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Query Cosmos DB data with Spark

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After you've added a linked service for your analytical store enabled Azure Cosmos DB database, you can use it to query the data using a Spark pool in your Azure Synapse Analytics workspace.

Loading Azure Cosmos DB analytical data into a dataframe

For initial exploration or quick analysis of data from an Azure Cosmos DB linked service, it's often easiest to load data from a container into a dataframe using a Spark-supported language like PySpark (A Spark-specific implementation of Python) or Scala (a Java-based language often used on Spark).

For example, the following PySpark code could be used to load a dataframe named df from the data in the my-container container connected to using the my_linked_service linked service, and display the first 10 rows of data:

 df = spark.read
     .format("cosmos.olap")\
     .option("spark.synapse.linkedService", "my_linked_service")\
     .option("spark.cosmos.container", "my-container")\
     .load()

display(df.limit(10))

Let's suppose the my-container container is used to store items similar to the following example:

{
    "productID": 123,
    "productName": "Widget",
    "id": "7248f072-11c3-42b1-a368-...",
    "_rid": "mjMaAL...==",
    "_self": "dbs/mjM...==/colls/mjMaAL...=/docs/mjMaAL...==/",
    "_etag": "\"54004b09-0000-2300-...\"",
    "_attachments": "attachments/",
    "_ts": 1655414791
}

The output from the PySpark code would be similar to the following table:

_rid	_ts	productID	productName	id	_etag
mjMaAL...==	1655414791	123	Widget	7248f072-11c3-42b1-a368-...	54004b09-0000-2300-...
mjMaAL...==	1655414829	124	Wotsit	dc33131c-65c7-421a-a0f7-...	5400ca09-0000-2300-...
mjMaAL...==	1655414835	125	Thingumy	ce22351d-78c7-428a-a1h5-...	5400ca09-0000-2300-...
...	...	...	...	...	...

The data is loaded from the analytical store in the container, not from the operational store; ensuring that there's no querying overhead on the operational store. The fields in the analytical data store include the application-defined fields (in this case productID and productName) and automatically created metadata fields.

After loading the dataframe, you can use its native methods to explore the data. For example, the following code creates a new dataframe containing only the productID and productName columns, ordered by the productName:

products_df = df.select("productID", "productName").orderBy("productName")

display(products_df.limit(10))

The output of this code would look similar this table:

productID	productName
125	Thingumy
123	Widget
124	Wotsit
...	...

Writing a dataframe to a Cosmos DB container

In most HTAP scenarios, you should use the linked service to read data into Spark from the analytical store. However you can write the contents of a dataframe to the container as shown in the following example:

mydf.write.format("cosmos.oltp")\
    .option("spark.synapse.linkedService", "my_linked_service")\
    .option("spark.cosmos.container", "my-container")\
    .mode('append')\
    .save()

Note

Writing a dataframe to a container updates the operational store and can have an impact on its performance. The changes are then synchronized to the analytical store.

Using Spark SQL to query Azure Cosmos DB analytical data

Spark SQL is a Spark API that provides SQL language syntax and relational database semantics in a Spark pool. You can use Spark SQL to define metadata for tables that can be queried using SQL.

For example, the following code creates a table named Products based on the hypothetical container used in the previous examples:

%%sql

-- Create a logical database in the Spark metastore
CREATE DATABASE mydb;

USE mydb;

-- Create a table from the Cosmos DB container
CREATE TABLE products using cosmos.olap options (
    spark.synapse.linkedService 'my_linked_service',
    spark.cosmos.container 'my-container'
);

-- Query the table
SELECT productID, productName
FROM products;

Tip

The %%sql keyword at the beginning of the code is a magic that instructs the Spark pool to run the code as SQL rather than the default language (which is usually set to PySpark).

By using this approach, you can create a logical database in your Spark pool that you can then use to query the analytical data in Azure Cosmos DB to support data analysis and reporting workloads without impacting the operational store in your Azure Cosmos DB account.

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