New Book on Apache Solr/Lucene (testmysearch.com)

Rauf Aliev’s Inside Apache Solr and Lucene: Algorithms and Engineering Deep Dive is a systems‑level walkthrough of the search engines’ internals, aimed at engineers building high‑throughput, terabyte‑scale search services. The book dissects core data structures (inverted index, posting lists, BKD trees), on‑disk formats and compression (variable‑byte, frame‑of‑reference, delta encoding), segment immutability/merging, and the full indexing pipeline. It also covers modern retrieval features crucial to ML-driven search: vector search and ANN (HNSW, vector quantization), hybrid search with Lucene, relevance models (TF‑IDF, BM25), learning‑to‑rank and re‑ranking, and advanced query execution (SIMD‑accelerated intersections, Block‑Max WAND pruning). For the AI/ML community this is practical gold: the book frames Solr/Lucene as a masterclass in trade‑offs between speed, memory, and I/O, with concrete engineering patterns for concurrency, partitioning, caching, and distributed coordination (SolrCloud, ZooKeeper, sharding/replication). If you work on embedding retrieval, production recommender pipelines, or large‑scale feature‑driven ranking, the technical deep dives—on ANN integration, scoring consistency across shards, pagination strategies, and JVM/IO tuning—offer actionable guidance for building resilient, high‑performance retrieval and ML inference systems.