Rules for creating metrics: requirements and tips

summary

Creates a summary metric data point for each time window (currently 1 minute). Use this if your NRQL query uses aggregator functions supported by the summary metric type, such as average, sum, min, or max.

Example rule-creation query:

SELECT summary(duration) AS 'service.responseTime' FROM Transaction 
WHERE appName = 'Data Points Staging' FACET name, appName, host

uniqueCount

Creates a uniqueCount metric data point for each 1-minute time window. Use this if your NRQL query uses the uniqueCount aggregator type.

Example rule-creation query:

FROM Transaction SELECT uniqueCount(request.headers.userAgent) 
AS 'server.request.header.userAgent.uniqueCount' 
WHERE appName = 'Browser Monitoring Router' FACET http.statusCode, name, appName, host

distribution

Creates a distribution metric data point for each 1-minute time window. Use this if your NRQL query uses aggregator functions such as percentile, histogram, min, max, average, sum, or count. Use only the attribute of interest as the argument, and discard the rest of the arguments from percentile or histogram. The generated metric supports any argument on percentile or histogram.

Example of creating a distribution rule:

SELECT distribution(duration) AS 'service.responseTime' FROM Transaction 
WHERE appName = 'Data Points Staging' FACET name, appName, host

Simple count: summary(1) and sum

If you want a metric that's a simple count of the events, logs, or spans that match a particular WHERE clause, use the summary(1) metric. This metric type counts the number of specified events, logs, or spans per minute. When querying the created metric, use the sum method to see the result.

Example: If you want to create a metric named foo.count that counts the transactions named foo, the NRQL would look like this:

FROM Transaction SELECT summary(1) AS 'foo.count' WHERE name = 'foo'

Then, you would query it like this:

FROM Metric SELECT sum(foo.count) SINCE 30 minutes ago

For more information about metrics, see our documentation about metric types.

Account limits

An account can have a maximum of 1,000 metric-creation rules.

Metric rule limits

A rule can:

• Create a maximum of 10 metrics.
• Use only one type of data (events, logs, or spans).
• Select a maximum of 20 attributes (facets) to include on a metric.

Time window limits

50K limit on unique metric-name/attribute-value combinations for a single metric in a 30 second time window. Normal cardinality limits on rollups will apply.

If the 50k in a 30 second window limit is exceeded, the rule is disabled and an NrIntegrationError event is created in that account that includes:

• The rule details
• A message about having too many facets
• A newRelicFeature attribute value of eventToMetric

Requirements

Requirements for naming a metric:

• Less than or equal to 255 (UTF-16) 16-bit code units. One way to ensure you are under the limit is to keep each string under 127 of whatever is easiest to count.

• No spaces.

• Start with a letter.

  Examples of strong metric names:

• rubyvm.memory.heap_used

• redis.container.cpu.percent

• memcached.process_virtual_memory.bytes

Length and structure

Decide on a name and structure that makes it easy for others to find, understand, and use this metric.

• We recommend keeping your metric name under 40 characters for ideal readability. Longer names can get cut off or overlap with other names.
• Your metric naming scheme will depend on your business logic. You may want to use namespaces to prefix your metric name, or your names may need to be more general.

Components within the name

If you want to create components within your metric name (like the source of metrics and the thing you’re measuring), we recommend going from broad to specific (left to right):

1. Use a dot to separate those components in order to be consistent with our New Relic metric names.

2. Then, use an underscore to separate words within the dots.

   Example:

   application.page_view.duration

   카피

Attributes

Avoid putting attributes in your metric name. Attributes are qualities of your metric that you can use to filter or facet your data, like cluster or availability zone.

Example: If you included availability zone in your metric name, it would mean, for that metric, you wouldn’t be able to see results across all availability zones.

Changing metric names

If you change a metric name, historical data will not be updated to that new name. To query or chart that historical data, you will need to specify the older metric name.