Partition imbalance
Partition imbalance is the uneven distribution of data or processing work across partitions, leaving some overloaded and others underutilized.
Latency
Partition imbalance is the uneven distribution of data-processing work across partitions, causing some partitions to be overloaded while others are underutilized.
- How it is used
- Identifies uneven partition work as a cause of higher latency and reduced throughput despite idle capacity elsewhere.
- How deep it goes
- Defines the failure mode, distinguishes its main causes, and discusses key selection and mitigations without a measurement procedure.
- Examples
- Contrasts user IDs with order statuses and illustrates changing demand through a product launch and a sale.
- What you can do after
- Evaluate/understand it by distinguishing data skew from access skew and choosing among rebalancing, added capacity, and load balancing.
Kafka for Architects
Partition load imbalance is an uneven distribution of records or processing work across partitions.
- How it is used
- Highlights partitioning as an architectural choice affecting resource consumption, consumer latency, and message ordering.
- How deep it goes
- Defines the imbalance, explains its regional cause and latency effects, and contrasts keyed routing with keyless distribution.
- Examples
- Uses region-skewed orders and timestamped application logs to illustrate keyed and keyless partitioning.
- What you can do after
- Diagnose overloaded and underutilized partitions by relating key choice to data volume and consumer processing latency.
Read how Kafka for Architects explains partition imbalance →
Related concepts
- meanproduces · A mean is a measure of central tendency, calculated as the arithmetic average of values or, for a random variable, as the probability-weighted average of its possible values.
- throughputproduces · The amount of work, data, or output a system processes or completes per unit of time.
- scalabilityproduces · Scalability is the ability of a system, process, or method to accommodate increasing workload, size, or demand while maintaining acceptable operation.
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