Move data by using the Azure Cosmos DB Spark Connector

The Azure Cosmos DB Spark Connector is a versatile tool that can enhance your Azure experience. It works as a bridge, connecting Spark to Azure Cosmos DB, enabling data read and write functionality. Moreover, it also supports several functionalities like flexible schema evolution, rate-limiting, change feed, and more.

First, let’s understand why we need the Azure Cosmos DB Spark connector. With the exponential rise of data, making sense out of it becomes a herculean task. Fortunately, Spark, an open-source distributed cluster-computing framework, provides an interface for data analytics. Its primary goal is to process and analyze large-scale data.

Azure Cosmos DB, on the other hand, is a multi-model database service designed for distributed data globally. It provides native support for NoSQL and its interoperability with multiple APIs, including SQL or Core (SQL), MongoDB, Cassandra, Tables, and Gremlin, offering a wide range of choices for developers.

Thanks to the Azure Cosmos DB Spark Connector, we can bridge these two powerful tools and process large-scale data in Cosmos DB using Spark.

Using Azure Cosmos DB Spark Connector

The Azure Cosmos DB Spark Connector supports two operations:

1. Reading data from Azure Cosmos DB to Spark
2. Writing data from Spark to Azure Cosmos DB

Here is a basic PySpark example to get you started:

df = spark.read\
.format("cosmos.oltp")\
.option("spark.synapse.linkedService", "")\
.option("spark.cosmos.container", "")\
.load()

df_transformed.write\
.format("cosmos.oltp")\
.option("spark.synapse.linkedService", "")\
.option("spark.cosmos.container", "")\
.mode('append')\
.save()


The PySpark script above retrieves data from a Cosmos DB container, performs Spark transformations (represented by df…) and writes the transformed data back to the Cosmos DB container.

Advanced Functionalities

Schema Inference and Evolution

The Azure Cosmos DB Spark Connector can infer reading schema from a Cosmos DB container. If your container’s data has a flexible schema, the connector can manage and evolve the schema based on incoming data.

Rate Limiting

Cosmos DB provides an explicit rate-limiting feature to prevent any accidental overuse. When Spark tries to consume more Request Units (RU) than allocated, Cosmos DB throws 429 exceptions. Azure Cosmos DB Spark Connector handles these exceptions natively and implements a backoff-retry policy.

Change Feed Support

Change Feed is a log of all modifications in the Azure Cosmos DB – insertions, updates, and deletes. The Azure Cosmos DB Spark connector can read this Change Feed, enabling scenarios like data replication, event-driven architectures, real-time analytics, and more.

In conclusion, the Azure Cosmos DB Spark Connector provides an efficient and straightforward way to move large-scale data between Spark and Azure Cosmos DB. Additionally, it offers various advanced features that pave the way for extensive data processing and analytics solutions. Undeniably, for DP-420 Candidates, mastering the Azure Cosmos DB Spark Connector is critical to designing and implementing native applications effectively on Microsoft Azure Cosmos DB.

Practice Test

True/False: The Azure Cosmos DB Spark connector allows you to connect Azure Cosmos DB with Spark applications.

• True
• False

Answer: True

Explanation: The Azure Cosmos DB Spark connector supports connectivity between Azure Cosmos DB and Spark applications, enabling reading, writing, querying and managing data.

True/False: The Azure Cosmos DB Spark Connector doesn’t support PySpark applications.

• True
• False

Answer: False

Explanation: The Azure Cosmos DB Spark connector supports PySpark applications as well.

Multiple select: What types of operations can you perform with Azure Cosmos DB Spark Connector?

• A. Read data
• B. Write data
• C. Query data
• D. Translate data

Answer: A, B, C

Explanation: The Azure Cosmos DB Spark Connector supports reading, writing and querying data from Azure Cosmos DB, but it does not support translating data.

Single select: Which Cosmos DB APIs does Azure Cosmos DB Spark Connector support?

• A. SQL API
• B. MongoDB API
• C. Both
• D. Neither

Answer: C. Both

Explanation: Azure Cosmos DB Spark Connector supports both SQL API and MongoDB API for reading and writing data to a Cosmos DB collection.

True/False: Azure Cosmos DB Spark Connector only works with Scala.

• True
• False

Answer: False

Explanation: Azure Cosmos DB Spark Connector works with not only Scala, but also Java and Python.

Single Select: What protocol does Azure Cosmos DB Spark Connector use to connect to Cosmos DB?

• A. Private Link
• B. Public Link
• C. Direct Mode
• D. None of the above

Answer: C. Direct Mode

Explanation: Azure Cosmos DB Spark Connector uses Direct Mode to connect to Cosmos DB.

Multiple Select: What are the benefits of Azure Cosmos DB Spark Connector?

• A. It helps to simplify data movement.
• B. It enables seamless integration with existing Spark jobs.
• C. It allows secure data access.
• D. It helps to translate data.

Answer: A, B, C

Explanation: Except data translation all are the benefits of Azure Cosmos DB Spark Connector.

Single Select: What SDK is available for Azure Cosmos DB Spark Connector?

