This article walks through the fundamentals of search engines, explaining inverted indexes, the explosion of index size, core Elasticsearch concepts, its distributed architecture, and how it powers the ELK stack for log analysis, all illustrated with clear diagrams and examples.
This article reviews major in‑memory inverted index compression techniques such as PForDelta, PEF, and MILC, explains their principles and trade‑offs, and details practical optimizations applied at 58.com to achieve query performance comparable to uncompressed indexes while reducing memory usage by about 35 percent.
This tutorial walks through building a simple Python search engine for big data, covering Bloom filter basics, tokenization with major and minor segmentation, inverted index creation, and implementing both simple and complex (AND/OR) queries, complete with code examples and visual illustrations.
This article explains how JD Daojia’s location system has evolved from simple distance calculations to a high‑performance, index‑driven backend service, detailing its core responsibilities, architectural stages, challenges, and the optimizations that improved accuracy, scalability, and resource usage.
This article demonstrates how to implement a basic Bloom filter, tokenization, and inverted index in Python to illustrate the core principles of big‑data search, including fast negative lookups, term segmentation, and support for AND/OR queries.
This article examines the architectural redesign of Alibaba's High‑Performance Time Series Database (HiTSDB), covering storage model changes, inverted‑index enhancements, a pipelined streaming aggregation engine, data‑migration strategies, and performance benchmarks that together deliver over tenfold query speed improvements.
The paper describes a high‑performance user‑behavior analysis system that processes hundreds of billions of daily logs for Meituan‑Dianping, using an inverted‑index structure with bitmap UUID sets and timestamp sequences, combined with Spark, Spring and Alluxio optimizations to cut query times from hours to under five seconds.
This article demonstrates how to build a basic big‑data search engine in Python using a Bloom filter, tokenisation, and an inverted index, showing step‑by‑step code examples that illustrate the core principles behind efficient data lookup.
This article explains the core of search engine technology by describing what an inverted index is, how it is built using single‑pass memory and multi‑way merge methods, how indexes can be partitioned and incrementally updated, and how Hadoop can be used for large‑scale indexing.
This article walks through implementing a basic big‑data search system in Python, covering Bloom filter basics, tokenization of text, inverted index construction, and how to combine these techniques to support fast AND/OR queries.
