How do I resolve ExecutorLostFailure "Slave lost" errors in Spark on Amazon EMR?
I submitted an Apache Spark application to an Amazon EMR cluster. The application fails with this error: "Most recent failure: Lost task 1209.0 in stage 4.0 (TID 31219, ip-xxx-xxx-xx-xxx.compute.internal, executor 115): ExecutorLostFailure (executor 115 exited caused by one of the running tasks) Reason: Slave lost"
This error indicates that a Spark task failed because a node terminated or became unavailable. There are many possible causes of this error. The following resolution covers these common root causes:
- High disk utilization
- Using Spot Instances for cluster nodes
- Aggressive Amazon Elastic Compute Cloud (Amazon EC2) Auto Scaling policies
High disk utilization
In Hadoop, NodeManager periodically checks the Amazon Elastic Block Store (Amazon EBS) volumes that are attached to the cluster's nodes. If disk utilization on a node that has one volume attached is greater than the YARN property yarn.nodemanager.disk-health-checker.max-disk-utilization-per-disk-percentage (default value 90%), then the node is considered unhealthy. When a node is unhealthy, ResourceManager kills all containers running on that node. ResourceManager doesn't schedule new containers on unhealthy nodes. For more information, see NodeManager in the Hadoop documentation.
If ResourceManager kills multiple executors because of unhealthy nodes, then the application fails with a "slave lost" error. To confirm that a node is unhealthy, review the NodeManager logs or the instance controller logs:
- The location of the NodeManager logs is defined in the YARN_LOG_DIR variable in yarn-env.sh.
- The instance controller logs are stored at /emr/instance-controller/log/instance-controller.log on the master node. The instance controller logs provide an aggregated view of all the nodes of the cluster.
If a node is unhealthy, the logs show an entry that looks like this:
2019-10-04 11:09:37,163 INFO Poller: InstanceJointStatusMap contains 40 entries (R:40): i-006baxxxxxx 1829s R 1817s ig-3B ip-xxx-xx-xx-xxx I: 7s Y:U 11s c: 0 am: 0 H:R 0.0%Yarn unhealthy Reason : 1/1 local-dirs are bad: /mnt/yarn; 1/1 log-dirs are bad: /var/log/hadoop-yarn/containers i-00979xxxxxx 1828s R 1817s ig-3B ip-xxx-xx-xx-xxx I: 7s Y:R 9s c: 3 am: 2048 H:R 0.0% i-016c4xxxxxx 1834s R 1817s ig-3B ip-xxx-xx-xx-xxx I: 13s Y:R 14s c: 3 am: 2048 H:R 0.0% i-01be3xxxxxx 1832s R 1817s ig-3B ip-xxx-xx-xx-xxx I: 10s Y:U 12s c: 0 am: 0 H:R 0.0%Yarn unhealthy Reason : 1/1 local-dirs are bad: /mnt/yarn; 1/1 log-dirs are bad: /var/log/hadoop-yarn/containers
To resolve this problem, increase the size of the EBS volumes that are attached to the core and task nodes. Or, delete unused data from HDFS.
If you're using Amazon EC2 Spot Instances for EMR cluster nodes—and one of those instances terminates—you might get a "slave lost" error. Spot Instances might terminate for the following reasons:
- The Spot Instant price is greater than your maximum price.
- There aren't enough unused EC2 instances to meet the demand for Spot Instances.
For more information, see Reasons for interruption.
To resolve this problem:
- Consider switching to On-Demand Instances.
- If you're using Amazon EMR release version 5.11.0 or earlier, consider upgrading to the latest version.
Amazon EC2 Auto Scaling policies
When a scaling policy performs many scale-in and scale-out events in sequence, a new node might get the same IP address that a previous node used. If a Spark application is running during a scale-in event, Spark adds the decommissioned node to the deny list to prevent an executor from launching on that node. If another scale-out event occurs and the new node gets the same IP address as the previously decommissioned node, YARN considers the new node valid and attempts to schedule executors on it. However, because the node is still on the Spark deny list, attempts to launch executors on that node fail. When you reach the maximum number of failures, the Spark application fails with a "slave lost" error.
To resolve this problem:
- Use less aggressive rules in your scaling policies. For more information, see Understanding automatic scaling rules.
- Increase the number of available IP addresses in the subnet. For more information, see VPC and subnet sizing.
To remove a node from the Spark deny list, decrease the Spark and YARN timeout properties, as shown in the following examples:
Add the following property in /etc/spark/conf/spark-defaults.conf. This reduces the amount of time that a node in the decommissioning state remains on the deny list. The default is one hour. For more information, see Configuring node decommissioning behavior.
Modify the following YARN property in /etc/hadoop/conf/yarn-site.xml. This property specifies the amount of time to wait for running containers and applications to complete before a decommissioning node transitions to the decommissioned state. The default is 3600 seconds.
For more information see, Spark enhancements for elasticity and resiliency on Amazon EMR.
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