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Pluralsight

Monitor Couchbase Clusters

via Pluralsight

Overview

This course covers different ways to monitor a Couchbase cluster - from gathering diagnostic data from logs and real-time statistics, to the analysis of running and completed N1QL queries. These are performed using the Couchbase UI, CLI and REST API.

An important part of maintaining a distributed database such as Couchbase is to monitor its state and performance on a continuous basis. In this course, Monitor Couchbase Clusters, You'll cover how a Couchbase environment can be monitored using the log files and the statistics which are generated. It also delves into the options available for query monitoring and analysis. First, you'll look at the log files which are generated by Couchbase and how log configurations can be modified. you'll also look into the feature of explicit logging and how this can be initiated from the Couchbase UI and also programmatically from the shell. Next, You'll then discover how to gathering cluster statistics including the resource utilization on each node as well as the cluster overall. These are performed using the Couchbase UI, the CLI, and the REST API. Finally, You'll then move on to query monitoring. Learn how you can access details of running queries and how long-running queries can be terminated. You'll also analyze the data for high-latency queries and the indexes which are present in a cluster. You'll explore the query profiling feature which allows query execution statistics to be returned along with the results so that real-time analysis can be performed. Once you’re done with this course, you will be quite the expert on different options available for monitoring a Couchbase cluster. From analyzing log data and real-time statistics to query executions, you will be able to keep an eye on the health and performance of your Couchbase cluster.

Syllabus

  • Course Overview 2mins
  • Managing and Configuring Logs 51mins
  • Monitoring Clusters in Couchbase 27mins
  • Collecting N1QL Query Statistics 60mins

Taught by

Kishan Iyer

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