title | description | services | author | ms.service | ms.topic | ms.date | ms.author | ms.reviewer |
---|---|---|---|---|---|---|---|---|
Troubleshoot Azure Data Factory | Microsoft Docs |
Learn how to troubleshoot external control activities in Azure Data Factory. |
data-factory |
nabhishek |
data-factory |
troubleshooting |
11/16/2020 |
abnarain |
craigg |
[!INCLUDEappliesto-adf-asa-md]
This article explores common troubleshooting methods for external control activities in Azure Data Factory.
For connector issues such as an encounter error using the copy activity, refer to Troubleshoot Azure Data Factory Connectors.
-
Message: Error 403.
-
Cause:
The Databricks access token has expired.
-
Recommendation: By default, the Azure Databricks access token is valid for 90 days. Create a new token and update the linked service.
-
Message:
Missing required field: settings.task.notebook_task.notebook_path.
-
Cause:
Bad authoring: Notebook path not specified correctly.
-
Recommendation: Specify the notebook path in the Databricks activity.
-
Message:
Cluster... does not exist.
-
Cause:
Authoring error: Databricks cluster does not exist or has been deleted.
-
Recommendation: Verify that the Databricks cluster exists.
-
Message:
Invalid Python file URI... Please visit Databricks user guide for supported URI schemes.
-
Cause:
Bad authoring.
-
Recommendation: Specify either absolute paths for workspace-addressing schemes, or
dbfs:/folder/subfolder/foo.py
for files stored in the Databricks File System (DFS).
-
Message:
{0} LinkedService should have domain and accessToken as required properties.
-
Cause:
Bad authoring.
-
Recommendation: Verify the linked service definition.
-
Message:
{0} LinkedService should specify either existing cluster ID or new cluster information for creation.
-
Cause:
Bad authoring.
-
Recommendation: Verify the linked service definition.
-
Message:
Node type Standard_D16S_v3 is not supported. Supported node types: Standard_DS3_v2, Standard_DS4_v2, Standard_DS5_v2, Standard_D8s_v3, Standard_D16s_v3, Standard_D32s_v3, Standard_D64s_v3, Standard_D3_v2, Standard_D8_v3, Standard_D16_v3, Standard_D32_v3, Standard_D64_v3, Standard_D12_v2, Standard_D13_v2, Standard_D14_v2, Standard_D15_v2, Standard_DS12_v2, Standard_DS13_v2, Standard_DS14_v2, Standard_DS15_v2, Standard_E8s_v3, Standard_E16s_v3, Standard_E32s_v3, Standard_E64s_v3, Standard_L4s, Standard_L8s, Standard_L16s, Standard_L32s, Standard_F4s, Standard_F8s, Standard_F16s, Standard_H16, Standard_F4s_v2, Standard_F8s_v2, Standard_F16s_v2, Standard_F32s_v2, Standard_F64s_v2, Standard_F72s_v2, Standard_NC12, Standard_NC24, Standard_NC6s_v3, Standard_NC12s_v3, Standard_NC24s_v3, Standard_L8s_v2, Standard_L16s_v2, Standard_L32s_v2, Standard_L64s_v2, Standard_L80s_v2.
-
Cause:
Bad authoring.
-
Recommendation: Refer to the error message.
-
Message:
There were already 1000 jobs created in past 3600 seconds, exceeding rate limit: 1000 job creations per 3600 seconds.
-
Cause:
Too many Databricks runs in an hour.
-
Recommendation: Check all pipelines that use this Databricks workspace for their job creation rate. If pipelines launched too many Databricks runs in aggregate, migrate some pipelines to a new workspace.
-
Message:
Could not parse request object: Expected 'key' and 'value' to be set for JSON map field base_parameters, got 'key: "..."' instead.
-
Cause:
Authoring error: No value provided for the parameter.
-
Recommendation: Inspect the pipeline JSON and ensure all parameters in the baseParameters notebook specify a nonempty value.
-
Message:
User:
SimpleUserContext{userId=..., name=[email protected], orgId=...}is not authorized to access cluster.
-
Cause: The user who generated the access token isn't allowed to access the Databricks cluster specified in the linked service.
-
Recommendation: Ensure the user has the required permissions in the workspace.
-
Message:
The cluster is in Terminated state, not available to receive jobs. Please fix the cluster or retry later.
-
Cause: The cluster was terminated. For interactive clusters, this issue might be a race condition.
-
Recommendation: To avoid this error, use job clusters.
-
Message:
Job execution failed.
-
Cause: Error messages indicate various issues, such as an unexpected cluster state or a specific activity. Often, no error message appears.
-
Recommendation: N/A
-
Message:
An error occurred while sending the request.
-
Cause: The network connection to the Databricks service was interrupted.
-
Recommendation: If you're using a self-hosted integration runtime, make sure that the network connection is reliable from the integration runtime nodes. If you're using Azure integration runtime, retry usually works.
The following table applies to U-SQL.
-
Message:
The access token is from the wrong tenant.
-
Cause: Incorrect Azure Active Directory (Azure AD) tenant.
