Azure Table Storage

Microsoft Docs - Azure Documentation

Overview of Azure Table Storage

Azure Table Storage is a NoSQL key-value store that accepts unstructured data and stores it as non-relational data. It is ideal for storing large amounts of semi-structured data that can be accessed with a high degree of parallelism. Table storage is a non-relational datastore that is schema-less. It is a more cost-effective, scalable, and flexible option for certain types of data compared to relational databases.

Key Concepts

  • Account: An Azure Storage account provides a unique namespace in Azure for your storage data. All objects in Azure Storage can be accessed through this namespace.
  • Table: A table is a collection of entities. There is no schema enforcement for entities within a table; each entity can have a different set of properties.
  • Entity: An entity is a set of properties, similar to a row in a database table. An entity can have up to 252 properties, plus the required PartitionKey and RowKey properties.
  • PartitionKey: This property, along with RowKey, forms the primary key for an entity. Entities with the same PartitionKey are co-located on the same storage node, which helps optimize query performance for entities within the same partition.
  • RowKey: This property, along with PartitionKey, forms the primary key for an entity. The RowKey uniquely identifies an entity within a partition.

When to Use Table Storage

Table storage is a good choice for storing the following types of data:

  • Data that doesn't require complex joins or transactions.
  • Data that can be accessed efficiently using a composite primary key (PartitionKey and RowKey).
  • Data with flexible schema requirements.
  • Large datasets where cost-effectiveness and scalability are paramount.

Examples include:

  • User data for web applications.
  • Address book data.
  • Device information or sensor data.
  • Metadata for blobs or files.

Key Features and Benefits

Scalability

Table storage can automatically scale to handle massive amounts of data and traffic, making it suitable for applications with unpredictable growth.

Performance

Queries that specify both the PartitionKey and RowKey are highly performant, returning data in milliseconds.

Cost-Effectiveness

Table storage offers a very low cost per gigabyte and per transaction, making it an economical choice for large datasets.

Flexibility (Schema-less)

Entities within the same table don't need to have the same set of properties, offering great flexibility in data modeling.

Availability and Durability

Azure Storage provides high availability and durability through geo-replication options.

Working with Table Storage

You can interact with Azure Table Storage using various methods:

  • Azure SDKs: Available for .NET, Java, Python, Node.js, and more.
  • Azure CLI: Command-line interface for managing Azure resources.
  • Azure PowerShell: Scripting language for managing Azure resources.
  • REST API: Direct access to Table Storage operations.

Example: Creating an Entity

Here's a conceptual example of what an entity might look like:


{
  "PartitionKey": "Users",
  "RowKey": "user123",
  "Email": "user123@example.com",
  "FirstName": "Jane",
  "LastName": "Doe",
  "IsActive": true,
  "LastLogin": "2023-10-27T10:00:00Z"
}

Limitations

While powerful, Table Storage has some limitations to consider:

  • No complex relationships or joins between entities.
  • No built-in support for transactions spanning multiple entities or tables.
  • Query capabilities are primarily focused on PartitionKey and RowKey lookups.

For more in-depth information and tutorials, please refer to the official Azure documentation links below.

See Also