A Chunked-Object Pattern for Multi-Region Large Payload Storage in Managed NoSQL Databases

TL;DR

This paper introduces a chunked-object pattern for storing large objects within managed NoSQL databases, overcoming size limits and reducing cross-region latency and consistency issues in multi-region deployments.

Contribution

The paper defines and implements a chunked-object pattern that stores large entities as ordered chunks within the database, eliminating reliance on external object storage.

Findings

Reduces cross-region time-to-consistency for large payloads.

Eliminates race conditions caused by replication lag.

Demonstrates effectiveness with over 200,000 transactions per hour.

Abstract

Many managed key-value and NoSQL databases - such as Amazon DynamoDB, Azure Cosmos DB, and Google Cloud Firestore - enforce strict maximum item sizes (e.g., 400 KB in DynamoDB). This constraint imposes significant architectural challenges for applications requiring low-latency, multi-region access to objects that exceed these limits. The standard industry recommendation is to offload payloads to object storage (e.g., Amazon S3) while retaining a pointer in the database. While cost-efficient, this "pointer pattern" introduces network overhead and exposes applications to non-deterministic replication lag between the database and the object store, creating race conditions in active-active architectures. This paper presents a "chunked-object" pattern that persists large logical entities as sets of ordered chunks within the database itself. We precisely define the pattern and provide a reference implementation using Amazon DynamoDB Global Tables. The design generalizes to any key-value store with per-item size limits and multi-region replication. We evaluate the approach using telemetry from a production system processing over 200,000 transactions per hour. Results demonstrate that the chunked-object pattern eliminates cross-system replication lag hazards and reduces p99 cross-region time-to-consistency for 1 MB payloads by keeping data and metadata within a single consistency domain.