HDInsight SDK for Python
Overview
The HDInsight SDK for Python provides classes and methods that allow you to manage your HDInsight clusters. It includes operations to create, delete, update, list, resize, execute script actions, monitor, get properties of HDInsight clusters, and more.
Prerequisites
- An Azure account. If you don't have one, get a free trial.
- Python
- pip
SDK Installation
The HDInsight SDK for Python can be found in the Python Package Index and can be installed by running:
pip install azure-mgmt-hdinsight
Authentication
The SDK first needs to be authenticated with your Azure subscription. Follow the example below to create a service principal and use it to authenticate. After this is done, you will have an instance of an HDInsightManagementClient, which contains many methods (outlined in below sections) that can be used to perform management operations.
Note
There are other ways to authenticate besides the below example that could potentially be better suited for your needs. All methods are outlined here: Authenticate with the Azure Management Libraries for Python
Authentication Example Using a Service Principal
First, login to Azure Cloud Shell. Verify you are currently using the subscription in which you want the service principal created.
az account show
Your subscription information is displayed as JSON.
{
"environmentName": "AzureCloud",
"id": "XXXXXXXX-XXXX-XXXX-XXXX-XXXXXXXXXXXX",
"isDefault": true,
"name": "XXXXXXX",
"state": "Enabled",
"tenantId": "XXXXXXXX-XXXX-XXXX-XXXX-XXXXXXXXXXXX",
"user": {
"cloudShellID": true,
"name": "XXX@XXX.XXX",
"type": "user"
}
}
If you're not logged into the correct subscription, select the correct one by running:
az account set -s <name or ID of subscription>
Important
If you have not already registered the HDInsight Resource Provider by another method (such as by creating an HDInsight Cluster through the Azure Portal), you need to do this once before you can authenticate. This can be done from the Azure Cloud Shell by running the following command:
az provider register --namespace Microsoft.HDInsight
Next, choose a name for your service principal and create it with the following command:
az ad sp create-for-rbac --name <Service Principal Name> --role Contributor --sdk-auth
The service principal information is displayed as JSON.
{
"clientId": "XXXXXXXX-XXXX-XXXX-XXXX-XXXXXXXXXXXX",
"clientSecret": "XXXXXXXX-XXXX-XXXX-XXXX-XXXXXXXXXXXX",
"subscriptionId": "XXXXXXXX-XXXX-XXXX-XXXX-XXXXXXXXXXXX",
"tenantId": "XXXXXXXX-XXXX-XXXX-XXXX-XXXXXXXXXXXX",
"activeDirectoryEndpointUrl": "https://login.microsoftonline.com",
"resourceManagerEndpointUrl": "https://management.azure.com/",
"activeDirectoryGraphResourceId": "https://graph.windows.net/",
"sqlManagementEndpointUrl": "https://management.core.windows.net:8443/",
"galleryEndpointUrl": "https://gallery.azure.com/",
"managementEndpointUrl": "https://management.core.windows.net/"
}
Copy the below Python snippet and fill in TENANT_ID, CLIENT_ID, CLIENT_SECRET, and SUBSCRIPTION_ID with the strings from the JSON that was returned after running the command to create the service principal.
from azure.mgmt.hdinsight import HDInsightManagementClient
from azure.common.credentials import ServicePrincipalCredentials
from azure.mgmt.hdinsight.models import *
# Tenant ID for your Azure Subscription
TENANT_ID = ''
# Your Service Principal App Client ID
CLIENT_ID = ''
# Your Service Principal Client Secret
CLIENT_SECRET = ''
# Your Azure Subscription ID
SUBSCRIPTION_ID = ''
credentials = ServicePrincipalCredentials(
client_id = CLIENT_ID,
secret = CLIENT_SECRET,
tenant = TENANT_ID
)
client = HDInsightManagementClient(credentials, SUBSCRIPTION_ID)
Cluster Management
Note
This section assumes you have already authenticated and constructed an HDInsightManagementClient instance and store it in a variable called client. Instructions for authenticating and obtaining an HDInsightManagementClient can be found in the Authentication section above.
