Partitioning is a critical concept in Azure Cosmos DB to ensure your applications can scale. Partitions in Azure Cosmos DB spread data across multiple logical partitions, attributing to scalability. Consider partitioning key as a logical divider of the data. To choose a partitioning strategy based on a specific workload, you must understand partitioning in depth and know how to optimize your workload.
Understanding Partitioning in Azure Cosmos DB
Azure Cosmos DB leverages horizontal partitioning to scale storage and throughput. Data in Cosmos DB is divided into smaller, manageable parts known as partitions. Two types of partition sets exist in Cosmos DB:
- Physical partitions: These are the fundamental units of storage and throughput in Cosmos DB. A physical partition can store up to 50 GB of data and consume up to 10,000 Request Units per second (RUs).
- Logical partitions: These form within a physical partition and are dictated by the partition key chosen at the creation of a container.
You choose a partition key when you create a container, and all items in the container will include a property that corresponds to this partition key. The value of the partition key determines the logical partition an item belongs to.
Choosing a Partitioning Strategy
The choice of partition key is crucial as it impacts the application’s scalability, cost-efficiency, and performance. Your partition key should evenly distribute write and read workloads across all logical partitions to prevent ‘hot’ partitions, resulting in throttled write or read operations.
Here’s a simple step-by-step strategy to choose a partitioning strategy based on specific workloads:
- Identify your workload: Define your application’s major workloads. A workload involves the set of queries, read, and write operations required by your application.
- Evaluate partition key options: For each workload, identify candidate properties for the partition key. A good partition key is one that spreads request and storage evenly across partitions and accommodates your workloads.
- Compare candidate keys: Compare partition key choices from each workload to a common key that could satisfy all workloads.
Let’s take an example of an e-commerce application where the workloads include order management, product catalog management, and customer management.
| Workload | Read/Write Intensity | Query Pattern | Suggested Partition Key |
| Order Management | High writes, Low Reads | Query by order ID, Query by customer ID | Order ID |
| Product Catalog | High reads, Low Writes | Query by product ID, Query by category | Product ID |
| Customer Management | Balanced reads and writes | Query by customer ID | Customer ID |
In the example above, if the query pattern overlaps, you could use a single partition key for all workloads, ensuring the key distributes the data and requests evenly. If it doesn’t, consider introducing a synthetic key or re-architect the workload.
Conclusion
Understanding Azure Cosmos DB’s partitioning and how to select a key to meet your specific workload is integral for any application. This will set your application up for success, allowing for optimal scaling, cost management, and performance on the Azure platform. For additional guidance or to deep dive into specific examples, Microsoft’s official documentation on Azure Cosmos DB is a great resource.
Practice Test
Choose the correct statement:
- a) Partitioning is optional in Azure Cosmos DB
- b) Partitioning is a requirement in Azure Cosmos DB
- c) Partitioning is only a requirement in relational databases
- d) None of the above
Answer: b) Partitioning is a requirement in Azure Cosmos DB
Explanation: Partitioning in Azure Cosmos DB allows the database to scale and distribute loads.
True or False: You can change a partition key at any time after setting it.
Answer: False
Explanation: The partition key is set at the creation of the container and can’t be changed afterwards without re-creating the container.
Which of the following are valid partitioning strategies in Azure Cosmos DB? (Select all that apply)
- a) Hash-based partitioning
- b) Range-based partitioning
- c) Geospatial partitioning
- d) Size-based partitioning
Answer: a) Hash-based partitioning, b) Range-based partitioning
Explanation: Azure Cosmos DB uses both hash-based and range-based partitioning.
True or False: Choosing a partitioning strategy based on a specific workload is a good practice in designing and implementing native applications using Microsoft Azure Cosmos DB?
Answer: True
Explanation: Choosing a correct partitioning strategy based on a specific workload can balance storage and throughput.
When would you choose the Hash-based partitioning strategy?
- a) When the total amount of data is not predictable
- b) When the total amount of data is predictable
- c) When you need to perform range queries
- d) When you need to store data in the same partition
Answer: a) When the total amount of data is not predictable
Explanation: Hash-based partitioning is optimal when the total amount of data isn’t predictable and when you need even data distribution.
True or False: You should store all related data in the same partition to speed up query performance.
Answer: False
Explanation: Storing too much data in the same partition can lead to an uneven distribution of data, and it can lead to partition key hotspotting.
The term “hot partition” refers to:
- a) A partition that contains a lot of data
- b) A partition that experiences high levels of read and write activity
- c) A partition that stores data in-memory
- d) A partition that has been recently created
Answer: b) A partition that experiences high levels of read and write activity
Explanation: A hot partition in Azure Cosmos DB is a partition that experiences disproportionately high levels of read/write activity.
