.5,
"50.0": 445.0,
"75.0": 722.5,
"95.0": 940.5,
"99.0": 980.1000000000001
}
}
}
}
--------------------------------------------------
// TESTRESPONSE[s/\.\.\./"took": $body.took,"timed_out": false,"_shards": $body._shards,"hits": $body.hits,/]
As you can see, the aggregation will return a calculated value for each percentile
in the default range. If we assume response times are in milliseconds, it is
@ -73,7 +78,9 @@ must be a value between 0-100 inclusive):
[source,js]
--------------------------------------------------
GET latency/data/_search
{
"size": 0,
"aggs" : {
"load_time_outlier" : {
"percentiles" : {
@ -84,6 +91,8 @@ must be a value between 0-100 inclusive):
}
}
--------------------------------------------------
// CONSOLE
// TEST[setup:latency]
<1> Use the `percents` parameter to specify particular percentiles to calculate
==== Keyed Response
@ -92,12 +101,13 @@ By default the `keyed` flag is set to `true` which associates a unique string ke
[source,js]
--------------------------------------------------
POST bank/account/_search?size=0
GET latency/data/_search
{
"size": 0,
"aggs": {
"balance_outlier": {
"load_time_outlier": {
"percentiles": {
"field": "balance",
"field": "load_time",
"keyed": false
}
}
@ -105,7 +115,7 @@ POST bank/account/_search?size=0
}
--------------------------------------------------
// CONSOLE
// TEST[setup:bank]
// TEST[setup:latency]
Response:
@ -115,35 +125,35 @@ Response:
...
"aggregations": {
"balance_outlier": {
"load_time_outlier": {
"values": [
{
"key": 1.0,
"value": 1462.8400000000001
"value": 9.9
},
{
"key": 5.0,
"value": 3591.85
"value": 29.500000000000004
},
{
"key": 25.0,
"value": 13709.333333333334
"value": 167.5
},
{
"key": 50.0,
"value": 26020.11666666667
"value": 445.0
},
{
"key": 75.0,
"value": 38139.648148148146
"value": 722.5
},
{
"key": 95.0,
"value": 47551.549999999996
"value": 940.5
},
{
"key": 99.0,
"value": 49339.16
"value": 980.1000000000001
}
]
}
@ -151,13 +161,6 @@ Response:
}
--------------------------------------------------
// TESTRESPONSE[s/\.\.\./"took": $body.took,"timed_out": false,"_shards": $body._shards,"hits": $body.hits,/]
// TESTRESPONSE[s/1462.8400000000001/$body.aggregations.balance_outlier.values.0.value/]
// TESTRESPONSE[s/3591.85/$body.aggregations.balance_outlier.values.1.value/]
// TESTRESPONSE[s/13709.333333333334/$body.aggregations.balance_outlier.values.2.value/]
// TESTRESPONSE[s/26020.11666666667/$body.aggregations.balance_outlier.values.3.value/]
// TESTRESPONSE[s/38139.648148148146/$body.aggregations.balance_outlier.values.4.value/]
// TESTRESPONSE[s/47551.549999999996/$body.aggregations.balance_outlier.values.5.value/]
// TESTRESPONSE[s/49339.16/$body.aggregations.balance_outlier.values.6.value/]
==== Script
@ -167,7 +170,9 @@ a script to convert them on-the-fly:
[source,js]
--------------------------------------------------
GET latency/data/_search
{
"size": 0,
"aggs" : {
"load_time_outlier" : {
"percentiles" : {
@ -183,6 +188,9 @@ a script to convert them on-the-fly:
}
}
--------------------------------------------------
// CONSOLE
// TEST[setup:latency]
<1> The `field` parameter is replaced with a `script` parameter, which uses the
script to generate values which percentiles are calculated on
<2> Scripting supports parameterized input just like any other script
@ -191,14 +199,16 @@ This will interpret the `script` parameter as an `inline` script with the `painl
[source,js]
--------------------------------------------------
GET latency/data/_search
{
"size": 0,
"aggs" : {
"load_time_outlier" : {
"percentiles" : {
"script" : {
"id": "my_script",
"params": {
"timeUnit" : 1000
"field": "load_time"
}
}
}
