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TerminationCode.md

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TerminationCode

  • USER_REQUEST: A user terminated the cluster directly. Parameters should include a username field that indicates the specific user who terminated the cluster. * JOB_FINISHED: The cluster was launched by a job, and terminated when the job completed. * INACTIVITY: The cluster was terminated since it was idle. * CLOUD_PROVIDER_SHUTDOWN: The instance that hosted the Spark driver was terminated by the cloud provider. * COMMUNICATION_LOST: Azure Databricks lost connection to services on the driver instance. For example, this can happen when problems arise in cloud networking infrastructure, or when the instance itself becomes unhealthy. * CLOUD_PROVIDER_LAUNCH_FAILURE: Azure Databricks experienced a cloud provider failure when requesting instances to launch clusters. * SPARK_STARTUP_FAILURE: The cluster failed to initialize. Possible reasons may include failure to create the environment for Spark or issues launching the Spark master and worker processes. * INVALID_ARGUMENT: Cannot launch the cluster because the user specified an invalid argument. For example, the user might specify an invalid runtime version for the cluster. * UNEXPECTED_LAUNCH_FAILURE: While launching this cluster, Databricks failed to complete critical setup steps, terminating the cluster. * INTERNAL_ERROR: Azure Databricks encountered an unexpected error that forced the running cluster to be terminated. Contact Databricks support for additional details. * SPARK_ERROR: The Spark driver failed to start. Possible reasons may include incompatible libraries and initialization scripts that corrupted the Spark container. * METASTORE_COMPONENT_UNHEALTHY: The cluster failed to start because the external metastore could not be reached. Refer to Troubleshooting. * DBFS_COMPONENT_UNHEALTHY: The cluster failed to start because Databricks File System (DBFS) could not be reached. * AZURE_RESOURCE_PROVIDER_THROTTLING: Azure Databricks reached the Azure Resource Provider request limit. Specifically, the API request rate to the specific resource type (compute, network, etc.) can’t exceed the limit. Retry might help to resolve the issue. For further information, see https://docs.microsoft.com/azure/virtual-machines/troubleshooting/troubleshooting-throttling-errors. * AZURE_RESOURCE_MANAGER_THROTTLING: Azure Databricks reached the Azure Resource Manager request limit which prevents the Azure SDK from issuing any read or write request to the Azure Resource Manager. The request limit is applied to each subscription every hour. Retry after an hour or changing to a smaller cluster size might help to resolve the issue. For further information, see https://docs.microsoft.com/azure/azure-resource-manager/resource-manager-request-limits. * NETWORK_CONFIGURATION_FAILURE: The cluster was terminated due to an error in the network configuration. For example, a workspace with VNet injection had incorrect DNS settings that blocked access to worker artifacts. * DRIVER_UNREACHABLE: Azure Databricks was not able to access the Spark driver, because it was not reachable. * DRIVER_UNRESPONSIVE: Azure Databricks was not able to access the Spark driver, because it was unresponsive. * INSTANCE_UNREACHABLE: Azure Databricks was not able to access instances in order to start the cluster. This can be a transient networking issue. If the problem persists, this usually indicates a networking environment misconfiguration. * CONTAINER_LAUNCH_FAILURE: Azure Databricks was unable to launch containers on worker nodes for the cluster. Have your admin check your network configuration. * INSTANCE_POOL_CLUSTER_FAILURE: Pool backed cluster specific failure. Refer to Pools for details. * REQUEST_REJECTED: Azure Databricks cannot handle the request at this moment. Try again later and contact Databricks if the problem persists. * INIT_SCRIPT_FAILURE: Azure Databricks cannot load and run a cluster-scoped init script on one of the cluster’s nodes, or the init script terminates with a non-zero exit code. Refer to Init script logs. * TRIAL_EXPIRED: The Databricks trial subscription expired.

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