micrometer

Micrometer calls their metrics "Meter primitives", and they support the following:

• Gauge
• TimeGauge
• Counter
• DistributionSummary
• FunctionCounter
• FunctionTimer
• Timer
• LongTaskTimer

None of these track time, so the registry is responsible for synthesizing the times at the moment of export.

All micrometer Meter types may contain "tags", which is a collection of key/value pairs. All of these Tags are converted to New Relic metric Attributes at export time.

Here is how the New Relic registry converts each of these types:

Gauge

We export the micrometer Gauge as a New Relic Gauge.

Attributes:

• baseUnit: (the gauge's base unit from the id)
• description: (the gauge's description)

TimeGauge

We export the micrometer TimeGauge as a New Relic Gauge.

Attributes:

• baseUnit: (the gauge's base unit from the id)
• description: (the gauge's description)

Counter

We export the micrometer Counter as a New Relic Count.

• value - delta between current count and previous count (should always be positive)

Attributes:

• baseUnit: (the counter's base unit from the id)
• description: (the counter's description)

DistributionSummary

Distribution Summary provides a summary of an event's distribution.

We take a snapshot and marshall it as:

• Snapshot Summary
  • name: "<name>.summary"
  • count: snapshot.count()
  • total: snapshot.total()
  • min: null
  • max: snapshot.max()

We also generate one Gauge per snapshot percentile:

• name: "<name>.percentiles"
• percentile: (the actual percentage number)

The framework also generates a Gauge for each histogram bucket.

FunctionCounter

The FunctionCounter is converted to a New Relic Count object.

Attributes:

• baseUnit: (the function counter's base unit from the id)
• description: (the function counter's description)

Timers

There are 3 kinds of timers: Timer, FunctionTimer, and LongTaskTimer. Aside from the basic Meter interface, these three do not share a common interface.

Timer

The micrometer Timer is intended to track a large number of short running events.

We export it as a Summary:

Summary:

• name: "<name>",
• count: timer.count() (the total number of times stop() has been called on this timer)
• total: the total time spent in all occurrences
• min: null
• max: timer.max() the maximum time seen for a single event

The summary has these attributes:

• baseTimeUnit: (the timer's base time unit)
• description: (the timer's description)

The framework also generates a Gauge for each histogram bucket.

FunctionTimer

The micrometer FunctionTimer is intended to track two monotonically increasing values: count (the number of total invocations) and time (total time spent for all invocations).

We export it a Summary:

Summary:

• name = "<name>",
• count: functionTimer.count()
• total: functionTimer.totalTime()
• min: null
• max: null

The summary has these attributes:

• baseTimeUnit: (the timer's base time unit)
• description: (the timer's description)

LongTaskTimer

The micrometer LongTaskTimer is intended to track the count and total duration of all in-flight long-running tasks. It is used to time tasks that are still ongoing (because the plain Timer only reports on completion).

We export it as a two gauges:

Gauge 1:

• name = "<name>.active_tasks",
• value = number of currently active tasks

Gauge 2:

• name = "<name>.total_duration",
• value - The cumulative duration of all active tasks in nanoseconds

Both metrics have these attributes:

• baseUnit: (the timer's base unit from the id)
• baseTimeUnit: hard coded to "NANOSECONDS"
• description: (the timer's description)