Intended for intermediate Java developers familiar with web development, networked services, and enterprise Java frameworks like Spring or Java EE. No prior experience in asynchronous or reactive programming is required. As enterprise applications become larger and more distributed, new architectural approaches like reactive designs, microservices, and event streams are required knowledge.

If you’re a software engineer, big data infrastructure engineer, or Elasticsearch developer, you’ll find this book useful. This Elasticsearch book will also help data professionals working in the e-commerce and FMCG industry who use Elastic for metrics evaluation and search analytics to get deeper insights for better business decisions. Prior experience with Elasticsearch will help you get the most out of this book.

深度實戰(zhàn)Electron開發(fā)本地音樂播放器 視頻

ElasticStack高級開發(fā)與架構實戰(zhàn) 視頻

Logging in Action

本書主要展示如何使用Elasticsearch構建可擴展的搜索應用程序。書中覆蓋了Elasticsearch的主要特性，從使用不同的分析器和查詢類型進行相關性調(diào)優(yōu)，到使用聚集功能進行實時性分析，還有地理空間搜索和文檔過濾等更多吸引人的特性。 全書共分兩個部分，*部分解釋了核心特性，內(nèi)容主要涉及Elasticsearch的介紹，數(shù)據(jù)的索引、更新和刪除，數(shù)據(jù)的搜索，數(shù)據(jù)的分析，使用相關性進行搜索，使用聚集來探索數(shù)據(jù)，文檔間的關系等；第二部分介紹每個特性工作的更多細節(jié)及其對性能和可擴展性的影響，以便對核心功能進行產(chǎn)品化，內(nèi)容主要涉及水平擴展和性能提升等。此外，本書還有6個附錄（網(wǎng)上下載），提供了讀者應該知道的特性，展示了關于地理空間搜索和聚集，如何管理Elasticsearch插件，學習在搜索結果中如何高亮查詢單詞，在生產(chǎn)環(huán)境中用來協(xié)助管理Elasticsearch的第三方的監(jiān)控工具有哪些，如何使用Percolator過濾為多個查詢匹配少量文檔，如何使用不同的建議器來實現(xiàn)自動完成的功能。

Wxpython in Action

Activiti, an open source process engine, uses Business ProcessModel and Notation to provide a standard way to capture businessprocesses and to convert those models into executable code. It'slightweight and integrates seamlessly with standard applicationsoftware such as Spring. Also included is a set of design andmanagement tools that even helps developers who have never used aprocess engine get productive quickly.

Self-collaboration Code Generation via ChatGPT

本篇博客詳細介紹了Python機器學習庫Scikit-learn的使用方法和主要特性。內(nèi)容涵蓋了如何安裝和配置Scikit-learn，Scikit-learn的主要特性，如何進行數(shù)據(jù)預處理，如何使用監(jiān)督學習和無監(jiān)督學習算法，以及如何評估模型和進行參數(shù)調(diào)優(yōu)。本文旨在幫助讀者深入理解Scikit-learn，并有效地應用在實際的機器學習任務中。