NB5 Docs► User Guide▼ Standard Metrics 🖺

NoSQLBench comes with a set of standard metrics that are part of every driver. Each driver enhances the metrics available by adding their own metrics with the NoSQLBench APIs. This section explains what the standard metrics are, and how to interpret them.


Within NoSQLBench, a data stream provider called an Input is responsible for providing the actual cycle number that will be used by consumer threads. Because different Input implementations may perform differently, a separate metric is provided to track the performance in terms of client-side overhead. The read-input metric is a timer that only measured the time it takes for a given activity thread to read the input value, nothing more.


A stride represents the work-unit for a thread within NoSQLBench. It allows a set of cycles to be logically grouped together for purposes of optimization -- or in some cases -- to simulate realistic client-side behavior over multiple operations. The stride is the number of cycles that will be allocated to each thread before it starts iterating on them.

The strides timer measures the time each stride takes, including all cycles within the stride. It starts measuring time before the cycle starts, and stops measuring after the last cycle in the stride has run.


Within NoSQLBench, each logical iteration of a statement is handled within a distinct cycle. A cycle represents an iteration of a workload. This corresponds to a single operation executed according to some statement definition.

The cycles metric is a timer that starts counting at the start of a cycle, before any specific activity behavior has control. It stops timing once the logical cycle is complete.


Each cycle of an activity has a metric which measures its internal service time, measured from the moment the cycle starts processing to the moment is fully complete. This is provided

When rate limiters are used, this sub-name identifies the service time component as


When a rate limiter is used, the waittime metric captures the notion of scheduling delay with respect to the requested rate. For example, if you specify a rate of 10 Kops/S, but at the 20 second mark, only 190Kops have completed, this represents one second of scheduling delay (10 Kops worth of operations at 10 Kops/S = 1 second). The cycles.waittime metric would thus indicate ~ 1S worth of waittime as the workload falling behind by about 1 second, although it would report in nanos.


When a rate limiter is used, the responsetime metric combines the servicetime and waittime values to yield a computed responsetime. This is a measure of how long a user would have had to wait for an operation to complete based on some ideal schedule, as described by a rate limiter. In this way, a rate limiter acts as both a minimal and a maximal target. It is presumed that the composed system is fast enough to run at the limited rate, thus any slow-downs which cause the system to run effectively behind schedule represent a user-impacting effect.


👉 This metric is provided directly by drivers. All conforming driver implementations should provide this metric as described below.

Each operation's execution is tracked with the result timer. This timer is used to measure ALL operations, even those with errors.


👉 This metric is provided directly by drivers. All conforming driver implementations should provide this metric as described below.

For operations which completed successfully with no exception, a separate result-success timer is used. When your workload is running well, both the result and result-success timer count the same number and rate of operations. This provides a useful cross-check between metrics.


👉 This metric is provided directly by drivers. All conforming driver implementations should provide this metric as described below. This happens automatically when the standard error handler implementation is used.

When the error handler sees an exception, the name of the exception is converted to a metric name with -error as the suffix. There will be one of these metric names created for each unique exception that occurs within an activity.

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