One of the standout characteristics of Azure Cosmos DB is its capability to scale out horizontally, with granular control on a per container basis. A key part of testing on DP-420 involves understanding how you can programmatically adjust throughput, based on requirements, to maintain optimal performance or manage costs.
Azure Cosmos DB offers flexibility and control over throughput at two levels. You can provide either database-level throughput, which is shared across all the containers (or collections) in the database, or provision throughput on a specific container.
Allocating the throughput at the database level is a cost-effective solution when you just started with the development but it might not scale well when you go in production as different containers might require different throughput. Allocating on the container level allows granular control and independent scalability, but it can be more costly.
Throughput in Azure Cosmos DB is measured in Request Units (RU/s). You can programmatically control it via the Azure SDK as shown in the following example:
<code>
// Creating a new database with shared throughput of 500 RU/s
await azureCosmosClient.CreateDatabaseIfNotExistsAsync(“DBName”, 500);
// Creating a new container in the above database with dedicated throughput of 1000 RU/s
await azureCosmosClient.GetDatabase(“DBName”).CreateContainerIfNotExistsAsync(“ContainerName”, “/partitionKey”, 1000);
</code>
Resource Governance in Azure Cosmos DB
On the other side of the coin, designing for resource governance is vital to ensure your application doesn’t unexpectedly drain resources or incur excessive costs. Cosmos DB, like other cloud services, operates as a shared resource environment and thus has mechanisms to govern resource usage.
For instance, Cosmos DB measures the computational resources, such as CPU, I/O, and memory utilized during request execution using Request Units (RUs). It places limits on the amount of RUs that can be consumed per second, per partition key range.
Moreover, Cosmos DB enforces a time limit on the execution of individual requests, depending on the total amount of RUs assigned to the database or container. If a request is expected to consume more RUs than the limit, Cosmos DB will prematurely terminate it and return a request rate too large (HTTP 429) status code.
With Azure Cosmos DB SDKs, you can inspect the `x-ms-request-charge` HTTP header to get the number of consumed RUs for each request.
<code>
// Check RUs consumed by a read operation
ItemResponse itemResponse = await container.ReadItemAsync(“id”, new PartitionKey(“partitionKeyValue”));
double requestCharge = itemResponse.RequestCharge;
Console.WriteLine(“Request charge of this read operation: {0}”, requestCharge);
</code>
In conclusion, when designing applications for Azure Cosmos DB, understanding and managing the granular scale units and resource governance requirements are essential. It enables you to create agile, scalable, and cost-effective apps that take full advantage of the cloud.
Remember, when it comes to preparing for the DP-420 exam, it’s not just about knowing how to utilize Azure Cosmos DB’s features. It’s about understanding how to design and implement solutions that work with these features how they are intended.
Practice Test
True or False: Granular scale units are often used in designing applications for Microsoft Azure Cosmos DB.
- True
- False
Answer: True.
Explanation: Granular scale units (adding more capacity in terms of RUs) are commonly used when designing or scaling applications on Azure Cosmos DB for improved performance and throughput.
Multiple Choice: What does RU in Azure Cosmos DB stand for?
- a) Resource Uniformity
- b) Resource Unit
- c) Rational Unit
- d) Resource Uplift
Answer: b) Resource Unit
Explanation: In Azure Cosmos DB, the Resource Unit (RU) is a measure of both CPU and IO resources.
True/False: With granular scale units in Azure Cosmos DB, you can scale up or down your database based on workload requirements.
- True
- False
Answer: True
Explanation: Granular scale units in Azure Cosmos DB provide the ability to dynamically scale up or down the database throughput based on the workload requirements.
Single Select: Which of the following factors determine the Cost of operation in Azure Cosmos DB?
- a) The number of index entries
- b) The item’s size
- c) Both a and b
- d) None of the above
Answer: c) Both a and b
Explanation: The cost of an operation in terms of RUs depends on both the number of index entries that the operation is creating/modifying and on the size of the item.
True/False: The design for granular scale units can help in instantaneously and independently scaling throughput and storage.
- True
- False
Answer: True
Explanation: Design for granular scale units allows throughput and storage to be instantaneously and independently scaled, which offers flexibility and cost-effectiveness in resource governance.
Which among the following is a factor when considering resource governance in Azure Cosmos DB?
- a) Ensuring efficient use of storage
- b) Scaling up or down in response to demand
- c) Effective use of RU/s
- d) All of the above
Answer: d) All the above.
Explanation: All aspects including efficient storage use, reactive scaling, and efficient use of RU/s, play significant roles in resource governance.
