{"ID":2832035,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.06852","arxiv_id":"2512.06852","title":"A Chunked-Object Pattern for Multi-Region Large Payload Storage in Managed NoSQL Databases","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.","short_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 excee...","url_abs":"https://arxiv.org/abs/2512.06852","url_pdf":"https://arxiv.org/pdf/2512.06852v1","authors":"[\"Manideep Reddy Chinthareddy\"]","published":"2025-12-07T14:06:09Z","proceeding":"cs.DB","tasks":"[\"cs.DB\",\"cs.DC\"]","methods":"[]","has_code":false}