Design partitioning for workloads that require multiple partition keys

Designing partitioning strategies for workloads that require multiple partition keys is a crucial aspect of building scalable and efficient applications using Microsoft Azure Cosmos DB. Partitioning is the process of distributing data across multiple physical partitions in order to achieve high throughput and low latency. Azure Cosmos DB allows developers to choose one or more partition keys to determine the data distribution pattern. In this article, we will discuss some important considerations and best practices for designing partitioning strategies for workloads with multiple partition keys.

Choose the Right Partition Key

The partition key plays a vital role in determining the scalability and performance of your application. It is important to choose a partition key that evenly distributes the data across partitions and avoids hotspots. When dealing with multiple partition keys, selecting the right combination of keys is critical. Consider attributes that are frequently accessed together and ensure they are part of the same partition key.

Composite Partition Keys

Azure Cosmos DB supports composite partition keys, which are combinations of two or more attributes as the partition key. This can be beneficial when you have multiple attributes that need to be considered together for efficient data access. For example, if your application frequently queries data based on both “Region” and “ProductCategory” attributes, you can create a composite partition key using both attributes.

"partitionKey": {
"paths": [
"/Region",
"/ProductCategory"
]
}

Data Skew and Cardinality

It’s important to consider the distribution and cardinality of data across multiple partition keys. Uneven data distribution can lead to data skew and hot partitions, impacting the scalability and performance of your application. Ensure that the combination of partition keys results in a large number of possible values with even distribution. If a partition key has low cardinality or too many occurrences of the same value, it may result in uneven distribution and hotspots.

Impact of Data Growth and Access Patterns

Analyze the growth rate and access patterns of your data to ensure the chosen partitioning strategy can scale efficiently. As your data size increases, the partitioning strategy should be able to accommodate the growth. Be mindful of how data will be accessed and distributed across partitions. Consider the frequency of data updates and the queries that will be performed. This analysis will help in selecting the right partitioning strategy.

Account for Cross-Partition Queries

When dealing with multiple partition keys, it’s important to evaluate the impact on cross-partition queries. Cross-partition queries involve accessing data from multiple partitions and can result in higher latency and reduced throughput. Design your data access patterns to minimize the need for cross-partition queries whenever possible. If cross-partition queries are unavoidable, optimize the queries by setting the appropriate requestOptions.MaxDegreeOfParallelism value to increase parallelism and improve query performance.

new FeedOptions
{
MaxDegreeOfParallelism = -1
}

Monitor and Optimize Performance

After implementing your partitioning strategy, continuously monitor the performance of your application. Azure Cosmos DB provides metrics and diagnostics that can help identify any performance bottlenecks. Monitor the Request Units (RUs) consumed by your queries and adjust the provisioned throughput and partitioning strategy accordingly to optimize performance.

In conclusion, designing partitioning strategies for workloads that require multiple partition keys requires careful consideration of data distribution, access patterns, and query optimization. By choosing the right combination of partition keys, using composite keys when necessary, and monitoring performance, you can ensure scalability, high throughput, and low latency for your native applications using Microsoft Azure Cosmos DB.