Change feed in Azure Cosmos DB - overview

Change feed support in Azure Cosmos DB works by listening to an Azure Cosmos container for any changes. It then outputs the sorted list of documents that were changed in the order in which they were modified. The changes are persisted, can be processed asynchronously and incrementally, and the output can be distributed across one or more consumers for parallel processing.

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 additional actions. Examples of additional actions include:

  • Triggering a notification or a call to an API, when an item is inserted or updated.
  • Real-time stream processing for IoT or real-time analytics processing on operational data.
  • Additional data movement by either synchronizing with a cache or a search engine or a data warehouse or archiving data to 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:

Using Azure Cosmos DB change feed to power real-time analytics and event-driven computing scenarios

Supported APIs and client SDKs

This feature is currently supported by the following Azure Cosmos DB APIs and client SDKs.

Client drivers Azure CLI SQL API Azure Cosmos DB's API for Cassandra Azure Cosmos DB's API for MongoDB Gremlin API Table API
.NET NA Yes Yes Yes Yes No
Java NA Yes Yes Yes Yes No
Python NA Yes Yes Yes Yes No
Node/JS NA Yes Yes Yes Yes No

Change feed and different operations

Today, you see all operations in the change feed. The functionality where you can control change feed, for specific operations such as updates only and not inserts is not yet available. You can add a “soft marker” on the item for updates and filter based on that when processing items in the change feed. Currently change feed doesn’t log deletes. Similar to the previous example, you can add a soft marker on the items that are being deleted, for example, you can add an attribute in the item called "deleted" and set it to "true" and set a TTL on the item, so that it can be automatically deleted. You can read the change feed for historic items (the most recent change corresponding to the item, it doesn't include the intermediate changes), for example, items that were added five years ago. If the item is not deleted you can read the change feed as far as the origin of your container.

Sort order of items in change feed

Change feed items come in the order of their modification time. This sort order is guaranteed per logical partition key.

Change feed in multi-region Azure Cosmos accounts

In a multi-region Azure Cosmos account, if a write-region fails over, change feed will work across the manual failover operation and it will be contiguous.

Change feed and Time to Live (TTL)

If a TTL (Time to Live) property is set on an item to -1, change feed will persist forever. If the data is not deleted, it will remain in the change feed.

Change feed and _etag, _lsn or _ts

The _etag format is internal and you should not take dependency on it, because it can change anytime. _ts is a modification or a creation timestamp. You can use _ts for chronological comparison. _lsn is a batch ID that is added for change feed only; it represents the transaction ID. Many items may have same _lsn. ETag on FeedResponse is different from the _etag you see on the item. _etag is an internal identifier and is used for concurrency control tells about the version of the item, whereas ETag is used for sequencing the feed.

Change feed use cases and scenarios

Change feed enables efficient processing of large datasets with a high volume of writes. Change feed also offers an alternative to querying an entire dataset to identify what has changed.

Use cases

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

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

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

  • Perform zero down-time migrations to another Azure Cosmos account or another Azure Cosmos container with a different logical partition key.

  • Implement lambda architecture using Azure Cosmos DB, where Azure Cosmos DB supports both real-time, batch and query serving layers, thus enabling lambda architecture with low TCO.

  • Receive and store event data from devices, sensors, infrastructure and applications, and process these events in real time, for example, using Spark. The following image shows how you can implement lambda architecture using Azure Cosmos DB via change feed:

Azure Cosmos DB-based lambda pipeline for ingestion and query


The following are some of the scenarios you can easily implement with change feed:

  • Within your serverless web or mobile apps, you can track events such as all the changes to your customer's profile, preferences, or their location and trigger certain actions, for example, sending push notifications to their devices using Azure Functions.

  • If you're using Azure Cosmos DB to build a game, you can, for example, use change feed to implement real-time leaderboards based on scores from completed games.

Working with change feed

You can work with change feed using the following options:

Change feed is available for each logical partition key within the container, and it can be distributed across one or more consumers for parallel processing as shown in the image below.

Distributed processing of Azure Cosmos DB change feed

Features of change feed

  • Change feed is enabled by default for all Azure Cosmos accounts.

  • You can use your provisioned throughput to read from the change feed, just like any other Azure Cosmos DB operation, in any of the regions associated with your Azure Cosmos database.

  • The change feed includes inserts and update operations made to items within the container. You can capture deletes by setting a "soft-delete" flag within your items (for example, documents) in place of deletes. Alternatively, you can set a finite expiration period for your items with the TTL capability. For example, 24 hours and use the value of that property to capture deletes. With this solution, you have to process the changes within a shorter time interval than the TTL expiration period.

  • Each change to an item appears exactly once in the change feed, and the clients must manage the checkpointing logic. If you want to avoid the complexity of managing checkpoints, the change feed processor provides automatic checkpointing and "at least once" semantics. See using change feed with change feed processor.

  • Only the most recent change for a given item is included in the change log. Intermediate changes may not be available.

  • The change feed is sorted by the order of modification within each logical partition key value. There is no guaranteed order across the partition key values.

  • Changes can be synchronized from any point-in-time, that is there is no fixed data retention period for which changes are available.

  • Changes are available in parallel for all logical partition keys of an Azure Cosmos container. This capability allows changes from large containers to be processed in parallel by multiple consumers.

  • Applications can request multiple change feeds on the same container simultaneously. ChangeFeedOptions.StartTime can be used to provide an initial starting point. For example, to find the continuation token corresponding to a given clock time. The ContinuationToken, if specified, wins over the StartTime and StartFromBeginning values. The precision of ChangeFeedOptions.StartTime is ~5 secs.

Change feed in APIs for Cassandra and MongoDB

Change feed functionality is surfaced as change stream in MongoDB API and Query with predicate in Cassandra API. To learn more about the implementation details for MongoDB API, see the Change streams in the Azure Cosmos DB API for MongoDB.

Native Apache Cassandra provides change data capture (CDC), a mechanism to flag specific tables for archival as well as rejecting writes to those tables once a configurable size-on-disk for the CDC log is reached. The change feed feature in Azure Cosmos DB API for Cassandra enhances the ability to query the changes with predicate via CQL. To learn more about the implementation details, see Change feed in the Azure Cosmos DB API for Cassandra.

Next steps

You can now proceed to learn more about change feed in the following articles: