Create and edit SLIs and SLOs

Service success is the ratio of the number of successful responses to the number of all requests. This effectively is an error rate, but you can filter it down, for example removing expected errors.

Valid events fields

FROM Transaction
WHERE entityGuid = '{entityGuid}'

Where {entityGuid} is the service's GUID.

Bad events fields

FROM TransactionError
WHERE entityGuid = '{entityGuid}' AND error.expected != true

Where {entityGuid} is the service's GUID.

A latency SLI measures the proportion of valid requests that were served faster than the threshold established as a good experience.

In order to determine that duration threshold, check how the service has been performing in the past weeks, and use that result as a realistic and achievable baseline. Afterwards, you can iterate on the SLI threshold, and align it with a more ambitious performance.

To select an appropriate value for the duration condition, one typical practice is to select the 95 percentile duration of the responses for the last 7 or 15 days. Find this duration threshold using the query builder, and use it to determine what you consider to be good events for your SLI:

SELECT percentile(duration, 95) FROM Transaction WHERE entityGuid = '{entityGuid}' since 7 days ago limit max

Valid events fields

FROM Transaction
WHERE entityGuid = '{entityGuid}' AND transactionType = 'Web'

Where {entityGuid} is the service's GUID.

Good events fields

FROM Transaction
WHERE entityGuid = '{entityGuid}' AND transactionType = 'Web' AND duration < {duration}

• Where {entityGuid} is the service's GUID.
• Where {duration} is the response time that you consider provides a good experience for your client service or end-user, in seconds.

Service success is the ratio of the number of successful responses to the number of all requests. This effectively is an error rate, but you can filter it down, for example removing expected errors.

Valid events fields

FROM Span
WHERE entity.guid = '{entityGuid}' AND (span.kind IN ('server', 'consumer') OR kind IN ('server', 'consumer'))

Where {entityGuid} is the service's GUID.

Bad events fields

FROM Span
WHERE entity.guid = '{entityGuid}' AND (span.kind IN ('server', 'consumer') OR kind IN ('server', 'consumer')) AND otel.status_code = 'ERROR'

Where {entityGuid} is the service's GUID.

A latency SLI measures the proportion of valid requests that were served faster than the threshold established as a good experience.

In order to determine that duration threshold, check how the service has been performing in the past weeks, and use that result as a realistic and achievable baseline. Afterwards, you can iterate on the SLI threshold, and align it with a more ambitious performance.

To select an appropriate value for the duration condition, one typical practice is to select the 95 percentile duration of the responses for the last 7 or 15 days. Find this duration threshold using the query builder, and use it to determine what you consider to be good events for your SLI:

SELECT percentile(duration.ms, 95) FROM Span WHERE entityGuid = '{entityGuid}' AND (span.kind IN ('server', 'consumer') OR kind IN ('server', 'consumer')) SINCE 7 days ago LIMIT MAX

Valid events fields

FROM Span
WHERE entity.guid = '{entityGuid}' AND (span.kind IN ('server', 'consumer') OR kind IN ('server', 'consumer'))

Where {entityGuid} is the service's GUID.

Good events fields

FROM Span
WHERE entity.guid = '{entityGuid}' AND (span.kind IN ('server', 'consumer') OR kind IN ('server', 'consumer')) AND duration.ms < {duration}

• Where {entityGuid} is the service's GUID.
• Where {duration} is the response time that you consider provides a good experience for your client service or end-user, in seconds.

Service success is the ratio of the number of successful responses to the number of all requests. This effectively is an error rate.

Valid data

FROM Metric
SELECT sum(getField(apm.service.transaction.duration, count))
WHERE appName = '{appName}'

Where {appName} is the APM app name.

Bad events fields

FROM Metric
SELECT sum(getField(apm.service.error.count, count))
WHERE appName = '{appName}' AND getField(`apm.service.error.count`, count) > 0

Where {appName} is the APM app name.

Imagine that good events are reported by a custom metric. Valid events counts could be the same.

Valid data

FROM Metric
SELECT sum(getField(apm.service.transaction.duration, count))
WHERE appName = '{appName}'

Where {appName} is the APM app name.

And, now we use the custom metric to discover the good events.

Good data

FROM Metric
SELECT sum(getField(newrelic.timeslice.value, count))
WHERE appName = '{appName}' AND metricTimesliceName = 'Custom/CrossClusterQuery/DataAvailability/status/success'

Where {appName} is the APM app name.

It's the proportion of page views that are served without errors.

Valid events fields

FROM PageView
WHERE entityGuid = '{entityGuid}'

Where {entityGuid} is the browser app GUID.

Bad events fields

FROM JavaScriptError
WHERE entityGuid = '{entityGuid}' AND firstErrorInSession IS true

Where {entityGuid} is the browser app GUID.

It’s the proportion of valid page views where the largest content element visible in the viewport was rendered faster than the threshold considered to correspond to a good experience.

Valid events fields

FROM PageViewTiming
WHERE entityGuid = '{entityGuid}' AND largestContentfulPaint IS NOT NULL

Where {entityGuid} is the browser app GUID.

Good events fields

FROM PageViewTiming
WHERE entityGuid = '{entityGuid}' AND largestContentfulPaint < '{largestContentfulPaint}'

• Where {entityGuid} is the browser app GUID.

• Where {largestContentfulPaint} is the amount of time (in milliseconds) to render the largest content element visible in the viewport that you consider provides a good experience for your end user. A frequent standard is 4000 ms.

  To determine a realistic number to use for {largestContentfulPaint} in your environment, one typical practice is to select the 95 percentile duration of the responses for the last 7 or 15 days. Find it by using the query builder:

  SELECT percentile(largestContentfulPaint, 95) FROM PageViewTiming WHERE entityGuid = '{entityGuid}' SINCE 7 days ago LIMIT MAX

  카피

It’s the proportion of page views where the time between a user's first interacion with the page and the time when the browser responds to that interaction is less than a certain threshold.

Valid events fields

FROM PageViewTiming
WHERE entityGuid = '{entityGuid}' AND interactionToNextPaint IS NOT NULL

Where {entityGuid} is the browser app GUID.

Good events fields

FROM PageViewTiming
WHERE entityGuid = '{entityGuid}' AND interactionToNextPaint < {interactionToNextPaint}

• Where {entityGuid} is the browser app GUID.

• Where {interactionToNextPaint} is the amount of time (in milliseconds) the browser should respond in to provide a good experience for your end user. A frequent standard is 300 ms.

  To determine a realistic number to use for {interactionToNextPaint} in your environment, one typical practice is to select the 95 percentile duration of the responses for the last 7 or 15 days. Find it by using the query builder:

  SELECT percentile(interactionToNextPaint, 95) FROM PageViewTiming WHERE entityGuid = '{entityGuid}' SINCE 7 days ago LIMIT MAX FACET deviceType

  카피

It’s the proportion of page views with a good cumulative layout shift (CLS). CLS is described as the total sum of all individual layout shift scores for every unexpected layout shift that occurs during the entire lifespan of the page. A layout shift occurs any time a visible element changes its position from one rendered frame to the next.

Valid events fields

FROM PageViewTiming
WHERE entityGuid = '{entityGuid}' AND cumulativeLayoutShift IS NOT NULL

Where {entityGuid} is the browser app GUID.

If you’d like to create separate SLIs to track CLS in desktop and mobile devices separately, add one of these clauses at the end of the field:

• and deviceType = 'Mobile'
• and deviceType = 'Desktop'

Good events fields

FROM PageViewTiming
WHERE entityGuid = '{entityGuid}' AND cumulativeLayoutShift < {cumulativeLayoutShift}

• Where {entityGuid} is the browser app GUID.

• Where {cumulativeLayoutShift} is a pre-set value. To provide a good user experience, your site should strive to have a CLS score of 0.1 or less. A CLS score of 0.25 or more is considered a poor user experience.

  If you’ve decided to create separate SLIs to track CLS in desktop and mobile devices separately when you defined the valid events query, add this clause at the end of the field:

  • and deviceType = 'Mobile'
  • and deviceType = 'Desktop'

  To determine a realistic number to select for {cumulativeLayoutShift} in your environment, one typical practice is to select the 75th percentile of page loads for the last 7 or 15 days, segmented across mobile and desktop devices. Find it by using the query builder:

  SELECT percentile(cumulativeLayoutShift, 95) FROM PageViewTiming WHERE  entityGuid = '{entityGuid}' since 7 days ago limit max facet deviceType

  카피

Success is the ratio of the number of successful synthectic checks to the number of all checks.

Valid events fields

FROM SyntheticCheck
WHERE entity.guid = '{entityGuid}'

Where {entityGuid} is the synthetic check's GUID.

Good events fields

FROM SyntheticCheck
WHERE entity.guid = '{entityGuid}' AND result='SUCCESS'

Where {entityGuid} is the synthetic check's GUID.

