Evaluate the throughput and data storage requirements for a specific workload

Azure Cosmos DB is a globally distributed, multi-model database service provided by Microsoft Azure. It offers high scalability, low latency, and automatic indexing of data, making it an excellent choice for storing and managing large volumes of data. When designing and implementing native applications using Azure Cosmos DB, it is essential to evaluate the throughput and data storage requirements to ensure optimal performance. In this article, we will explore how to evaluate the throughput and data storage needs for a specific workload using Azure Cosmos DB.

Throughput Requirements

Throughput refers to the number of database operations that can be performed in a given amount of time. When evaluating the throughput requirements for a workload, it is crucial to consider factors such as the number of requests per second, the size of the data being stored, and the expected response time.

Azure Cosmos DB allows you to provision throughput at the container level using two models: manual throughput provisioning and autoscale.

1. Manual Throughput Provisioning

With manual throughput provisioning, you need to specify the request units per second (RU/s) required for your workload. Request Units (RU) is the measure of throughput in Azure Cosmos DB and is a combination of read and write operations along with the size of the data being accessed.

To determine the required RU/s, you can start by estimating the number of read and write operations your workload will perform. Microsoft provides a handy tool called the Azure Cosmos DB Capacity Planner, which can assist you in estimating the required throughput. By inputting metrics such as the number of reads, writes, and queries per second, the tool provides an estimated RU/s requirement.

Once you have the estimated RU/s, you can provision the throughput accordingly. It is important to keep in mind that Azure Cosmos DB allows you to adjust the provisioned throughput dynamically, which gives you the flexibility to scale up or down as per your workload requirements.

2. Autoscale

Autoscale is a feature provided by Azure Cosmos DB that automatically adjusts the provisioned throughput based on the workload demands. With autoscale, you do not need to specify a fixed RU/s value. Instead, Azure Cosmos DB automatically scales the throughput up or down in response to the workload patterns. This ensures that you only pay for the throughput you actually need.

Data Storage Requirements

Apart from throughput, evaluating the data storage requirements is equally important when designing and implementing native applications using Azure Cosmos DB.

Azure Cosmos DB supports multiple data models such as key-value, document, column-family, and graph. Each data model has its own characteristics and usage patterns. When evaluating the data storage requirements, consider factors such as the data model, the size of the individual documents or entities, and the expected growth rate.

To estimate the data storage requirements, you can calculate the average document or entity size and multiply it by the expected number of documents or entities. For example, if you expect to store one million documents, and the average size of each document is 1 KB, then the total storage required would be approximately 1 GB.

It is important to note that Azure Cosmos DB automatically handles the partitioning and distribution of data across multiple nodes within a region or even globally. This offers elastic scalability and eliminates the need to manage data distribution manually.

Conclusion

When designing and implementing native applications using Azure Cosmos DB, evaluating the throughput and data storage requirements is crucial for ensuring optimal performance and cost efficiency. By considering factors such as the number of requests, response time, and data size, you can provision the right amount of throughput and estimate the required data storage accurately. Azure Cosmos DB provides flexible scalability options, allowing you to adjust the throughput dynamically based on workload demands. With its globally distributed nature, Azure Cosmos DB offers high availability and low latency for your native applications.