DP-420 Designing and Implementing Cloud-Native Applications Using Microsoft Azure Cosmos DB Questions and Answers

Box 1 =The request level options

Azure Cosmos DB offers five well-defined consistency levels: strong, bounded staleness, session, consistent prefix and eventual. You can configure the default consistency level on your Azure Cosmos DB account at any time2. The default consistency level appliesto all databases and containers under that account1. You can also override the default consistency level for a specific request by using the request options2.

Box 2 =The request level options

To modify the consistency level of a read operation in Azure Cosmos DB, you can use request-level options to override the account's default consistency setting. Therefore, to ensure that the read operations of App1 can request either consistent prefix or session consistency, you need to modify the request-level options for each operation.Reference: -https://docs.microsoft.com/en-us/azure/cosmos-db/consistency-levels

IS_NUMBER returns a Boolean value indicating if the type of the specified expression is a number.

"1234" is a string, not a number.

The partition key is what will determine how data is routed in the various partitions by Cosmos DB and needs to make sense in the context of your specific scenario. The IoT Device ID is generally the "natural" partition key for IoT applications.

Scenario: The iotdb database will contain two containers named con-iot1 and con-iot2.

Ensure that Azure Cosmos DB costs for IoT-related processing are predictable.

leaseCollectionPrefix: when set, the value is added as a prefix to the leases created in the Lease collection for this Function. Using a prefix allows two separate Azure Functions to share the same Lease collection by using different prefixes.

Scenario: Use Azure Functions to send notifications about product updates to different recipients.

Trigger the execution of two Azure functions following every update to any document in the con-product container.

Scenario: Develop an app named App2 that will run from the retail stores and query the data in account2. App2 must be limited to a single DNS endpoint when accessing account2.

By using Azure Private Link, you can connect to an Azure Cosmos account via a private endpoint. The private endpoint is a set of private IP addresses in a subnet within your virtual network.

When you're using Private Link with an Azure Cosmos account through a direct mode connection, you can use only the TCP protocol. The HTTP protocol is not currently supported.

Bounded staleness is frequently chosen by globally distributed applications that expect low write latencies but require total global order guarantee. Bounded staleness is great for applications featuring group collaboration and sharing, stock ticker, publish-subscribe/queueing etc.

Scenario: Application updates in con-product frequently cause HTTP status code 429 "Too many requests". You discover that the 429 status code relates to excessive request unit (RU) consumption during the updates.

App1 queries the con-product and con-productVendor containers.

Note: Request unit is a performance currency abstracting the system resources such as CPU, IOPS, and memory that are required to perform the database operations supported by Azure Cosmos DB.

Scenario:

Develop an app named App1 that will run from all locations and query the data in account1.

Once multi-region writes are configured, maximize the performance of App1 queries against the data in account1.

Whenever there are multiple solutions for a requirement, select the solution that provides the best performance, as long as there are no additional costs associated.

Azure Cosmos DB is a multi-model database that supports various data models, such as documents, key-value, graph, and column-family3. The core content-model of Cosmos DB’s database engine is based on atom-record-sequence (ARS), which allows it to store and query different types of data in a flexible and efficient way3.

To optimize the data store for reading data, you should consider the following factors:

The size and shape of your data

The frequency and complexity of your queries

The latency and throughput requirements of your application

The trade-offs between storage efficiency and query performance

Based on these factors, one possible design that you could implement is B. In a company container, create a document for each company. Embed the users into company documents. Use the company ID as the partition key.

This design has the following advantages:

It stores companiesand users as self-contained documents that can be easily retrieved by company ID1.

It avoids storing redundant data or creating additional containers for users1.

It allows you to browse by company and browse the users by company with simple queries1.

It shows a details page for the company and all the related users by fetching a single document1.

It leverages the benefitsof embedding data, such as reducing the number of requests, improving query performance, and simplifying data consistency2.

This design also has some limitations, such as:

It may not be suitable for some users who have data that is greater than 2 KB, as it could exceed the maximum document size limit of 2 MB2.

It may not be optimal forscenarios where users need to be associated with more than one company or queried independently from companies2.

It may not be scalable for companies that have more than 1,000 users, as it couldresult in hot partitions or throttling2.

Depending on your specific use case and requirements, you may need to adjust this design or choose a different one. For example, you could use a hybrid data model that combines embedding and referencing data2, or you could use a graph data model that expresses entities and relationships as vertices and edges.

You can set up alerts from the Azure Cosmos DB pane or the Azure Monitor service in the Azure portal.

Note: Alerts are used to set up recurring tests to monitor the availability and responsiveness of your Azure Cosmos DB resources. Alerts can send you a notification in the form of an email, or execute an Azure Function when one of your metrics reaches the threshold or if a specific event is logged in the activity log.

Azure Cosmos DB is a multi-model database that supports various data models, such as documents, key-value, graph, and column-family3. The core content-model of Cosmos DB’s database engine is based on atom-record-sequence (ARS), which allows it to store and query different types of data in a flexible and efficient way3.

To develop a model that supports order read operations and minimizes the number of requests, you should consider the following factors:

The size and shape of your data

The frequency and complexity of your queries

The latency and throughput requirements of your application

The trade-offs between storage efficiency and query performance

Based on these factors, one possible model that you could implement is B. Create a single database that contains one container. Create a separate document for each order and embed the order items into the order documents.

This model has the following advantages:

It stores orders and order items as self-contained documents that can be easily retrieved by order ID1.

It avoids storing redundant data or creating additional containers for order items1.

It allows you to view the order history of a customer with simple queries1.

It leverages the benefits of embedding data, such as reducing the number of requests, improving query performance, and simplifying data consistency2.

This model also has some limitations, such as:

It may not be suitable for some order items that have data that is greater than 2 KB, as it could exceed the maximum document size limit of 2 MB2.

It may not be optimal for scenarios where order items need to be queried independently from orders or aggregated by other criteria2.

It may not supporttransactions across multiple orders or customers, as transactions are scoped to a single logical partition2.

Depending on your specific use case and requirements, you may need to adjust this model or choose a different one. For example, you could use a hybrid data model that combines embedding and referencing data2, or you could use a graph data model that expresses entities and relationships as vertices and edges.

Box 1: No

Each physical partition should have its own index, but since no index is used, the query is not cross-partition.

Box 2: No

Index utilization is 0% and Index Look up time is also zero.

Box 3: Yes

A partition key index will be created, and the query will perform across the partitions.

You can facilitate aggregate data by using Change Feed and Azure Functions, and then use it for reporting.

Box 1: Yes

"path": "/*" is in includePaths.

Include the root path to selectively exclude paths that don't need to be indexed. This is the recommended approach as it lets Azure Cosmos DB proactively index any new property that may be added to your model.

Box 2: No

"path": "/firstName/?" is in excludePaths.

Box 3: Yes

"path": "/address/city/?" is in includePaths