The piece dives into the Incremental View Maintenance (IVM) framework integrated into StarRocks, highlighting its potential to optimize data processing. Incremental view maintenance allows for updates to views based on changes in base data without needing to recompute everything. This approach is particularly beneficial in scenarios with frequent data changes, where traditional methods can be inefficient. The author emphasizes the challenge of threading these changes through complex queries involving joins and aggregations. The article outlines the current implementation of StarRocks, which utilizes Partition Change Tracking (PCT). This method refreshes materialized views at the partition level, which is an improvement over full refreshes. However, it has limitations, especially if base tables lack partitioning. The new framework introduces Time-Varying Relations (TVRs), which track the evolution of data over time, allowing for more granular updates. By focusing on deltas between versions of base tables, StarRocks aims to reduce the computational load when data changes. A significant innovation discussed is the use of aggregate combinator functions. These functions streamline the aggregation process by minimizing overhead when processing new data. Instead of rescanning entire datasets, StarRocks can efficiently compute results based on recent changes. This is particularly critical when dealing with large volumes of data, as it directly impacts performance and resource utilization. Overall, the advancements in the IVM framework signify a move toward more responsive data processing capabilities in StarRocks.