Azure Storage Documentation

Azure Storage Tables: Best Practices

Azure Table Storage offers a NoSQL key-attribute store that is ideal for storing large amounts of structured, non-relational data. Following these best practices will help you optimize performance, manage costs, and ensure the reliability of your applications.

1. Design Your Partition and Row Keys Carefully

The PartitionKey and RowKey are the primary keys for entities in a table. They determine how data is distributed across storage nodes and how efficiently you can retrieve it.

• PartitionKey: Use this to distribute your data evenly across storage partitions.
  • Avoid hot partitions: If a single PartitionKey is accessed far more frequently than others, it can become a bottleneck. Distribute writes and reads across multiple PartitionKeys.
  • Group related data: Use PartitionKey to group entities that are frequently accessed together. This allows for efficient range queries and batch operations.
  • Consider your query patterns: Design PartitionKeys to align with the most common ways you'll query your data. For example, if you always query by date, consider using a date component in your PartitionKey.
• RowKey: Use this to uniquely identify an entity within a partition. It should be unique within a PartitionKey.
  • Sortable and sequential: If you need to retrieve entities in a specific order within a partition, make your RowKey sortable (e.g., timestamps, GUIDs, or sequential numbers).
  • Avoid extremely large RowKeys: While flexible, very large RowKeys can impact storage efficiency.
• Combination Strategy: A common and effective strategy is to use a combination of a partition identifier and a sequential identifier or timestamp. For example:

  
  PartitionKey = "CustomerID"
  RowKey = "Timestamp"  // e.g., "20231027T103000Z" or a sequential number
                          

  Or, for more even distribution:

  
  PartitionKey = "CustomerID_YYYYMMDD"
  RowKey = "Timestamp"
                          

2. Optimize Query Performance

Efficient querying is crucial for a responsive application.

• Use PartitionKey and RowKey in Filter Clauses: Queries that filter on both PartitionKey and RowKey are the most efficient because they can target a specific partition.
• Prefer Point Queries (Get Entity): Retrieving a single entity by its full PartitionKey and RowKey is extremely fast.
• Leverage Table Query Projections: Select only the properties you need using the select clause. This reduces the amount of data transferred and processed.

  
  // Example using Azure SDK for .NET
  var query = tableClient.Query(filter: $"PartitionKey eq '{partitionKey}' and RowKey eq '{rowKey}'", select: new List { "Property1", "Property2" });
                          

• Avoid Scans: Queries that scan entire tables or partitions without specific filters on PartitionKey and RowKey are inefficient and costly.
• Understand OData Filter Syntax: Familiarize yourself with the OData syntax for filtering to construct efficient queries.
• Use Indexes Sparingly: Table Storage does not have secondary indexes. If you need complex indexing, consider other Azure services like Azure Cosmos DB or Azure SQL Database.

3. Implement Efficient Data Operations

Batch operations and efficient updates can significantly improve throughput and reduce latency.

• Batch Operations: Group up to 100 entities into a single batch operation (insert, update, delete). This reduces the number of network round trips. Note that batch operations within a partition are atomic, but cross-partition batches are not.
• Use Merge or Insert/Replace Operations:
  • MergeEntity: Updates only the specified properties, leaving others unchanged.
  • ReplaceEntity: Replaces the entire entity with the new one.
  • InsertOrReplaceEntity: Inserts if the entity doesn't exist, otherwise replaces it.
  • InsertOrMergeEntity: Inserts if the entity doesn't exist, otherwise merges it.
  Choose the operation that best suits your needs to avoid unnecessary data transfer or unintended data loss.
• Handle Concurrency with ETags: Table Storage uses ETags for optimistic concurrency control. When retrieving an entity, you get its ETag. When updating, include the ETag. If the ETag doesn't match, the operation will fail, indicating that another client modified the entity. This prevents lost updates.

4. Manage Table Schema and Data Types

Table Storage is schema-less at the table level, but entity properties have types.

• Use Appropriate Data Types: Leverage the strongly typed properties provided by the Table Storage API (e.g., string, int, bool, DateTime, guid, double, decimal, binary). This improves query efficiency and data integrity.
• Avoid Storing Large Blobs Directly: For large binary data, consider using Azure Blob Storage and store a reference (URL or SAS token) to the blob in your Table Storage entity.
• Keep Entity Size Manageable: Each entity has a maximum size of 1MB. Design your entities to stay well within this limit.

5. Monitor and Scale

Regular monitoring and strategic scaling are key to maintaining performance.

• Monitor Throughput and Latency: Use Azure Monitor to track metrics like Request Units (RUs), latency, and error rates for your Table Storage account.
• Understand Request Units (RUs): Table Storage operations consume RUs. Optimize your queries and operations to minimize RU consumption, especially for read-heavy workloads. Range queries and scans consume more RUs than point queries.
• Scaling is Automatic: Table Storage automatically scales to handle increased load. However, if you experience performance issues, it's often due to hot partitions or inefficient queries, not a lack of capacity.

6. Security Considerations

Protect your data with appropriate security measures.

• Use Shared Access Signatures (SAS): Grant granular, time-limited access to specific tables or entities.
• Use Azure Active Directory (Azure AD) Authentication: For enhanced security and centralized management, integrate with Azure AD.
• Encrypt Data in Transit and at Rest: Azure Storage automatically encrypts data at rest and in transit using HTTPS.