Queue Tools Investigation

Overview

Klaudia's Queue Tools enable intelligent investigation and troubleshooting of message queue and event streaming issues affecting your Kubernetes workloads. When applications experience message processing failures, consumer lag, or queue connectivity problems, Klaudia can correlate application errors with queue health, consumer group state, and broker issues to provide actionable root cause analysis.

Message queues and event streams are critical components of modern distributed architectures. Issues like consumer lag, partition rebalancing, dead letter queue accumulation, and broker connectivity problems can cause cascading failures across your microservices. Klaudia's queue investigation tools help you quickly identify and resolve these issues before they impact your users.

Supported Tools

What Klaudia Can Do

General Capabilities

• Consumer Health Analysis: Identify stuck consumers, processing failures, and lag issues
• Connectivity Investigation: Troubleshoot connection failures and authentication issues
• Performance Analysis: Detect bottlenecks affecting message processing
• Error Correlation: Link application exceptions to underlying queue issues

Apache Kafka

• Identify broker connectivity and leader election problems
• Troubleshoot producer acknowledgment failures
• Examine topic configuration and retention issues

RabbitMQ

• Analyze message acknowledgment failures and redelivery loops
• Identify cluster partition (split-brain) scenarios
• Troubleshoot exchange binding and routing issues
• Detect memory and disk alarm conditions

When Klaudia Uses Queue Tools - Usage Examples

Klaudia automatically engages queue investigation tools in the following scenarios:

Root Cause Analysis (RCA)

When application pods show queue-related errors in their logs:

• Consumer connection failures or authentication errors
• Message processing timeouts or acknowledgment failures
• Producer delivery failures or timeout errors

Troubleshooting Unhealthy Resources

When investigating unhealthy pods that consume from or produce to queues:

• Application pods showing high CPU due to reprocessing loops
• Pods failing health checks due to queue connectivity
• Services with degraded performance from consumer lag

Chat Sessions

When you ask Klaudia questions about queue-related problems:

• "Why is my Kafka consumer group lagging behind?"
• "What's causing messages to pile up in RabbitMQ?"
• "Why are my consumers constantly rebalancing?"