Azure Table Storage: A Comprehensive Guide

Azure Table Storage is Microsoft's solution for NoSQL in the cloud. Businesses worldwide use it to power applications that need fast access to non-relational data that can’t be efficiently stored in relational databases. It is highly scalable, cost-effective, and can be integrated with other Azure services.

This guide covers everything you need to know about Azure Table Storage: how it works, key features, common use cases and benefits, and troubleshooting tips to help you resolve common issues.

An overview of Azure Table Storage

Azure Table Storage is a cloud-based NoSQL data store for storing structured data in a schemaless format. It is designed for large-scale applications that need fast and flexible access to key-value data without the overhead of traditional relational databases.

Data is stored in tables, with each table consisting of entities (similar to rows in a database) that contain properties (similar to columns). Unlike relational databases, Table Storage does not enforce relationships between entities.

Azure Table Storage is part of Azure Storage, which also includes Blob Storage, Queue Storage, and File Storage. It can be easily integrated with other Azure services like Functions, Logic Apps, and Power BI to build end-to-end solutions. Users can access Table Storage using REST APIs, the Azure SDKs, Storage Explorer, or Azure PowerShell.

For applications that require higher performance, availability, and global distribution, Azure Cosmos DB for Table is an alternative to Table Storage. It offers automatic secondary indexing, supports a serverless mode for cost efficiency, and provides enhanced scalability. Microsoft now also provides a unified Azure Tables SDK that works with both Azure Table Storage and Azure Cosmos DB for Table.

Key features of Azure Table Storage

• NoSQL storage: As there’s no predefined schema, you can store entities with varying properties in the same schema.
• Massive scalability: Can handle petabytes of data and millions of transactions per second.
• Fast query performance: Supports the Open Data Protocol (OData), which is a flexible and fast protocol for querying and updating data over REST APIs.
• Geo-replication: Supports redundancy and disaster recovery with geo-replicated storage options.

How does Azure Table Storage work?

Azure Table Storage is built on a distributed architecture that delivers high availability, scalability, and efficient access to structured NoSQL data. In this section, let’s talk about the core components and concepts that define how Azure Table Storage works.

Storage account

Every Azure Table Storage deployment begins with a Storage Account, which serves as a unique namespace in Azure. Each storage account can hold multiple tables and is assigned a unique endpoint URL.

Table

A Table is a collection of structured data, similar to a table in a relational database, but without a strict schema. Tables in Azure also do not enforce foreign keys, relationships, or fixed column types.

Entity

An Entity is a single record within a table, similar to a row in a relational database. Each entity can be up to 1 MB in size (for Cosmos DB the limit is 2 MB) and consists of three system-defined properties:

• PartitionKey: A string that determines the partition where the entity is stored.
• RowKey: A unique identifier for an entity within a partition.
• Timestamp: A system-generated property that records the last modification time of the entity.

Together, the PartitionKey and RowKey form the primary key for the entity. In addition to these system properties, an entity can have up to 252 custom properties, which can store data in different formats.

Properties

Each entity consists of properties, which store actual data values. Some of the supported data types for properties are: String, Binary, Boolean, DateTime, Double, GUID, Int32, and Int64.

Partitioning and indexing

Azure Table Storage is designed for high scalability, and partitioning is a key factor in its performance. The PartitionKey properties determine how data is distributed across storage nodes. Entities with the same PartitionKey are stored together for efficient querying. The combination of PartitionKey and RowKey is automatically indexed, which allows for quick retrieval of specific entities.

URL format and accessing Table Storage

Each table in Azure Table Storage has a unique URL endpoint that follows this format:

https://<your-storage-account>.table.core.windows.net/<table-name>

For example, if you have a storage account named mycloudacc and a table called Patients, the table’s URL would be:

https://mycloudacc.table.core.windows.net/patients

Entities in the table can be accessed via REST API requests using the PartitionKey and RowKey in the URL:

https://mycloudacc.table.core.windows.net/patients(PartitionKey='USA',RowKey='12345')

Azure Table Storage use cases

Table Storage’s schemaless structure, fast access times, and cost-effectiveness make it ideal for several use cases, including:

Storing log and telemetry data

Since it supports high write speeds and large-scale storage, Table Storage is an excellent choice for capturing and analyzing event-driven logs, telemetry and monitoring data from applications, IoT devices, and cloud services.

Web and mobile application data

Applications that need to store user profiles, preferences, session data, and application state can benefit from the low latency offered by Table Storage.

Storing metadata for large files

In cloud storage solutions, metadata about large files (such as media files, documents, and backups) can be stored in Table Storage while the actual files are stored in Azure Blob Storage.

E-commerce and product catalogs

Retail businesses use Table Storage for managing product catalogs, inventory details, and pricing information. The flexible schema allows for easy updates when new product attributes need to be added.

Storing configuration and settings

Table Storage is also used to store configuration settings for distributed applications, such as feature flags, access permissions, and environment-specific settings.

