Azure Cosmos DB
Azure Cosmos DB is a globally distributed database service built to provide low latency, high availability, scalable and responsive applications. Spark on the other hand is a lightning-fast unified analytics engine that allows processing of large volumes of data. Integrating Spark with Cosmos DB is a task often undertaken while working with big datasets or when you desire real-time analytics. At times, it might be necessary to write data back to the transactional store from Spark and that’s what we are going to focus on in this discussion.
Initial Setup
Before we embark on how to write data from Spark back into Cosmos DB, let’s briefly discuss the initial setup.
Firstly, you’ll need a running instance of Azure Cosmos DB established. You can set this up on the Azure portal by creating a new Azure Cosmos DB account.
Secondly, for your Spark environment, Databricks, an Apache Spark-based analytics platform can be utilized. It ensures a smooth integration with Cosmos DB by use of the Azure Cosmos DB Spark Connector – a high-performance connector that allows you to use Azure Cosmos DB as an input source or an output sink for Apache Spark jobs.
Once the Cosmos DB instance and Databricks spark environment are ready, you can now connect your Databricks notebook to Azure Cosmos DB using the Cosmos DB Spark Connector.
# setup the configurations for connecting Cosmos DB
connectionConfig = {
"Endpoint" : "https://your_account.documents.azure.com:443/",
"Masterkey" : "your_master_key",
"Database" : "your_database",
"Collection" : "your_collection"
}
# initialize spark session
spark = SparkSession.builder.master("local").appName("Cosmos DB connection").getOrCreate()
# read from Cosmos DB
df = spark.read.format("com.microsoft.azure.cosmosdb.spark").options(connectionConfig).load()
And that’s it for the setup. You’re now connected to Cosmos DB from Spark and ready to read and write data.
Writing From Spark to Cosmos DB
Writing data to Cosmos DB from Spark is a straightforward process. Just as we’ve read data into a dataframe using the Cosmos DB Spark Connector, we can write data back to Cosmos DB from Spark.
Here is a simple example of how you can accomplish this:
# Assuming we have a DataFrame df that we've processed and want to write back to Cosmos DB
df_to_write = df.select("column1", "column2", "column3")
# Write configuration
writeConfig = {
"Endpoint" : "https://your_account.documents.azure.com:443/",
"Masterkey" : "your_master_key",
"Database" : "your_database",
"Collection" : "your_collection",
"Upsert" : "true"
}
# Write the data from Spark DataFrame to Cosmos DB
df_to_write.write.format("com.microsoft.azure.cosmosdb.spark").options(writeConfig).save()
In the write configuration, we’ve included an “Upsert” option and set it to true. This means that if the documents we are writing already exist in the database, they will be updated; if they don’t exist, they will be inserted. This “upsert” operation is very useful for maintaining data consistency and integrity.
One important point to note is that Cosmos DB, being a NoSQL database, doesn’t enforce schema on the data. It means that the data you write back don’t necessarily need to have the same schema as the initial data in Cosmos DB. It gives flexibility to handle and store diverse and evolving data structures.
Conclusion
With a few lines of code, you can perform bidirectional data exchange between Spark and Cosmos DB. By following the steps above, you can write data back to Cosmos DB from your Spark application, providing a powerful combination of technologies for handling, processing, and storing big data. Moreover, the capability to integrate native applications offered by Microsoft Azure Cosmos DB, like in DP-420 Designing and Implementing Native Applications exam, bolsters your data handling prowess significantly.
Remember to use Azure’s Cosmos DB Spark Connector documentation for troubleshooting or deeper understanding, and continue to explore this exciting intersection of cloud, big data and analytics.
Practice Test
True or False: Spark can be used to write data back to Azure Cosmos DB.
- True
- False
Answer: True
Explanation: Spark can both read from and write data back to the Azure Cosmos DB transactional store.
What type of connector should be used to enable Spark to interact with Cosmos DB?
- A. Cosmos DB Spark connector
- B. Cosmos DB SQL connector
- C. Azure Data Lake connector
- D. Azure Blob Storage connector
Answer: A. Cosmos DB Spark connector
Explanation: Cosmos DB Spark connector allows for interaction between Spark and Cosmos DB. Other connector types will not function correctly in this context.
True or False: You can write data from Spark to Azure Cosmos DB using the Data Frame API.
- True
- False
Answer: True
Explanation: You can use the Data Frame API to write data from Spark back to Azure Cosmos DB.
In order to write data back into Cosmos DB, what mode should be used in Spark?
- A. Append mode
- B. Overwrite mode
- C. Read mode
- D. Write mode
Answer: B. Overwrite mode
Explanation: Overwrite mode is used to replace existing data in Cosmos DB with new data written back from Spark.
