Best Practices for Monitoring Distributed In-Memory Computing

When you add a distributed in-memory computing cluster to support existing systems or new APIs, you introduce additional moving parts that can be hard to track and troubleshoot for performance issues or failures.

Learn how the veterans monitor various components of a distributed cluster for network, memory, or node-specific issues, and troubleshoot to resolve issues. By the end of this session you'll have a handy checklist and set of tools to consider using for your own deployments.

This session will cover:

• How to monitor applications, cluster node logs and metrics, JVM, operating system, and the network
• What some of the best tools are for different scenarios, including:
  • Log-based monitoring including Logstash, Elasticsearch, Kibana or Splunk
  • Grafana
  • Application monitoring (throughput and latency, GC)
  • Node’s local metrics monitoring (memory/GC/CPU)
  • Network issues monitoring (checking node connectivity and latency)
  • GridGain Web Console
• Tips and tricks for how to configure and optimize monitoring

About The Moving Apache Ignite into Production Series

This webinar is the third in a series that will guide you through the best development, monitoring, and troubleshooting practices for deploying Apache Ignite across different topologies and use cases. Other topics include:

• Moving Apache Ignite into Production: An Initial Checklist
• Best Practices for Native Persistence and Data Recovery
• Best Practices for Deploying Apache Ignite in the Cloud