Which of the following can cause a hot partition? (Select all that apply)
- a) A bad choice of partition key
- b) Storing too much data in a single partition
- c) Locally-ordered monotonically increasing or decreasing values
- d) High throughput rate
Answer: a) A bad choice of partition key, b) Storing too much data in a single partition, c) Locally-ordered monotonically increasing or decreasing values
Explanation: All these factors can contribute to hot partitioning, which can lead to uneven data distribution and limit performance.
True or False: Performance is always the most important factor to consider when choosing a partitioning strategy.
Answer: False
Explanation: While performance is a critical factor, so too are scalability, reliability, durability, and cost effectiveness.
Which partitioning strategy does Azure Cosmos DB use for write operations?
- a) Hash-based partitioning
- b) Range-based partitioning
- c) Data is written to all partitions
- d) None of the above
Answer: a) Hash-based partitioning
Explanation: Azure Cosmos DB distributes writes across different physical partitions based on a hash of the partition key value.
Interview Questions
What key factors should you consider when designing a partitioning strategy for Microsoft Azure Cosmos DB?
You need to consider the following factors: equality of data distribution, volume of storage, and access patterns across partitions. You should also consider the maximum throughput provisioned for a single physical partition which is currently 10,000 RU/s and the maximum storage provisioned for a single physical partition which is currently 50 GB.
What is the best partition strategy for a write-heavy workload in Cosmos DB?
For a write-heavy workload, the best partition strategy would be to evenly distribute the workload amongst numerous partition key values. This helps you to leverage the full provisioned throughput and provide the best performance.
How can I ensure a uniform distribution of data and request volume in Cosmos DB?
You can ensure a uniform distribution through the choice of the right partition key. Ideally, the partition key should have a high cardinality and the access pattern should target a single partition key or a range of partition keys.
What are logical and physical partitions in Cosmos DB?
Logical partitions are formed based on the partition key value defined in items. All items with the same partition key value are stored in the same logical partition. Physical partitions on the other hand are internal resources used to manage storage and throughput. Multiple logical partitions can be mapped to a single physical partition.
Can the partition key be changed after a Cosmos DB container is created?
No, the partition key cannot be changed after a container is created in Cosmos DB. If you need to change it, then you’d have to delete the container/recreate it or migrate the data to a new container with the required partition key.
In a Cosmos DB, does Microsoft automatically manage physical partitions?
Yes, Microsoft automatically manages physical partitions in Cosmos DB. Whenever a physical partition reaches its storage limit, it splits into multiple partitions. Similarly, if a physical partition exceeds the throughput limit, Cosmos DB distributes the load over more physical partitions.
What is the main purpose of partition keys in Cosmos DB?
Partition keys in Cosmos DB are used to distribute data and throughput across physical partitions. They are responsible for ensuring both efficient storage and optimal query performance.
How does Cosmos DB handle hot partition scenarios?
Cosmos DB has a mechanism to handle hot partition scenarios, which can occur when one partition consumes more resources than others. It can split and redistribute data from a busy partition to less busy partitions. This process happens automatically and transparently, without affecting database performance or availability.
Why should you avoid a partition key that could result in “hotspotting” in Cosmos DB?
“Hotspotting” may cause an uneven distribution of data and request load, resulting in only one partition having significantly more data or requests. This can lead to inefficient use of provisioned throughput, potential throttling, and imperfect scalability.
Can you provide some examples of good partition keys in Cosmos DB?
Good partition keys typically have a high cardinality and distribute requests and data uniformly across partitions. Examples of good partition keys might include attributes such as CustomerId, OrderId, or DeviceId, depending on the nature of your data and operations.
What is the benefit of selecting a partition key that evenly distributes write and read operations across all logical partitions in Cosmos DB?
The benefit of evenly distributing operations across all logical partitions is to fully leverage provisioned throughput. This helps prevent throttling, improves scalability, and ensures the maximum performance of your Cosmos DB.
What happens if the total size of all items with the same partition key value exceed the limit of 20 GB in Cosmos DB?
If the total size of all items with the same partition key value exceed the limit of 20 GB, the operation will fail because a partition key cannot span multiple physical partitions in Cosmos DB.
Can a single item in Cosmos DB be larger than 2MB?
No, a single item in Cosmos DB cannot exceed the maximum limit of 2MB, regardless of the provisioned throughput or the number of partition keys.
What could be potential issues if a bad partition key is chosen in Cosmos DB?
A bad partition key could result in a hot partition, where one partition consumes more resources than others. This could lead to inefficient usage of provisioned throughput, potential throttling, and limited scalability.
Can throughput be controlled at a partition key granularity level in Cosmos DB?
No, throughput in Cosmos DB is controlled at the level of the container and cannot be managed or provisioned for a specific partition key. However, by choosing a high-cardinality partition key that distributes writes and reads across all logical partitions, you can achieve a more balanced throughput utilization.
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