-
Recommendation: Incorrect Azure Active Directory (Azure AD) tenant.
-
Message:
We cannot accept your job at this moment. The maximum number of queued jobs for your account is 200.
-
Cause: This error is caused by throttling on Data Lake Analytics.
-
Recommendation: Reduce the number of submitted jobs to Data Lake Analytics. Either change Data Factory triggers and concurrency settings on activities, or increase the limits on Data Lake Analytics.
-
Message:
This job was rejected because it requires 24 AUs. This account's administrator-defined policy prevents a job from using more than 5 AUs.
-
Cause: This error is caused by throttling on Data Lake Analytics.
-
Recommendation: Reduce the number of submitted jobs to Data Lake Analytics. Either change Data Factory triggers and concurrency settings on activities, or increase the limits on Data Lake Analytics.
-
Message:
Forbidden. ACL verification failed. Either the resource does not exist or the user is not authorized to perform the requested operation.<br/> <br/> User is not able to access Data Lake Store. <br/> <br/> User is not authorized to use Data Lake Analytics.
-
Cause: The service principal or certificate doesn't have access to the file in storage.
-
Recommendation: Verify that the service principal or certificate that the user provides for Data Lake Analytics jobs has access to both the Data Lake Analytics account, and the default Data Lake Storage instance from the root folder.
-
Message:
Forbidden. ACL verification failed. Either the resource does not exist or the user is not authorized to perform the requested operation.<br/> <br/> User is not able to access Data Lake Store. <br/> <br/> User is not authorized to use Data Lake Analytics.
-
Cause: The service principal or certificate doesn't have access to the file in storage.
-
Recommendation: Verify that the service principal or certificate that the user provides for Data Lake Analytics jobs has access to both the Data Lake Analytics account, and the default Data Lake Storage instance from the root folder.
-
Message:
Cannot find the 'Azure Data Lake Store' file or folder.
-
Cause: The path to the U-SQL file is wrong, or the linked service credentials don't have access.
-
Recommendation: Verify the path and credentials provided in the linked service.
-
Message:
Forbidden. ACL verification failed. Either the resource does not exist or the user is not authorized to perform the requested operation.<br/> <br/> User is not able to access Data Lake Store. <br/> <br/> User is not authorized to use Data Lake Analytics.
-
Cause: The service principal or certificate doesn't have access to the file in storage.
-
Recommendation: Verify that the service principal or certificate that the user provides for Data Lake Analytics jobs has access to both the Data Lake Analytics account, and the default Data Lake Storage instance from the root folder.
-
Message:
Cannot resolve the account of AzureDataLakeAnalytics. Please check 'AccountName' and 'DataLakeAnalyticsUri'.
-
Cause: The Data Lake Analytics account in the linked service is wrong.
-
Recommendation: Verify that the right account is provided.
-
Message:
Error Id: E_CQO_SYSTEM_INTERNAL_ERROR (or any error that starts with "Error Id:").
-
Cause: The error is from Data Lake Analytics.
-
Recommendation: The job was submitted to Data Lake Analytics, and the script there, both failed. Investigate in Data Lake Analytics. In the portal, go to the Data Lake Analytics account and look for the job by using the Data Factory activity run ID (don't use the pipeline run ID). The job there provides more information about the error, and will help you troubleshoot.
If the resolution isn't clear, contact the Data Lake Analytics support team and provide the job Universal Resource Locator (URL), which includes your account name and the job ID.
-
Message:
Invalid HttpMethod: '%method;'.
-
Cause: The Httpmethod specified in the activity payload isn't supported by Azure Function Activity.
-
Recommendation: The supported Httpmethods are: PUT, POST, GET, DELETE, OPTIONS, HEAD, and TRACE.
-
Message:
Response Content is not a valid JObject.
-
Cause: The Azure function that was called didn't return a JSON Payload in the response. Azure Data Factory (ADF) Azure function activity only supports JSON response content.
-
Recommendation: Update the Azure function to return a valid JSON Payload such as a C# function may return
(ActionResult)new OkObjectResult("{\"Id\":\"123\"}");
-
Message: Azure function activity missing function key.
-
Cause: The Azure function activity definition isn't complete.
-
Recommendation: Check that the input Azure function activity JSON definition has a property named
functionKey
.
-
Message:
Azure function activity missing function name.
-
Cause: The Azure function activity definition isn't complete.
-
Recommendation: Check that the input Azure function activity JSON definition has a property named
functionName
.
-
Message:
Call to provided Azure function '%FunctionName;' failed with status-'%statusCode;' and message - '%message;'.
-
Cause: The Azure function details in the activity definition may be incorrect.
-
Recommendation: Fix the Azure function details and try again.
-
Message:
Azure function activity missing functionAppUrl.
-
Cause: The Azure function activity definition isn't complete.
-
Recommendation: Check that the input Azure Function activity JSON definition has a property named
functionAppUrl
.
-
Message:
There was an error while calling endpoint.
-
Cause: The function URL may be incorrect.
-
Recommendation: Verify that the value for
functionAppUrl
in the activity JSON is correct and try again.
