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Kafka uses different strategies to assign partitions to consumers in a group efficiently.

Exponential backoff helps manage retries by increasing wait times to reduce system overload.

Log replication ensures data consistency by requiring majority acknowledgment before committing changes.

Jitter helps spread out client retries to avoid traffic spikes.

Backpressure helps manage message flow in Kafka during traffic spikes.

ZooKeeper watches notify clients of changes, avoiding constant polling and reducing server load.

Fallbacks allow systems to handle failures safely and maintain user experience.

Load shedding helps systems reject excess requests to maintain performance during high demand.