Which of the following is a prerequisite for writing data back to Azure Cosmos DB from Apache Spark?
- A. Creation of a Spark cluster
- B. Installation of the MongoDB Connector
- C. Installation of the Apache Kafka Connector
- D. Creation of a Hadoop cluster
Answer: A. Creation of a Spark cluster
Explanation: A Spark cluster is a prerequisite for writing data back to Azure Cosmos DB from Spark.
True or False: Both batch and streaming data can be written back to Azure Cosmos DB from Spark.
- True
- False
Answer: True
Explanation: Spark supports both batch and streaming data operations in writing data back to Azure Cosmos DB.
What method is used in Spark DataFrameWriter to write data back to Azure Cosmos DB?
- A. write.save()
- B. write.start()
- C. write.stream()
- D. write.format()
Answer: A. write.save()
Explanation: The write.save() method is used in Spark DataFrameWriter to write data into Azure Cosmos DB.
True or False: Spark streaming cannot write data in real-time into Azure Cosmos DB.
- True
- False
Answer: False
Explanation: Spark supports real-time data processing, which allows it to write data into Azure Cosmos DB in real-time.
Which parameter should be set to true to enable upserts when writing data back to Azure Cosmos DB from Spark?
- A. cosmos.enableUpsert
- B. cosmos.writeUpsert
- C. cosmos.spark.upsert
- D. cosmos.db.upsert
Answer: A. cosmos.enableUpsert
Explanation: The parameter cosmos.enableUpsert should be set to true to enable upsert operations, which will update an existing record or insert a new record if it does not already exist.
True or False: If partition key isn’t defined explicitly when writing data from Spark, it will choose a random partition.
- True
- False
Answer: True
Explanation: If the partition key is not explicitly set when writing data from Spark to Azure Cosmos DB, the Cosmos DB Spark connector will choose a random partition. This could lead to inefficient data distribution.
Interview Questions
How do you connect Apache Spark to Azure Cosmos DB?
You use the Azure Cosmos DB Spark connector, which allows your Spark jobs to read from and write directly to Azure Cosmos DB.
Which Cosmos DB API should be used to work with Spark?
The Cosmos DB SQL API should be used.
What does Azure Cosmos DB Spark connector provide?
The Azure Cosmos DB Spark connector provides a way to write data from Spark DataFrames to Azure Cosmos DB collections, and to read data from Azure Cosmos DB collections into Spark DataFrames.
Is it necessary to convert a DataFrame to an RDD before writing it to Azure Cosmos DB?
No, it is not necessary. The Cosmos DB Spark connector can write DataFrames directly to Cosmos DB.
How can data be partitioned when writing from Spark to Cosmos DB?
The Cosmos DB connector automatically partitions the data when writing it to Cosmos DB. The number of partitions is equal to the number of Spark tasks.
Can Azure Cosmos DB Spark connector handle schema evolution?
Yes, Azure Cosmos DB Spark connector is designed to handle schema evolution.
What should be ensured while saving a DataFrame to a Azure Cosmos DB collection?
It should be ensured that the DataFrame has a column with the name id and it’s unique.
What happens if an error occurs while writing data to Azure Cosmos DB from Spark?
If an error occurs while writing data, the Spark job will fail and an exception will be thrown.
Can you specify the throughputs for writes in Cosmos DB?
Yes, you can specify the write throughput by configuring the ‘WriteThroughputBudget’ setting in the connection configuration.
What is the role of ‘Upsert’ in writing data back to Azure Cosmos DB?
‘Upsert’ is a configuration setting that when set to true, the connector will use the upsert functionality of Cosmos DB. This means that if a document with a given ‘id’ already exists, it will be updated instead of creating a new one.
What happens when ‘Upsert’ is set to false?
When ‘Upsert’ is set to false, the connector will attempt to insert all documents. If a document with the same ‘id’ already exists, then the operation will fail.
Is it possible to write a streaming DataFrame to Azure Cosmos DB?
Yes, you can write a streaming DataFrame to Cosmos DB using Azure Cosmos DB Spark connector.
How to handle exceptions while writing to Cosmos DB using Spark connector?
You can handle exceptions by defining a task failure listener in your Spark application to handle any failures that occur during write operations.
Why would someone need to write from Apache Spark back to Azure Cosmos DB?
This can be necessary for various data processing tasks, such as machine learning model training, data cleansing, aggregation and other transformations, where the results need to be saved back into Cosmos DB for further use or analysis.
What is the main advantage of using Azure Cosmos DB Spark connector?
The main advantage of using the Azure Cosmos DB Spark connector is that it allows direct reading/writing to/from Azure Cosmos DB, hence, removing the need for any intermediaries. This enhances the performance and reduces the latency drastically.
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