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⚡ One HLD concept. 60 seconds. Interview ready
Understanding Event Time vs Processing Time in Stream Processing
Event time is when an event occurs, while processing time is when it is handled.
⚡ One HLD concept. 60 seconds. Interview ready
Transactional Outbox Pattern: Never Lose Events!
The Transactional Outbox Pattern ensures database updates and event publishing are synchronized to avoid losing events.
⚡ One HLD concept. 60 seconds. Interview ready
HLD: Late-Arriving Events - Drop, Update, or Replay?
Late-arriving events can be dropped, updated, or routed based on system needs.
⚡ One HLD concept. 60 seconds. Interview ready
Understanding Watermarks in Stream Processing
Watermarks help stream processing systems manage event time and late events.
⚡ One HLD concept. 60 seconds. Interview ready
Understanding Distributed Clocks in Systems Design
In distributed systems, timestamps don't always reflect the true order of events.
⚡ One HLD concept. 60 seconds. Interview ready
Understanding Lamport Timestamps in Distributed Systems
Lamport Timestamps help order events in distributed systems while preserving causality.
⚡ One HLD concept. 60 seconds. Interview ready
Understanding Physical vs Logical Time in Distributed Systems
Physical time tells when events happen, while logical time tells their order.
⚡ One HLD concept. 60 seconds. Interview ready
Kafka Streams Windowing: Tumbling vs Hopping Windows Explained
Kafka Streams uses tumbling and hopping windows for time-based data aggregation.
⚡ One HLD concept. 60 seconds. Interview ready
Time-Based Partitioning: Scaling Time-Series Databases
Time-based partitioning organizes time-series data into smaller, efficient segments for better querying.