Azure Table Storage is best suited for queries that require fast, scalable, and cost-effective storage of structured, non-relational data. Key scenarios include:
1. PartitionKey-based: Queries targeting a specific partition key perform optimally due to the natural partitioning scheme.
2. RowKey-based: Within a partition, querying by row key provides efficient access to individual entities.
3. Point queries: Fetching single entities using both partition and row keys ensures optimal performance.
To optimize query performance:
– Design an effective partitioning strategy, considering query patterns and load balancing across partitions.
– Minimize cross-partition queries as they can lead to higher latency and reduced throughput.
– Use continuation tokens for large result sets, enabling pagination and reducing response times.
– Avoid unnecessary properties in projections to reduce payload size and improve query speed.
– Leverage ETag values for optimistic concurrency control, ensuring data consistency without additional round-trips.
– Cache frequently accessed data to minimize requests and reduce latency.
