A notable part of preparing for the exam “DP-420: Designing and Implementing Native Applications Using Microsoft Azure Cosmos DB” is understanding how to perform a query against the transactional store from Apache Spark. In this post, we will explore a few fundamental aspects and provide illustrative examples to help ensure your readiness for this topic.
I. Azure Cosmos DB Connector for Apache Spark
The Azure Cosmos DB Spark connector provides a way for Apache Spark to interact bi-directionally with Azure Cosmos DB. This allows Spark to read data from and write data to the transactional Cosmos DB in a performant and scalable way.
To use Cosmos DB connector with Spark, you need to configure your spark-submit command correctly. Here’s an example
./bin/spark-submit –packages com.azure.cosmos.spark:azure-cosmos-spark_3-1_2-12:4.4.1 –class com.yourcompany.classname /path/to/your.jar
In the above command, we are telling the spark-submit command to include the Azure Cosmos DB Spark connector package.
II. Reading data from Cosmos DB with Spark
Once we have the connector setup, we can start using Spark to read data from Cosmos DB. Here’s how:
readConfig = {
“spark.cosmos.accountEndpoint” : “https://your-account.documents.azure.com:443/”,
“spark.cosmos.accountKey” : “your-account-key”,
“spark.cosmos.database” : “your-database”,
“spark.cosmos.container” : “your-container”
}
df = spark.read.format(“cosmos.oltp”).options(readConfig).load()
df.show()
In the above code, we have specified the accountEndpoint, accountKey, database and container, we are connecting to. Then, we use `spark.read.format(“cosmos.oltp”)` to specify we are reading from Cosmos DB.
III. Writing data to Cosmos DB with Spark
For writing data to Cosmos DB, we need a different set of configurations:
writeConfig = {
“spark.cosmos.accountEndpoint” : “https://your-account.documents.azure.com:443/”,
“spark.cosmos.accountKey” : “your-account-key”,
“spark.cosmos.database” : “your-database”,
“spark.cosmos.container” : “your-container”,
“spark.cosmos.write.strategy” : “ItemOverwrite”
}
df.write.format(“cosmos.oltp”).options(writeConfig).mode(‘append’).save()
Note the additional property here: `spark.cosmos.write.strategy` is set to “ItemOverwrite”, which ensures that if a record already exists in the database, it will be overwritten.
IV. Querying data from Cosmos DB with Spark
We can perform SQL-like queries on our Cosmos DB data using Spark. Once we have loaded the data into a DataFrame, we can use SparkSQL functions to query our data:
df.createOrReplaceTempView(“people”)
sqlDF = spark.sql(“SELECT name FROM people WHERE age >= 13 AND age <= 19")
sqlDF.show()
In this example, we’re querying a table of people and selecting those who are between 13 and 19 years old.
This post provides an overview of how to interact with Azure Cosmos DB using Apache Spark. Understanding these concepts will significantly aid your performance in the DP-420 exam where you’re required to design and implement native applications using Microsoft Azure Cosmos DB. As a best practice, always follow the latest recommended configurations and strategies from Microsoft Azure’s official documentation.
Practice Test
True or False: Azure Cosmos DB does not support performing queries from Spark.
- Answer: False
Explanation: Azure Cosmos DB provides support to perform queries from Apache Spark. This makes data processing efficient.
Which of the following services are required to query the transactional store from Spark in Azure Cosmos DB?
- a) Azure Storage
- b) Azure SQL Database
- c) Apache Spark Connector For Cosmos DB
- d) Azure Databricks
Answer: c) Apache Spark Connector For Cosmos DB, d) Azure Databricks
Explanation: The Apache Spark Connector For Cosmos DB is used to connect and query the data in Cosmos DB from Spark. Azure Databricks is an Apache Spark-based analytics platform that can also be used in this context.
True or False: Azure Cosmos DB does not support multiple data models.
- Answer: False
Explanation: Azure Cosmos DB supports multiple data models, which can handle a variety of data types, and supports querying from platforms like Spark.
True or False: Azure Cosmos DB provides global distribution, which can be leveraged when performing a query against the transactional store from Spark.
- Answer: True
Explanation: Azure Cosmos DB provides global distribution for latency and throughput, able to execute queries at a low latency dependent on geographical location.
Which of the following is not a feature of Azure Cosmos DB?
- a) Global distribution
- b) Streaming Analytics
- c) Multiple data models
- d) Turnkey global distribution
Answer: b) Streaming Analytics
Explanation: Streaming Analytics is not a feature of Azure Cosmos DB, it is a separate Azure service.
True or False: Spark cannot read and write data to Azure Cosmos DB in real-time.
- Answer: False
Explanation: Spark can indeed read and write data to Azure Cosmos DB in real-time by using the Azure Cosmos DB Spark connector.
Multiple Select: Which of the following are capable of performing a query against the transactional store from Spark in Azure Cosmos DB?
- a) Apache Spark
- b) Azure Data Lake
- c) PySpark
- d) Apache Hadoop
Answer: a) Apache Spark, c) PySpark
Explanation: Apache Spark and PySpark can perform a query against the transactional store from Spark in Azure Cosmos DB with the help of the Azure Cosmos DB Spark connector.
