Concepts
Partition keys, also called hash keys, are used to distribute data across multiple partitions. A high-cardinality partition key is one that has a large number of distinct values. For example, a user ID that is unique to each user or a UUID (Universal Unique Identifier) which guarantees uniqueness across distributed systems. High cardinality ensures that the data is not skewed to a small number of partitions which could cause ‘hot spots’, leading to performance bottlenecks and throttling in databases.
Importance of Balanced Partition Access
Balanced partition access is integral to the optimized performance of a database. A well-balanced system ensures each partition is being read and written to roughly equally, thereby maximizing throughput and minimizing the risk of overloading individual partitions. Imbalanced partitions can lead to uneven workloads where some nodes are idle and others are overwhelmed. This can delay the processing of data, increase latency, and lead to a poor user experience.
Example of a Low vs High Cardinality Partition Key
Consider an example of a social media application storing user posts in a DynamoDB table:
| Low Cardinality Key | High Cardinality Key |
|---|---|
| PostType | UserID |
| Image | 123e4567-e89b-12d3-a456-426614174000 |
| Video | 987e6543-f21c-34d5-a543-586617451002 |
| Text | 789e1234-a56c-67d8-b432-536812347999 |
In the low cardinality example, you use the type of post as the partition key. Since there are only a few post types, this creates a small number of partitions with potentially many items, leading to hot partitions and potentially throttled access.
In contrast, the high cardinality example uses a unique UserID or a UUID as the partition key. This approach ensures that each item (user post) is distributed across a large number of partitions, leading to a more balanced access pattern.
Best Practices for Using High-Cardinality Keys
- Use UUIDs or Hashes: If natural keys with high cardinality are not available, use UUIDs or cryptographic hashes of a composite key to ensure uniformity.
- Composite Keys: Combine multiple attributes to form a unique key. For example, concatenating
userIDwithtimestampor other unique attributes can form a partition key that is highly unique. - Avoid Sequential Keys: Sequential keys such as timestamps or monotonically increasing numbers should be avoided as the partition key since they can lead to hot partitions. If needed, these can be part of a composite key with high cardinality attributes.
- Understand the Access Patterns: Not all high-cardinality keys are optimal. Understand the application’s access patterns and choose a key that will not only distribute the data evenly but also cater to query requirements effectively.
- Monitor Access Patterns: Use CloudWatch or DynamoDB’s metrics to monitor the read and write activity across partitions. If certain partitions are accessed more frequently, it may be time to reassess the partition key design.
- Add Randomness to Partition Keys: If you have no choice but to use a low cardinality key, consider adding a random number or string to the key to distribute writes more evenly.
Conclusion
High-cardinality partition keys are paramount when designing scalable, high-performance applications using AWS services such as DynamoDB. They help in preventing the creation of hot spots and ensure that the workload is balanced across all partitions. Through careful planning and observing best practices, developers can ensure that their database architecture will sustain growth in data and demand without compromising on performance.
Answer the Questions in Comment Section
True or False: High-cardinality partition keys help prevent “hot” partitions in Amazon DynamoDB.
- (A) True
- (B) False
Answer: A
Explanation: High-cardinality partition keys distribute read and write operations more evenly among partitions, helping to prevent single partitions from becoming “hot,” which can lead to throttling and uneven performance.
In Amazon DynamoDB, what is the effect of using a low-cardinality partition key?
- (A) Improved performance
- (B) Risk of throttling
- (C) Easier data retrieval
- (D) Higher scalability
Answer: B
Explanation: Low-cardinality partition keys can cause operations to concentrate on a small number of partitions. This increased load can lead to throttling and reduced performance.
True or False: It is ideal to use only the ‘date’ field as a partition key for an Amazon DynamoDB table storing user activities.
- (A) True
- (B) False
Answer: B
Explanation: Using just the ‘date’ field as a partition key may cause a high concentration of requests on certain dates, leading to uneven access patterns. A combined key with higher cardinality would balance the partition access better.