Create a Cluster
A new cluster can be created by calling client.clusters.create().
Samples
Code samples for creating several common types of HDInsight clusters are available: HDInsight Python Samples.
Example
This example demonstrates how to create a Spark cluster with 2 head nodes and 1 worker node.
Note
You first need to create a Resource Group and Storage Account, as explained below. If you have already created these, you can skip these steps.
Creating a Resource Group
You can create a resource group using the Azure Cloud Shell by running
az group create -l <Region Name (i.e. eastus)> --n <Resource Group Name>
Creating a Storage Account
You can create a storage account using the Azure Cloud Shell by running:
az storage account create -n <Storage Account Name> -g <Existing Resource Group Name> -l <Region Name (i.e. eastus)> --sku <SKU i.e. Standard_LRS>
Now run the following command to get the key for your storage account (you will need this to create a cluster):
az storage account keys list -n <Storage Account Name>
The below Python snippet creates a Spark cluster with 2 head nodes and 1 worker node. Fill in the blank variables as explained in the comments and feel free to change other parameters to suit your specific needs.
# The name for the cluster you are creating
cluster_name = ""
# The name of your existing Resource Group
resource_group_name = ""
# Choose a username
username = ""
# Choose a password
password = ""
# Replace <> with the name of your storage account
storage_account = "<>.blob.core.windows.net"
# Storage account key you obtained above
storage_account_key = ""
# Choose a region
location = ""
container = "default"
params = ClusterCreateProperties(
cluster_version="3.6",
os_type=OSType.linux,
tier=Tier.standard,
cluster_definition=ClusterDefinition(
kind="spark",
configurations={
"gateway": {
"restAuthCredential.enabled_credential": "True",
"restAuthCredential.username": username,
"restAuthCredential.password": password
}
}
),
compute_profile=ComputeProfile(
roles=[
Role(
name="headnode",
target_instance_count=2,
hardware_profile=HardwareProfile(vm_size="Large"),
os_profile=OsProfile(
linux_operating_system_profile=LinuxOperatingSystemProfile(
username=username,
password=password
)
)
),
Role(
name="workernode",
target_instance_count=1,
hardware_profile=HardwareProfile(vm_size="Large"),
os_profile=OsProfile(
linux_operating_system_profile=LinuxOperatingSystemProfile(
username=username,
password=password
)
)
)
]
),
storage_profile=StorageProfile(
storageaccounts=[StorageAccount(
name=storage_account,
key=storage_account_key,
container=container,
is_default=True
)]
)
)
client.clusters.create(
cluster_name=cluster_name,
resource_group_name=resource_group_name,
parameters=ClusterCreateParametersExtended(
location=location,
tags={},
properties=params
))
Get Cluster Details
To get properties for a given cluster:
client.clusters.get("<Resource Group Name>", "<Cluster Name>")
Example
You can use get to confirm that you have successfully created your cluster.
my_cluster = client.clusters.get("<Resource Group Name>", "<Cluster Name>")
print(my_cluster)
The output should look like:
{'additional_properties': {}, 'id': '/subscriptions/XXXXXXXX-XXXX-XXXX-XXXX-XXXXXXXXXXXX/resourceGroups/<Resource Group Name>/providers/Microsoft.HDInsight/clusters/<Cluster Name>', 'name': '<Cluster Name>', 'type': 'Microsoft.HDInsight/clusters', 'location': '<Location>', 'tags': {}, 'etag': 'XXXXXXXX-XXXX-XXXX-XXXX-XXXXXXXXXXXX', 'properties': <azure.mgmt.hdinsight.models.cluster_get_properties_py3.ClusterGetProperties object at 0x0000013766D68048>}
List Clusters
List Clusters Under The Subscription
client.clusters.list()
List Clusters By Resource Group
client.clusters.list_by_resource_group("<Resource Group Name>")
Note
Both list() and list_by_resource_group() return a ClusterPaged object. Calling advance_page() returns a list of clusters on that page and advances the ClusterPaged object to the next page. This can be repeated until a StopIteration exception is raised, indicating that there are no more pages.