Benefits of using Azure Table Storage

Next, let’s explore some business benefits of choosing Azure Table Storage as a cloud-based data storage solution:

• Cost-effective storage: Azure Table Storage provides a pay-as-you-go pricing model, which makes it a budget-friendly option for businesses that need to store large amounts of structured data without the expense of traditional databases.
• Fast read and write performance: With its key-based access model, Table Storage enables quick data retrieval and high-speed writes for data-intensive applications.
• High availability and durability: Built on Azure’s global infrastructure, Table Storage provides 99.99% availability and automatic data replication across multiple data centers. This keeps your data always accessible and protected against hardware failures or regional outages.
• Secure data storage: Microsoft provides built-in security features such as encryption at rest, role-based access control (RBAC), and network-level security options to protect sensitive business data.
• REST API and SDK support: Azure Table Storage can be accessed via REST APIs and supports multiple programming languages through the Azure SDKs.

Azure Table Storage troubleshooting guide

Next, let’s discuss some common Table Storage issues and how to troubleshoot them:

Authentication errors

Authentication issues occur when an application fails to connect to an Azure Table Storage account.

Symptoms:

• AuthenticationFailed or 403 Forbidden errors.
• Access is denied even with correct storage account credentials.

Troubleshooting:

• Double-check the storage account name and key in your application.
• Instead of storing keys manually, consider using Azure Managed Identities for secure authentication.
• If using SAS tokens, verify their validity, permissions, and expiration time.
• If using Azure Role-Based Access Control (RBAC), ensure that the user or service principal has the necessary roles assigned, such as Storage Table Data Contributor.
• If using Azure Storage Firewall and Virtual Networks, confirm that your client’s IP address is whitelisted.

Connection timeouts

Connection timeouts occur when a request to Azure Table Storage takes too long to get a response.

Symptoms:

• Delays in API responses.
• Error messages like TimeoutException or 503 Server Busy.

Troubleshooting:

• Go to Azure Status to check if there are any ongoing outages.
• Reduce the number of requests by using efficient partition keys and filters to target specific entities.
• Use Azure Monitor to track metrics like TableCapacity, TotalRequests, and SuccessE2ELatency.
• If you're exceeding the partition throughput limits, consider scaling out by using multiple partitions or migrating to Azure Cosmos DB Table API for automatic scaling.

Slow query performance

When queries take too long to execute, it can lead to delays and reduced application efficiency.

Symptoms:

• Queries take longer to execute than expected.
• Increased latency during peak load hours.
• Timeouts occur when fetching large data sets.

Troubleshooting:

• Use efficient partition keys and row keys. Well-distributed partition keys improve query performance.
• Reduce the number of returned attributes by selecting only the required properties.
• If possible, perform batch transactions to reduce the number of round trips to the server.
• Regularly analyze your storage metrics to identify slow queries.

Partition hotspot issues

A high load on a specific partition can cause bottlenecks and performance degradation.

Symptoms:

• High latency or request failures for entities within a single partition.
• Throttling errors (503 Server Busy) or transaction failures.

Troubleshooting:

• Choose a partition key that evenly distributes data to prevent hotspots.
• If your partition key has a large number of entities, consider sharding the data across multiple partitions.
• Use Azure Monitor and Application Insights or a dedicated monitoring tool like Site24x7 to identify and address high transaction volumes.

Hitting storage capacity limits

Azure Table Storage has limits on the number of partitions and entity sizes. If you exceed these limits, writes may fail.

Symptoms:

• Write operations fail with "Quota Exceeded" errors.
• Slow performance when retrieving large amounts of data.

Troubleshooting:

• Check storage quotas to make sure you are not exceeding them.
• If a single table is hitting its storage limit, distribute data across multiple tables.
• Optimize how you distribute data across partitions to prevent a single partition from becoming a bottleneck.
• If your workload requires global distribution and unlimited scaling, consider migrating to Azure Cosmos DB Table API.

Unsecured access keys

If access keys for your Azure Table Storage account are not properly secured, unauthorized users can gain access to your data.

Symptoms:

• You notice unauthorized access logs in Azure Monitor.
• Unexpected data modifications or deletions occur.

Troubleshooting:

• Regenerate and rotate access keys regularly to limit potential exposure.
• Use Azure Managed Identities to reduce reliance on access keys.
• Store access keys in Azure Key Vault instead of hardcoding them.
• Restrict the scope of permissions using Azure Role-Based Access Control (RBAC).
• Monitor activity logs in Azure Security Center for suspicious access attempts.

Misconfigured network security rules

Misconfigured network security settings can lead to unauthorized access or prevent legitimate users from connecting to your storage account.

Symptoms:

• Users cannot access Azure Table Storage from approved locations.
• Security alerts indicating possible unauthorized access attempts.

