MQ Admin debugging FAQ
Step 1: Check Queue Managers and Channels
Verify all required Queue Managers are RUNNING.
Check channel statuses. A channel stuck in RETRYING, STOPPED, or INACTIVE when messages are expected is a
common culprit.
DISPLAY CHSTATUS(*)Step 2: Locate the Stalled Messages
For queues, use the current depth command to identify where messages are building up.
A consistently high current depth, especially on a transmission queue, points to a downstream issue.
If messages are not being consumed, the application may be down, delayed, or dealing with a poison message.
Step 1: Check Queue Managers and Channels
Verify all required Queue Managers are RUNNING.
Check channel statuses. A channel stuck in RETRYING, STOPPED, or INACTIVE when messages are expected is a
common culprit.
DISPLAY CHSTATUS(*)DISPLAY QUEUE(*) CURDEPTH
Step 3: Investigate the Dead-Letter Queue (DLQ)
If a message cannot be delivered, IBM MQ will often place it on a DLQ. Ensure a DLQ is defined for the Queue Manager.
Display the DLQ contents and inspect whether failed messages are accumulating.
Browse DLQ messages where possible to determine whether malformed or unexpected payloads are the root cause.
DISPLAY QUEUE(SYSTEM.DEAD.LETTER.QUEUE) CURDEPTHStep 4: Understand the Failure Reason Codes
IBM MQ provides specific reason codes for each failure.
Use the official IBM documentation or a trusted decoder to pinpoint the exact source of the failure.

Step 5: Clear the Blockage (If Safe to Do So)
If the issue is a simple backlog, start the related channel or restart the consumer application.
For poison messages, once the application is corrected, messages on the DLQ can be deleted, returned to the original queue, or redirected to a test queue.
Connect to the Queue Manager and clear the blockage only after confirming message safety.
Step 6: Proactive Prevention
QueueMetrix Lamaxu is purpose built to expose IBM MQ statuses, queue depths, events, channel health and statistics into enterprise observability tools through JMX, REST and structured logs, reducing manual investigation effort and surfacing these exact bottlenecks far earlier in the incident lifecycle.