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Troubleshooting managed feature store

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In this article, learn how to troubleshoot common problems you might encounter with the managed feature store in Azure Machine Learning.

Issues found when creating and updating a feature store

You might encounter these issues when you create or update a feature store:

ARM Throttling Error

Symptom

Feature store creation or update fails. The error might look like this:

{
  "error": {
    "code": "TooManyRequests",
    "message": "The request is being throttled as the limit has been reached for operation type - 'Write'. ..",
    "details": [
      {
        "code": "TooManyRequests",
        "target": "Microsoft.MachineLearningServices/workspaces",
        "message": "..."
      }
    ]
  }
}

Solution

Run the feature store create/update operation at a later time. Since the deployment occurs in multiple steps, the second attempt might fail because some of the resources already exist. Delete those resources and resume the job.

RBAC permission errors

To create a feature store, the user needs the Contributor and User Access Administrator roles (or a custom role that covers the same set, or a super set, of the actions).

Symptom

If the user doesn't have the required roles, the deployment fails. The error response might look like the following one

{
  "error": {
    "code": "AuthorizationFailed",
    "message": "The client '{client_id}' with object id '{object_id}' does not have authorization to perform action '{action_name}' over scope '{scope}' or the scope is invalid. If access was recently granted, please refresh your credentials."
  }
}

Solution

Grant the Contributor and User Access Administrator roles to the user on the resource group where the feature store is to be created. Then, instruct the user to run the deployment again.

For more information, see Permissions required for the feature store materialization managed identity role.

Duplicated materialization identity ARM ID issue

Once the feature store is updated to enable materialization for the first time, some later updates on the feature store might result in this error.

Symptom

When the feature store is updated using the SDK/CLI, the update fails with this error message:

Error:

{
  "error":{
    "code": "InvalidRequestContent",
    "message": "The request content contains duplicate JSON property names creating ambiguity in paths 'identity.userAssignedIdentities['/subscriptions/{sub-id}/resourceGroups/{rg}/providers/Microsoft.ManagedIdentity/userAssignedIdentities/{your-uai}']'. Please update the request content to eliminate duplicates and try again."
  }
}

Solution

The issue involves the ARM ID of the materialization_identity ARM ID format.

From the Azure UI or SDK, the ARM ID of the user-assigned managed identity uses lower case resourcegroups. See this example:

  • (A): /subscriptions/{sub-id}/resourcegroups/{rg}/providers/Microsoft.ManagedIdentity/userAssignedIdentities/{your-uai}

When the feature store uses the user-assigned managed identity as its materialization_identity, its ARM ID is normalized and stored, with resourceGroups. See this example:

  • (B): /subscriptions/{sub-id}/resourceGroups/{rg}/providers/Microsoft.ManagedIdentity/userAssignedIdentities/{your-uai}

In the update request, you might use a user-assigned identity that matches the materialization identity, to update the feature store. When you use that managed identity for that purpose, while using the ARM ID in format (A), the update fails and it returns the earlier error message.

To fix the issue, replace the string resourcegroups with resourceGroups in the user-assigned managed identity ARM ID. Then, run the feature store update again.

Older versions of azure-mgmt-authorization package don't work with AzureMLOnBehalfOfCredential

Symptom

When you use the setup_storage_uai script provided in the featurestore_sample folder in the azureml-examples repository, the script fails with this error message:

AttributeError: 'AzureMLOnBehalfOfCredential' object has no attribute 'signed_session'

Solution:

Check the version of the installed azure-mgmt-authorization package, and verify that you're using a recent version, at least 3.0.0 or later. An old version, for example 0.61.0, doesn't work with AzureMLOnBehalfOfCredential.

Feature Set Spec Create Errors

Invalid schema in feature set spec

Before you register a feature set into the feature store, define the feature set spec locally, and run <feature_set_spec>.to_spark_dataframe() to validate it.