@ -206,6 +216,8 @@ This will interpret the `script` parameter as an `inline` script with the `painl
}
}
--------------------------------------------------
// CONSOLE
// TEST[setup:latency,stored_example_script]
[[search-aggregations-metrics-percentile-aggregation-approximation]]
==== Percentiles are (usually) approximate
@ -252,7 +264,9 @@ This balance can be controlled using a `compression` parameter:
[source,js]
--------------------------------------------------
GET latency/data/_search
{
"size": 0,
"aggs" : {
"load_time_outlier" : {
"percentiles" : {
@ -265,6 +279,9 @@ This balance can be controlled using a `compression` parameter:
}
}
--------------------------------------------------
// CONSOLE
// TEST[setup:latency]
<1> Compression controls memory usage and approximation error
The TDigest algorithm uses a number of "nodes" to approximate percentiles -- the
@ -298,7 +315,9 @@ The HDR Histogram can be used by specifying the `method` parameter in the reques
[source,js]
--------------------------------------------------
GET latency/data/_search
{
"size": 0,
"aggs" : {
"load_time_outlier" : {
"percentiles" : {
@ -312,6 +331,9 @@ The HDR Histogram can be used by specifying the `method` parameter in the reques
}
}
--------------------------------------------------
// CONSOLE
// TEST[setup:latency]
<1> `hdr` object indicates that HDR Histogram should be used to calculate the percentiles and specific settings for this algorithm can be specified inside the object
<2> `number_of_significant_value_digits` specifies the resolution of values for the histogram in number of significant digits
@ -326,7 +348,9 @@ had a value.
[source,js]
--------------------------------------------------
GET latency/data/_search
{
"size": 0,
"aggs" : {
"grade_percentiles" : {
"percentiles" : {
@ -337,5 +361,7 @@ had a value.
}
}
--------------------------------------------------
// CONSOLE
// TEST[setup:latency]
<1> Documents without a value in the `grade` field will fall into the same bucket as documents that have the value `10`.

77
docs/reference/aggregations/metrics/percentile-rank-aggregation.asciidoc
View File

@ -24,17 +24,21 @@ Let's look at a range of percentiles representing load time:
[source,js]
--------------------------------------------------
GET latency/data/_search
{
"size": 0,
"aggs" : {
"load_time_outlier" : {
"load_time_ranks" : {
"percentile_ranks" : {
"field" : "load_time", <1>
"values" : [15, 30]
"values" : [500, 600]
}
}
}
}
--------------------------------------------------
// CONSOLE
// TEST[setup:latency]
<1> The field `load_time` must be a numeric field
The response will look like this:
@ -45,15 +49,16 @@ The response will look like this:
...
"aggregations": {
"load_time_outlier": {
"load_time_ranks": {
"values" : {
"15": 92,
"30": 100
"500.0": 55.00000000000001,
"600.0": 64.0
}
}
}
}
--------------------------------------------------
// TESTRESPONSE[s/\.\.\./"took": $body.took,"timed_out": false,"_shards": $body._shards,"hits": $body.hits,/]
From this information you can determine you are hitting the 99% load time target but not quite
hitting the 95% load time target
@ -64,13 +69,14 @@ By default the `keyed` flag is set to `true` associates a unique string key with
[source,js]
--------------------------------------------------
POST bank/account/_search?size=0
GET latency/data/_search
{
"size": 0,
"aggs": {
"balance_outlier": {
"load_time_ranks": {
"percentile_ranks": {
"field": "balance",
"values": [25000, 50000],
"field": "load_time",
"values": [500, 600],
"keyed": false
}
}
@ -78,7 +84,7 @@ POST bank/account/_search?size=0
}
--------------------------------------------------
// CONSOLE
// TEST[setup:bank]
// TEST[setup:latency]
Response:
@ -88,15 +94,15 @@ Response:
...
