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Azure Well-Architected Framework review – Azure Cosmos DB for NoSQL

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This article describes the best practices for Azure Cosmos DB for NoSQL. These best practices ensure that you can deploy solutions on Azure Cosmos DB that are efficient, reliable, secure, optimized for cost, and operationally excellent. This guidance focuses on the five pillars of architecture excellence in the Well-Architected Framework:

This review guide assumes that you have a working knowledge of Azure Cosmos DB and are well versed with its features. For more information, see Azure Cosmos DB for NoSQL.

Prerequisites

Understanding the Well-Architected Framework pillars can help produce a high-quality, stable, and efficient cloud architecture. We recommend that you start by reviewing your workload using the Azure Well-Architected Framework Review assessment.

For more context, review various reference architectures that reflect the considerations from this guide in their design. These architectures include, but aren't limited to:

Reliability

As with any cloud service, failures can occur both on the service and the workload side. It's impossible to prevent all potential failures, but it's a better goal to minimize the effects a single failing component can have on your entire workload. This section includes considerations and recommendations to minimize the consequences of a one-off failure.

Design checklist

Recommendations

Recommendation Benefit
Distribute your Azure Cosmos DB account across availability zones (when available). Availability zones provide distinct power, networking, and cooling isolating hardware failures to a subset of your replicas. Azure Cosmos DB has multiple replicas that span across a single random availability zone when the availability zones feature isn't used. If the availability zone feature is used, replicas span across multiple availability zones.
Configure your Azure Cosmos DB account to span at least two regions. Spanning multiple regions prevents your account from being entirely unavailable if there's an isolated region outage.
Enable service-managed failover for your account. Service-managed failover allows Azure Cosmos DB to change the write region of a multiple-region account to preserve availability. This change occurs without user interaction. Understand the tradeoffs with service-managed failover and plan for forced failover if necessary. For more information, see building highly available applications.
Validate availability by testing failover manually with service-managed failover temporarily disabled. Temporarily disabling service-manage failover allows you to validate the end-to-end high availability of your application with a manual failover started using a script or the Azure portal. Afterwards, you can reenable service-managed failover.

Azure Policy definitions

Security

Security is a critical part of any architecture that can be easily overlooked for convenience. Bolster the security of your final workload by considering various security best practices up-front before the first resource or proof of concept is created. This section includes considerations and recommendations to reduce the number of security vulnerabilities for your final workload.

Design checklist

Recommendations

Recommendation Benefit
Implement, at a minimum, the data protection and identity management security baselines. Go through the security baseline including identity management and data protection. Implement the recommendations to secure your Azure Cosmos DB account.
Disable public endpoints and use private endpoints whenever possible. Avoid leaving unnecessary or unused public endpoints available for surface area attacks to your account.
Use role-based access control to limit control-plane access to specific identities and groups and within the scope of well-defined assignments. Use role-based access control to prevent unintended access to your account. Assign appropriate roles and permissions to users or applications accessing Azure Cosmos DB.
Create virtual network endpoints and rules to limit access to the account. Implement virtual network service endpoints and firewall rules to restrict access to your Azure Cosmos DB account. Use network security groups (NSGs) to control inbound and outbound traffic to and from the Azure Cosmos DB resources. Limiting access to trusted networks and applying appropriate network security measures helps protect your data from unauthorized access.
Follow best software development practices for secure access to data. Follow secure coding practices and perform secure code reviews when developing applications that interact with Azure Cosmos DB. Protect against common security vulnerabilities like injection attacks, cross-site scripting (XSS), or insecure direct object references (IDOR). Implement input validation, parameterized queries, and appropriate error handling for common HTTP status codes to prevent security risks.
Monitor control-plane logs for breaches. Monitoring helps you track access patterns and audit logs, ensuring that your database remains secure and compliant with relevant data protection regulations. Monitoring data-plane metrics can also help identify unfamiliar patterns that might reveal a security breach. For more information, see security checklist for Azure databases.
Enable Microsoft Defender for Azure Cosmos DB Microsoft Defender detects attempts to exploit databases in your Azure Cosmos DB for NoSQL account. Defender detects potential SQL injections, suspicious access patterns, and other potential exploitation.

Azure Policy definitions

Cost optimization

Your workload's characteristics and the implementation of your solution can influence the final cost of running in Azure. Consider main drivers like your partitioning strategy, consistency level, replication, and write type when designing your workload. When sizing your workload, consider the read/write nature of your data, the size of average items, normalization, and TTL. This section includes considerations and recommendations to streamline costs for your workload.