Symptom

When a user runs <feature_set_spec>.to_spark_dataframe(), various schema validation failures can occur if the feature set dataframe schema isn't aligned with the feature set spec definition.

For example:

  • Error message: azure.ai.ml.exceptions.ValidationException: Schema check errors, timestamp column: timestamp is not in output dataframe
  • Error message: Exception: Schema check errors, no index column: accountID in output dataframe
  • Error message: ValidationException: Schema check errors, feature column: transaction_7d_count has data type: ColumnType.long, expected: ColumnType.string

Solution

Check the schema validation failure error, and update the feature set spec definition accordingly, for both the column names and types. For examples:

  • update the source.timestamp_column.name property to correctly define the timestamp column names.
  • update the index_columns property to correctly define the index columns.
  • update the features property to correctly define the feature column names and types.
  • if the feature source data is of type csv, verify that the CSV files are generated with column headers.

Next, run <feature_set_spec>.to_spark_dataframe() again to check if the validation passed.

If the SDK defines the feature set spec, the infer_schema option is also recommended as the preferred way to autofill the features, instead of manually typing in the values. The timestamp_column and index columns can't be autofilled.

For more information, see the Feature Set Spec schema document.

Can't find the transformation class

Symptom

When a user runs <feature_set_spec>.to_spark_dataframe(), it returns this error: AttributeError: module '<...>' has no attribute '<...>'

For example:

  • AttributeError: module '7780d27aa8364270b6b61fed2a43b749.transaction_transform' has no attribute 'TransactionFeatureTransformer1'

Solution

The feature transformation class is expected to have its definition in a Python file under the root of the code folder. The code folder can have other files or sub folders.

Set the value of the feature_transformation_code.transformation_class property to <py file name of the transformation class>.<transformation class name>.

For example, if the code folder looks like this

code/
└── my_transformation_class.py

and the my_transformation_class.py file defines the MyFeatureTransformer class, set feature_transformation_code.transformation_class to be my_transformation_class.MyFeatureTransformer

FileNotFoundError on code folder

Symptom

If the feature set spec YAML is manually created, and the SDK doesn't generate the feature set, the error can happen. The command runs <feature_set_spec>.to_spark_dataframe() returns error FileNotFoundError: [Errno 2] No such file or directory: ....

Solution

Check the code folder. It should be a subfolder under the feature set spec folder. In the feature set spec, set feature_transformation_code.path as a relative path to the feature set spec folder. For example:

feature set spec folder/
├── code/
│ ├── my_transformer.py
│ └── my_orther_folder
└── FeatureSetSpec.yaml

In this example, the feature_transformation_code.path property in the YAML should be ./code

Note

When you use create_feature_set_spec function in azureml-featurestore to createa FeatureSetSpec python object, it can take any local folder as the feature_transformation_code.path value. When the FeatureSetSpec object is dumped to form a feature set spec yaml in a target folder, the code path will be copied into the target folder, and the feature_transformation_code.path property updated in the spec yaml.

Feature set CRUD Errors

Feature set GET fails due to invalid FeatureStoreEntity

Symptom

When you use the feature store CRUD client to GET a feature set - for example, fs_client.feature_sets.get(name, version)"` - you might see this error:


Traceback (most recent call last):

  File "/home/trusted-service-user/cluster-env/env/lib/python3.8/site-packages/azure/ai/ml/operations/_feature_store_entity_operations.py", line 116, in get

    return FeatureStoreEntity._from_rest_object(feature_store_entity_version_resource)

  File "/home/trusted-service-user/cluster-env/env/lib/python3.8/site-packages/azure/ai/ml/entities/_feature_store_entity/feature_store_entity.py", line 93, in _from_rest_object

    featurestoreEntity = FeatureStoreEntity(

  File "/home/trusted-service-user/cluster-env/env/lib/python3.8/site-packages/azure/ai/ml/_utils/_experimental.py", line 42, in wrapped

    return func(*args, **kwargs)

  File "/home/trusted-service-user/cluster-env/env/lib/python3.8/site-packages/azure/ai/ml/entities/_feature_store_entity/feature_store_entity.py", line 67, in __init__

    raise ValidationException(

azure.ai.ml.exceptions.ValidationException: Stage must be Development, Production, or Archived, found None

This error can also happen in the FeatureStore materialization job, where the job fails with the same error trace back.

