Seth Payne

Getting the most out of your search deployment isn't just about writing the perfect query; it's about ensuring the underlying system is perfectly sized for your workload. For many, this has meant facing a difficult choice. If your search indexes are large but your query and indexing rates are moderate, you may have been forced to scale up to more expensive, higher-tiered nodes simply to get the storage capacity you need. This often leads to overprovisioning compute resources and unnecessary costs.

By Seth Payne, Lead Product Manager at MongoDB By Daniel Ernst, Search Engineering Lead at MongoDB By Arjun Gurumurthy, Staff Engineer at MongoDB By Archana Srinivasan, Sr. Technical Account Manager at AWS By Rashmikant Vyas (RK), Sr. Technical Account Manager at AWS By Sergio Ariel de la Campa, Sr. Technical Account Manager at AWS MongoDB customers were seeking a solution to handle Atlas Search index rebuild in case of a problem with underlying hardware or scaling activities.

作者：Seth Payne 特别声明：截止撰写该博文，在目前的公开发行版中，该功能还不能公开测试。这个功能将在未来的发行版中发布。 长期以来，Elastic Platform 一直被视为搜索用例和机器生成数据的分析系统。 分析专注于处理摄入的数据，其中重要的思想是如何在 Elasticsearch 中索引数据时优化结构数据。 Kibana 公开 Elasticsearch 聚合并使用它们来创建交互式仪表板、可视化和警报。 但随着 Elastic Platform 被更广泛地用作搜索、安全、可观察性和通用分析平台，分析师用户需要能够将数据作为摄入的数据，对其进行转换以适应摄入后的调查需求，并从底层 Elasticsearch 索引数据中获得洞察力 。 他们需要一个简洁、集成和高效的工作流，由丰富且富有表现力的查询支持，其中搜索、过滤、聚合和转换通过单个查询表达式执行，几乎没有 UI 上下文切换。 为了解决这些挑战，Elastic 团队目前正在开发 Elasticsearch 查询语言 (ESQL)。 ESQL 为 Elastic...