Microsoft Azure Cosmos DB is a schema-less, multi-model database service designed for robust and scalable application development. A unique feature of this platform is the change feed.
The change feed in Cosmos DB is an ordered list of documents within a container, organized according to their modification time. The change feed is persistent, which means once an item appears in the feed, it will always be there. The change feed supports a dynamic set size, managing the throughput efficiently to digest the change feed efficiently, and can be pulled for data changes asynchronously, or pushed out to a set of registered observers.
To effectively utilize this feature, it is important to manage the number of change feed instances. This is where the Change Feed Estimator comes to the forefront.
The Change Feed Estimator
The Change Feed Estimator is an API that estimates the lag between the current instant and the change feed’s update on each physical partition. This estimation shows the number of outstanding changes that have not been read by a consumer. By efficiently managing the number of change feed instances, multiple instances can be distributed across different regions giving your application both balance and high availability.
How to Use the Change Feed Estimator
Firstly, you need to create a new `ChangeFeedEstimator`:
ChangeFeedEstimator estimator =
client.GetContainer(databaseId, containerId).GetChangeFeedEstimator(leaseContainerId, processorName);
In the above example, `databaseId` is the ID of the source Azure Cosmos container’s database, `containerId` is the ID of the source Azure Cosmos container, `leaseContainerId` is the ID of the leases Azure Cosmos container, and `processorName` is the name of the change feed processor.
After creating the estimator, you can get the estimation as follows:
IAsyncEnumerator
while (await enumerator.MoveNextAsync())
{
Console.WriteLine($”Partition {enumerator.Current.LeaseToken} at {enumerator.Current.EstimatedLag} items.”);
}
From this example, you’ll note that the estimation is per partition. This is because the change feed operations work at a partition level, which makes it easily parallelized and distributed.
Summing up the Benefits of Change Feed
Using the change feed and change feed estimator in Azure Cosmos DB offers several benefits:
- Real-time reaction: Since the feed operates in real-time, applications can react promptly to any changes in the data. This enables a more responsive and dynamic application.
- Distributed processing: Since the change feed is partitioned, it allows for distributed data processing. This means that the workload can be spread across multiple machines or processes, which boosts overall performance and scalability.
- Reliability: Since the change feed is persistent, it guarantees that all changes will eventually be read. This is particularly important in systems where consistency and completeness of data is a must.
In conclusion, Azure Cosmos DB’s Change Feed, in combination with the Change Feed Estimator, allows developers to build scalable, distributed and real-time applications. The estimator helps manage the number of instances and provides visibility into how much outstanding work remains to be processed. By effectively using these tools, you can ensure the change feed’s processing is balanced and highly available.
Practice Test
True or False: Change Feed Estimator is not a feature of Microsoft Azure Cosmos DB.
- True
- False
Answer: False
Explanation: Change Feed Estimator is a feature of Microsoft Azure Cosmos DB that can help estimate the number of remaining changes in the change feed.
Which of the following can be managed by using the Change Feed Estimator?
- a) Number of change feed instances
- b) Network traffic
- c) Data replication
- d) Storage capacity
Answer: a) Number of change feed instances
Explanation: The Change Feed Estimator is primarily used for managing the number of change feed instances in Microsoft Azure Cosmos DB.
True or False: The Change Feed Estimator can be used to calculate the number of changes waiting to be read for a partition key range.
- True
- False
Answer: True
Explanation: The Change Feed Estimator calculates the number of changes awaiting to be read for a partition key range, based on the difference between the state of the change feed and the state persisted by a change feed processor.
_____________ is an efficient way to continuously read insert and update operations done on items within an Azure Cosmos container.
- a) Change Feed
- b) Bulk Executor
- c) Azure SQL Server
- d) Cosmos Map
Answer: a) Change Feed
Explanation: Change Feed in Azure Cosmos DB is an efficient way to read insert and update operations done on items within an Azure Cosmos container.
True or False: The Change Feed Estimator feature of Azure Cosmos DB doesn’t support auto-scaling.
- True
- False
Answer: False
Explanation: The Change Feed Estimator helps in auto-scaling by estimating the number of remaining changes in the change feed.
Which of the following method is used to get an instance of Change Feed Estimator in Azure Cosmos DB?
