Azure Documentation

Scaling Azure Storage Tables with Azure Cosmos DB

This tutorial explores strategies for scaling applications that rely on Azure Storage Tables, particularly when facing limitations of the traditional Table storage service. We will introduce Azure Cosmos DB for Table as a powerful alternative for achieving higher throughput, lower latency, and global distribution.

Understanding Azure Storage Tables

Azure Storage Tables is a NoSQL key-value store that allows you to store large amounts of structured, non-relational data. It's cost-effective and highly scalable for many common scenarios. However, it has specific limits on request units (RUs) and partitioning that can become a bottleneck for high-demand applications.

Common Limitations of Azure Storage Tables

• Throughput Limits: While scalable, there are soft limits on transaction rates per partition and storage account.
• Partitioning: Effective partitioning is crucial for performance but can be complex to manage for extremely large datasets or high concurrency.
• Global Distribution: Native Azure Storage Tables are regionally deployed. Achieving multi-region writes or reads requires additional complexity.
• Request Unit (RU) Management: Performance is metered by RUs, and managing RU consumption can be challenging under unpredictable loads.

Introducing Azure Cosmos DB for Table

Azure Cosmos DB is Microsoft's globally distributed, multi-model database service. Azure Cosmos DB for Table offers an API that is compatible with the Azure Storage Table API, allowing you to migrate existing Table storage applications with minimal code changes while unlocking significant scalability and performance benefits.

Key Benefits of Azure Cosmos DB for Table

• Elastic Scalability: Independently scale storage and throughput (RUs) up or down.
• Low Latency: Guaranteed single-digit millisecond latency for reads and writes, even globally.
• Global Distribution: Turnkey global distribution with active-active replication.
• High Availability: Built-in high availability with 99.999% availability guarantees.
• Comprehensive SLAs: Service Level Agreements covering throughput, latency, availability, and consistency.
• Advanced Features: Change Feed, TTL, backup and restore, and more.

Migrating to Azure Cosmos DB for Table

Migrating your Azure Storage Tables to Azure Cosmos DB for Table can be achieved through various strategies:

1. Lift-and-Shift (Direct Migration): For many applications, you can create a Cosmos DB account, provision a table with desired throughput, and point your application to the new endpoint. This often requires minimal code changes, primarily updating connection strings and potentially some SDK configurations.
2. Phased Migration: Gradually migrate specific tables or partitions. This allows for testing and validation in a production environment before a full cutover.
3. Data Migration Tools: Utilize tools like Azure Data Factory or custom scripts to copy data from existing Azure Storage Tables to Cosmos DB for Table. This is often necessary for large datasets or when a direct API switch isn't feasible initially.

When migrating, consider the following:

• Partition Key Choice: A well-designed partition key is still critical for performance and scalability in Cosmos DB for Table.
• Throughput Provisioning: Choose between manual or autoscale throughput based on your workload predictability.

Performance Tuning and Best Practices

Even with Cosmos DB for Table, optimal performance requires attention to detail:

• Optimize Partition Keys: Distribute your data and requests evenly across partitions to avoid "hot partitions."
• Batch Operations: Use batch inserts and updates to reduce the number of requests and improve efficiency.
• Indexing: Cosmos DB for Table automatically indexes all properties. Understand the query patterns to optimize performance.
• Select Properties: Project only the properties you need in your queries to reduce data transfer.
• Monitor RUs: Keep an eye on Request Unit consumption through Azure Monitor and adjust provisioned throughput as needed.

Here's a simple example of how your connection string might change:

// Azure Storage Tables connection string example
"DefaultEndpointsProtocol=https;AccountName=yourstorageaccount;AccountKey=YOUR_ACCOUNT_KEY;EndpointSuffix=core.windows.net"

// Azure Cosmos DB for Table connection string example
"AccountEndpoint=https://yourcosmosdbaccount.table.cosmos.azure.com:443/;AccountKey=YOUR_COSMOSDB_KEY;"
            

Conclusion

Azure Storage Tables is an excellent solution for many use cases. However, when your application demands higher throughput, lower latency, global reach, or more robust SLAs, Azure Cosmos DB for Table provides a seamless path to scale. By understanding the differences and employing the right migration and tuning strategies, you can ensure your applications remain performant and scalable in the cloud.