When designing and implementing native applications using Microsoft Azure Cosmos DB for exam DP-420, understanding throughput and data storage requirements for a specific workload is essential. This involves analyzing and understanding how your application reads, writes, and queries data in Cosmos DB.
Azure Cosmos DB manages resources such as CPU, Memory, and I/O via the concept of Request Unit (RU). Every operation in Cosmos DB, including reads, writes, and queries, consumes a certain amount of RUs based on factors such as item size, item indexing, query complexity among others.
Understanding Throughput:
The throughput of Azure Cosmos DB is measured in Request Units per second (RU/s). When setting up a Cosmos DB account, it’s vital to specify the level of throughput required by the application. This is done by setting a provisioned throughput level (in RU/s).
For examining the workload for a specific Cosmos DB use case, we can use the Azure Cosmos DB capacity planner. This tool guides you in estimating the required RU/s for a workload based on parameters like item size, read operations per second, write operations per second, etc.
For example, if we have an item size of 1KB, with 100 read operations per second and 50 write operations per second, the capacity planner might recommend a provisioned throughput of around 700 RU/s for an optimal performance.
Understanding Data Storage Requirements:
The underlying physical storage is automatically managed by Azure Cosmos DB. When the data grows, it automatically allocates more storage, and similarly, it reclaims the storage when data is deleted. Thus, the storage is directly proportional to the amount of data stored in the database.
Let’s consider an example. If a document size is 2 KB, and we have 1 million such documents, the total storage consumed will be approximately 2 GB.
Furthermore, Azure Cosmos DB also stores indexes to improve the query performance, and the size of these indexes also should be considered in the storage. Therefore, while estimating the storage, you should take into account both the size of the data and the indexes.
Comparing Throughput and Data Storage:
When considering the overall cost and performance, both throughput and data storage play key roles. While high provisioned throughput can lead to optimal performance, it can also result in higher costs. On the other hand, more data storage not only increases the cost but also the need for more throughput as the size of the data and indexes increase.
| Throughput | Data Storage | |
|---|---|---|
| Performance Impact | High provisioned throughput leads to optimal performance | More data needs more throughput for optimal performance |
| Cost Implication | High throughput equals high cost | More data equals more storage cost, and potential increase in throughput needs |
In conclusion, while preparing for DP-420 Designing and Implementing Native Applications Using Microsoft Azure Cosmos DB exam, it’s crucial to have a foundational understanding of throughput and data storage requirements for a specific workload, as it helps in making crucial design decisions. Use tools like Azure Cosmos DB capacity planner to estimate your requirements, keeping in mind the trade-offs between performance and cost.
Practice Test
1) True/False: Throughput in Microsoft Azure Cosmos DB is measured in Request Units (RU/s).
- True
- False
Answer: True
Explanation: Request Units (RU/s) measure the amount of throughput. Every operation like reads, writes or queries consumes some amount of RU/s.
2) To evaluate the throughput needs for a specific workload in Microsoft Azure Cosmos DB, which of the following factors need to be considered?
- A) Data volume
- B) Read and write rates
- C) Query patterns
- D) Stored procedure execution frequency
Answer: A, B, C, D
Explanation: All these factors can affect the throughput needs. Hence, they need to be considered in the evaluation.
3) Single Choice: Which performance level secures a certain amount of RU/s in Microsoft Azure Cosmos DB?
- A) Provisioned throughput
- B) Serverless throughput
- C) Partitioned throughput
- D) Default throughput
Answer: A) Provisioned throughput
Explanation: Provisioned throughput secures a certain amount of RU/s for a database or container in Cosmos DB.
4) True/False: You can change the throughput of your Azure Cosmos DB account at any time without incurring any further costs.
- True
- False
Answer: False
Explanation: While you can alter the throughput settings, it might result in additional costs depending on the amount of RU/s provisioned.
5) In Microsoft Azure Cosmos DB, if you have data storage requirements that exceed_______, you should consider partitioning your data?
- A) 20 GB
- B) 10 GB
- C) 5 GB
- D) 2 GB
Answer: B) 10 GB
Explanation: A single partition in Cosmos DB has a limit of 10 GB of storage. Therefore, if the data storage requirement exceeds 10 GB, you should consider partitioning.
6) True/False: Data storage requirements in Cosmos DB directly affect the number of partition keys required.
- True
- False
Answer: True
Explanation: The size of data determines how many partition keys are required to distribute the data effectively across different partitions.
7) Which of the following are types of throughput in Azure Cosmos DB?
- A) Manual throughput
- B) Autoscale throughput
- C) Shared throughput
- D) Scheduled throughput
Answer: A, B, C
Explanation: Azure Cosmos DB offers Manual, Autoscale and Shared throughput. There is no option called Scheduled throughput.
