Optimizing cache performance is a continuum and determining what stays in cache and what is evicted is a common dilemma. Given limited capacity of cache, data contend to stay in cache and programs strive to classify data that best suits cache ability. However, prediction of data longevity is complex and is a function of several external parameters. So, an efficient algorithm driving enterprise workloads works with realistic goal of obtaining near-optimal cache performance. We have been working with distributed cache over last 5 years and serving several customers with diverse workloads. Our role has gradually evolved into helping these workloads to fine-tune the distributed cache based specific data access patterns, thereby optimizing performance. Drawing from this experience, this session illustrates popular eviction algorithms and their tradeoffs, leveraging real-world examples to illustrate their impact. Attendees will learn cache optimization strategies that can significantly enhance their application performance.
Cache optimization is an ongoing challenge due to limited capacity and unpredictable data longevity. Achieving near-optimal cache performance requires tailoring eviction strategies to specific access patterns. Drawing on five years of experience with distributed cache across diverse workloads, this session shares practical insights into popular eviction algorithms, their trade-offs, and real-world strategies to boost application performance.