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.
