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126 questions, 454 terms and 400 topics in 20 areas.
15 results for “Competing Consumers”
Competing Consumers
Multiple identical consumers reading from one queue, so throughput scales with consumer count and work is distributed automatically.
Consumer Group
A set of consumers that cooperatively read one stream, with each partition assigned to exactly one member, so the group collectively processes every message once.
Contract Testing
Verifying that a provider satisfies the expectations each of its consumers actually relies on, without running all the services together.
Data Contract
An explicit, versioned, enforced agreement between a data producer and its consumers about schema, semantics, quality and change policy.
Event Stream
An append-only, retained log of events that many independent consumers read at their own position, and can re-read.
Offset Management
How a consumer records its position in a stream, and the decision that determines whether processing is at-least-once or at-most-once.
Publish/Subscribe
A messaging pattern where each published message is delivered to every interested subscriber, rather than to one competing worker.
Schema Evolution
Changing a table's structure over time while keeping existing data readable and existing consumers working.
Visibility Timeout
The period after a consumer receives a message during which it is hidden from other consumers, before it becomes available again for redelivery.
Orders must trigger inventory reservation, a confirmation email, an analytics record and a fraud check. Queue, topic, or both — and what breaks if you choose wrong?
What the interviewer is testing Whether you know the difference between work distribution and notification — a distinction that produces one of the most confusi
A service writes to its database and then publishes an event to Kafka. Sometimes consumers see an event for a record that does not exist, and sometimes a record exists with no event. Why, and how do you fix it?
What the interviewer is testing Recognition of the dual write problem — one of the most common defects in event driven systems and one that testing rarely catch
Consumer lag on a Kafka topic grows during peak and does not recover overnight. You add consumers and nothing improves. Why?
The mechanism Each partition is assigned to exactly one consumer within a group. With ten partitions and ten consumers, an eleventh consumer joins the group, is
Competing Consumers
Scaling throughput with instances, at the cost of ordering.
Contract Testing
Verifying what consumers actually rely on, without a shared environment.
Contract Tests
Capturing what consumers actually use, not what the API documents.