When selecting a partition key for a DynamoDB table, you should ensure it has:
- (A) Low uniqueness
- (B) High uniqueness
- (C) Simple data type
- (D) Predictable access patterns
Answer: B
Explanation: High uniqueness in a partition key, implying high cardinality, helps prevent hot partitions by evenly distributing data and traffic across them.
Which technique can be used to increase a partition key’s cardinality in Amazon DynamoDB?
- (A) Using random numbers as suffixes
- (B) Decreasing the write capacity units (WCUs)
- (C) Decreasing the read capacity units (RCUs)
- (D) Storing all the data in a single partition
Answer: A
Explanation: Adding random numbers as suffixes or using other techniques to provide a more unique partition key increases its cardinality and leads to a more uniform data distribution.
True or False: When using Amazon DynamoDB, you should avoid using hashes or uuids as partition keys because they have low cardinality.
- (A) True
- (B) False
Answer: B
Explanation: Hashes or UUIDs actually have high cardinality since they are designed to be unique. They can be good choices for partition keys in Amazon DynamoDB.
True or False: The only way to handle ‘hot’ partitions in Amazon DynamoDB is by resharding your table.
- (A) True
- (B) False
Answer: B
Explanation: Resharding is one method to handle ‘hot’ partitions, but other strategies include using higher-cardinality partition keys or implementing caching mechanisms.
Select all the practices that can help you maintain uniform access across DynamoDB partitions:
- (A) Using a primary key with low cardinality
- (B) Introducing a hash function to the partition key value
- (C) Write sharding across partition key values
- (D) Monitoring access patterns and adjusting provisioned capacity accordingly
Answer: B, C, D
Explanation: A hash function increases key randomness, write sharding involves creating additional partition key values to distribute the load, and monitoring and adjusting capacity can help handle throughput requirements more effectively.
True or False: Amazon DynamoDB automatically adjusts partitions and their load distribution based on the partition key.
- (A) True
- (B) False
Answer: A
Explanation: DynamoDB automatically manages the creation and rebalancing of partitions, but the efficiency of this process is highly dependent on the choice of a good partition key.
In a scenario where userID is a low-cardinality key, how can you improve partition key cardinality for a DynamoDB table?
- (A) Prefix userID with the current timestamp
- (B) Postfix userID with a sequential number
- (C) Combining userID with another high-cardinality attribute
- (D) Increase the number of read capacity units (RCUs) for the table
Answer: C
Explanation: Combining the low-cardinality userID with another high-cardinality attribute can create a composite key that is much more unique, thus improving the partition key cardinality.
True or False: To achieve balanced partition access in Amazon DynamoDB, your partition key should be based on attributes with highly variable access patterns.
- (A) True
- (B) False
Answer: B
Explanation: Highly variable access patterns can lead to unpredictable loads on partitions. Instead, partition keys should allow for predictable and evenly distributed access, which can be achieved with high cardinality and low variability in access.
What is the recommended practice if you have a small number of high-traffic partition keys in Amazon DynamoDB?
- (A) Move the high-traffic keys to a separate table
- (B) Replicate the high-traffic keys across multiple partitions
- (C) Apply exponential backoff in your request retries
- (D) Implement write sharding to distribute the workload
Answer: D
Explanation: Write sharding by appending or prepending random values to the partition key, or using a deliberate pattern, helps to distribute the workload across multiple partitions, reducing the likelihood of creating ‘hot’ partitions.
Great explanation on using high-cardinality partition keys for balanced partition access!
Can anyone explain how high-cardinality partition keys impact DynamoDB read and write capacity?
Thanks for the helpful post!
Could you use a composite key as a strategy for high-cardinality keys?
Nice article, very informative 🙂
How would you estimate the cardinality of your partition keys beforehand?
Thanks, this was exactly what I needed.
What are some common mistakes when choosing partition keys?