How We Solved the Logstash Compute Problem with the “DBeast Monitor”
In the realm of data ingestion and processing, ensuring the smooth operation of tools like Logstash is paramount for efficiency and reliability. Recently, our team encountered a significant challenge with high CPU usage in Logstash, which led to decreased data ingest rates. This article outlines the journey we embarked on to diagnose and solve this problem, leveraging the insights provided by the DBeast monitor.
Identifying the Problem
Our journey began when we noticed intermittent spikes in CPU usage by Logstash, coinciding with a drop in data ingestion rates. This issue was first observed in the Stack monitoring dashboard within the “Logstash — Kibana” section. Unfortunately, I cannot display the actual dashboard images here, we will use demo data to illustrate the observed phenomena.
Initial Analysis
To further investigate, we drilled down into the details of the problematic Logstash instance by accessing its specific dashboard. Here, we observed that the CPU usage spikes were occurring simultaneously with exceptions, suggesting a correlation between the two events.
Pinpointing the Pipeline
Our next step involved identifying which pipeline was causing the exceptions. By examining different sections of the dashboard, we determined the problematic pipeline. To confirm our suspicion, we temporarily removed the pipeline and observed that the CPU spikes ceased, conclusively indicating that this pipeline was the root cause of the issue.
Investigating the Pipeline
Upon clicking on the pipeline name, we were directed to the pipeline analytics dashboard, which provided detailed information about the exceptions. This detailed view allowed us to pinpoint the issue to a specific Ruby plugin within the pipeline.
Resolving the Issue
With the problematic component identified, we proceeded to modify the Ruby code in the configuration files of the plugin. This intervention effectively resolved the CPU usage issue, as confirmed by subsequent monitoring of the Logstash dashboard, which showed normalized CPU usage and restored data ingestion rates.
Summary
The resolution of this issue underscores the importance of thorough monitoring and analysis in the management of data processing systems. By leveraging the “DBeast monitor”, we were able to systematically identify and address the root cause of the Logstash computes problem. This experience has not only improved our system’s efficiency but also enriched our team’s problem-solving toolkit.
You can try DBeast Monitor today in our playground:
https://play.dbeast.co/a/dbeast-dbeastmonitor-app
To get the most recent version, please visit our GitHub release page and download it from there.
https://github.com/dbeast-co/dbeast-monitor/releases