• A. Node.js
• B. Python
• C. Scala
• D. All of the above

Answer: D. All of the above

Explanation: Azure Cosmos DB Spark Connector has SDKs available for Python, Scala, and Node.js.

True/False: Azure Cosmos DB Spark Connector doesn’t support writing Cosmos DB stored procedures.

• True
• False

Answer: True

Explanation: Currently, Azure Cosmos DB Spark Connector does not support writing stored procedures to Cosmos DB.

Multiple Select: Using Azure Cosmos DB Spark Connector, you can read data from:

• A. Azure Table storage
• B. Azure SQL Database
• C. Azure Cosmos DB
• D. Azure Data Lake

Answer: C. Azure Cosmos DB

Explanation: The Azure Cosmos DB Spark Connector only supports reading data from Azure Cosmos DB.

Interview Questions

What is the Azure Cosmos DB Spark Connector used for?

The Azure Cosmos DB Spark Connector is used to move data to and from Azure Cosmos DB using Apache Spark. It enables real-time data analytics and exploration, and large-scale transformations of data in Cosmos DB.

How does the Azure Cosmos DB Spark Connector support schema inference and evolution?

The Azure Cosmos DB Spark Connector uses the schema inference and evolution to automatically map and convert the Azure Cosmos DB data model into a Spark SQL Data Frame, and vice-versa. This conversion allows seamless reading and writing of data.

What is the role of the Azure Cosmos DB Change Feed in the Spark Connector?

The Change Feed in Azure Cosmos DB Spark Connector is an ordered, guaranteed, and durable (as durable as the data in the database) source of records for all changes in a Cosmos DB container. It allows obtaining a sorted list of documents in the order in which they were modified.

What is the benefit of using Azure Cosmos DB together with Apache Spark?

The integration of Azure Cosmos DB with Apache Spark provides powerful, real-time data analytics capabilities. With the Connector, one can transfer data to and from Azure Cosmos DB, perform real-time analytics, machine learning, and other data science tasks.

Can you mention two write configuration options for Azure Cosmos DB Spark Connector?

Write configuration options for the Azure Cosmos DB Spark Connector include ‘WriteThroughputBudget’, which is used to limit the number of request units consumed per second when writing data, and ‘Upsert’ used to enable updates of existing records.

How to control the parallelism of an Azure Cosmos DB Spark job?

The parallelism of a Spark job can be controlled by two properties: ‘spark.cosmos.read.forceEventualConsistency’ and ‘spark.cosmos.write.maxIngestionTaskParallelism’. The first property ensures eventual consistency, and the second property limits the maximum degree of parallelism during ingestion.

What is the ‘Upsert’ configuration option used for in the Azure Cosmos DB Spark Connector?

The ‘Upsert’ configuration option is used to enable updates of existing records. If set to true, Spark will update existing items if they already exist in Azure Cosmos DB, otherwise, it will insert new items.

Can Azure Cosmos DB Spark Connector handle large amounts of data without consuming all available request units (RUs)?

Yes, the Azure Cosmos DB Spark Connector uses efficient techniques such as partitioning and batching to handle large volumes of data without consuming all available request units or causing excessive throttling.

What purpose does the ‘spark.cosmos.read.maxBufferedItemCount’ configuration option serve in the Azure Cosmos DB Spark Connector?

The ‘spark.cosmos.read.maxBufferedItemCount’ configuration option is used to tune the internal buffering for Cosmos DB item reads. It controls how many items the connector should buffer while reading from Cosmos DB.

Can you perform advanced analytics and AI task using Azure Cosmos DB Spark Connector?

Yes, the integration of Azure Cosmos DB with Apache Spark enables powerful real-time analytics, machine learning, and AI capabilities. It allows the transformation and analysis of large-scale, operational data collected in Azure Cosmos DB.

How does Azure Cosmos DB Spark Connector handle schema discrepancies?

During the reading process, if Azure Cosmos DB Spark Connector encounters documents with different schemas than inferred, it drops those documents and proceeds ahead. The connector also logs the ID and partition key value of the documents for the user to take appropriate action.

Does the Azure Cosmos DB Spark Connector support cross-language usability?

Yes, Azure Cosmos DB Spark Connector supports cross-language usability. It can be used with Python, Scala, and Java and supports SQL queries as well.

What is the ‘spark.cosmos.write.bulkEnabled’ configuration option in the Azure Cosmos DB Spark Connector?

The ‘spark.cosmos.write.bulkEnabled’ configuration option enables the bulk execution mode when writing data to Azure Cosmos DB. When set to true, writes are batched and sent in a single request to Cosmos DB, improving the throughput and reducing the consumed request units.

Besides schema inference, what other inconsistency handling does Azure Cosmos DB Spark Connector provide?

Besides schema inference, the Azure Cosmos DB Spark Connector provides data skipping. If documents have a different schema than inferred or have inconsistent data types, the connector skips those documents and continues processing the remaining data.

Is it possible to do incremental reading of data with Azure Cosmos DB Spark Connector?

Yes, it’s possible to do incremental reading of data with Azure Cosmos DB Spark Connector using Change Feed. This feature allows you to process changes in Azure Cosmos DB as they arrive, providing near-real-time data processing capabilities.