Solution

Start a notebook session with the new version of SDKS

  • If it uses azure-ai-ml, update to azure-ai-ml==1.8.0.
  • If it uses the feature store dataplane SDK, update it to azureml-featurestore== 0.1.0b2.

In the notebook session, update the feature store entity to set its stage property, as shown in this example:

from azure.ai.ml.entities import DataColumn, DataColumnType
 
account_entity_config = FeatureStoreEntity(

    name="account",

    version="1",

    index_columns=[DataColumn(name="accountID", type=DataColumnType.STRING)],

    stage="Development",

    description="This entity represents user account index key accountID.",

    tags={"data_typ": "nonPII"},

)

poller = fs_client.feature_store_entities.begin_create_or_update(account_entity_config)

print(poller.result())

When you define the FeatureStoreEntity, set the properties to match the properties used when it was created. The only difference is to add the stage property.

Once the begin_create_or_update() call returns successfully, the next feature_sets.get() call and the next materialization job should succeed.

Feature Retrieval job and query errors

When a feature retrieval job fails, check the error details. Go to the run detail page, select the Outputs + logs tab, and examine the logs/azureml/driver/stdout file.

If user runs the get_offline_feature() query in the notebook, cell outputs directly show the error.

Feature retrieval specification resolution errors

Symptom

The feature retrieval query/job shows these errors:

  • Invalid feature
code: "UserError"
mesasge: "Feature '<some name>' not found in this featureset."
  • Invalid feature store URI:
message: "the Resource 'Microsoft.MachineLearningServices/workspaces/<name>' under resource group '<>>resource group name>'->' was not found. For more details please go to https://aka.ms/ARMResourceNotFoundFix",
code: "ResourceNotFound"
  • Invalid feature set:
code: "UserError"
message: "Featureset with name: <name >and version: <version> not found."

Solution

Check the content in the feature_retrieval_spec.yaml that the job uses. Make sure all the feature store URI, feature set name/version, and feature names are valid and exist in the feature store.

To select features from a feature store, and generate the feature retrieval spec YAML file, use of the utility function is recommended.

This code snippet uses the generate_feature_retrieval_spec utility function.

from azureml.featurestore import FeatureStoreClient
from azure.ai.ml.identity import AzureMLOnBehalfOfCredential

featurestore = FeatureStoreClient(
credential = AzureMLOnBehalfOfCredential(),
subscription_id = featurestore_subscription_id,
resource_group_name = featurestore_resource_group_name,
name = featurestore_name
)

transactions_featureset = featurestore.feature_sets.get(name="transactions", version = "1")

features = [
    transactions_featureset.get_feature('transaction_amount_7d_sum'),
    transactions_featureset.get_feature('transaction_amount_3d_sum')
]

feature_retrieval_spec_folder = "./project/fraud_model/feature_retrieval_spec"
featurestore.generate_feature_retrieval_spec(feature_retrieval_spec_folder, features)

File feature_retrieval_spec.yaml not found when using a model as input to the feature retrieval job

Symptom

When you use a registered model as a feature retrieval job input, the job fails with this error:

ValueError: Failed with visit error: Failed with execution error: error in streaming from input data sources
    VisitError(ExecutionError(StreamError(NotFound)))
=> Failed with execution error: error in streaming from input data sources
    ExecutionError(StreamError(NotFound)); Not able to find path: azureml://subscriptions/{sub_id}/resourcegroups/{rg}/workspaces/{ws}/datastores/workspaceblobstore/paths/LocalUpload/{guid}/feature_retrieval_spec.yaml

Solution:

When you provide a model as input to the feature retrieval step, the model expects to find the retrieval spec YAML file under the model artifact folder. The job fails if that file is missing.