True or False: The Azure Cosmos DB Spark connector supports the Spark to Cosmos DB Server version
- Answer: False
Explanation: Apache Spark to Cosmos DB Server version 0 and later are supported by the Azure Cosmos DB Spark connector.
Which of the following is NOT a supported data model for Azure Cosmos DB?
- a) Key-Value
- b) Graph
- c) Relational
- d) Column-family
Answer: c) Relational
Explanation: Azure Cosmos DB supports key-value, graph, column-family and document data models, but not relational.
True or False: You can use the Cosmos DB Spark connector with managed and unmanaged Spark clusters.
- Answer: True
Explanation: The Azure Cosmos DB Spark connector can be used with both managed Spark clusters, such as Azure Databricks, and unmanaged clusters, providing flexibility and a wide range of options.
Which service can you use along with Spark and Azure Cosmos DB for real-time analytics and AI?
- a) Azure Storage
- b) Azure Machine Learning
- c) Azure Functions
- d) Azure Kubernetes Service
Answer: b) Azure Machine Learning
Explanation: Azure Machine Learning can be used along with Spark and Azure Cosmos DB to create a powerful environment for real-time analytics and AI.
Interview Questions
What is Spark in the context of Azure Cosmos DB?
Apache Spark is an open-source, distributed computing system used for big data processing and analytics. Azure Cosmos DB provides a Spark connector that enables you to read from and write to Azure Cosmos DB using Spark APIs.
How can you perform a query against the transactional store from Spark?
You can perform a query against the transactional store from Spark by using the Spark connector provided by Azure Cosmos DB. You simply load your Cosmos DB data into a DataFrame and then perform operations using Spark SQL commands.
What is Spark SQL?
Spark SQL is a Spark module for structured data processing. Unlike basic Spark RDD API, the interfaces provided by Spark SQL provide Spark with more information about the structure of both the data and the computation being performed, which allows Spark to run more optimizations.
How does Azure Cosmos DB handle data partitioning for Spark?
Azure Cosmos DB automatically manages data partitioning to provide elastic scale. During read operations from Spark, the Spark connector for Azure Cosmos DB performs parallelized queries against all partitions in the transactional store to maximize throughput.
Why does Azure Cosmos DB ensure idempotent writes in Spark?
Azure Cosmos DB ensures idempotent writes in Spark to maintain data consistency and avoid duplicate writes when a task is rerun. If a job fails and Spark needs to rerun a task, ensuring idempotent writes prevents duplicates and inconsistencies in the data.
What are the supported data models and APIs of Azure Cosmos DB for Spark?
Azure Cosmos DB supports key-value (table), column-family (Cassandra), document (SQL and MongoDB), and graph (Gremlin) data models. The corresponding APIs for these models are all supported by the Spark connector in Azure Cosmos DB.
What’s the role of DataFrame in connecting Spark to Azure Cosmos DB?
A DataFrame represents a distributed collection of data organized into named columns. It’s conceptually equivalent to a table in a relational database. When connecting Spark to Azure Cosmos DB, DataFrames allow manipulating of Cosmos DB data using Spark’s domain-specific language for structured data.
How can I write DataFrame or RDD data to Azure Cosmos DB using Spark?
You can write DataFrame or RDD data to Azure Cosmos DB using the write() function provided by Spark connector. Typically, you would convert your data to a DataFrame or RDD, and then use the write() function to save your data into Azure Cosmos DB.
How can one optimize the performance of Spark queries on Azure Cosmos DB?
You can optimize performance by using predicate push down to only read the required data, using explicit schema for faster initialization of jobs, and using bulk write operations for efficient data ingestion.
What is the use of a change feed in Azure Cosmos DB for Spark?
Change feed in Azure Cosmos DB for Spark is a log of all the inserts and updates that occurred in your Cosmos DB. Using the change feed support in the Spark connector, you can read these changes in real-time, which can be very useful for scenarios like data replication, event sourcing, and triggers.
Can you perform transactional operations using Spark Connector in Azure Cosmos DB?
No, transactional operations are not supported by the Spark connector. Operations are executed individually, and there is no way to run several operations in a single transaction.
Can I perform real-time analytics with Azure Cosmos DB and Spark?
Yes, leveraging Azure Cosmos DB’s change feed functionality and Spark’s real-time computation capabilities, you can build real-time analytics solutions.
How can I manage upper bound limitations while working with Spark and Azure Cosmos DB?
While working with the Spark connector for Azure Cosmos DB, too much parallelism can overwhelm the service and lead to throttling. This can be managed by setting an upper bound on concurrent requests which can be configured in the client by setting the
spark.cosmos.throughputControlEnabledconfiguration to
true.
Is it possible to handle schema evolutions in Azure Cosmos DB using Spark?
Yes, the Spark connector for Azure Cosmos DB handles the schema evolution scenario, mapping nullable fields when it reads the data, and handling missing properties in the data without an exception.
How does the Spark connector for Cosmos DB handle data consistency?
The Spark connector supports “Eventual” and “Session” consistency levels offered by Azure Cosmos DB. “Eventual” consistency provides the best performance and availability, while “Session” consistency guarantees linearizability and read-your-own-writes consistency within a single session.
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