As the demand for data analytics and artificial intelligence (AI) continues to grow, the volume of data used in these fields has skyrocketed, highlighting the need for enhanced computational capabilities. Cache, as an accelerator for data and AI computations, plays a vital role in improving performance. However, it is crucial to recognize that data analytics and AI have distinct data access patterns, necessitating different cache strategies.
In this session, we will share our practical observations on data access patterns in the analytical SQL and AI training domains, drawn from extensive experience with large-scale systems. We will present evaluation results from various caching strategies used in analytical SQL and AI, and provide recommendations tailored to different use cases.
Our journey has been enriched by learning from big internet companies, allowing us to identify best practices in the following areas:
- Traffic patterns for analytical SQL and cache strategy recommendations: We will explore the data access patterns specific to analytical SQL and discuss effective cache strategies to optimize performance in this domain.
- Traffic patterns for AI training and cache efficiency measurement: We will delve into the unique traffic patterns involved in AI training processes and explore ways to measure cache efficiency for different AI training workflows. Understanding the nuances of AI training traffic is essential in optimizing cache utilization.
- Cache capacity planning based on real-time metrics: We will highlight the importance of real-time metrics in determining cache capacity planning. By leveraging dynamic insights into the working set, organizations can optimize cache capacity to meet the demands of data analytics and AI workloads effectively.
- Adaptive caching admission and eviction for uncertain traffic patterns: Uncertainty in traffic patterns can present challenges in caching. We will discuss the concept of adaptive caching, which allows for dynamic admission and eviction strategies to accommodate uncertain traffic patterns effectively.
