Introduction to Azure Cosmos DB Best Practices

Azure Cosmos DB is a globally distributed, multi-model database service that allows you to elastically and independently scale throughput and storage. To maximize its benefits and ensure optimal performance, reliability, and cost-effectiveness, adhering to best practices is crucial. This document provides a comprehensive overview of these practices.

Tip: Regularly review and adapt your Cosmos DB implementation as your application evolves and new features become available.

Performance Best Practices

Optimizing the performance of your Azure Cosmos DB database is key to delivering a responsive and scalable application. This involves careful consideration of data modeling, querying, and resource provisioning.

Indexing Strategies

Azure Cosmos DB automatically indexes all data by default. While this is convenient, understanding and customizing your indexing policy can significantly improve read performance and reduce costs.

  • Selective Indexing: Index only the properties that are frequently queried. Exclude properties that are rarely or never used in queries to reduce index size and RU consumption.
  • Composite Indexes: For queries involving multiple sort or filter criteria on different properties, consider creating composite indexes.
  • Index Kind: Choose the appropriate index kind (e.g., Range, Spatial, Composite) based on your query patterns.
  • Inclusion/Exclusion Paths: Use inclusion and exclusion paths in your indexing policy to fine-tune what gets indexed.

Example of a selective indexing policy:


{
    "indexingMode": "consistent",
    "automatic": true,
    "includedPaths": [
        { "path": "/*" }
    ],
    "excludedPaths": [
        { "path": "/path/to/rarely/queried/property/*" }
    ]
}

Partitioning

Effective partitioning is the cornerstone of scalability in Azure Cosmos DB. A well-chosen partition key distributes requests and storage evenly across logical partitions.

  • High Cardinality Partition Keys: Choose a partition key with a high number of distinct values. This ensures data and requests are spread across many physical partitions.
  • Even Distribution: Aim for an even distribution of data and request volume across your partition key values. Avoid "hot" partitions that receive a disproportionate amount of traffic.
  • Single-Valued Partition Keys: For optimal performance and scalability, prefer partition keys with a single, common value if your data model allows, or keys that naturally lead to a good distribution.
  • Avoid Sequential Keys: Keys like timestamps or sequential IDs can lead to hot partitions.

Query Optimization

Well-written queries are essential for efficient data retrieval and minimal Request Unit (RU) consumption.

  • Filter Early: Apply filters as early as possible in your query to reduce the amount of data processed.
  • Avoid `SELECT *`: Specify only the fields you need in your `SELECT` clause to reduce network traffic and RU cost.
  • Use Projection: Utilize projections in your queries to return only the necessary properties.
  • Leverage Indexes: Ensure your queries can effectively use your defined indexes.
  • Partition Key in WHERE clause: When querying within a single logical partition, include the partition key in your `WHERE` clause for maximum efficiency.

Example of efficient projection:


SELECT c.id, c.name FROM c WHERE c.category = "electronics"

Throughput Management

Provisioning the right amount of throughput (RUs/sec) is critical for performance and cost. Azure Cosmos DB offers both manual and autoscale throughput modes.

  • Autoscale: For workloads with unpredictable traffic, autoscale is often the most cost-effective and performant option. It automatically scales throughput up and down based on demand.
  • Manual Throughput: For predictable workloads, manual throughput can be more cost-effective if provisioned correctly.
  • Provision for Peak Load: Provision enough throughput to handle your peak load, but avoid over-provisioning.
  • Monitor RU Consumption: Regularly monitor your RU consumption to identify bottlenecks and optimize provisioned throughput.

Consistency Levels

Azure Cosmos DB offers five consistency levels, each with trade-offs between consistency, availability, and latency. Choose the level that best suits your application's needs.

  • Strong: Highest consistency, lowest availability, highest latency.
  • Bounded Staleness: Guarantees that data will not be stale beyond a specified limit.
  • Session: Default. Provides consistency within a client session.
  • Consistent Prefix: Reads are guaranteed to be at least as up-to-date as the previous read.
  • Eventual: Lowest consistency, highest availability, lowest latency.
Tip: Most applications can function effectively with the default Session consistency. Consider Eventual for scenarios where some staleness is acceptable to maximize availability and minimize latency.

Cost Optimization

Managing costs in Azure Cosmos DB involves optimizing throughput, storage, and indexing.

Throughput Cost

  • Right-size your RUs: Avoid over-provisioning. Use autoscale for variable workloads.
  • Optimize queries: Inefficient queries consume more RUs.
  • Batch operations: Group multiple operations into fewer requests where possible.
  • Choose the right API: Some APIs may have different RU consumption characteristics for similar operations.

Storage Cost

Storage is billed based on the total amount of data stored across all items in your container.

  • Data Archiving: Implement data lifecycle management. Move older, less frequently accessed data to cheaper storage solutions if applicable.
  • Efficient Data Models: Avoid storing redundant or unnecessary data.

Indexing Cost

Indexing consumes RUs during writes and also contributes to storage costs.

  • Selective Indexing: Only index the fields you actively query.
  • Index Exclusions: Exclude large, rarely queried arrays or complex objects from indexing.

Security Best Practices

Securing your Azure Cosmos DB data is paramount.

  • Role-Based Access Control (RBAC): Grant least privilege to users and applications. Use specific read, write, or admin roles.
  • Keys Management: Securely manage your account keys. Use Azure Key Vault for storing and rotating keys.
  • Network Security: Configure virtual network rules and private endpoints to restrict access to your Cosmos DB account.
  • Data Encryption: Data is encrypted at rest by default. Ensure you understand and configure encryption at transit.

Monitoring and Diagnostics

Proactive monitoring helps identify performance issues, track usage, and detect potential problems.

  • Azure Monitor: Leverage Azure Monitor for metrics like RU consumption, latency, storage usage, and availability.
  • Diagnostic Logs: Enable diagnostic logs to capture detailed information for troubleshooting.
  • Alerting: Set up alerts for key metrics (e.g., high RU consumption, high latency, failed requests) to be notified of issues proactively.

Development Patterns

Adopting robust development patterns can enhance application resilience and maintainability.

  • Idempotency: Design operations to be idempotent to handle retries gracefully.
  • Batching: Use bulk operations or batching for efficient multi-item operations.
  • Error Handling: Implement robust error handling, including retry logic for transient failures (e.g., 429 Too Many Requests).
  • SDK Usage: Utilize the official Azure Cosmos DB SDKs for your preferred language and stay updated with the latest versions.