-
Message:
Azure function activity missing Method in JSON.
-
Cause: The Azure function activity definition isn't complete.
-
Recommendation: Check that the input Azure function activity JSON definition has a property named
method
.
-
Message:
Azure function activity missing LinkedService definition in JSON.
-
Cause: The Azure function activity definition isn't complete.
-
Recommendation: Check that the input Azure function activity JSON definition has linked service details.
-
Message:
AzureMLExecutePipeline activity '%activityName;' has invalid value for property '%propertyName;'.
-
Cause: Bad format or missing definition of property
%propertyName;
. -
Recommendation: Check if the activity
%activityName;
has the property%propertyName;
defined with correct data.
-
Message:
AzureMLExecutePipeline activity missing LinkedService definition in JSON.
-
Cause: The AzureMLExecutePipeline activity definition isn't complete.
-
Recommendation: Check that the input AzureMLExecutePipeline activity JSON definition has correctly linked service details.
-
Message:
AzureMLExecutePipeline activity has wrong LinkedService type in JSON. Expected LinkedService type: '%expectedLinkedServiceType;', current LinkedService type: Expected LinkedService type: '%currentLinkedServiceType;'.
-
Cause: Incorrect activity definition.
-
Recommendation: Check that the input AzureMLExecutePipeline activity JSON definition has correctly linked service details.
-
Message:
AzureMLService linked service has invalid value for property '%propertyName;'.
-
Cause: Bad format or missing definition of property '%propertyName;'.
-
Recommendation: Check if the linked service has the property
%propertyName;
defined with correct data.
-
Message:
Request sent to Azure Machine Learning for operation '%operation;' failed with http status code '%statusCode;'. Error message from Azure Machine Learning: '%externalMessage;'.
-
Cause: The Credential used to access Azure Machine Learning has expired.
-
Recommendation: Verify that the credential is valid and retry.
-
Message:
Request sent to Azure Machine Learning for operation '%operation;' failed with http status code '%statusCode;'. Error message from Azure Machine Learning: '%externalMessage;'.
-
Cause: The credential provided in Azure Machine Learning Linked Service is invalid, or doesn't have permission for the operation.
-
Recommendation: Verify that the credential in Linked Service is valid, and has permission to access Azure Machine Learning.
-
Message:
Request sent to Azure Machine Learning for operation '%operation;' failed with http status code '%statusCode;'. Error message from Azure Machine Learning: '%externalMessage;'.
-
Cause: The properties of the activity such as
pipelineParameters
are invalid for the Azure Machine Learning (ML) pipeline. -
Recommendation: Check that the value of activity properties matches the expected payload of the published Azure ML pipeline specified in Linked Service.
-
Message:
Request sent to Azure Machine Learning for operation '%operation;' failed with http status code '%statusCode;'. Error message from Azure Machine Learning: '%externalMessage;'.
-
Cause: The published Azure ML pipeline endpoint doesn't exist.
-
Recommendation: Verify that the published Azure Machine Learning pipeline endpoint specified in Linked Service exists in Azure Machine Learning.
-
Message:
Request sent to Azure Machine Learning for operation '%operation;' failed with http status code '%statusCode;'. Error message from Azure Machine Learning: '%externalMessage;'.
-
Cause: There is a server error on Azure Machine Learning.
-
Recommendation: Retry later. Contact the Azure Machine Learning team for help if the issue continues.
-
Message:
Azure ML pipeline run failed with status: '%amlPipelineRunStatus;'. Azure ML pipeline run Id: '%amlPipelineRunId;'. Please check in Azure Machine Learning for more error logs.
-
Cause: The Azure ML pipeline run failed.
-
Recommendation: Check Azure Machine Learning for more error logs, then fix the ML pipeline.
-
Message:
Please provide value for the required property '%propertyName;'.
-
Cause: The required value for the property has not been provided.
-
Recommendation: Provide the value from the message and try again.
-
Message:
The type of the property '%propertyName;' is incorrect.
-
Cause: The provided property type isn't correct.
-
Recommendation: Fix the type of the property and try again.
-
Message:
An invalid json is provided for property '%propertyName;'. Encountered an error while trying to parse: '%message;'.
-
Cause: The value for the property is invalid or isn't in the expected format.
-
Recommendation: Refer to the documentation for the property and verify that the value provided includes the correct format and type.
-
Message:
The storage connection string is invalid. %errorMessage;
-
Cause: The connection string for the storage is invalid or has incorrect format.
-
Recommendation: Go to the Azure portal and find your storage, then copy-and-paste the connection string into your linked service and try again.
-
Message:
Error calling the endpoint '%url;'. Response status code: '%code;'
-
Cause: The request failed due to an underlying issue such as network connectivity, DNS failure, server certificate validation, or timeout.
-
Recommendation: Use Fiddler/Postman to validate the request.
-
Message:
The linked service type '%linkedServiceType;' is not supported for '%executorType;' activities.
-
Cause: The linked service specified in the activity is incorrect.
-
Recommendation: Verify that the linked service type is one of the supported types for the activity. For example, the linked service type for HDI activities can be HDInsight or HDInsightOnDemand.
