Enable the analytical store on a container

To enable the analytical store on a container in Microsoft Azure Cosmos DB, you can follow a few simple steps. The analytical store feature allows you to perform real-time analytics on your operational data without impacting the performance of your transactional workloads. By enabling the analytical store, you can gain valuable insights from your data and run ad-hoc queries using familiar SQL syntax.

Step 1: Create a Container

First, you need to create a container in your Azure Cosmos DB account. You can do this using the Azure portal, Azure CLI, or any of the available SDKs. Make sure to choose a suitable partition key for your container, as it determines the distribution of data across the database.

Step 2: Enable Analytical Store

Once you have created the container, you can enable the analytical store for it. The analytical store can be enabled at the time of container creation or by modifying the container properties later.

To enable the analytical store during container creation, include the analyticalStoreEnabled property in the ThroughputProperties object. Set it to true to enable the feature. Here’s an example of creating a container with the analytical store enabled using the Azure CLI:

az cosmosdb sql container create \
--account-name \
--database-name \
--name \
--partition-key-path '/partitionKey' \
--analytical-store-enabled true


Alternatively, you can enable the analytical store for an existing container by updating the container properties. Here’s an example of modifying a container using JavaScript SDK:

const containerDefinition = {
id: "",
partitionKey: {
paths: ["/partitionKey"],
kind: "Hash"
},
analyticalStoreEnabled: true
};

const { resource: updatedContainer } = await container.replace(containerDefinition);


Step 3: Querying Analytical Store

Once the analytical store is enabled, you can start querying your data. The analytical store provides an integrated Apache Spark environment for running analytical queries. You can use SQL commands to query your container’s data. The spark namespace is used for referencing the analytical store.

Here’s an example of querying the analytical store using SQL syntax:

// Query the container's operational data
SELECT * FROM c

Step 4: Cost Considerations

Enabling the analytical store incurs additional costs, as it utilizes separate provisioned throughput and storage. The throughput provisioned for your container is shared between the operational and analytical workloads. Keep in mind that data written to the analytical store is billed independently.

It’s important to monitor and optimize the resource usage of your analytical queries to ensure cost-effectiveness and efficient utilization of resources.

In conclusion, enabling the analytical store on a container in Azure Cosmos DB allows you to leverage real-time analytics on your operational data. By following the steps outlined above, you can gain valuable insights from your data using familiar SQL queries. Remember to consider the additional cost implications and optimize your resource usage accordingly.