  • Design an indexing policy that's considers the operations and queries you commonly make in your workload.
  • Determine a partition key or set of partition keys which have a value that has high cardinality and does not change. Use the existing guidance and best practices to help select an appropriate partition key. Also, consider your indexing policy when determining a partition key.
  • Select a throughput allocation schema that's appropriate for your workload. Review the benefits of standard and autoscale throughput distributed at the database or container level. Also, consider serverless when appropriate. Review your workload's traffic patterns in the context of selecting a throughput allocation scheme.
  • Consider consistency levels as they relate to your workload. Also, consider if client sessions should alter the default consistency level.
  • Calculate the expected overall data storage for your workload. The size of items and indexes all influence your data storage cost. Calcuate the impact of replication and backup on storage costs.
  • Create a strategy to automatically remove older items that are no longer used or necessary. If required, export these items to a lower-cost storage solution before they are removed.
  • Evaluate your most common queries that minimize cross-partition lookups. Use this information to inform the process of selecting a partition key or customizing an indexing policy.

Recommendations

Recommendation Benefit
Monitor RU/s utilization and patterns. Use metrics to monitor RU consumption from the very beginning of your solution. Use queries and other data research techniques to find antipatterns in your application code.
Customize your indexing policy to map to your workload. The default indexing policy indexes all paths in an item, and this policy can have significant impacts to RU consumption and costs. Use an indexing policy designed based on only the paths that you need to index for your common queries. For write-heavy workloads, disable automatic indexing of columns not used in queries.
Select partition key[s] that are ideal for your workload. The partition key[s] should distribute throughput consumption and data storage evenly across logical partitions. The selection should also minimize the number of unbounded cross-partition queries. Avoid hot partitions that receive a disproportionate amount of traffic, as unbalance partitions can increase throughput costs and transient errors. Use the most common search queries to determine potential partition key[s] that likely executes only single-partition or bounded cross-partition queries.
Use serverless or provisioned throughput, manual provisioning or autoscale, at the database or container level when appropriate for your workload. Compare the provisioned throughput types and select the appropriate option for your workload. Generally, smaller and dev/test workloads might benefit from serverless throughput or manual shared throughput at the database level. Larger, mission-critical workloads might benefit from provisioned throughput assigned at the container level.
Configure the default consistency level for your application. When appropriate, downgrade the default consistency level in client sessions. You might not always need to change the standard default consistency level or override it in client sessions. Consider the higher costs associated with reads at stronger consistency levels.
For dev/test workloads, use the Azure Cosmos DB emulator. The Azure Cosmos DB emulator is an option for dev/test and continuous integration that can save on the costs of these common workloads for your development team. The emulator is also available as a Docker container image.
Use transactional batch operations Design partitions to take advantage of transactional batch operations within a logical partition key for inserting. Use batch operations in client-side SDKS for inserting, updating, or deleting multiple documents in a single transaction request. This step can reduce the number of individual requests and can eventually lead to better throughput efficiency.
Use projection to reduce throughput costs of large query result sets. Author queries to only project the minimal number of fields required from a result set. If calculations on fields are necessary, evaluate the throughput cost of performing those calculations server-side versus client-side.
Avoid using unbounded cross-partition queries. Evaluate and author queries to ensure they search within a single logical partition whenever possible. Use query filters to control which logical partitions the query targets. If a query must search across logical partitions, bound the query to only search a subset of logical partitions instead of a full scan.
Implement time-to-live (TTL) to remove unused items. Use TTL to automatically delete data that's no longer needed. Manage storage costs by removing expired or obsolete data. If necessary, export the expired data to a lower-cost storage solution.
Consider an analytical store for heavy aggregations. Azure Cosmos DB analytical store automatically syncs your data to a separate column store to optimize for large aggregations, reporting, and analytical queries.

Azure Policy definitions

Operational excellence

Workloads must be monitored after they're deployed to make sure they perform as intended. Further, monitoring of workloads can help unlock new efficiencies not immediately obvious during the planning phase. In catastrophic scenarios, diagnostic data is the key to uncovering why a high severity incident might have occurred. This section includes considerations and recommendations to monitor events and characteristics of your workloads.

Design checklist

  • Draft a log and metrics monitoring strategy to differentiate between different workloads, flag exceptional scenarios, track patterns in exceptions/errors, and track host machine performance.
  • Design large workloads to use bulk operations whenever possible.
  • Define multiple alerts to monitor throttling, analyze throughput allocation, and track the size of your data.
  • Design a monitoring strategy for availability of your solution across regions.
  • Create and enforce best practices for automating the deployment of your Azure Cosmos DB for NoSQL account and resources.
  • Plan expected metric thresholds based on partition and index design. Ensure that there's a plan to monitor those metrics to determine how close they are to the planned thresholds.