8) True/False: Evaluating workload for Cosmos DB simply involves reviewing the amount of data to be stored.
- True
- False
Answer: False
Explanation: Along with the volume of data to be stored, factors like read/write rates, query patterns, and stored procedure execution frequency need to be evaluated.
9) Single Choice: What is the unit of RU charge in Cosmos DB?
- A) Per second
- B) Per minute
- C) Per hour
- D) Per day
Answer: A) Per second
Explanation: The cost of all database operations in Cosmos DB is normalized and expressed in terms of Request Units (RU) per second.
10) True/False: Autoscale throughput in Cosmos DB automatically scales the number of RUs based on the workload.
- True
- False
Answer: True
Explanation: Autoscale throughput is a feature in Cosmos DB that automatically adjusts the throughput of your database based on the workload.
Interview Questions
What is throughput in the context of Microsoft Azure Cosmos DB?
In Microsoft Azure Cosmos DB, throughput refers to the total amount of resources consumed to perform database operations, like read, write, or query activities, measured in Request Units per second (RU/s).
What factors influence the throughput of Azure Cosmos DB?
The throughput of Azure Cosmos DB is influenced by several factors, including the size and complexity of documents, the indexing policy, the consistency level, and the type and volume of operations (reads, writes, queries).
How can you calculate the required throughput for a specific workload in Azure Cosmos DB?
Azure Cosmos DB provides the Capacity Planner tool to calculate the required throughput for a specific workload. The tool uses details like the number of reads and writes per second, the data size, and the item size to estimate the required RU/s.
What are Request Units (RU/s) in Cosmos DB and why are they important in evaluating throughput for a specific workload?
In Cosmos DB, Request Units (RU/s) represents a measure of throughput. They help quantify the resources required to perform a database operation such as read and write operations. Ensuring you provision an adequate number of RU/s for your workload can enhance performance and prevent throttling.
Why is it necessary to evaluate data storage requirements for a specific workload in Azure Cosmos DB?
Evaluating data storage requirements helps ensure sufficient storage capacity for your data. It also helps in cost optimization, as you pay for the amount of storage consumed in Azure Cosmos DB.
How does partitioning impact the throughput and data storage in Azure Cosmos DB?
Partitioning in Azure Cosmos DB spreads the data across multiple physical partitions for efficient data distribution. It impacts throughput by enabling concurrent operations across partitions, and affects data storage by distributing data storage requirements, allowing data to scale over time.
How does the consistency level affect the throughput in Azure Cosmos DB?
Cosmos DB provides five consistency levels – strong, bounded staleness, session, consistent prefix, and eventual. Higher consistency levels like strong and bounded staleness require more request units (RU/s), thereby affecting the throughput.
What is data redundancy, and how does it impact storage requirements?
Data redundancy involves storing multiple copies of the same data. In Azure Cosmos DB, data redundancy provides high availability and reliability but also increases the storage requirements.
How do indexing policies impact throughput in Cosmos DB?
Indexing policies in Cosmos DB can impact throughput as they consume request units (RU/s) when creating or updating indexes. A complex indexing policy may consume more throughput as compared to a simplified one.
How can you increase the provisioned throughput in Cosmos DB?
You can increase the provisioned throughput in Cosmos DB by increasing the number of request units per second (RU/s) in either the Azure portal, using ARM templates, or programmatically using SDKs.
How does the item size affect the throughput in Azure Cosmos DB?
The item size affects the number of request units (RU/s) consumed for read or write operations. Larger items require more RU/s, thereby impacting the throughput.
What are the storage costs associated with Azure Cosmos DB?
In Azure Cosmos DB, you are billed for the total amount of data stored in your account, including the metadata, index data, and the replicated data in secondary regions.
What is the role of Time to Live (TTL) in managing storage requirements in Cosmos DB?
Time to Live (TTL) in Cosmos DB allows items to be automatically deleted after a specific period, thereby managing storage usage and potentially reducing storage costs.
How does the workload type (read-heavy, write-heavy, or balanced) affect the throughput and data storage in Cosmos DB?
A read-heavy workload consumes more RU/s for read operations, while a write-heavy workload consumes more for write operations. A balanced workload evenly distributes RU/s across reads and writes. The type of workload also impacts storage, with write-heavy workloads requiring more storage capacity.
How can Azure Monitor and Azure Metrics help in evaluating throughput and data storage requirements?
Azure Monitor and Azure Metrics provide insights into the performance, availability, and usage patterns of your Cosmos DB account. They aid in tracking storage growth, monitoring throughput utilization, and diagnosing performance issues, thereby helping in evaluating throughput and data storage requirements.
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