Kafka Partitions and Ordering Explained
Kafka guarantees ordering within a partition, not across the whole topic. The key idea is choosing the right message key so related events go to the same partition.
KafkaDistributed SystemsMessagingOrdering
Interview Prep
System Design Interview Questions
Distributed systems, messaging, scalability, caching, queues, consistency, and backend architecture interview prep.
Showing 18 of 18 questions
What are idempotency keys and why are they important?
Idempotency keys prevent duplicate side effects when clients retry requests. They are critical for payments, order creation, purchases, booking systems, and any API where running the same operation twice would be dangerous.
ReliabilityAPIsDistributed SystemsPayments
Exactly-once vs at-least-once delivery in Kafka
At-least-once means messages are not lost but may be processed more than once. Exactly-once means the final processing effect happens once, which usually requires transactions, idempotency, or careful system design.
KafkaDistributed SystemsReliabilityIdempotency
What are common rate limiting strategies in system design?
Rate limiting controls how many requests a client can make within a time period. Common strategies include fixed window, sliding window, token bucket, and leaky bucket.
System DesignRate LimitingAPIsScalabilityDistributed Systems
What is consistent hashing and why is it useful?
Consistent hashing distributes keys across servers in a way that minimizes remapping when servers are added or removed. It is useful for caches, sharding, load balancing, and routing users to stable backend nodes.
System DesignConsistent HashingDistributed SystemsCachingSharding
How would you safely transfer money between accounts?
A safe money transfer system must handle concurrency, consistency, deadlocks, retries, duplicate requests, and distributed system failures while ensuring money is never lost or duplicated.
System DesignConcurrencyTransactionsDistributed SystemsConsistencyBackend
LRU Cache Design
Understand how an LRU cache works, how Java LinkedHashMap can implement it, and what interviewers expect beyond the code.
CachingJavaHashMapLinkedHashMapSystem Design
Why Use Kafka?
Understand when Kafka and similar messaging systems become useful: moving from direct service calls to durable event-driven communication.
KafkaEvent Driven ArchitectureMessagingSystem Design
What are common caching strategies in system design?
Caching improves latency and reduces load by storing frequently used data closer to the application or user. Common strategies include cache-aside, read-through, write-through, write-behind, write-around, TTL-based caching, and CDN caching.
System DesignCachingRedisScalabilityPerformanceDistributed Systems
What are tail latency and p99 latency?
Latency in system design is the time interval between the start of a request from a client to the delivery of the result back from the server. Tail latency describes the slowest requests in a system. p99 latency means 99% of requests are faster than this value, while the slowest 1% are at or above it.
System DesignLatencyp99PerformanceDistributed SystemsSRE
What is a dead letter queue and why is it useful?
A dead letter queue stores messages that could not be processed successfully after retries or validation failures. It helps the main pipeline keep moving while preserving failed messages for debugging, alerting, and replay.
System DesignMessagingKafkaDLQReliabilityDistributed Systems
Deadlock prevention strategies
Deadlock happens when threads wait forever on each other's locks. The most practical prevention strategy is consistent lock ordering, plus timeouts, smaller lock scope, and better system design.
ConcurrencyDeadlockLocksJavaSystem Design
Availability concepts in system design
Availability means the system can successfully serve users when they need it. This page explains how availability is measured, improved, and discussed in system design interviews.
AvailabilityReliabilitySLOSystem DesignHigh Availability
Timeouts and retries in system design
Timeouts prevent services from waiting forever, while retries help recover from temporary failures. Used badly, retries can overload dependencies and cause cascading failures.
ReliabilityTimeoutsRetriesBackoffSystem Design
Circuit breaker pattern in system design
A circuit breaker protects your service from repeatedly calling a failing dependency. It fails fast, gives the dependency time to recover, and helps prevent cascading failures.
ReliabilityCircuit BreakerResilienceMicroservicesSystem Design
Graceful degradation in system design
Graceful degradation means keeping the most important user flows working even when optional features or dependencies fail.
ReliabilityAvailabilityGraceful DegradationResilienceSystem Design
Reliability concepts in system design
Reliability means a system performs its intended function correctly and consistently over time. It includes availability, correctness, durability, recovery, observability, and predictable behavior during failures.
ReliabilityAvailabilitySLOError BudgetSystem Design
Health checks in system design
Health checks help infrastructure decide whether an instance is alive, ready for traffic, or should be restarted. They are essential for load balancing, deployments, and auto-recovery.
Health ChecksReliabilityAvailabilityKubernetesSystem Design