A metric event in Vector represents a numerical operation performed on a time series. In Vector, unlike in other tools, metrics are first-class citizens. They are not represented as logs. This makes them interoperable with various metrics services without the need for any transformation.
Vector’s metric data model favors accuracy and correctness over ideological purity. Vector’s metric types are thus an agglomeration of various metric types found in the wild, such as Prometheus and Statsd. This ensures that metrics are correctly interoperable between systems.
Schema
A Vector metric event represents a numerical operation
performed on a time series. Unlike other tools, metrics
in Vector are first class citizens, they are not represented
as structured logs. This makes them interoperable with
various metrics services without the need for any
transformation.
Vector’s metric data model favors accuracy and correctness over
ideological purity. Therefore, Vector’s metric types are a
conglomeration of various metric types found in the wild, such as
Prometheus and Statsd. This ensures metric data is correctly
interoperable between systems.
A gauge represents a point-in-time value
that can increase and decrease. Vector’s
internal gauge type represents changes to
that value. Gauges should be used to track
fluctuations in values, like current memory
or CPU usage.
Also called a timer. A histogram samples
observations (usually things like request
durations or response sizes) and counts them
in configurable buckets. It also provides a
sum of all observed values.
Similar to a histogram, a summary samples
observations (usually things like request
durations and response sizes). While it also
provides a total count of observations and a
sum of all observed values, it calculates
configurable quantiles over a sliding time
window.