- a) GetChangeFeedEstimator()
- b) CreateChangeFeedEstimator()
- c) InstantiateChangeFeedEstimator()
- d) CallChangeFeedEstimator()
Answer: a) GetChangeFeedEstimator()
Explanation: The GetChangeFeedEstimator() method is used to get an instance of Change Feed Estimator in Azure Cosmos DB.
True or False: You cannot manage the backlog of change feed instances using the Change Feed Estimator.
- True
- False
Answer: False
Explanation: The Change Feed Estimator is designed to help manage the backlog of change feed instances by estimating the number of remaining changes in the change feed.
Which of the following metrics cannot be estimated by the Change Feed Estimator?
- a) Number of changes awaiting to be read
- b) Number of change feed instances
- c) Amount of data storage used
- d) Number of insert and update operations
Answer: c) Amount of data storage used
Explanation: The Change Feed Estimator is primarily used to estimate the number of changes waiting to be read and the number of change feed instances. It does not estimate the amount of data storage used.
Is it obligatory to use Change Feed Estimator for managing change feed instances when working with Microsoft Azure Cosmos DB?
- a) Yes
- b) No
Answer: b) No
Explanation: While the Change Feed Estimator can assist with managing change feed instances, it is not obligatory to use it.
The Change Feed Processor and Change Feed Estimator:
- a) perform the same tasks
- b) cannot work together
- c) complement each other
- d) are alternatives to each other
Answer: c) complement each other
Explanation: The Change Feed Processor and Change Feed Estimator can be used together where the former processes changes and the latter estimates the number of remaining changes in the feed.
Interview Questions
What is the primary function of the change feed estimator in Microsoft Azure Cosmos DB?
The change feed estimator is used for maintaining and controlling the number of change feed instances in a Cosmos DB. It provides an interface to estimate the amount of changes that still need to be processed in your change feeds.
How does the change feed estimator help in managing the number of change feed instances?
The change feed iterator offers an interface for getting lease token ranges and their corresponding remaining work in terms of the number of documents. This information can be used to scale processing up or down by increasing or decreasing the number of instances reading from the change feed.
What is the purpose of a lease token in the context of the change feed estimator?
A lease token is used to represent a logical partition key range that change feed instances distribute and process. It’s used to scale and distribute processing of change feed over multiple instances.
Define the term “Estimatble Change Feed” in context of Azure Cosmos DB.
The Estimatable Change Feed allows the client library to interact with the change feed and get an estimation of remaining work required in terms of number of documents that have not been processed yet.
What Azure component can be monitored for scalability with the help of the change feed estimator?
The Azure Functions can be monitored for scalability with the help of the change feed estimator.
Can the change feed estimator be used in mono-partitioned Cosmos DB collections?
Yes, it can be used in both mono-partitioned and multi-partitioned Cosmos DB collections to manage the number of change instances.
How does LeaseToken Expiration work in Cosmos DB’s Change Feed?
Lease tokens do not expire in Cosmos DB’s Change Feed. They are kept alive and updated as long as the host that holds that lease is not down.
What is the role of Azure Functions triggered by Cosmos DB Change Feed?
Azure Functions triggered by Cosmos DB Change Feed help to perform real-time data processing as the data changes in the database.
How many types of change feed processing models exist in Cosmos DB?
There are two types of change feed processing models: Static and Dynamic distribution models.
What is the difference between a Static and Dynamic distribution model in change feed processing?
In a static distribution, a fixed amount of partitions is predefined, while in a dynamic distribution, leases (or partitions) can increase or decrease based on the work estimation generated by the Change Feed Estimator.
How do you define the “Remaining Work” in the Change Feed Estimator?
The “Remaining Work” is a measure of the estimated number of documents that have not yet been processed by a Change Feed processor instance.
Why is the Change Feed Estimator important for optimizing resource use in Azure Cosmos DB?
The Change Feed Estimator allows developers to scale processing based on the actual number of changes to process, avoiding overutilization or underutilization of resources, and hence, save operational costs.
What programming languages are supported for using the Change Feed Estimator in Cosmos DB?
The Change Feed Estimator is currently supported in .NET Standard SDK only.
Can data be re-read from Change Feed in Cosmos DB?
Yes, Change Feed in Cosmos DB keeps track of the changes in data over time, and hence information can be read again from it.
Can the Change Feed Estimator be used to manage cross-region replications in Cosmos DB?
No, the Change Feed Estimator is not designed for managing cross-region replication, but instead for managing and scaling Change Feed processor instances based on the volume of changes in the data.
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