[DOCS] Adds HTTP response count example to Painless examples (#54412)
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@ -15,19 +15,24 @@ more about the Painless scripting language in the
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* <<painless-group-by>>
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* <<painless-bucket-script>>
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NOTE: While the context of the following examples is the {transform} use case,
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the Painless scripts in the snippets below can be used in other {es} search
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aggregations, too.
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[discrete]
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[[painless-top-hits]]
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==== Getting top hits by using scripted metric
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==== Getting top hits by using scripted metric aggregation
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This snippet shows how to find the latest document, in other words the document
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with the earliest timestamp. From a technical perspective, it helps to achieve
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the function of a <<search-aggregations-metrics-top-hits-aggregation>> by using
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scripted metric aggregation which provides a metric output.
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scripted metric aggregation in a {transform}, which provides a metric output.
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[source,js]
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--------------------------------------------------
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"latest_doc": {
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"aggregations": {
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"latest_doc": {
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"scripted_metric": {
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"init_script": "state.timestamp_latest = 0L; state.last_doc = ''", <1>
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"map_script": """ <2>
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@ -45,6 +50,7 @@ scripted metric aggregation which provides a metric output.
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return last_doc
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"""
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}
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}
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}
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--------------------------------------------------
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// NOTCONSOLE
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@ -70,7 +76,8 @@ You can retrieve the last value in a similar way:
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[source,js]
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--------------------------------------------------
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"latest_value": {
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"aggregations": {
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"latest_value": {
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"scripted_metric": {
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"init_script": "state.timestamp_latest = 0L; state.last_value = ''",
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"map_script": """
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@ -88,6 +95,7 @@ You can retrieve the last value in a similar way:
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return last_value
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"""
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}
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}
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}
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--------------------------------------------------
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// NOTCONSOLE
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@ -97,12 +105,14 @@ You can retrieve the last value in a similar way:
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[[painless-time-features]]
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==== Getting time features as scripted fields
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This snippet shows how to extract time based features by using Painless. The
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snippet uses an index where `@timestamp` is defined as a `date` type field.
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This snippet shows how to extract time based features by using Painless in a
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{transform}. The snippet uses an index where `@timestamp` is defined as a `date`
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type field.
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[source,js]
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--------------------------------------------------
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"script_fields": {
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"aggregations": {
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"script_fields": {
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"hour_of_day": { <1>
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"script": {
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"lang": "painless",
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@ -121,7 +131,9 @@ snippet uses an index where `@timestamp` is defined as a `date` type field.
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"""
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}
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}
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}
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},
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...
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}
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--------------------------------------------------
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// NOTCONSOLE
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@ -327,3 +339,63 @@ the buckets you want to use for the variable. In this particular case, `min` and
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`max` are variables mapped to `time_frame.gte.value` and `time_frame.lte.value`.
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<3> Finally, the script substracts the start date of the session from the end
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date which results in the duration of the session.
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[discrete]
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[[painless-count-http]]
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==== Counting HTTP responses by using scripted metric aggregation
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You can count the different HTTP response types in a web log data set by using
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scripted metric aggregation as part of the {transform}. The example below
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assumes that the HTTP response codes are stored as keywords in the `response`
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field of the documents.
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[source,js]
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--------------------------------------------------
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"aggregations": { <1>
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"responses.counts": { <2>
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"scripted_metric": { <3>
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"init_script": "state.responses = ['error':0L,'success':0L,'other':0L]", <4>
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"map_script": """ <5>
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def code = doc['response.keyword'].value;
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if (code.startsWith('5') || code.startsWith('4')) {
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state.responses.error += 1 ;
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} else if(code.startsWith('2')) {
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state.responses.success += 1;
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} else {
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state.responses.other += 1;
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}
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""",
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"combine_script": "state.responses", <6>
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"reduce_script": """ <7>
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def counts = ['error': 0L, 'success': 0L, 'other': 0L];
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for (responses in states) {
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counts.error += responses['error'];
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counts.success += responses['success'];
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counts.other += responses['other'];
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}
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return counts;
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"""
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}
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},
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...
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}
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--------------------------------------------------
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// NOTCONSOLE
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<1> The `aggregations` object of the {transform} that contains all aggregations.
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<2> Object of the `scripted_metric` aggregation.
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<3> This `scripted_metric` performs a distributed operation on the web log data
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to count specific types of HTTP responses (error, success, and other).
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<4> The `init_script` creates a `responses` array in the `state` object with
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three properties (`error`, `success`, `other`) with long data type.
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<5> The `map_script` defines `code` based on the `response.keyword` value of the
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document, then it counts the errors, successes, and other responses based on the
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first digit of the responses.
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<6> The `combine_script` returns `state.responses` from each shard.
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<7> The `reduce_script` creates a `counts` array with the `error`, `success`,
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and `other` properties, then iterates through the value of `responses` returned
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by each shard and assigns the different response types to the appropriate
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properties of the `counts` object; error responses to the error counts, success
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responses to the success counts, and other responses to the other counts.
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Finally, returns the `counts` array with the response counts.
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