After spending years building distributed systems, we’ve learned that handling data changes in real time is one of those challenges that initially seems simple but quickly becomes complex. Today, we will examine how the Azure Cosmos DB Change Feed revolutionizes real-time data processing and why it might be the solution we’ve been seeking.
The Challenge of Data Change Tracking
Many applications heavily rely on tracking data modifications – both additions and updates. Traditionally, architects default to polling-based approaches, which come with significant drawbacks:
- High resource consumption
- Delayed results
- Business intelligence lag
- Frustrated stakeholders waiting for timely analytics
Enter Cosmos DB Change Feed
Imagine having a dedicated observer monitoring your database 24/7, meticulously recording every change. That’s essentially what Cosmos DB Change Feed provides – a continuous stream of modifications occurring within the Cosmos DB container. Every document insertion or update is captured and made available for processing in real time.
The real breakthrough here is that we could do much more than just analytics. We can use Change Feed for:
1. Event notifications: Real-time messaging for status updates via event-driven architectures.
2. Data replication: Syncing a read-only replica for reporting without impacting performance.
3. Cache invalidation: Auto-refreshing cached data on updates to prevent staleness.
Setting up the Change Feed in Cosmos DB is straightforward. First, we need a container with a partition key. Change Feed works at the container level, so make sure you’ve designed partitions correctly. Then, we enable the Change Feed in the container’s settings or through the SDK. The key configurations to consider include lease containers (which track processing state), polling intervals (to control how often changes are read), and retention policies (to decide how long changes are kept). Once configured, we can use Azure Functions, Event Hubs, or custom processors to start consuming the Change Feed and power real-time applications!
Let’s consider the following use case to implement the Cosmos DB Feed.
A financial services company needs a secure, scalable audit logging system for tracking changes to customer records. The system must:
- Capture all modifications (e.g., profile updates, account changes).
- Maintain a history of changes for compliance and forensic investigations.
- Optimize storage and query performance for auditing and reporting.
By leveraging Cosmos DB Change Feed, we can:
- Capture data changes in real time.
- Transform changes into an optimized audit schema.
- Store only deltas (field-level changes) instead of full snapshots.
For the use case, the high-level architecture will look like this.
In this case, instead of storing entire objects repeatedly for updates, we can store only changes in a dedicated audit container with a time-series model:
We can use an Azure Function that listens to the Cosmos DB Change Feed and writes transformed data into the audit log.
Once changes are stored efficiently, querying becomes fast and cost-effective. For example, we can query to find all email changes for a user using the following query.
Understanding Limitations
The Change Feed in Cosmos DB is powerful but comes with challenges. It only captures inserts and updates; the deletions capture is in preview right now. Throughput limitations can also be an issue—high-volume workloads need properly scaled consumers. Since Change Feed is partition-based, cross-partition order isn’t guaranteed, which can be tricky in some cases.