True/False: In Azure Cosmos DB, you cannot reserve RUs at the database level.
- True
- False
Answer: False.
Explanation: In Azure Cosmos DB, you can reserve RUs at the database level, which are shared among all the containers in the database.
Single Select: What do you need to manage as a part of Resource Governance in Azure Cosmos DB?
- a) Containers
- b) Items
- c) Indexes
- d) All of the above
Answer: d) All of the above
Explanation: Resource governance includes managing all these aspects – containers, items, and indexes in Azure Cosmos DB.
Multiple Choice: What is a benefit of designing for granular scale units in Azure Cosmos DB?
- a) Cost-effectiveness
- b) High Performance
- c) Flexibility
- d) All of the above
Answer: d) All of the above
Explanation: Designing for granular scale units provides cost-effectiveness due to usage-based pricing, high performance due to high-speed database operations, and flexibility due to dynamic scaling capabilities.
True/False: In Azure Cosmos DB, logical partitions are a factor to consider when designing for granular scale units.
- True
- False
Answer: True
Explanation: In Azure Cosmos DB, logical partitions are a significant factor in the design of granular scale units as they affect the scalability and performance of the database.
Interview Questions
What is a Request Unit (RU) in Azure Cosmos DB?
A Request Unit (RU) in Azure Cosmos DB is the measure of throughput or performance delivered by Azure Cosmos DB. It abstracts the system resources such as memory, CPU, and I/O necessary to perform database transactions.
How is the throughput provisioned in Azure Cosmos DB?
Throughput in Azure Cosmos DB is provisioned in terms of Request Units (RUs). Users can provision throughput at either a database level that is shared among the containers or at a per-container level.
How can we estimate the Request Units (RU) needed for an operation in Azure Cosmos DB?
To estimate the RU’s needed for an operation, Azure Cosmos DB provides the Request Unit calculator. We can use it for operations like create, read, update, delete on items, and queries.
What are the benefits of granular scale units in Azure Cosmos DB?
Granular scale units allow the optimization of Azure Cosmos DB cost and performance. It helps managing the throughput and storage, scales instantaneously to adjust workloads and offers global distribution without worrying about resource management.
How does resource governance work in Azure Cosmos DB?
In Azure Cosmos DB, resource governance works by isolating resources used by each tenant. This is done using a combination of techniques including resource governance and rate limiting to ensure that DB resources are shared fairly among tenants.
What happens when you exceed the provisioned throughput in Azure Cosmos DB?
If the consumed throughput exceeds the provisioned throughput, Azure Cosmos DB will return with an HTTP 429 status code, indicating that too many requests have been sent in a given amount of time. The client SDKs handle such errors and automatically retry the operation.
What factors influence the number of Request Units (RUs) required for a write operation in Cosmos DB?
The complexity of the operation, the size of the document, indexing, and triggers or stored procedures related to the operation are all factors influencing the RUs required for a write operation in Cosmos DB.
How to balance the system load in Azure Cosmos DB?
Azure Cosmos DB automatically partitions data to balance the database system load. Further, it also provides manual partition key selection that allows granular control on data distribution.
How can you monitor the performance of Azure Cosmos DB?
Azure Cosmos DB provides built-in metrics, detailed diagnostics, and real-time telemetry to monitor the database’s performance. Azure Monitor and Azure Log Analytics can be used for this purpose.
What is partitioning in Azure Cosmos DB and what is its purpose?
Partitioning in Azure Cosmos DB is the division of data into smaller logical pieces, based on a specified partition key. Its purpose is to distribute data for scalability and manageability.
How do you handle peaks in Azure Cosmos DB?
Azure Cosmos DB allows you to elastically scale the throughput (RUs) to manage peak workloads. You can increase the provisioned throughput just before the peak load and decrease it after the peak.
How does automatic and instant scalability happen in Azure Cosmos DB?
Automatic and instant scalability in Azure Cosmos DB is achieved through horizontal partitioning or sharding. It’s done by distributing data across multiple partitions, with each partition having its own resources.
How can you improve the performance of Azure Cosmos DB queries?
Performance can be improved by selecting an appropriate partition key, efficient indexing, optimizing the query, and avoiding cross-partition queries.
How do granular scale units contribute to cost-effectiveness in Azure Cosmos DB?
Granular scale units allow for precise control over the provisioned throughput, helping to optimize cost. Users can precisely allocate and pay for only the throughput they need.
How is data distributed across regions in Azure Cosmos DB?
In Azure Cosmos DB, data is automatically replicated across all the selected regions for global distribution. The data gets partitioned and each partition gets replicated across the regions.
extutorials.com