To fix the issue, package the feature_retrieval_spec.yaml in the root folder of the model artifact folder before registering the model.

Observation Data isn't joined with any feature values

Symptom

After users run the feature retrieval query/job, the output data gets no feature values. For example, a user runs the feature retrieval job to retrieve features transaction_amount_3d_avg and transaction_amount_7d_avg with these results:

transactionID accountID timestamp is_fraud transaction_amount_3d_avg transaction_amount_7d_avg
83870774-7A98-43B... A1055520444618950 2023-02-28 04:34:27 0 null null
25144265-F68B-4FD... A1055520444618950 2023-02-28 10:44:30 0 null null
8899ED8C-B295-43F... A1055520444812380 2023-03-06 00:36:30 0 null null

Solution

Feature retrieval does a point-in-time join query. If the join result shows empty, try these potential solutions:

  • Either extend the temporal_join_lookback range in the feature set spec definition, or temporarily remove it. This allows the point-in-time join to look back further (or infinitely) into the past, before the observation event time stamp, to find the feature values.
  • If source.source_delay is also set in the feature set spec definition, make sure that temporal_join_lookback > source.source_delay.

If none of these solutions work, get the feature set from feature store, and run <feature_set>.to_spark_dataframe() to manually inspect the feature index columns and timestamps. The failure could happen because:

  • the index values in the observation data don't exist in the feature set dataframe
  • no feature value, with a timestamp value before the observation timestamp, exists.

In these cases, if the feature enabled offline materialization, you might need to backfill more feature data.

User or managed identity doesn't have proper RBAC permission on the feature store

Symptom:

The feature retrieval job/query fails with this error message in the logs/azureml/driver/stdout file:

Traceback (most recent call last):
  File "/home/trusted-service-user/cluster-env/env/lib/python3.8/site-packages/azure/ai/ml/_restclient/v2022_12_01_preview/operations/_workspaces_operations.py", line 633, in get
    raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
azure.core.exceptions.HttpResponseError: (AuthorizationFailed) The client 'XXXX' with object id 'XXXX' does not have authorization to perform action 'Microsoft.MachineLearningServices/workspaces/read' over scope '/subscriptions/XXXX/resourceGroups/XXXX/providers/Microsoft.MachineLearningServices/workspaces/XXXX' or the scope is invalid. If access was recently granted, please refresh your credentials.
Code: AuthorizationFailed

Solution:

  1. If the feature retrieval job uses a managed identity, assign the AzureML Data Scientist role on the feature store to the identity.
  2. If the problem happens when
  • the user runs code in an Azure Machine Learning Spark notebook
  • that notebook uses the user's own identity to access the Azure Machine Learning service

assign the AzureML Data Scientist role on the feature store to the user's Microsoft Entra identity.

Azure Machine Learning Data Scientist is a recommended role. User can create their own custom role with the following actions

  • Microsoft.MachineLearningServices/workspaces/datastores/listsecrets/action
  • Microsoft.MachineLearningServices/workspaces/featuresets/read
  • Microsoft.MachineLearningServices/workspaces/read

For more information about RBAC setup, see Manage access to managed feature store.

