Scalability and performance targets for Queue Storage
This reference details scalability and performance targets for Azure Storage. The scalability and performance targets listed here are high-end targets, but are achievable. In all cases, the request rate and bandwidth achieved by your storage account depends upon the size of objects stored, the access patterns utilized, and the type of workload your application performs.
Make sure to test your service to determine whether its performance meets your requirements. If possible, avoid sudden spikes in the rate of traffic and ensure that traffic is well-distributed across partitions.
When your application reaches the limit of what a partition can handle for your workload, Azure Storage begins to return error code 503 (Server Busy) or error code 500 (Operation Timeout) responses. If 503 errors are occurring, consider modifying your application to use an exponential backoff policy for retries. The exponential backoff allows the load on the partition to decrease, and to ease out spikes in traffic to that partition.
Scale targets for Queue Storage
| Resource | Target |
|---|---|
| Maximum size of a single queue | 500 TiB |
| Maximum size of a message in a queue | 64 KiB |
| Maximum number of stored access policies per queue | 5 |
| Maximum request rate per storage account | 20,000 messages per second, which assumes a 1-KiB message size |
| Target throughput for a single queue (1-KiB messages) | Up to 2,000 messages per second |