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

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.

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.

Azure Co smos DB is a multi-model database that supports various data models, such as documents, key-value, graph, and column-family 3 .  The core content-model of Cosmos DB’s database engine is based on atom-record-sequence (ARS), which allows it to store and query differ ent types of data in a flexible and efficient way 3 .

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 ID 1 .

It avoids storing redundant data or creating additional containers for order items 1 .

It allows you to view the order history of a customer with simple queries 1 .

It leverages the benefits of embedding data, such as reducing the number of requests, im proving query performance, and simplifying data consistency 2 .

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 MB 2 .

It may not be optimal for scenarios where order items need to be queried independently from orders or ag gregated by other criteria 2 .

It may not support transactions across multiple orders or customers, as transactions are scoped to a single logical partition 2 .

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 data 2 , or you could use a graph data model that expresses entities and relationships as vertices and edges.

The cost of a point read for a 1 KB item is 1 RU.  The cost of other operatio ns depends on factors such as item size, indexing policy, consistency level, and query complexity 1 . To minimize the consumption of RUs, you can optimize these factors according to your application needs.

For your scenario, one possible way to minimize the consumption of RUs associated to the reads by App1 is to  change the consistency level of read requests to consistent prefix . Consistent prefix is a lower consistency level than session, which is the default consistency level for Azure Cosmos DB.  Lower consistency levels consume fewer RUs than higher consistency levels 2 .  Consistent prefix guarantees that reads neve r see out-of- order writes and that monotonic reads are preserved 1 . This may be suitable for your application if you can tolerate some eventual consistency.

Instead create an Azure function that uses Azure Cosmos DB Core (SQL) API change feed as a trigger and Azure event hub as the output.

The Azure Cosmos DB change feed is a mechanism to get a continuous and incremental feed of records from an Azure Cosmos container as those records are being created or modified. Change feed support works by listening to container for any changes. It then outputs the sorted list of documents that were changed in the order in which they were modified.

The following diagram represents the data flow and components involved in the solution:

Box 1: No

Four containers with 1000 RU/s each.

Box 2: No

Max 8000 RU/s for db2. 8 containers, so 1000 RU/s for each container.

Box 3: Yes

Max 8000 RU/s for db2. 8 containers, so 1000 RU/s for each container. Can very well add an additional container.