Amrit Sarkar

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Search Engineer Consultant
Lucidworks

Amrit Sarkar is Search Engineer and Consultant at Lucidworks Inc, California-based enterprise search technology company, with 4+ years experience in search domain and big data, e-commerce and product. He is working primarily on running search-based applications on Kubernetes, and developing and improving core components of Apache Solr.

Amrit Sarkar is speaking at the following session/s

Monitoring Apache Solr Ecosystem on Kubernetes

Wednesday | 11:35AM - 12:15PM | Columbia 3/4

Kubernetes is fast becoming the operating system for the Cloud and brings a ubiquity which 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 can be deployed on Kubernetes on large-scale for a plethora of use cases. For such scale, an effective metric dashboards, log analytics, monitoring, and alerting system is a requirement to make sure abnormal behaviors are detected, error diagnostics are performed and ability to fine-tune the entire ecosystem to reach best possible performance.

In this talk, we discuss and compare various monitoring and analytics tools for Solr ecosystem running on Kubernetes. From inbuilt features to third-party tools which provide powerful yet easy to use dynamic dashboards and OpenTracing support. The session concludes with a short demo of the strategy adopted.

Takeaway
Kubernetes is fast becoming the default tool to host applications including Apache Solr, and effective monitoring and analytics tools are required for mission-critical use-cases. Attendees will take away a deep sense of understanding of the type of tools they require to achieve for theirs.

Intended Audience
Enthusiasts looking for running Apache Solr ecosystem effectively on Kubernetes. Product Managers looking for analytics tools for overall application performance on Kubernetes. Developers looking for dynamic and effective monitoring and altering systems for their application for further diagnostics on Kubernetes.

Level:
All Levels