<![CDATA[This study presents a hybrid framework for snapshotting and event replay optimization aimed at improving performance and scalability in event-sourced systems, where state reconstruction frequently experiences significant latency due to the replay of extensive event streams. Four snapshotting strategies were used to test this method: No Snapshotting, Fixed-Interval, Adaptive, and the new Hybrid method. The Adaptive strategy used dynamic triggers to make snapshots when an aggregate had more than 1,000 accesses in a five-minute window, when more than fifteen minutes had passed since the last snapshot, or when its event stream had more than 5,000 events. The Hybrid method used these adaptive triggers along with a fixed base interval of 5,000 events. For hot aggregates, the interval was cut in half to balance systematic coverage with runtime responsiveness. We built a prototype system in Java with Spring Boot and PostgreSQL. We used Kryo for snapshot serialization and Kafka to make controlled workloads. We ran tests on a 16-core Intel Xeon server to see how well it could handle event distributions that were uniform, skewed (Zipfian), or bursty, with anywhere from 10,000 to 1,000,000 events. We ran each configuration ten times and used 95% confidence intervals to find the average of the results. The results show that the Hybrid strategy consistently cut recovery latency, getting it down to 40 seconds under burst loads. This is a 58% improvement over Fixed-Interval and a 38% improvement over Adaptive. Throughput and responsiveness during normal operation were not affected, which is important. These results show that Hybrid snapshotting strikes a good balance between speed of recovery and storage overhead. This makes it a good choice for real-time, event-sourced applications. In the future, we will look into automatic tuning of snapshot intervals and adaptive thresholds, as well as adding support for distributed deployments and complex event schemas.]]>
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