Hadoop Job hangs at ACCEPTED, with yarn resourcemanager log java.net.UnknownHostException

ONLINECAST

3 min read

·

07-10-2024

6

0

Share

Hadoop Job Stuck at ACCEPTED? Decoding the "java.net.UnknownHostException" in YARN Resource Manager Logs

The Problem:

Imagine this: you've submitted a Hadoop job, you see it's in the "ACCEPTED" state, but it just hangs there, refusing to progress. You look at the YARN Resource Manager logs and find the dreaded "java.net.UnknownHostException" error. This can be frustrating, as your job is stuck in limbo, preventing you from getting your desired results.

Scenario and Code:

Let's say you're running a MapReduce job on your Hadoop cluster. You see the following in your YARN Resource Manager logs:

2023-07-27 15:14:05,345 INFO org.apache.hadoop.yarn.server.resourcemanager.scheduler.fifo.FifoScheduler: Submitting application application_1690510633532_0001
2023-07-27 15:14:05,347 INFO org.apache.hadoop.yarn.server.resourcemanager.scheduler.fifo.FifoScheduler: Accepted application application_1690510633532_0001
2023-07-27 15:14:05,350 WARN org.apache.hadoop.yarn.server.resourcemanager.scheduler.fifo.FifoScheduler: Unable to launch container for application_1690510633532_0001
java.net.UnknownHostException: node-1
	at java.net.InetAddress.getAllByName0(InetAddress.java:1226)
	at java.net.InetAddress.getAllByName(InetAddress.java:1179)
	at org.apache.hadoop.yarn.server.resourcemanager.scheduler.ResourceScheduler.allocateContainers(ResourceScheduler.java:1236)
	at org.apache.hadoop.yarn.server.resourcemanager.scheduler.ResourceScheduler.allocateContainers(ResourceScheduler.java:963)
	at org.apache.hadoop.yarn.server.resourcemanager.scheduler.fifo.FifoScheduler.allocateContainers(FifoScheduler.java:246)
	at org.apache.hadoop.yarn.server.resourcemanager.scheduler.fifo.FifoScheduler.allocate(FifoScheduler.java:195)
	at org.apache.hadoop.yarn.server.resourcemanager.scheduler.Scheduler.run(Scheduler.java:230)
	at java.lang.Thread.run(Thread.java:748)

Analysis and Clarification:

This "java.net.UnknownHostException" error usually points to a DNS resolution issue. The Resource Manager is unable to resolve the hostname "node-1", which is likely the hostname of one of your cluster nodes. This means the YARN Resource Manager cannot communicate with the node to launch containers, causing the job to get stuck in the "ACCEPTED" state.

Possible Causes:

• Incorrect DNS configuration: The DNS server configured for your Hadoop cluster is either unavailable or unable to resolve the hostname "node-1".
• Name resolution conflict: The hostname "node-1" might be already in use by another system within your network, causing a clash.
• Network connectivity issues: The YARN Resource Manager might have a problem connecting to the network, preventing it from resolving hostnames.
• Typo in the hostname: Double check the hostname "node-1" is correctly entered in the configuration files and matches the actual hostname of the node.

Solutions:

• Verify DNS configuration: Check that the DNS server is configured correctly and accessible to your Hadoop cluster.
• Inspect network connectivity: Test the network connectivity between the YARN Resource Manager and the nodes in your cluster.
• Correct hostnames: Ensure that all hostnames are correctly spelled and match the actual hostnames of the nodes.
• Resolve hostname conflicts: If a hostname conflict exists, reconfigure the conflicting system or choose a different hostname.

Additional Value:

• Using the /etc/hosts File: For quick troubleshooting, you can temporarily add the hostname and IP address of the node to the /etc/hosts file on the YARN Resource Manager. This bypasses DNS resolution but is not a long-term solution.
• Check Logs for More Clues: Scrutinize the logs on the specific node mentioned in the error. Look for any networking errors or misconfigurations on that node.

Conclusion:

The "java.net.UnknownHostException" in your YARN Resource Manager logs usually signifies a DNS resolution problem. By carefully examining the DNS configuration, network connectivity, and hostnames, you can often resolve the issue and get your Hadoop jobs back on track. Remember to monitor and troubleshoot the logs for more context and to ensure smooth and efficient operation of your Hadoop cluster.