Example
The following example prints the properties of all clusters for the current subscription:
clusters_paged = client.clusters.list()
while True:
try:
for cluster in clusters_paged.advance_page():
print(cluster)
except StopIteration:
break
Delete a Cluster
To delete a cluster:
client.clusters.delete("<Resource Group Name>", "<Cluster Name>")
Update Cluster Tags
You can update the tags of a given cluster like so:
client.clusters.update("<Resource Group Name>", "<Cluster Name>", tags={<Dictionary of Tags>})
Example
client.clusters.update("<Resource Group Name>", "<Cluster Name>", tags={"tag1Name" : "tag1Value", "tag2Name" : "tag2Value"})
Resize Cluster
You can resize a given cluster's number of worker nodes by specifying a new size like so:
client.clusters.resize("<Resource Group Name>", "<Cluster Name>", target_instance_count=<Num of Worker Nodes>)
Cluster Monitoring
The HDInsight Management SDK can also be used to manage monitoring on your clusters via the Operations Management Suite (OMS).
Enable OMS Monitoring
Note
To enable OMS Monitoring, you must have an existing Log Analytics workspace. If you have not already created one, you can learn how to do that here: Create a Log Analytics workspace in the Azure portal.
To enable OMS Monitoring on your cluster:
client.extension.enable_monitoring("<Resource Group Name>", "<Cluster Name>", workspace_id="<Workspace Id>")
View Status Of OMS Monitoring
To get the status of OMS on your cluster:
client.extension.get_monitoring_status("<Resource Group Name", "Cluster Name")
Disable OMS Monitoring
To disable OMS on your cluster:
client.extension.disable_monitoring("<Resource Group Name>", "<Cluster Name>")
Script Actions
HDInsight provides a configuration method called script actions that invokes custom scripts to customize the cluster.
Note
More information on how to use script actions can be found here: Customize Linux-based HDInsight clusters using script actions
Execute Script Actions
To execute script actions on a given cluster:
script_action1 = RuntimeScriptAction(name="<Script Name>", uri="<URL To Script>", roles=[<List of Roles>]) #valid roles are "headnode", "workernode", "zookeepernode", and "edgenode"
client.clusters.execute_script_actions("<Resource Group Name>", "<Cluster Name>", <persist_on_success (bool)>, script_actions=[script_action1]) #add more RuntimeScriptActions to the list to execute multiple scripts
Delete Script Action
To delete a specified persisted script action on a given cluster:
client.script_actions.delete("<Resource Group Name>", "<Cluster Name", "<Script Name>")
List Persisted Script Actions
Note
list() and list_persisted_scripts() return a RuntimeScriptActionDetailPaged object. Calling advance_page() returns a list of RuntimeScriptActionDetail on that page and advances the RuntimeScriptActionDetailPaged object to the next page. This can be repeated until a StopIteration exception is raised, indicating that there are no more pages. See the example below.
To list all persisted script actions for the specified cluster:
client.script_actions.list_persisted_scripts("<Resource Group Name>", "<Cluster Name>")
Example
scripts_paged = client.script_actions.list_persisted_scripts(resource_group_name, cluster_name)
while True:
try:
for script in scripts_paged.advance_page():
print(script)
except StopIteration:
break
List All Scripts' Execution History
To list all scripts' execution history for the specified cluster:
client.script_execution_history.list("<Resource Group Name>", "<Cluster Name>")
Example
This example prints all the details for all past script executions.
script_executions_paged = client.script_execution_history.list("<Resource Group Name>", "<Cluster Name>")
while True:
try:
for script in script_executions_paged.advance_page():
print(script)
except StopIteration:
break
Azure SDK for Python
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