User or Managed Identity doesn't have proper RBAC permission to Read from the Source Storage or Offline store

Symptom

The feature retrieval job/query fails with the following error message in the logs/azureml/driver/stdout file:

An error occurred while calling o1025.parquet.
: java.nio.file.AccessDeniedException: Operation failed: "This request is not authorized to perform this operation using this permission.", 403, GET, https://{storage}.dfs.core.windows.net/test?upn=false&resource=filesystem&maxResults=5000&directory=datasources&timeout=90&recursive=false, AuthorizationPermissionMismatch, "This request is not authorized to perform this operation using this permission. RequestId:63013315-e01f-005e-577b-7c63b8000000 Time:2023-05-01T22:20:51.1064935Z"
    at org.apache.hadoop.fs.azurebfs.AzureBlobFileSystem.checkException(AzureBlobFileSystem.java:1203)
    at org.apache.hadoop.fs.azurebfs.AzureBlobFileSystem.listStatus(AzureBlobFileSystem.java:408)
    at org.apache.hadoop.fs.Globber.listStatus(Globber.java:128)
    at org.apache.hadoop.fs.Globber.doGlob(Globber.java:291)
    at org.apache.hadoop.fs.Globber.glob(Globber.java:202)
    at org.apache.hadoop.fs.FileSystem.globStatus(FileSystem.java:2124)

Solution:

  • If the feature retrieval job uses a managed identity, assign the Storage Blob Data Reader role on the source storage, and offline store storage, to the identity.
  • This error happens when the notebook uses the user's identity to access the Azure Machine Learning service to run the query. To resolve the error, assign the Storage Blob Data Reader role to the user's identity on the source storage and offline store storage account.

Storage Blob Data Reader is the minimum recommended access requirement. Users can also assign roles - for example, Storage Blob Data Contributor or Storage Blob Data Owner - with more privileges.

Training job fails to read data generated by the build-in Feature Retrieval Component

Symptom

A training job fails with the error message that the training data doesn't exist, the format is incorrect, or there's a parser error:

FileNotFoundError: [Errno 2] No such file or directory
  • format isn't correct.
ParserError:

Solution

The built-in feature retrieval component has one output, output_data. The output data is a uri_folder data asset. It always has this folder structure:

<training data folder>/
├── data/
│ ├── xxxxx.parquet
│ └── xxxxx.parquet
└── feature_retrieval_spec.yaml

The output data is always in parquet format. Update the training script to read from the "data" sub folder, and read the data as parquet.

generate_feature_retrieval_spec() fails due to use of local feature set specification

Symptom:

This python code generates a feature retrieval spec on a given list of features:

featurestore.generate_feature_retrieval_spec(feature_retrieval_spec_folder, features)

If the features list contains features defined by a local feature set specification, the generate_feature_retrieval_spec() fails with this error message:

AttributeError: 'FeatureSetSpec' object has no attribute 'id'

Solution:

A feature retrieval spec can only be generated using feature sets registered in Feature Store. To fix the problem:

  • Register the local feature set specification as a feature set in the feature store
  • Get the registered feature set
  • Create feature lists again using only features from registered feature sets
  • Generate the feature retrieval spec using the new features list

The get_offline_features() query takes a long time

Symptom:

Running get_offline_features to generate training data, using a few features from feature store, takes too long to finish.

Solutions:

Check these configurations:

  • Verify that each feature set used in the query, has temporal_join_lookback set in the feature set specification. Set its value to a smaller value.
  • If the size and timestamp window on the observation dataframe are large, configure the notebook session (or the job) to increase the size (memory and core) of the driver and executor. Additionally, increase the number of executors.

Feature Materialization Job Errors

When the feature materialization job fails, follow these steps to check the job failure details:

  1. Navigate to the feature store page: https://ml.azure.com/featureStore/{your-feature-store-name}.
  2. Go to the feature set tab, select the relevant feature set, and navigate to the Feature set detail page.
  3. From feature set detail page, select the Materialization jobs tab, then select the failed job to open it in the job details view.
  4. On the job detail view, under the Properties card, review the job status and error message.
  5. You can also go to the Outputs + logs tab, then find the stdout file from the logs\azureml\driver\stdout file.

After a fix is applied, you can manually trigger a backfill materialization job to verify that the fix works.