"aggregations": {
"balance_outlier": {
"load_time_ranks": {
"values": [
{
"key": 25000.0,
"value": 48.537724935732655
"key": 500.0,
"value": 55.00000000000001
},
{
"key": 50000.0,
"value": 99.85567010309278
"key": 600.0,
"value": 64.0
}
]
}
@ -104,8 +110,7 @@ Response:
}
--------------------------------------------------
// TESTRESPONSE[s/\.\.\./"took": $body.took,"timed_out": false,"_shards": $body._shards,"hits": $body.hits,/]
// TESTRESPONSE[s/48.537724935732655/$body.aggregations.balance_outlier.values.0.value/]
// TESTRESPONSE[s/99.85567010309278/$body.aggregations.balance_outlier.values.1.value/]
==== Script
@ -115,11 +120,13 @@ a script to convert them on-the-fly:
[source,js]
--------------------------------------------------
GET latency/data/_search
{
"size": 0,
"aggs" : {
"load_time_outlier" : {
"load_time_ranks" : {
"percentile_ranks" : {
"values" : [3, 5],
"values" : [500, 600],
"script" : {
"lang": "painless",
"source": "doc['load_time'].value / params.timeUnit", <1>
@ -132,6 +139,8 @@ a script to convert them on-the-fly:
}
}
--------------------------------------------------
// CONSOLE
// TEST[setup:latency]
<1> The `field` parameter is replaced with a `script` parameter, which uses the
script to generate values which percentile ranks are calculated on
<2> Scripting supports parameterized input just like any other script
@ -140,15 +149,17 @@ This will interpret the `script` parameter as an `inline` script with the `painl
[source,js]
--------------------------------------------------
GET latency/data/_search
{
"size": 0,
"aggs" : {
"load_time_outlier" : {
"load_time_ranks" : {
"percentile_ranks" : {
"values" : [3, 5],
"values" : [500, 600],
"script" : {
"id": "my_script",
"params": {
"timeUnit" : 1000
"field": "load_time"
}
}
}
@ -156,6 +167,8 @@ This will interpret the `script` parameter as an `inline` script with the `painl
}
}
--------------------------------------------------
// CONSOLE
// TEST[setup:latency,stored_example_script]
==== HDR Histogram
@ -172,12 +185,14 @@ The HDR Histogram can be used by specifying the `method` parameter in the reques
[source,js]
--------------------------------------------------
GET latency/data/_search
{
"size": 0,
"aggs" : {
"load_time_outlier" : {
"load_time_ranks" : {
"percentile_ranks" : {
"field" : "load_time",
"values" : [15, 30],
"values" : [500, 600],
"hdr": { <1>
"number_of_significant_value_digits" : 3 <2>
}
@ -186,6 +201,8 @@ The HDR Histogram can be used by specifying the `method` parameter in the reques
}
}
--------------------------------------------------
// CONSOLE
// TEST[setup:latency]
<1> `hdr` object indicates that HDR Histogram should be used to calculate the percentiles and specific settings for this algorithm can be specified inside the object
<2> `number_of_significant_value_digits` specifies the resolution of values for the histogram in number of significant digits
@ -200,16 +217,20 @@ had a value.
[source,js]
--------------------------------------------------
GET latency/data/_search
{
"size": 0,
"aggs" : {
"grade_ranks" : {
"load_time_ranks" : {
"percentile_ranks" : {
"field" : "grade",
"field" : "load_time",
"values" : [500, 600],
"missing": 10 <1>
}
}
}
}
--------------------------------------------------
<1> Documents without a value in the `grade` field will fall into the same bucket as documents that have the value `10`.
// CONSOLE
// TEST[setup:latency]
<1> Documents without a value in the `load_time` field will fall into the same bucket as documents that have the value `10`.