Scalability and performance targets for Azure Table storage

Azure Table storage is a NoSQL key-attribute store that accepts un-structured data. It's ideal for storing large amounts of manageable data. This document outlines the scalability and performance targets for Azure Table storage.

Understanding Throughput

Throughput in Azure Table storage is measured in Request Units (RUs). A request unit is a normalized measure of throughput. Different types of operations consume different numbers of RUs.

Request Unit Consumption

• A read operation typically consumes 1 RU.
• A write operation typically consumes 2 RUs.
• Batch operations consume RUs based on the number of entities involved, with a minimum of 2 RUs per operation in the batch.

Understanding RU consumption is crucial for capacity planning and cost management.

Partitioning for Scalability

Azure Table storage organizes data into partitions. A partition is a set of entities with the same partition key. The partition key is a string value that identifies the partition to which an entity belongs. All entities within a partition are stored together and are generally ordered by their row key within that partition.

Partition Key Design

A well-designed partition key is essential for achieving high scalability and performance. Consider the following:

• Distribute data evenly across partitions to avoid hot spots.
• Keep partition sizes manageable. Large partitions can lead to slower query performance.
• Use partition keys that align with common query patterns.

Performance Targets

Azure Table storage offers high throughput and low latency. The specific performance targets can vary based on factors like partition design, the number of partitions, and the type of operations being performed.

Table Storage Limits

As of the latest updates, Azure Table storage provides the following general targets:

• Storage Capacity: Virtually unlimited.
• Throughput per Storage Account: Scales automatically, but design is key. Individual partitions have higher limits than the overall account.
• Transactions per Second: Can reach tens of thousands, especially with well-partitioned data and appropriate client-side batching.
• Latency: Typically single-digit milliseconds for reads and writes within a partition.

Best Practices for Scalability

To maximize the scalability and performance of your Azure Table storage solution, follow these best practices:

1. Design Your Partition Key Wisely

This is the most critical aspect of scaling with Table storage. Aim for partition keys that distribute your data and workload evenly.

2. Leverage Batch Operations

Combine multiple insert, update, or delete operations into a single batch transaction. This significantly reduces the number of individual requests and improves efficiency.

// Example of a batch operation (conceptual)
const batch = tableClient.createBatch();
const entity1 = { partitionKey: "user1", rowKey: "profile", name: "Alice" };
const entity2 = { partitionKey: "user1", rowKey: "settings", theme: "dark" };

batch.createEntity(entity1);
batch.updateEntity(entity2, "replace"); // or merge

await tableClient.submitBatch(batch);
            

3. Use Row Keys Effectively

Row keys uniquely identify an entity within a partition. They are also used for sorting within a partition. Choose them to optimize your query patterns.

4. Monitor Your Performance

Use Azure Monitor to track your storage account's performance metrics, including RU consumption, latency, and error rates. Identify and address any bottlenecks.

5. Consider Table Design Patterns

Explore common table design patterns such as the 80/20 pattern or the time-series pattern to suit your specific application needs.

Scaling Beyond a Single Partition

While individual partitions can handle high loads, the true power of Table storage comes from having many partitions. When your data or workload grows, your strategy should be to add more partitions with new partition keys rather than trying to scale a single, massive partition indefinitely.

By carefully designing your data model and leveraging the features of Azure Table storage, you can build highly scalable and performant applications.