Change feed design patterns in Azure Cosmos DB

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APPLIES TO: NoSQL

The Azure Cosmos DB change feed enables efficient processing of large datasets that have a high volume of writes. Change feed also offers an alternative to querying an entire dataset to identify what has changed. This article focuses on common change feed design patterns, design tradeoffs, and change feed limitations.

Azure Cosmos DB is well-suited for IoT, gaming, retail, and operational logging applications. A common design pattern in these applications is to use changes to the data to trigger other actions. Examples of these actions include:

  • Triggering a notification or a call to an API when an item is inserted, updated, or deleted.
  • Real-time stream processing for IoT or real-time analytics processing on operational data.
  • Data movement such as synchronizing with a cache, a search engine, a data warehouse, or cold storage.

The change feed in Azure Cosmos DB enables you to build efficient and scalable solutions for each of these patterns, as shown in the following image:

Diagram that shows using Azure Cosmos DB change feed to power real-time analytics and event-driven computing scenarios.

Event computing and notifications

The Azure Cosmos DB change feed can simplify scenarios that need to trigger a notification or send a call to an API based on a certain event. You can use the change feed processor to automatically poll your container for changes and then call an external API each time there's a write, update, or delete.

You can also selectively trigger a notification or send a call to an API based on specific criteria. For example, if you're reading from the change feed by using Azure Functions, you can put logic into the function to send a notification only if a condition is met. Although the Azure Function code would execute for each change, the notification would be sent only if the condition is met.

Real-time stream processing

The Azure Cosmos DB change feed can be used for real-time stream processing for IoT or real-time analytics processing on operational data. For example, you might receive and store event data from devices, sensors, infrastructure, and applications, and then process these events in real time by using Spark. The following image shows how you can implement a lambda architecture by using the Azure Cosmos DB change feed:

Diagram that shows an Azure Cosmos DB-based lambda pipeline for ingestion and query.

In many cases, stream processing implementations first receive a high volume of incoming data into a temporary message queue such as Azure Event Hubs or Apache Kafka. The change feed is a great alternative due to Azure Cosmos DB's ability to support a sustained high rate of data ingestion with guaranteed low read and write latency. The advantages of the Azure Cosmos DB change feed over a message queue include:

Data persistence

Data that's written to Azure Cosmos DB shows up in the change feed. The data is retained in the change feed until it's deleted if you read by using latest version mode. Message queues typically have a maximum retention period. For example, Azure Event Hubs offers a maximum data retention of 90 days.

Query ability

In addition to reading from an Azure Cosmos DB container's change feed, you can run SQL queries on the data that's stored in Azure Cosmos DB. The change feed isn't a duplication of data that's already in the container, but rather, it's just a different mechanism of reading the data. Therefore, if you read data from the change feed, the data is always consistent with queries of the same Azure Cosmos DB container.

High availability

Azure Cosmos DB offers up to 99.999% read and write availability. Unlike many message queues, Azure Cosmos DB data can be easily multiple-regionally distributed and configured with an recovery time objective (RTO) of zero.

After you process items in the change feed, you can build a materialized view and persist aggregated values back in Azure Cosmos DB. If you're using Azure Cosmos DB to build a game, for example, you can use change feed to implement real-time leaderboards based on scores from completed games.

Data movement

You can also read from the change feed for real-time data movement.

For example, the change feed helps you perform the following tasks efficiently:

  • Update a cache, search index, or data warehouse with data that's stored in Azure Cosmos DB.

  • Perform zero-downtime migrations to another Azure Cosmos DB account or to another Azure Cosmos DB container that has a different logical partition key.

  • Implement an application-level data tiering and archival. For example, you can store "hot data" in Azure Cosmos DB and age out "cold data" to other storage systems such as Azure Blob Storage.

When you have to denormalize data across partitions and containers, you can read from your container's change feed as a source for this data replication. Real-time data replication with the change feed can guarantee only eventual consistency. You can monitor how far the change feed processor lags behind in processing changes in your Azure Cosmos DB container.

Event sourcing

The event sourcing pattern involves using an append-only store to record the full series of actions on that data. Azure Cosmos DB's change feed is a great choice as a central data store in event sourcing architectures where all data ingestion is modeled as writes (no updates or deletes). In this case, each write to Azure Cosmos DB is an "event" and you'll have a full record of past events in the change feed. Typical uses of the events published by the central event store are for integration with external systems. Because there is no time limit for retention in the change feed, you can replay all past events by reading from the beginning of your Cosmos container's change feed.

You can have multiple change feed consumers subscribe to the same container's change feed. Aside from the lease container's provisioned throughput, there is no cost to utilize the change feed. The change feed is available in every container regardless of whether it is utilized.

Azure Cosmos DB is a great central append-only persistent data store in the event sourcing pattern because of its strengths in horizontal scalability and high availability. In addition, the change feed processor offers an "at least once" guarantee, ensuring that you don't miss processing any events.

Current limitations

The change feed has multiple modes that each have important limitations that you should understand. There are several areas to consider when you design an application that uses the change feed in either latest version mode or all versions and deletes mode.

Intermediate updates

In latest version mode, only the most recent change for a specific item is included in the change feed. When processing changes, you read the latest available item version. If there are multiple updates to the same item in a short period of time, it's possible to miss processing intermediate updates. If you would like to replay past individual updates to an item, you can model these updates as a series of writes instead or use all versions and deletes mode.

Deletes

The change feed latest version mode doesn't capture deletes. If you delete an item from your container, it's also removed from the change feed. The most common method of handling deletes is to add a soft marker on the items that are being deleted. You can add a property called deleted and set it to true at the time of deletion. This document update shows up in the change feed. You can set a Time to Live (TTL) on this item so that it can be automatically deleted later.

Retention

The change feed in latest version mode has an unlimited retention. As long as an item exists in your container, it's available in the change feed.

Guaranteed order

All change feed modes have a guaranteed order within a partition key value, but not across partition key values. You should select a partition key that gives you a guarantee of meaningful order.

For example, consider a retail application that uses the event sourcing design pattern. In this application, different user actions are each "events," which are modeled as writes to Azure Cosmos DB. Imagine if some example events occurred in the following sequence:

  1. Customer adds Item A to their shopping cart.
  2. Customer adds Item B to their shopping cart.
  3. Customer removes Item A from their shopping cart.
  4. Customer checks out and shopping cart contents are shipped.

A materialized view of current shopping cart contents is maintained for each customer. This application must ensure that these events are processed in the order in which they occur. For example, if the cart checkout were to be processed before Item A's removal, it's likely that Item A would have shipped to the customer, and not the item the customer wanted instead, Item B. To guarantee that these four events are processed in order of their occurrence, they should fall within the same partition key value. If you select username (each customer has a unique username) as the partition key, you can guarantee that these events show up in the change feed in the same order in which they're written to Azure Cosmos DB.

Examples

Here are some real-world change feed code examples for latest version mode that extend beyond the scope of the provided samples:

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