Kafka Partition Count Calculator

Work out kafka partition count instantly with clear inputs, formula shown and shareable results.

About Kafka Partition Count Calculator

Partition count is the larger of two requirements: enough partitions to carry the throughput at per-partition limits, and at least as many as consumer instances since a partition is consumed by exactly one member of a group. Replication multiplies the write load the brokers must sustain, so a factor of three triples disk and network write volume.

Formula

Partition count

throughput partitions = ceil(target / per-partition throughput); partitions = max(that, consumer instances); broker writes = target x replication factor

Worked example

With the default values below, Kafka Partition Count Calculator returns Partitions required of 24. Change any input above and every figure here is recalculated in your browser.

Inputs used

Target topic throughputminimum 0.001 MB/s: 250 MB/s
Throughput per partitionminimum 0.001 MB/s: 12 MB/s
Consumer instances in the groupminimum 1: 24
Replication factoraccepts 1–6: 3

Results

Partitions required: 24
Partitions needed for throughput alone: 21
Total broker write throughput including replication: 750.0 MB/s
Consumers that would sit idle: 0

Step by step

1Values used
2Partition count
3Partitions required= 24
4Partitions needed for throughput alone= 21
5Total broker write throughput including replication= 750.0 MB/s
6Consumers that would sit idle= 0

Assumptions and limits

The result is derived only from the 4 inputs listed above. Anything not among them is not part of the calculation.
In particular, this calculator does not take inflation, tax, fees or charges or compounding frequency into account.
Figures are rounded for display to 1 decimal place, so totals built from several rounded parts can differ slightly from the unrounded arithmetic.

How these calculators are built and tested is described on the Methodology page. If a figure looks wrong, please report it.

Frequently Asked Questions

Why can partitions not be reduced?

Kafka only supports increasing partition count, and increasing it changes key-to-partition mapping, which breaks ordering guarantees for existing keys. Over-provision modestly rather than under-provisioning.

Is a very high partition count harmful?

Yes. Each partition costs file handles, memory for buffers and leader-election work, and end-to-end latency rises. Tens of thousands per cluster is the practical ceiling.