Core Insights - Inverted index is a data structure that maps each term to a list of documents containing that term, facilitating quick document retrieval based on keywords [1][3] - The construction of inverted indexes involves three main steps: text preprocessing, dictionary generation, and the creation of inverted record tables [1] - Inverted index technology is widely used in various data processing fields, demonstrating significant practical value, especially in search engines, log analysis systems, and recommendation systems [3] Industry Applications - Elasticsearch and similar systems utilize inverted indexes for millisecond-level text retrieval responses in full-text search engines [3] - Log analysis systems leverage inverted indexes to quickly locate specific error messages or user behavior patterns [3] - The combination of inverted indexes and vector retrieval technology is advancing Retrieval-Augmented Generation (RAG) technology, supporting both exact matching and semantic similarity searches [3] Company Developments - StarRocks, a next-generation real-time analytical database, showcases significant advantages in inverted index technology, supporting full-text search and efficient text data queries [5] - The enterprise version of StarRocks, known as Jingzhou Database, enhances inverted index performance with distributed construction capabilities, handling petabyte-scale indexing tasks [8] - Tencent has adopted StarRocks as the core technology platform for building a large-scale vector retrieval system, overcoming performance and scalability challenges of traditional retrieval solutions [8] Performance Improvements - The solution based on StarRocks has achieved over 80% reduction in query response time compared to traditional methods while supporting larger data processing needs [8] - The optimized inverted index structure and query algorithms in Tencent's system enable complex multidimensional query conditions while maintaining millisecond-level response times [8]

以下文章来源于DataFunSummit ，作者海博 DataFunSummit . DataFun社区旗下账号，专注于分享大数据、人工智能领域行业峰会信息和嘉宾演讲内容，定期提供资 料合集下载。 INTRODUCTION 海博 理想汽车 分 享 嘉 宾 大数据工程师 专注于大数据计算领域，曾参与过多个数据平台的建设。目前负责理想汽车 OLAP 引擎 StarRocks 和时序引擎 MatrixDB 的应用和周边生态的建设 。 01 海量数据分析的挑战 首先来介绍一下理想汽车海量数据分析场景。 1. 背景：海量数据分析驱动汽车数字化、智能化 与互联网数据分析不同，汽车制造业的数据分析场景主要围绕车辆数据进行分析，除了企业经营数据，大部分 数据是从车端采集而来。车辆数据主要包括三类： 车机埋点数据：来自于车辆上类似 pad 的车机，其中会有一些行为埋点数据，采集分析后用于驱动智能 座舱的迭代。 这些来自车端的数据每天都会达到万亿级别，通过采集、分析这些海量数据，再应用回车辆，从而打造更智能 的车，以数据去驱动汽车的数字化、智能化。 2. 海量数据分析面临的问题 在海量数据分析过程中会面临诸多问题，主要包括三个方 ...