-
Message:
The type of the property '%propertyName;' is incorrect. The expected type is %expectedType;.
-
Cause: The type of the provided property isn't correct.
-
Recommendation: Fix the property type and try again.
-
Message:
The cloud type is unsupported or could not be determined for storage from the EndpointSuffix '%endpointSuffix;'.
-
Cause: The cloud type is unsupported or couldn't be determined for storage from the EndpointSuffix.
-
Recommendation: Use storage in another cloud and try again.
-
Message:
No response from the endpoint. Possible causes: network connectivity, DNS failure, server certificate validation or timeout.
-
Cause: Network connectivity, DNS failure, server certificate validation or timeout.
-
Recommendation: Validate that the endpoint you are trying to hit is responding to requests. You may use tools like Fiddler/Postman.
The following table applies to Azure Batch.
-
Message:
Hit unexpected exception and execution failed.
-
Cause:
Can't launch command, or the program returned an error code.
-
Recommendation: Ensure that the executable file exists. If the program started, verify that stdout.txt and stderr.txt were uploaded to the storage account. It's a good practice to include logs in your code for debugging.
-
Message:
Cannot access user batch account; please check batch account settings.
-
Cause: Incorrect Batch access key or pool name.
-
Recommendation: Verify the pool name and the Batch access key in the linked service.
-
Message:
Cannot access user storage account; please check storage account settings.
-
Cause: Incorrect storage account name or access key.
-
Recommendation: Verify the storage account name and the access key in the linked service.
-
Message:
Operation returned an invalid status code 'BadRequest'.
-
Cause: Too many files in the
folderPath
of the custom activity. The total size ofresourceFiles
can't be more than 32,768 characters. -
Recommendation: Remove unnecessary files, or Zip them and add an unzip command to extract them.
For example, use
powershell.exe -nologo -noprofile -command "& { Add-Type -A 'System.IO.Compression.FileSystem'; [IO.Compression.ZipFile]::ExtractToDirectory($zipFile, $folder); }" ; $folder\yourProgram.exe
-
Message:
Cannot create Shared Access Signature unless Account Key credentials are used.
-
Cause: Custom activities support only storage accounts that use an access key.
-
Recommendation: Refer to the error description.
-
Message:
The folder path does not exist or is empty: ...
-
Cause: No files are in the storage account at the specified path.
-
Recommendation: The folder path must contain the executable files you want to run.
-
Message:
There are duplicate files in the resource folder.
-
Cause: Multiple files of the same name are in different sub-folders of folderPath.
-
Recommendation: Custom activities flatten folder structure under folderPath. If you need to preserve the folder structure, zip the files and extract them in Azure Batch by using an unzip command.
For example, use
powershell.exe -nologo -noprofile -command "& { Add-Type -A 'System.IO.Compression.FileSystem'; [IO.Compression.ZipFile]::ExtractToDirectory($zipFile, $folder); }" ; $folder\yourProgram.exe
-
Message:
Batch url ... is invalid; it must be in Uri format.
-
Cause: Batch URLs must be similar to
https://mybatchaccount.eastus.batch.azure.com
-
Recommendation: Refer to the error description.
-
Message:
An error occurred while sending the request.
-
Cause: The batch URL is invalid.
-
Recommendation: Verify the batch URL.
-
Message:
The batch ID for Spark job is invalid. Please retry your job.
-
Cause: There was an internal problem with the service that caused this error.
-
Recommendation: This issue could be transient. Retry your job after sometime.
-
Message:
Could not determine the region from the provided storage account. Please try using another primary storage account for the on demand HDI.
-
Cause: There was an internal error while trying to determine the region from the primary storage account.
-
Recommendation: Try another storage.
-
Message:
Service Principal or the MSI authenticator are not instantiated. Please consider providing a Service Principal in the HDI on demand linked service which has permissions to create an HDInsight cluster in the provided subscription and try again.
-
Cause: There was an internal error while trying to read the Service Principal or instantiating the MSI authentication.
-
Recommendation: Consider providing a service principal, which has permissions to create an HDInsight cluster in the provided subscription and try again. Verify that the Manage Identities are set up correctly.
-
Message:
Failed to submit the job '%jobId;' to the cluster '%cluster;'. Error: %errorMessage;.
-
Cause: The error message contains a message similar to
The remote name could not be resolved.
. The provided cluster URI might be invalid. -
Recommendation: Verify that the cluster hasn't been deleted, and that the provided URI is correct. When you open the URI in a browser, you should see the Ambari UI. If the cluster is in a virtual network, the URI should be the private URI. To open it, use a Virtual Machine (VM) that is part of the same virtual network.
For more information, see Directly connect to Apache Hadoop services.
-
Cause: If the error message contains a message similar to
A task was canceled.
, the job submission timed out. -
Recommendation: The problem could be either general HDInsight connectivity or network connectivity. First confirm that the HDInsight Ambari UI is available from any browser. Then check that your credentials are still valid.
If you're using a self-hosted integrated runtime (IR), perform this step from the VM or machine where the self-hosted IR is installed. Then try submitting the job from Data Factory again.
