In modern data-driven ecosystems, Extract, Transform, Load (ETL) flows serve as the backbone of data integration pipelines. These flows facilitate the seamless movement of data across disparate systems and formats, streamlining processes that range from data acquisition to preparation for analysis. However, the pervasive use of ETL flows introduces a pressing challenge-how to bound the maintenance cost of an ever-expanding number of flows. In this paper, we describe an end-to-end prototype for ETL flow refactoring, aimed at reducing the maintenance cost, which keeps the human in the loop for refactoring decisions. Our prototype adopts and significantly extends the gSpan Frequent Subgraph Mining (FSM) algorithm to apply it to real-world ETL use cases in the context of the IBM DataStage™ data integration tool. We report on real customer workloads, share their statistics and evaluate the performance of our prototype. We found potential for up to 32% maintenance cost reduction on the use cases we analyzed after removing duplicate flows. We also share an anonymized version of the workloads with the research community.

Modern key-value stores, object stores, Internet proxy caches, and Content Delivery Networks (CDN) often manage objects of diverse sizes, e.g., blobs, video files of different lengths, images with varying resolutions, and small documents. In such workloads, size-aware cache policies outperform size-oblivious algorithms. Unfortunately, existing size-aware algorithms tend to be overly complicated and computationally expensive. Our work follows a more approachable pattern; we extend the prevalent (size-oblivious) TinyLFU cache admission policy to handle variable-sized items. Implementing our approach inside two popular caching libraries only requires minor changes. We show that our algorithms yield competitive or better hit-ratios and byte hit-ratios compared to the state-of-the-art size-aware algorithms such as AdaptSize, LHD, LRB, and GDSF. Further, a runtime comparison indicates that our implementation is faster by up to 3× compared to the best alternative, i.e., it imposes a much lower CPU overhead.

Developing a silver bullet software cache management policy is a daunting task due to the variety of potential workloads. In this paper, we investigate an adaptivity mechanism for software cache management schemes which offer tuning parameters targeted at the frequency vs. recency bias in the workload. The goal is automatic tuning of the parameters for best performance based on the workload without any manual intervention. We study two approaches for this problem, a hill climbing solution and an indicator based solution. In hill climbing, we repeatedly reconfigure the system hoping to find its best setting. In the indicator approach, we estimate the workloads' frequency vs. recency bias and adjust the parameters accordingly in a single swoop.