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[DOCS] Changes scripted metric to filter aggs in transforms example #54167

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[DOCS] Changes scripted metric to filter aggs in transforms example.
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[DOCS] Adds introductory sentence to the example.
szabosteve Mar 30, 2020
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85 changes: 35 additions & 50 deletions docs/reference/transform/examples.asciidoc
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Expand Up @@ -188,20 +188,15 @@ or flight stats for any of the featured destination or origin airports.


[[example-clientips]]
==== Finding suspicious client IPs by using scripted metrics
==== Finding suspicious client IPs

With {transforms}, you can use
{ref}/search-aggregations-metrics-scripted-metric-aggregation.html[scripted
metric aggregations] on your data. These aggregations are flexible and make
it possible to perform very complex processing. Let's use scripted metrics to
identify suspicious client IPs in the web log sample dataset.

We transform the data such that the new index contains the sum of bytes and the
number of distinct URLs, agents, incoming requests by location, and geographic
destinations for each client IP. We also use a scripted field to count the
specific types of HTTP responses that each client IP receives. Ultimately, the
example below transforms web log data into an entity centric index where the
entity is `clientip`.
In this example, we use the web log sample dataset to identify suspicious client
IPs. We transform the data such that the new index contains the sum of bytes and
the number of distinct URLs, agents, incoming requests by location, and
geographic destinations for each client IP. We also use filter aggregations to
count the specific types of HTTP responses that each client IP receives.
Ultimately, the example below transforms web log data into an entity centric
index where the entity is `clientip`.

[source,console]
----------------------------------
Expand Down Expand Up @@ -230,30 +225,17 @@ PUT _transform/suspicious_client_ips
"agent_dc": { "cardinality": { "field": "agent.keyword" }},
"geo.dest_dc": { "cardinality": { "field": "geo.dest" }},
"responses.total": { "value_count": { "field": "timestamp" }},
"responses.counts": { <4>
"scripted_metric": {
"init_script": "state.responses = ['error':0L,'success':0L,'other':0L]",
"map_script": """
def code = doc['response.keyword'].value;
if (code.startsWith('5') || code.startsWith('4')) {
state.responses.error += 1 ;
} else if(code.startsWith('2')) {
state.responses.success += 1;
} else {
state.responses.other += 1;
}
""",
"combine_script": "state.responses",
"reduce_script": """
def counts = ['error': 0L, 'success': 0L, 'other': 0L];
for (responses in states) {
counts.error += responses['error'];
counts.success += responses['success'];
counts.other += responses['other'];
}
return counts;
"""
}
"success" : { <4>
"filter": {
"term": { "response" : "200"}}
},
"error404" : {
"filter": {
"term": { "response" : "404"}}
},
"error503" : {
"filter": {
"term": { "response" : "503"}}
},
"timestamp.min": { "min": { "field": "timestamp" }},
"timestamp.max": { "max": { "field": "timestamp" }},
Expand All @@ -277,11 +259,13 @@ PUT _transform/suspicious_client_ips
to synchronize the source and destination indices. The worst case
ingestion delay is 60 seconds.
<3> The data is grouped by the `clientip` field.
<4> This `scripted_metric` performs a distributed operation on the web log data
to count specific types of HTTP responses (error, success, and other).
<4> Filter aggregation that counts the occurrences of successful (`200`)
responses in the `response` field. The following two aggregations (`error404`
and `error503`) count the error responses by error codes.
<5> This `bucket_script` calculates the duration of the `clientip` access based
on the results of the aggregation.


After you create the {transform}, you must start it:

[source,console]
Expand All @@ -290,6 +274,7 @@ POST _transform/suspicious_client_ips/_start
----------------------------------
// TEST[skip:setup kibana sample data]


Shortly thereafter, the first results should be available in the destination
index:

Expand All @@ -299,6 +284,7 @@ GET sample_weblogs_by_clientip/_search
----------------------------------
// TEST[skip:setup kibana sample data]


The search result shows you data like this for each client IP:

[source,js]
Expand All @@ -313,22 +299,20 @@ The search result shows you data like this for each client IP:
"src_dc" : 2.0,
"dest_dc" : 2.0
},
"success" : 2,
"error404" : 0,
"error503" : 0,
"clientip" : "0.72.176.46",
"agent_dc" : 2.0,
"bytes_sum" : 4422.0,
"responses" : {
"total" : 2.0,
"counts" : {
"other" : 0,
"success" : 2,
"error" : 0
}
"total" : 2.0
},
"url_dc" : 2.0,
"timestamp" : {
"duration_ms" : 5.2191698E8,
"min" : "2019-11-25T07:51:57.333Z",
"max" : "2019-12-01T08:50:34.313Z"
"min" : "2020-03-16T07:51:57.333Z",
"max" : "2020-03-22T08:50:34.313Z"
}
}
}
Expand All @@ -337,11 +321,12 @@ The search result shows you data like this for each client IP:
// NOTCONSOLE

NOTE: Like other Kibana sample data sets, the web log sample dataset contains
timestamps relative to when you installed it, including timestamps in the future.
The {ctransform} will pick up the data points once they are in the past. If you
installed the web log sample dataset some time ago, you can uninstall and
timestamps relative to when you installed it, including timestamps in the
future. The {ctransform} will pick up the data points once they are in the past.
If you installed the web log sample dataset some time ago, you can uninstall and
reinstall it and the timestamps will change.


This {transform} makes it easier to answer questions such as:

* Which client IPs are transferring the most amounts of data?
Expand Down