Troubleshooting:

• Ensure that only trusted IP addresses are whitelisted in the Azure Storage firewall settings.
• Use Virtual Network (VNet) Service Endpoints to only allow access from specific subnets.
• Enable Microsoft Entra ID authentication for extra security instead of just using shared keys.
• Regularly audit and review Azure Security Center for potential threats or unauthorized access.

Incorrect time settings affecting query results

Azure Table Storage requires precise timestamps for operations like retrieving entities.

Symptoms:

• Incorrect or missing results when querying tables.
• Unexpected behavior when filtering data by time-based attributes.

Troubleshooting:

• Ensure that the application client system's clock is synchronized with a Network Time Protocol (NTP) server.
• Use UTC time instead of local time when storing timestamps.
• Validate timestamp formats to ensure that they follow the ISO 8601 standard (e.g., yyyy-MM-ddThh:mm:ssZ).
• If filtering by timestamps, use PartitionKey or RowKey along with Timestamp for better query performance.

Incorrect data serialization

You are unable to store or retrieve data from Table Storage due to serialization issues.

Symptoms:

• Errors related to unsupported data types in requests.
• Inconsistent data retrieval or failed deserialization attempts.

Troubleshooting:

• Ensure that your application is using supported data types such as String, Int32, Double, or Boolean.
• When storing date-time values, use ISO 8601 format (UTC-based, e.g., 2023-12-01T12:00:00Z) to avoid inconsistencies.
• For large data types, consider storing binary objects in Blob Storage and saving only their reference in Table Storage.
• Test serialization and deserialization using the Azure Storage SDK.

Partition key imbalance

Uneven distribution of data across PartitionKeys can lead to performance bottlenecks and scalability issues.

Symptoms:

• Some partitions experience high traffic while others remain underutilized.
• Increased latency for specific queries or operations.

Troubleshooting:

• Use logging or analytics tools to identify uneven distribution.
• Choose a PartitionKey that ensures even distribution, such as a hash of a unique identifier.
• Combine multiple attributes to create a more balanced PartitionKey.
• Continuously monitor partition usage and adjust your strategy as needed.
• For advanced partitioning needs, consider using Azure Cosmos DB with the Table API.

Data corruption or loss

Data corruption or loss can occur due to application bugs, misconfigurations, or accidental deletions.

Symptoms:

• Missing or incomplete data in queries.
• Inconsistent data across partitions.

Troubleshooting:

• Use Azure Storage’s soft delete feature to recover accidentally deleted data.
• Add validation checks in your application to ensure data integrity.
• Implement versioning for entities to track changes and recover from corruption.
• Use Azure Backup or custom scripts to create regular backups of your data.
• Enable logging and auditing to identify the root cause of data corruption or loss.

Azure Table Storage best practices

To get the best performance, security, and scalability from Azure Table Storage, follow these best practices:

• Avoid storing access keys in application code. Use Azure Managed Identities to authenticate your applications securely without having to manage secrets.
• Assign the least privilege necessary for users and applications. Use Azure Storage firewall rules to restrict access to trusted networks only.
• Generate SAS tokens with specific permissions instead of exposing account keys. Always set expiration dates to limit exposure risks.
• Partition keys should distribute data evenly to avoid hotspots. Avoid highly concentrated partitions with too many transactions.
• Queries that use both PartitionKey and RowKey perform faster. Design unique RowKeys to prevent duplicates and enable efficient filtering.
• Use select projections to retrieve only the needed properties instead of fetching full entities. This reduces bandwidth and improves query speed.
• When inserting or updating multiple entities in the same partition, use batch operations to reduce transaction costs. Azure Table Storage supports up to 100 entities per batch.
• Use indexed properties like PartitionKey and RowKey instead of scanning the entire table. Paginate results when dealing with large data sets.
• Avoid hot partitions by distributing data evenly. Consider using randomized or hashed PartitionKeys for high-write scenarios.
• Regularly check Azure Monitor or a dedicated monitoring tool like Site24x7 to track key performance metrics and identify performance bottlenecks.
• If you need global distribution and automatic scaling, consider migrating to Azure Cosmos DB Table API, which offers more flexibility.
• Store timestamps in UTC format for consistent date-based queries. This helps maintain accurate records of entity updates.
• Set up automated backups using Azure Data Factory or Azure Blob Storage.
• Export data regularly to Azure SQL Database or Azure Blob Storage for long-term retention.
• Use retry logic in your application to handle transient failures caused by network latency or service throttling. Implement exponential backoff to avoid overwhelming the service.
• Enable soft delete for Table Storage to prevent accidental data loss and allow recovery of deleted data within a configurable retention period.
• Use Azure Private Link to establish a private connection between your virtual network and Azure Table Storage. This prevents unnecessary exposure to the public internet.

Conclusion

Azure Table Storage is a reliable, performant, and scalable data storage solution for data-intensive applications. We hope that the insights shared in this guide have helped you develop a deep understanding of Azure Table Storage and given you the confidence to start using it.

To set up end-to-end monitoring for your Azure Table Storage instance, don’t forget to try out Site24x7.