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3 changed files with 23 additions and 23 deletions
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@ -38,7 +38,7 @@ in a table below the chart. The table is sorted by correlation coefficients that
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range from 0 to 1. Attributes with higher correlation values are more likely to
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contribute to high latency transactions. By default, the attribute with the
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highest correlation value is added to the chart. To see the latency distribution
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for other attributes, hover over their row in the table.
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for other attributes, select their row in the table.
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If a correlated attribute seems noteworthy, use the **Filter** quick links:
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@ -47,12 +47,14 @@ the selected value.
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* `-` creates a new query in the {apm-app} to filter out transactions containing
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the selected value.
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In this example screenshot, transactions with the field
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`labels.orderPriceRange` and value `large` are skewed to the right with slower
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response times than the overall latency distribution. If you select the `+`
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filter in the appropriate row of the table, it creates a new query in the
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{apm-app} for transactions with this attribute. With the "noise" now filtered
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out, you can begin viewing sample traces to continue your investigation.
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You can also click the icon beside the field name to view and filter its most
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popular values.
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In this example screenshot, there are transactions that are skewed to the right
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with slower response times than the overall latency distribution. If you select
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the `+` filter in the appropriate row of the table, it creates a new query in
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the {apm-app} for transactions with this attribute. With the "noise" now
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filtered out, you can begin viewing sample traces to continue your investigation.
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[discrete]
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[[correlations-error-rate]]
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@ -67,25 +69,23 @@ is determined by its {ecs-ref}/ecs-event.html#field-event-outcome[event.outcome]
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value. For example, APM agents set the `event.outcome` to `failure` when an HTTP
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transaction returns a `5xx` status code.
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// The chart highlights the failed transactions in the overall latency distribution for the transaction group.
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If there are attributes that have a statistically significant correlation with
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failed transactions, they are listed in a table. The table is sorted by scores,
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which are mapped to high, medium, or low impact levels. Attributes with high
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impact levels are more likely to contribute to failed transactions.
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// By default, the attribute with the highest score is added to the chart. To see a different attribute in the chart, hover over its row in the table.
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The chart highlights the failed transactions in the overall latency distribution
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for the transaction group. If there are attributes that have a statistically
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significant correlation with failed transactions, they are listed in a table.
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The table is sorted by scores, which are mapped to high, medium, or low impact
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levels. Attributes with high impact levels are more likely to contribute to
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failed transactions. By default, the attribute with the highest score is added
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to the chart. To see a different attribute in the chart, select its row in the
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table.
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For example, in the screenshot below, the field
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`kubernetes.pod.name` and value `frontend-node-59dff47885-fl5lb` has a medium
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impact level and existed in 19% of the failed transactions.
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For example, in the screenshot below, there are attributes such as a specific
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node and pod name that have medium impact on the failed transactions.
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[role="screenshot"]
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image::apm/images/correlations-failed-transactions.png[Failed transaction correlations]
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TIP: Some details, such as the failure and success percentages, are available
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only when the
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<<observability-enable-inspect-es-queries,observability:enableInspectEsQueries>>
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advanced setting is enabled.
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Select the `+` filter to create a new query in the {apm-app} for transactions
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with this attribute. You might do his for multiple attributes--each time
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filtering out more and more noise and bringing you closer to a diagnosis.
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with one or more of these attributes. If you are unfamiliar with a field, click
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the icon beside its name to view its most popular values and optionally filter
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on those values too. Each time that you add another attribute, it is filtering
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out more and more noise and bringing you closer to a diagnosis.
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