Azure Table Storage achieves scalability through partitioning and load balancing. Data is organized into partitions based on the PartitionKey, allowing for horizontal scaling across multiple storage nodes. This ensures high throughput and low latency as data grows.
To monitor performance, use Azure Monitor to track metrics like total requests, average latency, and throttling rates. Set up alerts for anomalies or thresholds that may indicate issues. Analyze logs with Log Analytics to identify trends and potential bottlenecks.
For maintaining performance, consider these strategies:
1. Optimize PartitionKey design to distribute workload evenly.
2. Use Entity Group Transactions for batch operations within a single partition.
3. Cache frequently accessed data to reduce read operations.
4. Implement retries with exponential backoff for transient failures.
5. Monitor and adjust provisioned throughput based on usage patterns.
6. Regularly review and update access policies and security settings.