For more information, read Ambari Web UI.
-
Cause: When the error message contains a message similar to
User admin is locked out in Ambari
orUnauthorized: Ambari user name or password is incorrect
, the credentials for HDInsight are incorrect or have expired. -
Recommendation: Correct the credentials and redeploy the linked service. First verify that the credentials work on HDInsight by opening the cluster URI on any browser and trying to sign in. If the credentials don't work, you can reset them from the Azure portal.
For ESP cluster, reset the password through self service password reset.
-
Cause: When the error message contains a message similar to
502 - Web server received an invalid response while acting as a gateway or proxy server
, this error is returned by HDInsight service. -
Recommendation: A 502 error often occurs when your Ambari Server process was shut down. You can restart the Ambari Services by rebooting the head node.
-
Connect to one of your nodes on HDInsight using SSH.
-
Identify your active head node host by running
ping headnodehost
. -
Connect to your active head node as Ambari Server sits on the active head node using SSH.
-
Reboot the active head node.
For more information, look through the Azure HDInsight troubleshooting documentation. For example:
-
-
Cause: When the error message contains a message similar to
Unable to service the submit job request as templeton service is busy with too many submit job requests
orQueue root.joblauncher already has 500 applications, cannot accept submission of application
, too many jobs are being submitted to HDInsight at the same time. -
Recommendation: Limit the number of concurrent jobs submitted to HDInsight. Refer to Data Factory activity concurrency if the jobs are being submitted by the same activity. Change the triggers so the concurrent pipeline runs are spread out over time.
Refer to HDInsight documentation to adjust
templeton.parallellism.job.submit
as the error suggests.
-
Message:
Could not get the status of the application '%physicalJobId;' from the HDInsight service. Received the following error: %message;. Please refer to HDInsight troubleshooting documentation or contact their support for further assistance.
-
Cause: HDInsight cluster or service has issues.
-
Recommendation: This error occurs when ADF doesn't receive a response from HDInsight cluster when attempting to request the status of the running job. This issue might be on the cluster itself, or HDInsight service might have an outage.
Refer to HDInsight troubleshooting documentation at https://docs.microsoft.com/azure/hdinsight/hdinsight-troubleshoot-guide, or contact their support for further assistance.
-
Message:
Hadoop job failed with exit code '%exitCode;'. See '%logPath;/stderr' for more details. Alternatively, open the Ambari UI on the HDI cluster and find the logs for the job '%jobId;'. Contact HDInsight team for further support.
-
Cause: The job was submitted to the HDI cluster and failed there.
-
Recommendation:
- Check Ambari UI:
- Ensure that all services are still running.
- From Ambari UI, check the alert section in your dashboard.
- For more information on alerts and resolutions to alerts, see Managing and Monitoring a Cluster.
- Review your YARN memory. If your YARN memory is high, the processing of your jobs may be delayed. If you do not have enough resources to accommodate your Spark application/job, scale up the cluster to ensure the cluster has enough memory and cores.
- Run a Sample test job.
- If you run the same job on HDInsight backend, check that it succeeded. For examples of sample runs, see Run the MapReduce examples included in HDInsight
- If the job still failed on HDInsight, check the application logs and information, which to provide to Support:
- Check whether the job was submitted to YARN. If the job wasn't submitted to yarn, use
--master yarn
. - If the application finished execution, collect the start time and end time of the YARN Application. If the application didn't complete the execution, collect Start time/Launch time.
- Check and collect application log with
yarn logs -applicationId <Insert_Your_Application_ID>
. - Check and collect the yarn Resource Manager logs under the
/var/log/hadoop-yarn/yarn
directory. - If these steps are not enough to resolve the issue, contact Azure HDInsight team for support and provide the above logs and timestamps.
- Check whether the job was submitted to YARN. If the job wasn't submitted to yarn, use
-
Message:
Hadoop job failed with transient exit code '%exitCode;'. See '%logPath;/stderr' for more details. Alternatively, open the Ambari UI on the HDI cluster and find the logs for the job '%jobId;'. Try again or contact HDInsight team for further support.
-
Cause: The job was submitted to the HDI cluster and failed there.
-
Recommendation:
- Check Ambari UI:
- Ensure that all services are still running.
- From Ambari UI, check the alert section in your dashboard.
- For more information on alerts and resolutions to alerts, see Managing and Monitoring a Cluster.
- Review your YARN memory. If your YARN memory is high, the processing of your jobs may be delayed. If you do not have enough resources to accommodate your Spark application/job, scale up the cluster to ensure the cluster has enough memory and cores.
- Run a Sample test job.
- If you run the same job on HDInsight backend, check that it succeeded. For examples of sample runs, see Run the MapReduce examples included in HDInsight
- If the job still failed on HDInsight, check the application logs and information, which to provide to Support:
- Check whether the job was submitted to YARN. If the job wasn't submitted to yarn, use
--master yarn
. - If the application finished execution, collect the start time and end time of the YARN Application. If the application didn't complete the execution, collect Start time/Launch time.
