Elasticsearch: Optimized Data Architecture, Enhanced Web Crawler, and Autoscaling in Elastic Enterprise Search V.7.12

Elasticsearch B.V, behind Elasticsearch and the Elastic Stack, announced an updated data architecture, enhanced web crawler, and autoscaling to support consistent search performance, scalability, and relevancy across the Enterprise Search solution in the 7.12 release.

The company’s Enterprise Search introduces a reimagined underlying data architecture optimized for performance, relevance, and capacity management. With this new data structure, deployments may benefit from up to 70% improvement in storage efficiency, up to 40% reduction in indexing latency, and improvements to results relevance across App Search and Workplace Search.

The Elastic App Search web crawler, introduced in beta, adds several performance and stability improvements, along with better support for web crawling standards such as robot.txt. With the App Search web crawler, users can leverage simple point-and-click tools to extract publicly accessible web content into their App Search engine without any coding required.

The firm’s Enterprise Search also inherits Index Lifecycle Management (ILM) policies from the Elastic Stack to automatically manage logs and analytics, and can roll additional ILM features into App Search and Workplace Search.

Autoscaling on Elastic Cloud allows Enterprise Search users to proactively set predefined rules that monitor storage usage, whether that storage comes from content, logs, or analytics. When a threshold is met, autoscaling automatically increases customers’ storage capacity based on predefined rules. With autoscaling, users can drive greater insights into their search platform with less overhead.

Resource:
Blog: What’s new in Elastic Enterprise Search 7.12