In order to define your new SLI, choose one of these three options:

• Entity data: Base the SLI on standard data coming from our agents or your own custom events. This is the most common option. If this is your choice, select the entity (for example, APM service) you want to use.
• Custom data: Alternatively, you can base the SLI on your custom NRDB events or dimensional metrics. Use this option when you can't relate the service level data to a specific entity, or when you want to relate the service level directly to a workload.
• Metric data: Based on the data coming from Prometheus, OTel or your own custom dimensional metrics.

In this step, you'll configure the SLI queries that determine which event is valid, or good, or bad.

If you associate the SLI with an APM service or a browser app, New Relic will suggest some typical SLI and their queries. We'll use the latest data as a baseline for your service level objectives, and you will be able to edit the SLI and SLO afterwards.

If you're using a different type of entity, you want to query dimensional metrics, or you want to customize the baseline values provided by New Relic, you can customize the SLI to your needs. For instance, you can use the WHERE clause to filter out health checks. You could also use different event types on each queries; in this case, make sure that each valid event corresponds only to one or less events on the good or bad query.

The account where the data is gathered from matches the account of the entity that the SLI refers to. Please see the section above to know what goes into each field.

On the right you'll see the final queries, and at the bottom you'll get a preview of the number of valid and good/bad events in the last days.

Here’s an example of the percentage-based success rate for a dimensional metric, let’s convert it into the valid/good events for SLI:

FROM Metric
SELECT percentage(sum(scrooge_do_expire_count), 
WHERE status = 'success') AS 'Success Rate'
WHERE env='production' 
AND status != 'attempt'

For the valid queries we would just copy the outside WHERE clause:

FROM Metric
SELECT sum(scrooge_do_expire_count))
WHERE env='production'
AND status != 'attempt'

While the good event would be the outside WHERE clause and WHERE clause from the percentage function:

FROM Metric
SELECT sum(scrooge_do_expire_count))
WHERE env='production'
AND status != 'attempt'
AND status = 'success'

The four aggregation functions that we currently support are count(), sum(), getField() and getCdfCount(). Count and sum are available for all event types, while getField and getCdfCount are only available when selecting from Metric.

Use the count() function with event data to count the number of valid/good/bad events.

The sum() function is helpful if you have pre-aggregated counters in event data or dimensional metrics. It requires a parameter: the attribute to use in the sum.

Use the getField() and getCdfCount() functions to see how often a distribution metric attribute is below or at a threshold. Both functions require an attribute, and getCdfCount() also requires a threshold to measure value against.

Example using count():

FROM JavaScriptError
SELECT count(*)
WHERE entityGuid = '{entityGuid}' AND firstErrorInSession IS true

Example using sum():

FROM ServerlessSample
SELECT sum(provider.errors.Sum)
WHERE awsAccountId = 'XXX'  And provider LIKE 'LambdaFunction%'

Example using getField() combined with getCdfCount():

FROM Metric
SELECT getField(`newrelic.goldenmetrics.synth.monitor.medianDurationS`, count) as 'Valid'

FROM Metric
SELECT getCdfCount(`newrelic.goldenmetrics.synth.monitor.medianDurationS`, 0.5) as 'Good'

You can also use wildcards in your SLI queries, here's an example:

FROM ApiGatewaySample
SELECT sum(provider.cache%Count.Sum)
WHERE awsAccountId = 'XXX'

In this step you'll get a preview of the SLI value, and you'll add one SLO for this SLI: Just select the length of the time window and the percentage target. The chart on the right will help you anticipate whether the target you're setting is feasible or if it's often missed.

Rolling time-window SLOs are supported. With a rolling time-window, the SLO compliance takes into account the last N days. Every minute, the oldest data drops out of the current calculation and new data replaces it.

Select a short name for your SLI which helps you recognize what it's measuring.

We recommend that you add tags to your SLI, so you can later use them for searching, filtering, and grouping SLIs on the UI.

You can set any tag that's meaningful to your organization. A dropdown will suggest useful tag keys such as the following:

• owner: The team or business unit that owns this service level, and will react when the SLO target is missed.

• category: A keyword that describes what the SLI is measuring, such as latency. If you follow the suggested service level flow, New Relic will populate this tag for you, and you may later edit it.

• environment: The environment that the service level is measuring, and that makes sense to your use case.

• maturity: Useful to communicate to your stakeholders how stable the SLO is. We recommend that you use tag values such as test, commitment, or aspirational.

• user_journey and application: These kinds of tags help you group the SLIs that apply to the same user experience, whether it's a whole user journey, or just a specific application.

  Additionally, the dropdown also displays the related entity tags, so you can quickly add them to the SLI as well.

  

  To finish, you may optionally add a description for that service level.