Queues in PostgreSQL | Citus Con: An Event for Postgres 2022

Summary

This talk delves into optimizing Postgres for queue-like workloads, addressing key issues such as concurrency, thundering herd problems, and the need for dynamic statistics. It explores the potential for Postgres extensions to create specialized queue systems, enhancing performance. The speaker also highlights practical solutions, such as using libraries in popular programming languages like Python, Ruby, and Java to simplify implementation. Overall, the session provides valuable insights on balancing Postgres' transactional power with the challenges of high-scale queuing, while emphasizing the importance of finding the right balance between simplicity and complexity.

Takeaways

• 😀 Postgres queues face challenges with concurrency due to competing transactions that hit the same rows, which limits real concurrency in systems.
• 😀 The idea of secretly implementing a circular scanning mechanism under the hood could help solve some of the concurrency problems, allowing for improved isolation between transactions.
• 😀 Using LISTEN and NOTIFY in Postgres for work distribution can lead to a 'thundering herd' effect, where many workers wake up simultaneously to process a single job, which is inefficient.
• 😀 Postgres could improve the way it handles worker wake-ups, potentially reducing unnecessary wake-ups and optimizing the number of workers needed for each job.
• 😀 Queue-like workloads in volatile tables can confuse the system's statistics, as sudden changes in table size can cause poor query plans when stats don't reflect the actual state of the table.
• 😀 Postgres could benefit from enhancements in statistics management for volatile tables to avoid poorly planned queries, especially in scenarios with highly dynamic data.
• 😀 There's potential for Postgres to integrate specialized queuing systems through extensions, leveraging recent features like the table AM (Access Method) system to create optimized tables for queuing.
• 😀 Developers can use libraries in languages like Python, Ruby, and Java that offer simplified queuing solutions on top of Postgres, making it easier to implement queuing without directly manipulating the database.
• 😀 Using Postgres for queuing is attractive because it integrates with existing business logic, allowing developers to manage workloads without introducing additional systems.
• 😀 At extreme scales, such as hyperscale environments, Postgres may not be the best fit for queuing, and other systems designed for massive queues might be more appropriate, but Postgres works well for typical, moderate-scale use cases.

Q & A

What are some concurrency issues when using Postgres for queuing systems?

Concurrency issues arise when multiple transactions compete for the same rows, often leading to inefficiencies. Since all transactions are trying to modify or access the same parts of the table, this can prevent real concurrency, making the queue system less efficient. The speaker suggests the idea of using a circular queue under the hood to help mitigate this problem.

What is the 'thundering herd' problem, and how does it impact Postgres-based queuing systems?

The 'thundering herd' problem occurs when multiple workers, such as powerful computers, are all notified of a new job and wake up to check the queue. If only one job is available, all workers compete for it, leading to unnecessary table hits. This causes inefficiencies. A more subtle wake-up system that only triggers enough workers to handle available jobs could help solve this.

How do volatile tables affect Postgres performance, especially in queue systems?

Volatile tables, where the number of rows can fluctuate rapidly (sometimes from millions of rows to none), can cause inaccurate statistics in Postgres. This misleads the query planner, potentially leading to inefficient query execution plans. Better handling of such statistics could improve performance for queuing systems using volatile tables.

What is the potential for Postgres extensions to improve queuing systems?

Postgres extensions could provide a more efficient way to handle queue-like workloads. Specifically, creating a table type optimized for queues—where items are inserted at one end and removed from another—could improve performance. The Table AM (Access Method) system in recent releases opens up possibilities for building such extensions.

Are there existing tools or libraries that can help developers implement queues in Postgres?

Yes, popular programming languages like Python, Ruby, and Java have libraries designed to implement queue systems efficiently. These libraries abstract away the complexity of working directly with Postgres, making it easier for developers to set up queuing without needing deep database knowledge.

Why might using Postgres for queuing systems be advantageous compared to other specialized systems?

Using Postgres for queuing offers operational simplicity since developers are already familiar with the system, reducing the need for additional moving parts. Postgres also integrates well with transactional workloads, making it a reliable choice for many applications, especially when full-fledged distributed transaction systems might be overkill.

What trade-offs should developers consider when using Postgres versus specialized queue systems?

Postgres is a good choice for moderate-scale queuing systems due to its ease of use and integration with other database features. However, for high-end or extreme scale queuing needs, specialized systems may offer better performance and scalability. The decision should depend on the specific requirements and scale of the workload.

What role do statistics play in Postgres queue systems, and how can they be improved?

Statistics in Postgres help the query planner determine the most efficient way to execute a query. For volatile tables in queue systems, outdated or inaccurate statistics can lead to poor query plans. Improving the handling of statistics in such tables could result in better performance by ensuring the query planner has accurate data to work with.

How can Postgres be optimized to handle queue-like workloads more effectively?

Postgres could be optimized for queue-like workloads by introducing improvements such as more efficient handling of concurrency, reducing the thundering herd problem, and improving the statistics gathering for volatile tables. Additionally, developing specialized extensions for queuing systems could improve overall performance and scalability.

What are the challenges of scaling Postgres for high-concurrency queue workloads?

Scaling Postgres for high-concurrency workloads involves challenges such as managing row-level contention, ensuring accurate statistics for volatile data, and handling the potential inefficiencies introduced by the thundering herd problem. Addressing these issues requires careful design, including potential extensions and optimizations to handle these specific use cases.