Optimizing Apache Cassandra for a Social Media Feed
Last week on the PlanetCassandra Global Meetup Hartmut Armbuster showed us his experience and expertise with Apache Cassandra by showing how he tackled the design and implementation of the data model for a social media feed.
Hartmut began by outlining the access patterns for the social media feed:
- Fetching a paginated list of posts.
- Retrieving statistics for each post (impressions, likes, comments).
- Determining if the current user has interacted with a post (likes, bookmarks).
- Fetching author information for each post.
- Getting a count of new posts since the user last viewed their feed.
With these patterns in mind, Hartmut detailed the initial database schema design and the corresponding queries. This initial design, while functional, involved a large number of sequential queries, which would result in unacceptable latency.The core of the presentation focused on an iterative schema refinement process. Hartmut demonstrated how to optimize the schema and process flow through several key steps:
- Consolidating tables: Combining the “user likes post” and “user bookmarked post” tables into a single “user relationships post” table to reduce the number of queries.
- Parallelizing queries: Executing independent queries concurrently to minimize wait times.
- Modifying primary keys: Adjusting primary keys in the “post stats” and “user relationships post” tables to enable bulk queries, drastically reducing the number of requests.
- Caching: Implementing an in-memory cache for user information to further reduce database load.
These optimizations resulted in a significant reduction in the number of database queries and a dramatic improvement in response time. What started as 81 sequential queries was transformed into 4-23 parallel queries, all executed in just two subsequent steps.Hartmut also discussed the importance of choosing the right tools for the job. He highlighted his choice of non-blocking IO, asynchronous drivers, reactive programming with Mutiny and Quercus, and Kotlin as the programming language. He emphasized that while Apache Cassandra can be a powerful tool for such use cases, it’s crucial to make an informed decision based on the specific requirements and constraints of the project.
Key Takeaways:
- Careful schema design is crucial for optimizing Apache Cassandra performance.
- Understanding and optimizing access patterns is essential.
- Parallelizing queries and leveraging caching can significantly reduce latency.
- Reactive programming can be a powerful paradigm for building highly performant and scalable applications with Apache Cassandra.
- While Cassandra is powerful, it is not always the right choice. Consider your needs and choose the database that fits best.
The presentation concluded with a live trace from Hartmut’s experimental stack, demonstrating the optimized solution’s performance. The entire API request processing, including fetching and processing data for 20 posts, was completed in under 4 milliseconds.
The meetup was a great success, providing valuable insights into optimizing Apache Cassandra for a real-world use case. The community is encouraged to contribute ideas and presentations for future meetups. The next meetup is scheduled for March 19th.To learn more and explore the code examples, check out the repository shared during the presentation.
Thank you to Hartmut and all the organizers for this informative session!