Invalid Offline Store Configuration

Symptom

The materialization job fails with this error message in the logs/azureml/driver/stdout file:

Caused by: Status code: -1 error code: null error message: InvalidAbfsRestOperationExceptionjava.net.UnknownHostException: adlgen23.dfs.core.windows.net

java.util.concurrent.ExecutionException: Operation failed: "The specified resource name contains invalid characters.", 400, HEAD, https://{storage}.dfs.core.windows.net/{container-name}/{fs-id}/transactions/1/_delta_log?upn=false&action=getStatus&timeout=90

Solution

Use the SDK to check the offline storage target defined in the feature store:


from azure.ai.ml import MLClient
from azure.ai.ml.identity import AzureMLOnBehalfOfCredential

fs_client = MLClient(AzureMLOnBehalfOfCredential(), featurestore_subscription_id, featurestore_resource_group_name, featurestore_name)

featurestore = fs_client.feature_stores.get(name=featurestore_name)
featurestore.offline_store.target

You can also check the offline storage target on the feature store UI overview page. Verify that both the storage and container exist, and that the target has this format:

/subscriptions/{sub-id}/resourceGroups/{rg}/providers/Microsoft.Storage/storageAccounts/{storage}/blobServices/default/containers/{container-name}

Materialization Identity doesn't have proper RBAC permission on the feature store

Symptom:

The materialization job fails with this error message in the logs/azureml/driver/stdout file:

Traceback (most recent call last):
  File "/home/trusted-service-user/cluster-env/env/lib/python3.8/site-packages/azure/ai/ml/_restclient/v2022_12_01_preview/operations/_workspaces_operations.py", line 633, in get
    raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
azure.core.exceptions.HttpResponseError: (AuthorizationFailed) The client 'XXXX' with object id 'XXXX' does not have authorization to perform action 'Microsoft.MachineLearningServices/workspaces/read' over scope '/subscriptions/XXXX/resourceGroups/XXXX/providers/Microsoft.MachineLearningServices/workspaces/XXXX' or the scope is invalid. If access was recently granted, please refresh your credentials.
Code: AuthorizationFailed

Solution:

Assign the Azure Machine Learning Data Scientist role on the feature store to the materialization identity (a user assigned managed identity) of the feature store.

Azure Machine Learning Data Scientist is a recommended role. You can create your own custom role with these actions:

  • Microsoft.MachineLearningServices/workspaces/datastores/listsecrets/action
  • Microsoft.MachineLearningServices/workspaces/featuresets/read
  • Microsoft.MachineLearningServices/workspaces/read

For more information, see Permissions required for the feature store materialization managed identity role.

Materialization identity doesn't have proper RBAC permission to read from the storage

Symptom

The materialization job fails with this error message in the logs/azureml/driver/stdout file:

An error occurred while calling o1025.parquet.
: java.nio.file.AccessDeniedException: Operation failed: "This request is not authorized to perform this operation using this permission.", 403, GET, https://{storage}.dfs.core.windows.net/test?upn=false&resource=filesystem&maxResults=5000&directory=datasources&timeout=90&recursive=false, AuthorizationPermissionMismatch, "This request is not authorized to perform this operation using this permission. RequestId:63013315-e01f-005e-577b-7c63b8000000 Time:2023-05-01T22:20:51.1064935Z"
    at org.apache.hadoop.fs.azurebfs.AzureBlobFileSystem.checkException(AzureBlobFileSystem.java:1203)
    at org.apache.hadoop.fs.azurebfs.AzureBlobFileSystem.listStatus(AzureBlobFileSystem.java:408)
    at org.apache.hadoop.fs.Globber.listStatus(Globber.java:128)
    at org.apache.hadoop.fs.Globber.doGlob(Globber.java:291)
    at org.apache.hadoop.fs.Globber.glob(Globber.java:202)
    at org.apache.hadoop.fs.FileSystem.globStatus(FileSystem.java:2124)

Solution:

Assign the Storage Blob Data Reader role, on the source storage, to the materialization identity (a user-assigned managed identity) of the feature store.

