[DOCS] Refresh APM correlation screenshots (#116723)

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Lisa Cawley 2021-11-15 08:18:45 -08:00 committed by GitHub
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@ -38,7 +38,7 @@ in a table below the chart. The table is sorted by correlation coefficients that
range from 0 to 1. Attributes with higher correlation values are more likely to
contribute to high latency transactions. By default, the attribute with the
highest correlation value is added to the chart. To see the latency distribution
for other attributes, hover over their row in the table.
for other attributes, select their row in the table.
If a correlated attribute seems noteworthy, use the **Filter** quick links:
@ -47,12 +47,14 @@ the selected value.
* `-` creates a new query in the {apm-app} to filter out transactions containing
the selected value.
In this example screenshot, transactions with the field
`labels.orderPriceRange` and value `large` are skewed to the right with slower
response times than the overall latency distribution. If you select the `+`
filter in the appropriate row of the table, it creates a new query in the
{apm-app} for transactions with this attribute. With the "noise" now filtered
out, you can begin viewing sample traces to continue your investigation.
You can also click the icon beside the field name to view and filter its most
popular values.
In this example screenshot, there are transactions that are skewed to the right
with slower response times than the overall latency distribution. If you select
the `+` filter in the appropriate row of the table, it creates a new query in
the {apm-app} for transactions with this attribute. With the "noise" now
filtered out, you can begin viewing sample traces to continue your investigation.
[discrete]
[[correlations-error-rate]]
@ -67,25 +69,23 @@ is determined by its {ecs-ref}/ecs-event.html#field-event-outcome[event.outcome]
value. For example, APM agents set the `event.outcome` to `failure` when an HTTP
transaction returns a `5xx` status code.
// The chart highlights the failed transactions in the overall latency distribution for the transaction group.
If there are attributes that have a statistically significant correlation with
failed transactions, they are listed in a table. The table is sorted by scores,
which are mapped to high, medium, or low impact levels. Attributes with high
impact levels are more likely to contribute to failed transactions.
// 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.
The chart highlights the failed transactions in the overall latency distribution
for the transaction group. If there are attributes that have a statistically
significant correlation with failed transactions, they are listed in a table.
The table is sorted by scores, which are mapped to high, medium, or low impact
levels. Attributes with high impact levels are more likely to contribute to
failed transactions. By default, the attribute with the highest score is added
to the chart. To see a different attribute in the chart, select its row in the
table.
For example, in the screenshot below, the field
`kubernetes.pod.name` and value `frontend-node-59dff47885-fl5lb` has a medium
impact level and existed in 19% of the failed transactions.
For example, in the screenshot below, there are attributes such as a specific
node and pod name that have medium impact on the failed transactions.
[role="screenshot"]
image::apm/images/correlations-failed-transactions.png[Failed transaction correlations]
TIP: Some details, such as the failure and success percentages, are available
only when the
<<observability-enable-inspect-es-queries,observability:enableInspectEsQueries>>
advanced setting is enabled.
Select the `+` filter to create a new query in the {apm-app} for transactions
with this attribute. You might do his for multiple attributes--each time
filtering out more and more noise and bringing you closer to a diagnosis.
with one or more of these attributes. If you are unfamiliar with a field, click
the icon beside its name to view its most popular values and optionally filter
on those values too. Each time that you add another attribute, it is filtering
out more and more noise and bringing you closer to a diagnosis.

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