// multi-utility computation suite · offline · instant · precise
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dev.event-driven-throughput Calculator
Calculates event-driven system throughput, consumer lag, and partition count from event rate and processing latency. Kafka consumer throughput depends on partition count — a single consumer can process messages from one partition at a time, so partition count = max parallelism.
Inputs
Events Per Sec
Reference formula or conversion factor shown for context.
Consumer Workers
Energy transferred by a force over a displacement (J = N·m).
Processing Ms
Duration of the process. Make sure units match the rate inputs (seconds, minutes, or hours).
Queue Depth
Vertical extent downward, or thickness of a layer. For tanks: affects pressure at the base (P = ρgh).
Results
consumer capacity (events/s)
The arithmetic total of all input values.
lag rate (events/s)
The value at the specified point or condition.
workers needed (no lag)
Sample size or count used in the calculation.
queue fills in (s)
Sample size or count used in the calculation.
utilization
Fraction of capacity actually being used. Above 80–85% is typically considered high utilisation; above 95% leads to queuing and latency spikes.
headroom
Buffer between current performance and a limit or target. Positive headroom is safety margin; zero or negative headroom means the limit has been hit.