Scaling Search Clusters with Apache Solr and Kubernetes

Presented at virtual Activate 2020. Kubernetes is fast becoming the operating system for the Cloud and brings a ubiquity that has the potential for massive benefits for technology organizations. Applications/Microservices are moved to orchestration tools like Kubernetes to leverage features like horizontal autoscaling, fault tolerance, CICD, and more. Apache Solr is an open-source search engine platform built on an Apache Lucene library. It offers Apache Lucene’s search capabilities in a user-friendly way. Lucidworks runs over a thousand distributed-mode Apache Solr Clusters spread across several machines for a plethora of use-cases around Search and Analytics. The traffic demands a massive scale which creates scenarios of in-depth micro-management like operating systems upgrade, scaling cluster dynamically, etc, affecting the overall search experience. This talk is focussed on the journey taken by Lucidworks on addressing scaling clusters horizontally and vertically, on the basis of query traffic load, data ingestion throughput or any other relevant metrics by extending capabilities of Kubernetes and Apache Solr to achieve true physical and logical autoscaling, satisfying modern era SLAs and infrastructure cost. The talk concludes with how the solution adopted opens up the future scope of fine-grained scaling of search clusters.

Amrit Sarkar, Cloud Search Reliability Engineer, Lucidworks

Intended Audience:
Search Architects, Developers, and Product Managers who wants to implemented intuitive and smart scaling solutions for search clusters and systems.

Attendee Takeaway:
Attendees will walk away with strong design intuition of auto-scaling search clusters based on traffic and other factors.

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