Resource-efficient blocking: Optimizing the trade-off between effectiveness and scalability in entity resolution

Deep Learning has revolutionized Entity Resolution (ER) by enabling high-accuracy matching through dense vector embeddings. However, this paradigm shift transforms the traditional blocking step into a high-dimensional similarity search problem, introducing a massive computational bottleneck that threatens scalability. While modern ER frameworks increasingly rely on blocking, the so-called indexing, to generate candidate pairs, the architectural design of this retrieval stage is often treated as a black box, with little attention paid to the resource implications of high-dimensional models. This paper conducts a rigorous design space exploration of blocking architectures, evaluating nine candidate generation strategies across graph-based, partition-based, and hash-based families. A primary contribution is a systematic analysis of Vector Quantization —specifically Product Quantization and Scalar Quantization—demonstrating that quantization is not merely a memory optimization, but a critical requirement for deploying state-of-the-art embeddings at scale. We evaluate these architectures on seven real-world datasets, measuring the interplay between embedding granularity, recall, and computational cost. Our results reveal that while graph-based methods offer peak precision, quantized partitioning methods reduce memory footprints by up to 96% with negligible loss in recall, effectively democratizing high-performance ER on commodity hardware. The study culminates in a decision framework for designing scalable, resource-aware blocking pipelines.