- Check and collect application log with
yarn logs -applicationId <Insert_Your_Application_ID>
. - Check and collect the yarn Resource Manager logs under the
/var/log/hadoop-yarn/yarn
directory. - If these steps are not enough to resolve the issue, contact Azure HDInsight team for support and provide the above logs and timestamps.
- Check whether the job was submitted to YARN. If the job wasn't submitted to yarn, use
-
Message:
MSI authentication is not supported on storages for HDI activities.
-
Cause: The storage linked services used in the HDInsight (HDI) linked service or HDI activity, are configured with an MSI authentication that isn't supported.
-
Recommendation: Provide full connection strings for storage accounts used in the HDI linked service or HDI activity.
-
Message:
Failed to initialize the HDInsight client for the cluster '%cluster;'. Error: '%message;'
-
Cause: The connection information for the HDI cluster is incorrect, the provided user doesn't have permissions to perform the required action, or the HDInsight service has issues responding to requests from ADF.
-
Recommendation: Verify that the user information is correct, and that the Ambari UI for the HDI cluster can be opened in a browser from the VM where the IR is installed (for a self-hosted IR), or can be opened from any machine (for Azure IR).
-
Message:
An invalid json is provided for script action '%scriptActionName;'. Error: '%message;'
-
Cause: The JSON provided for the script action is invalid.
-
Recommendation: The error message should help to identify the issue. Fix the json configuration and try again.
Check Azure HDInsight on-demand linked service for more information.
-
Message:
Failed to submit Spark job. Error: '%message;'
-
Cause: ADF tried to create a batch on a Spark cluster using Livy API (livy/batch), but received an error.
-
Recommendation: Follow the error message to fix the issue. If there isn't enough information to get it resolved, contact the HDI team and provide them the batch ID and job ID, which can be found in the activity run Output in ADF Monitoring page. To troubleshoot further, collect the full log of the batch job.
For more information on how to collect the full log, see Get the full log of a batch job.
-
Message:
Spark job failed, batch id:%batchId;. Please follow the links in the activity run Output from ADF Monitoring page to troubleshoot the run on HDInsight Spark cluster. Please contact HDInsight support team for further assistance.
-
Cause: The job failed on the HDInsight Spark cluster.
-
Recommendation: Follow the links in the activity run Output in ADF Monitoring page to troubleshoot the run on HDInsight Spark cluster. Contact HDInsight support team for further assistance.
For more information on how to collect the full log, see Get the full log of a batch job.
-
Message:
The batch with ID '%batchId;' was not found on Spark cluster. Open the Spark History UI and try to find it there. Contact HDInsight support for further assistance.
-
Cause: The batch was deleted on the HDInsight Spark cluster.
-
Recommendation: Troubleshoot batches on the HDInsight Spark cluster. Contact HDInsight support for further assistance.
For more information on how to collect the full log, see Get the full log of a batch job, and share the full log with HDInsight support for further assistance.
-
Message:
Failed to create the on demand HDI cluster. Cluster or linked service name: '%clusterName;', error: '%message;'
-
Cause: The error message should show the details of what went wrong.
-
Recommendation: The error message should help to troubleshoot the issue.
-
Message:
Failed to delete the on demand HDI cluster. Cluster or linked service name: '%clusterName;', error: '%message;'
-
Cause: The error message should show the details of what went wrong.
-
Recommendation: The error message should help to troubleshoot the issue.
-
Message:
The file path should not be null or empty.
-
Cause: The provided file path is empty.
-
Recommendation: Provide a path for a file that exists.
-
Message:
HDInsightOnDemand linked service does not support execution via SelfHosted IR. Your IR name is '%IRName;'. Please select an Azure IR instead.
-
Cause: The HDInsightOnDemand linked service doesn't support execution via SelfHosted IR.
-
Recommendation: Select an Azure IR and try again.
-
Message:
HDInsight cluster URL '%clusterUrl;' is incorrect, it must be in URI format and the scheme must be 'https'.
-
Cause: The provided URL isn't in correct format.
-
Recommendation: Fix the cluster URL and try again.
-
Message:
Failed to connect to HDInsight cluster: '%errorMessage;'.
-
Cause: Either the provided credentials are wrong for the cluster, or there was a network configuration or connection issue, or the IR is having problems connecting to the cluster.
-
Recommendation:
-
Verify that the credentials are correct by opening the HDInsight cluster's Ambari UI in a browser.
-
If the cluster is in Virtual Network (VNet) and a self-hosted IR is being used, the HDI URL must be the private URL in VNets, and should have '-int' listed after the cluster name.
For example, change
https://mycluster.azurehdinsight.net/
tohttps://mycluster-int.azurehdinsight.net/
. Note the-int
aftermycluster
, but before.azurehdinsight.net
-
If the cluster is in VNet, the self-hosted IR is being used, and the private URL was used, and yet the connection still failed, then the VM where the IR is installed had problems connecting to the HDI.
Connect to the VM where the IR is installed and open the Ambari UI in a browser. Use the private URL for the cluster. This connection should work from the browser. If it doesn't, contact HDInsight support team for further assistance.
