Explore fundamental principles and best practices for designing reliable event delivery systems, including message ordering, fault tolerance, and guaranteed delivery. This beginner-friendly quiz highlights core strategies to ensure consistent and dependable event processing across distributed architectures.
Which term best describes a system that ensures each event is delivered to its recipient exactly once, with no duplicates or missing events?
Explanation: Exactly-once delivery ensures that each event is delivered a single time, preventing both duplicates and losses. At-most-once delivery may result in missed events if failures occur. Eventually consistent delivery focuses on eventual agreement, not delivery guarantees. Random delivery is not a recognized event delivery guarantee. Only exactly-once delivery fully meets the criteria in the question.
If a network error causes an event to be lost before reaching its destination, which feature helps the system resend it automatically?
Explanation: Automatic retry allows the system to detect failed deliveries and attempt to resend the lost event, increasing reliability. One-way hash relates to verification, not delivery. Concurrent indexing handles search efficiency, not message delivery. Static binding is unrelated to dynamic event transmission. Only automatic retry addresses the scenario given.
In a distributed system where order matters, what mechanism ensures that events arrive in the same sequence as they were sent?
Explanation: Message sequencing assigns an order to events so recipients process them in the original send order, which is critical for consistent system behavior. Random polling retrieves messages non-sequentially. Stateless routing does not track message order. Batch merging might combine data, but does not assure event order. Message sequencing is the correct mechanism for ordered delivery.
Which concept describes storing events so they can survive system crashes or power failures until they are successfully delivered?
Explanation: Persistent storage ensures events are saved on non-volatile media, protecting them during crashes until delivery is confirmed. Volatile caching uses memory, losing data during failures. On-the-fly compression reduces data size but does not address durability. Ephemeral queuing involves temporary storage, risk of data loss. Only persistent storage safeguards event integrity for reliability.
Why is designing event consumers to be idempotent important for reliable event delivery?
Explanation: Idempotency ensures that processing an event multiple times causes no unintended effects, which is crucial when duplicates might occur. Increasing delivery speed is unrelated to idempotency. Event encryption secures data, not delivery logic. Retries are still needed for lost events. Safe duplicate processing is the central benefit of idempotent consumers.
What technique can be used to detect if an event was not received within a certain time by its recipient?
Explanation: Timeout monitoring tracks whether events are acknowledged within a set timeframe, enabling systems to react if delivery fails. Decryption keys are for security, not delivery confirmation. Deep learning is used for AI, not event receipt checks. Static linking relates to program compilation, not network communication. Timeout monitoring directly addresses undetected delivery failures.
Which approach helps prevent an event from being processed more than once in a distributed system?
Explanation: Storing unique event identifiers allows systems to track and disregard duplicate events, ensuring each is processed once. Increasing bandwidth and compression may improve performance, but not duplicate detection. Using random delays does not prevent the same event from being processed repeatedly. Unique identifiers are the key preventive measure in this scenario.
Which action confirms that an event has been successfully received and processed by the recipient?
Explanation: Sending an acknowledgement informs the sender that the event was successfully received and possibly processed, supporting reliable delivery. Applying a hash function validates data integrity, not delivery status. Lazy evaluation refers to delayed processing in code, not to network communication. Assigning priorities sets order of handling, not confirmation. Acknowledgements are fundamental in event delivery.
What can be used to balance the load by distributing events among multiple processors or recipients?
Explanation: Load balancing distributes events efficiently across multiple processors or recipients, ensuring no single component is overloaded. Selective broadcasting targets specific recipients without distributing the workload evenly. Manual error coding relates to error handling, not load distribution. Legacy queuing might refer to outdated systems and doesn't inherently balance load. Load balancing is the correct concept for scaling.
Which method allows a system to recover and continue delivering events after an unexpected crash or restart?
Explanation: Checkpointing involves saving the system's state so, after a crash or restart, event delivery can resume from the last saved point. Data whitening is a data transformation unrelated to event recovery. Loose coupling refers to reducing dependencies between components, not to failure recovery. Incremental parsing deals with processing data piecemeal, not recovery. Checkpointing directly ensures continuity in delivery after failures.