Recommendations

Recommendation Benefit
Ensure application developers are using the latest version of the developer SDK. Each Azure Cosmos DB for NoSQL SDK has a minimum recommended version. For more information, see .NET SDK and Java SDK.
Create identifiers in the client application to differentiate workloads. Consider flags, such as the user-agent suffix, to identify what workload each log entry or metric should be associated with.
Capture supplemental diagnostics using the developer SDK. Use the diagnostics injection techniques for each SDK to add supplemental information about the workload alongside default metrics and logs. For more information, see .NET SDK and Java SDK.
Create alerts associated with host machine resources. Connectivity and availability issues might occur due to client-side host machine issues. Monitor resources such as CPU, memory, and storage on host machines with client applications using the Azure Cosmos DB for NoSQL SDKs.
Use the bulk features of client SDKs for large operations. Scenarios that require a high degree of throughput benefit from using the bulk feature of the SDK. The bulk feature automatically manages and batches operations to maximize throughput.
Create alerts for throughput throttling. Use alerts to track throughput throttling beyond expected thresholds. Over time, review and adjust alerts as you learn more about your workload in relation to Azure Cosmos DB. The Normalized RU Consumption metric is a metric that measures the percentage utilization of provisioned throughput on a database or container. If this metric is consistently at 100%, requests likely return a transient error.
Track query performance using metrics. Use metrics to track the performance of your top queries over time. Evaluate if there are efficiencies to be found by updating th indexing policy or changing queries. If query performance is poor, troubleshoot performance and apply query best practices. For more information, see query performance tips.
Use templates to automatically deploy account resources. Consider Azure Resource Manager, Bicep, or Terraform templates to automate the deployment of your account and subsequent resources. Ensure that your team is using the same templates to deploy to other nonproduction environments.
Track key metrics to identify common problems in your workload. Use specific metrics to find common problems in your workload including, but not limited to; RU utilization, RU utilization by partition, throttling, and request volumes by type. For more information, see monitor data reference.

Azure Policy definitions

Performance efficiency

  • Define a performance baseline for your application. Measure how many concurrent users and transactions you might need to handle. Consider workload characteristics such as your average user flow, common operations, and spikes in usage.
  • Research your most common and most complex queries. Identify queries that use multiple lookups, joins, or aggregates. Consider these queries in any design considerations for the partition key or indexing policy.
  • For the most common queries, determine the number of results you expect per page. This number will help formalize a buffered item count for prefetched results.
  • Research your target users. Determine which Azure regions are closest to them.
  • Identify queries that use one or more ordering fields. Also, identify operations that impact multiple fields. Include these fields explicitly in the indexing policy design.
  • Design items so their corresponding JSON documents are as small as possible. Considering splitting data cross multiple items if necessary.
  • Identify queries on child arrays and determine if they are candidates for more efficient subqueries.
  • Determine if your workload requires an analytical store. Consider analytical stores and services like Azure Synapse Link for extremely complex queries.
Recommendation Benefit
Configure your throughput based on your performance baseline. Use tools like the capacity calculator to determine the amount of throughput required for your performance baseline. Use features like autoscale to scale your actual throughput to more closely match your actual workload. Monitor your actual throughput consumption afterwards and make adjustments.
Use optimization techniques on the client and server sides when appropriate. Take advantage of the built-in integrated cache. Configure the SDK to manage how many items are prefetched (buffered) and returned for each page.
Deploy Azure Cosmos DB for NoSQL to regions closest to your end users. Reduce latency by deploying Azure Cosmos DB for NoSQL to the regions closest to your end users as much as possible. Take advantage of read replication to provide performant read performance regardless of how you configure write (single or multiple regions). Configure the (.NET/Java) SDK to prefer regions closer to your end user.
Configure the SDK for Direct mode. Direct mode is the preferred option for best performance. This mode allows your client to open TCP connections directly to partitions in the service and send requests directly with no intermediary gateway. This mode offers better performance because there are fewer network hops.
Disable indexing for bulk operations. If there are many insert/replace/upsert operations, disable indexing to improve the speed of the operation while using the bulk support of the corresponding SDK. Indexing can be immediately reenabled later.
Create composite indexes for fields that are used in complex operations. Composite indexes can increase the efficiency of operations on multiple fields by orders of magnitude. In many cases, use composite indexes for ORDER BY statements with multiple fields.
Optimize host client machines for the SDKs. For most common case, use at least 4-cores and 8-GB memory on 64-bite host machines using the SDKs (.NET/Java). Also, enable accelerated networking on host machines.
Use the singleton pattern for the CosmosClient class in most SDKs. Use the client class in most SDKs as a singleton. The client class manages its own lifecycle and is designed to not be disposed. Constantly creating and disposing of instances can result in reduced performance.
Keep item sizes less than 100 KB in size. Larger items consumer more throughput for common read and write operations. Queries on larger items that project all fields can also have a significant throughput cost.
Use subqueries strategically to optimize queries that join large data sets. Queries that join child arrays can increase in complexity if multiple arrays are involved and not filtered. For example, a query that joins more than two arrays of at least 10 items each can expand to 1,000+ tuples. Optimize self-join expressions by using subqueries to filter the arrays before joining arrays within the item. For cross-partition queries, optimize your query to include a filter on the partition key to optimize the routing of your query to the least amount of partitions possible.
Use analytical workloads for the most complex queries. If you run frequent aggregations or join queries over large containers, consider enabling the analytical store and doing queries in Azure Synapse Analytics.

Azure Policy definitions

Extra resources

Consider more resources related to Azure Cosmos DB for NoSQL.

Azure Architecture Center guidance

Cloud Adoption Framework guidance

Next steps

Deploy an Azure Cosmos DB for NoSQL account using the a Bicep template