Storage Blob Data Reader is the minimum recommended access requirement. You can also assign roles with more privileges; for example, Storage Blob Data Contributor or Storage Blob Data Owner.

For more information about RBAC configuration, see Permissions required for the feature store materialization managed identity role.

Materialization identity doesn't have proper RBAC permission to write data to the offline store

Symptom

The materialization job fails with this error message in the logs/azureml/driver/stdout file:

An error occurred while calling o1162.load.
: java.util.concurrent.ExecutionException: java.nio.file.AccessDeniedException: Operation failed: "This request is not authorized to perform this operation using this permission.", 403, HEAD, https://featuresotrestorage1.dfs.core.windows.net/offlinestore/fs_xxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx_fsname/transactions/1/_delta_log?upn=false&action=getStatus&timeout=90
    at com.google.common.util.concurrent.AbstractFuture$Sync.getValue(AbstractFuture.java:306)
    at com.google.common.util.concurrent.AbstractFuture$Sync.get(AbstractFuture.java:293)
    at com.google.common.util.concurrent.AbstractFuture.get(AbstractFuture.java:116)
    at com.google.common.util.concurrent.Uninterruptibles.getUninterruptibly(Uninterruptibles.java:135)
    at com.google.common.cache.LocalCache$Segment.getAndRecordStats(LocalCache.java:2410)
    at com.google.common.cache.LocalCache$Segment.loadSync(LocalCache.java:2380)
    at com.google.common.cache.LocalCache$S

Solution

Assign the Storage Blob Data Reader role, on the source storage, to the materialization identity (a user-assigned managed identity) of the feature store.

Storage Blob Data Contributor is the minimum recommended access requirement. You can also assign roles with more privileges; for example, Storage Blob Data Owner.

For more information about RBAC configuration, see Permissions required for the feature store materialization managed identity role.

Streaming job output to a notebook results in failure

Symptom:

When using the feature store CRUD client to stream materialization job results to notebook using fs_client.jobs.stream("<job_id>"), the SDK call fails with an error

HttpResponseError: (UserError) A job was found, but it is not supported in this API version and cannot be accessed.

Code: UserError

Message: A job was found, but it is not supported in this API version and cannot be accessed.

Solution:

When the materialization job is created (for example, by a backfill call), it might take a few seconds for the job to properly initialize. Run the jobs.stream() command again a few seconds later. The issue should be gone.

Invalid Spark configuration

Symptom:

A materialization job fails with this error message:

Synapse job submission failed due to invalid spark configuration request

{

"Message":"[..] Either the cores or memory of the driver, executors exceeded the SparkPool Node Size.\nRequested Driver Cores:[4]\nRequested Driver Memory:[36g]\nRequested Executor Cores:[4]\nRequested Executor Memory:[36g]\nSpark Pool Node Size:[small]\nSpark Pool Node Memory:[28]\nSpark Pool Node Cores:[4]"

}

Solution:

Update the materialization_settings.spark_configuration{} of the feature set. Make sure that these parameters use memory size amounts, and a total number of core values, both less than what the instance type, as defined by materialization_settings.resource, provides:

spark.driver.cores spark.driver.memory spark.executor.cores spark.executor.memory

For example, for instance type standard_e8s_v3, this Spark configuration is one of the valid options.


transactions_fset_config.materialization_settings = MaterializationSettings(

    offline_enabled=True,

    resource = MaterializationComputeResource(instance_type="standard_e8s_v3"),

    spark_configuration = {

        "spark.driver.cores": 4,

        "spark.driver.memory": "36g",

        "spark.executor.cores": 4,

        "spark.executor.memory": "36g",

        "spark.executor.instances": 2

    },

    schedule = None,

)

fs_poller = fs_client.feature_sets.begin_create_or_update(transactions_fset_config)

Next steps