ElasticSearch是一个基于Lucene的分布式实时搜索引擎解决方案。属于Elastic Stack的一部分,同时另有 logstash, kibana, beats等开源项目。

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相关搜索揭开了相关工作的神秘面纱。使用Elasticsearch,它将教会您如何将引人入胜的搜索结果返回给用户,帮助您理解并利用基于lucene的搜索引擎的内部原理。

对这项技术

用户已经习惯并期待即时的相关搜索结果。要做到这一点,你必须掌握搜索引擎。然而对于许多开发者来说,相关性排名是神秘或令人困惑的。

关于这本书

相关搜索使主题变得更清晰,并向您展示了搜索引擎是一个可编程的相关框架。您将学习如何应用Elasticsearch或Solr到您的企业独特的排名问题。该书演示了如何编写相关性程序,以及如何合并辅助数据源、分类法、文本分析和个性化。在实践中,相关性框架还需要软技能,例如与涉众协作,以发现业务的正确相关性需求。到最后,你呢?你能在搜索产品上实现一个可证明、可衡量的相关性改进的良性循环吗?

里面有什么

  • 调试技术相关性

  • 搜索引擎的特性应用到实际问题

  • 使用用户界面来指导搜索者

  • 一个系统的方法相关性

  • 专注于改善搜索的商业文化
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Detecting performance issues due to suboptimal code during the development process can be a daunting task, especially when it comes to localizing them after noticing performance degradation after deployment. Static analysis has the potential to provide early feedback on performance problems to developers without having to run profilers with expensive (and often unavailable) performance tests. We develop a VSCode tool that integrates the static performance analysis results from Infer via code annotations and decorations (surfacing complexity analysis results in context) and side panel views showing details and overviews (enabling explainability of the results). Additionally, we design our system for interactivity to allow for more responsiveness to code changes as they happen. We evaluate the efficacy of our tool by measuring the overhead that the static performance analysis integration introduces in the development workflow. Further, we report on a case study that illustrates how our system can be used to reason about software performance in the context of a real performance bug in the ElasticSearch open-source project. Demo video: https://www.youtube.com/watch?v=-GqPb_YZMOs Repository: https://github.com/ipa-lab/vscode-infer-performance

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