-
If self-hosted IR isn't being used, then the HDI cluster should be accessible publicly. Open the Ambari UI in a browser and check that it opens up. If there are any issues with the cluster or the services on it, contact HDInsight support team for assistance.
The HDI cluster URL used in ADF linked service must be accessible for ADF IR (self-hosted or Azure) in order for the test connection to pass, and for runs to work. This state can be verified by opening the URL from a browser either from VM, or from any public machine.
-
-
Message:
User name and password cannot be null or empty to connect to the HDInsight cluster.
-
Cause: Either the user name or the password is empty.
-
Recommendation: Provide the correct credentials to connect to HDI and try again.
-
Message:
Failed to read the content of the hive script. Error: '%message;'
-
Cause: The script file doesn't exist or ADF couldn't connect to the location of the script.
-
Recommendation: Verify that the script exists, and that the associated linked service has the proper credentials for a connection.
-
Message:
Failed to create ODBC connection to the HDI cluster with error message '%message;'.
-
Cause: ADF tried to establish an Open Database Connectivity (ODBC) connection to the HDI cluster, and it failed with an error.
-
Recommendation:
- Confirm that you correctly set up your ODBC/Java Database Connectivity (JDBC) connection.
- For JDBC, if you're using the same virtual network, you can get this connection from:
Hive -> Summary -> HIVESERVER2 JDBC URL
- To ensure that you have the correct JDBC set up, see Query Apache Hive through the JDBC driver in HDInsight.
- For Open Database (ODB), see Tutorial: Query Apache Hive with ODBC and PowerShell to ensure that you have the correct setup.
- For JDBC, if you're using the same virtual network, you can get this connection from:
- Verify that Hiveserver2, Hive Metastore, and Hiveserver2 Interactive are active and working.
- Check the Ambari user interface (UI):
- Ensure that all services are still running.
- From the Ambari UI, check the alert section in your dashboard.
- For more information on alerts and resolutions to alerts, see Managing and Monitoring a Cluster .
- If these steps are not enough to resolve the issue, contact the Azure HDInsight team.
- Confirm that you correctly set up your ODBC/Java Database Connectivity (JDBC) connection.
-
Message:
Hive execution through ODBC failed with error message '%message;'.
-
Cause: ADF submitted the hive script for execution to the HDI cluster via ODBC connection, and the script has failed on HDI.
-
Recommendation:
- Confirm that you correctly set up your ODBC/Java Database Connectivity (JDBC) connection.
- For JDBC, if you're using the same virtual network, you can get this connection from:
Hive -> Summary -> HIVESERVER2 JDBC URL
- To ensure that you have the correct JDBC set up, see Query Apache Hive through the JDBC driver in HDInsight.
- For Open Database (ODB), see Tutorial: Query Apache Hive with ODBC and PowerShell to ensure that you have the correct setup.
- For JDBC, if you're using the same virtual network, you can get this connection from:
- Verify that Hiveserver2, Hive Metastore, and Hiveserver2 Interactive are active and working.
- Check the Ambari user interface (UI):
- Ensure that all services are still running.
- From the Ambari UI, check the alert section in your dashboard.
- For more information on alerts and resolutions to alerts, see Managing and Monitoring a Cluster .
- If these steps are not enough to resolve the issue, contact the Azure HDInsight team.
- Confirm that you correctly set up your ODBC/Java Database Connectivity (JDBC) connection.
-
Message:
The main storage has not been initialized. Please check the properties of the storage linked service in the HDI linked service.
-
Cause: The storage linked service properties are not set correctly.
-
Recommendation: Only full connection strings are supported in the main storage linked service for HDI activities. Verify that you are not using MSI authorizations or applications.
-
Message:
Failed to prepare the files for the run '%jobId;'. HDI cluster: '%cluster;', Error: '%errorMessage;'
-
Cause: The credentials provided to connect to the storage where the files should be located are incorrect, or the files do not exist there.
-
Recommendation: This error occurs when ADF prepares for HDI activities, and tries to copy files to the main storage before submitting the job to HDI. Check that files exist in the provided location, and that the storage connection is correct. As ADF HDI activities do not support MSI authentication on storage accounts related to HDI activities, verify that those linked services have full keys or are using Azure Key Vault.
-
Message:
Could not open the file '%filePath;' in container/fileSystem '%container;'.
-
Cause: The file doesn't exist at specified path.
-
Recommendation: Check whether the file actually exists, and that the linked service with connection info pointing to this file has the correct credentials.
-
Message:
The file storage has not been initialized. Please check the properties of the file storage linked service in the HDI activity.
-
Cause: The file storage linked service properties are not set correctly.
-
Recommendation: Verify that the properties of the file storage linked service are properly configured.
-
Message:
The script storage has not been initialized. Please check the properties of the script storage linked service in the HDI activity.
-
Cause: The script storage linked service properties are not set correctly.
-
Recommendation: Verify that the properties of the script storage linked service are properly configured.
-
Message:
The storage linked service type '%linkedServiceType;' is not supported for '%executorType;' activities for property '%linkedServicePropertyName;'.
-
Cause: The storage linked service type isn't supported by the activity.
-
Recommendation: Verify that the selected linked service has one of the supported types for the activity. HDI activities support AzureBlobStorage and AzureBlobFSStorage linked services.
For more information, read Compare storage options for use with Azure HDInsight clusters
-
Message:
The '%value' provided for commandEnvironment is incorrect. The expected value should be an array of strings where each string has the format CmdEnvVarName=CmdEnvVarValue.
-
Cause: The provided value for
commandEnvironment
is incorrect. -
Recommendation: Verify that the provided value is similar to:
\"variableName=variableValue\" ]
Also verify that each variable appears in the list only once.
-
Message:
The commandEnvironment already contains a variable named '%variableName;'.
-
Cause: The provided value for
commandEnvironment
is incorrect. -
Recommendation: Verify that the provided value is similar to:
\"variableName=variableValue\" ]
Also verify that each variable appears in the list only once.
-
Message:
The certificate or password is wrong for ADLS Gen 1 storage.
-
Cause: The provided credentials are incorrect.
-
Recommendation: Verify that the connection information in ADLS Gen 1 linked to the service, and verify that the test connection succeeds.
-
Message:
The value '%value;' for the required property 'TimeToLive' in the on demand HDInsight linked service '%linkedServiceName;' has invalid format. It should be a timespan between '00:05:00' and '24:00:00'.
-
Cause: The provided value for the required property
TimeToLive
has an invalid format. -
Recommendation: Update the value to the suggested range and try again.
-
Message:
The value '%value;' for the property 'roles' is invalid. Expected types are 'zookeeper', 'headnode', and 'workernode'.
-
Cause: The provided value for the property
roles
is invalid. -
Recommendation: Update the value to be one of the suggestions and try again.
-
Message:
The connection string in HCatalogLinkedService is invalid. Encountered an error while trying to parse: '%message;'.
-
Cause: The provided connection string for the
HCatalogLinkedService
is invalid. -
Recommendation: Update the value to a correct Azure SQL connection string and try again.
-
Message:
Failed to create on demand HDI cluster. Cluster name is '%clusterName;'.
-
Cause: The cluster creation failed, and ADF did not get an error back from HDInsight service.
-
Recommendation: Open the Azure portal and try to find the HDI resource with provided name, then check the provisioning status. Contact HDInsight support team for further assistance.
-
Message:
Only Azure Blob storage accounts are supported as additional storages for HDInsight on demand linked service.
-
Cause: The provided additional storage was not Azure Blob storage.
-
Recommendation: Provide an Azure Blob storage account as an additional storage for HDInsight on-demand linked service.
-
Message:
No response from the endpoint. Possible causes: network connectivity, DNS failure, server certificate validation or timeout.
-
Cause: This issue is due to either Network connectivity, a DNS failure, a server certificate validation, or a timeout.
-
Recommendation: Validate that the endpoint you are trying to hit is responding to requests. You may use tools like Fiddler/Postman.
-
Message:
Error calling the endpoint '%url;'. Response status code: '%code;'
-
Cause: The request failed due to an underlying issue such as network connectivity, a DNS failure, a server certificate validation, or a timeout.
-
Recommendation: Use Fiddler/Postman to validate the request.
To use Fiddler to create an HTTP session of the monitored web application:
-
Download, install, and open Fiddler.
-
If your web application uses HTTPS, go to Tools > Fiddler Options > HTTPS.
-
If your application uses TLS/SSL certificates, add the Fiddler certificate to your device.
Go to: Tools > Fiddler Options > HTTPS > Actions > Export Root Certificate to Desktop.
-
Turn off capturing by going to File > Capture Traffic. Or press F12.
-
Clear your browser's cache so that all cached items are removed and must be downloaded again.
-
Create a request:
-
Select the Composer tab.
-
Set the HTTP method and URL.
-
If needed, add headers and a request body.
-
Select Execute.
-
-
Turn on traffic capturing again, and complete the problematic transaction on your page.
-
Go to: File > Save > All Sessions.
For more information, see Getting started with Fiddler.
When you observe that the activity is running much longer than your normal runs with barely no progress, it may happen to be stuck. You can try canceling it and retry to see if it helps. If it’s a copy activity, you can learn about the performance monitoring and troubleshooting from Troubleshoot copy activity performance; if it’s a data flow, learn from Mapping data flows performance and tuning guide.
Error message: The payload including configurations on activity/dataSet/linked service is too large. Please check if you have settings with very large value and try to reduce its size.
Cause: The payload for each activity run includes the activity configuration, the associated dataset(s) and linked service(s) configurations if any, and a small portion of system properties generated per activity type. The limit of such payload size is 896KB as mentioned in Data Factory limits section.
Recommendation: You hit this limit likely because you pass in one or more large parameter values from either upstream activity output or external, especially if you pass actual data across activities in control flow. Please check if you can reduce the size of large parameter values, or tune your pipeline logic to avoid passing such values across activities and handle it inside the activity instead.
For more troubleshooting help, try these resources: