What is the Claim-Check Pattern in Event-Driven Systems?
Understanding How to Handle Large & Sensitive Payloads in Distributed Systems
In distributed systems, communication between services often relies on message passing. Messages are sent between components to trigger actions, share data, or synchronize states. However, as systems grow in complexity, the size of these messages can become a significant challenge. Transmitting messages containing large payloads directly can overwhelm the message broker, consume excessive bandwidth, and slow down processing. Additionally, some message brokers have size limits for messages, making it impossible to send large payloads directly.
What is the Claim-Check Pattern?
The Claim-Check Pattern is a messaging design pattern that separates the transmission of large data payloads from the actual message. Instead of sending the entire payload within the message, the pattern stores the payload in an external storage system (e.g., a database, blob storage, or file system) and sends only a reference to the stored data in the message. This reference is often called a "claim check," akin to a ticket you receive at a coat check. This is achieved through a multi-step process:
Store the Payload: When a service needs to send a large message, it first uploads the payload to an external storage system. This could be a cloud storage service like Amazon S3, Azure Blob Storage, or a distributed file system.
Generate a Reference: Once the payload is stored, the service or database generates a unique reference (e.g., a URL, file path, or database key) that points to the stored data.
Send the Claim Check: Instead of sending the entire payload, the sender service sends a lightweight message containing only the reference (claim check) and any necessary metadata.
Retrieve the Payload: The receiving service uses the claim check to fetch the payload from the external storage system when needed.
Claim-Check with Event Sourcing for Privacy
Event Sourcing is a pattern in which an application's state is determined by a sequence of events. However, storing all information directly in events can raise privacy concerns when dealing with sensitive data. Claim-Check improves privacy within Event Sourcing by ensuring sensitive data is not directly stored within the event stream.
Storing Sensitive Data Separately: Instead of embedding sensitive data (e.g., personally identifiable information or PII) directly in events, store it in a secure external storage system.
Generating Claim Check for Sensitive Data: Include only a claim check (reference) in the event log. This ensures that sensitive data is not exposed in the event stream.
Access Control for Payload Retrieval: Implement strict access controls on the external storage system to ensure that only authorized services or users can retrieve the sensitive data.
Audit Trail: Event sourcing naturally provides an audit trail of events. Combined with access logs for the storage location, a complete audit trail of data access is maintained.
Data Retention Policies: Apply data retention policies to the external storage system to automatically delete or anonymize sensitive data after a specified period, ensuring compliance with privacy regulations like GDPR.
Data Encryption: Sensitive data can be encrypted before storage, providing an additional layer of security.
Pros and Cons
Pros
Reduced Message Size: By storing large payloads externally, messages become lightweight, improving performance and reducing bandwidth usage.
Enhanced Security: Sensitive data can be stored separately and accessed with controlled authorization. Separate retention policies can be applied to data and metadata.
Increased Scalability: Smaller messages reduce the load on message brokers, enabling the system to handle higher message volumes.
Cost Efficiency: Storing large payloads in cost-effective storage systems (e.g., object storage) can be cheaper than transmitting them through a message broker.
Cons
Increased Complexity: The pattern introduces additional components (e.g., external storage) and steps (e.g., uploading and retrieving payloads), which can complicate the system.
Latency Overhead: Fetching payloads from external storage adds latency, which may not be acceptable in low-latency systems.
Dependency on External Storage: The system becomes dependent on the availability/performance of the external storage system. If the storage system becomes unavailable, messages cannot be fully processed.
Potential for Orphaned Data: If the claim check is lost or not processed, the stored data may become orphaned, consuming storage space without purpose.
If you enjoyed this article, please hit the ❤️ like button.
If you think someone else will benefit from this, then please 🔁 share this post.
Great topic. Thanksssss mate