Monitoring and Managing Resource Consumption on a Single Node.

Monitoring and Managing Resource Consumption on a Single Node.

Applies To: Distributed Systems (Hadoop, Spark, etc.), Standalone Servers 
Category: Troubleshooting Performance, Resource Management 

Issue Summary  

A single node within a cluster or a standalone server is experiencing disproportionately high CPU, memory, or disk I/O utilization, leading to performance bottlenecks, instability, or even node failure. 

Possible Cause(s)  

List common reasons why this issue may occur.  

  1. Workload imbalance: A specific application or job is preferentially scheduled on or is generating excessive load on that node. 

  1. Misconfigured services: A service on the node is misconfigured, leading to resource leaks or inefficient operation. 

  1. Hardware issues: Underlying hardware problems (e.g., failing disk, faulty RAM) leading to performance degradation. 

  1. Background processes: Uncontrolled background processes (e.g., backups, indexing, monitoring agents) consume resources. 

  1. Network bottlenecks: Heavy network traffic specifically routing through or terminating on that node. 

Step-by-Step Resolution  

  1. 1. Identify Resource logs: 

  1. To identify processes consuming the most CPU, memory, or I/O. 

top 

  1. To identify the detailed disk I/O statistics. 

iostat -dx 1 

  1. 2. Check the Node Manager is up and running it down, start it.

    1. yarn node -list

  1. 3. Check Configuration: 

  1. Review the configuration files for the high-resource processes/services on that node. Look for non-standard settings or resource limits. 

Additional Notes: 

  • Implement robust monitoring (e.g., Prometheus, Grafana, Nagios) to track resource usage on all nodes and receive alerts for anomalies. 

  • Regularly review cluster health and job execution patterns to detect imbalances early. 

    • Related Articles

    • Resource Allocation and Scheduler Configuration

      Resource Allocation and Scheduler Configuration Category: Administration → Resource Management Applies to: Apache Hadoop 2.x, 3.x Issue Summary This document outlines critical configurations for resource allocation and scheduler management within ...
    • Managing HDFS Space and Replication

      Managing HDFS Space and Replication Category: Troubleshooting → HDFS Applies To: Apache Hadoop HDFS 2.x, 3.x Issue summary: Effective management of HDFS disk space and data replication is crucial for the stability, performance, and data availability ...
    • NewEvol Release Note 1.4.0

      Following points were covered in the version 1.4.0: New feature: 12 Enhancement: 22 Issues resolved: 16 Known Issues to be Fixed: 2
    • Kafka Retention, Cleanup, and Performance Tuning

      Kafka Retention, Cleanup, and Performance Tuning Category: Administration → Kafka Applies to: Apache Kafka 2.x 3.x Issue Summary This document outlines critical configurations and best practices for managing data retention, ensuring efficient ...
    • Understanding Hadoop Logs – Types, Use Cases, and Common Locations.

      Category: Troubleshooting → Logging and montoring Applies To: Hadoop HA cluster. Issue Summary In a distributed Hadoop HA cluster, component logs are the primary source of truth for monitoring system health, diagnosing failures, and troubleshooting ...