July 7th, 2024

Solving Concurrency Bugs Using Schedules and Imagination

Ankush Menat highlights challenges of concurrency bugs in business apps, stresses importance of addressing them. He introduces schedule diagrams as a visual debugging tool, offering a practical approach to identify and resolve concurrency issues efficiently. Menat demonstrates the effectiveness of schedule diagrams through examples, urging developers to leverage them for debugging.

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Solving Concurrency Bugs Using Schedules and Imagination

Ankush Menat discusses the challenges of dealing with concurrency bugs in business applications, emphasizing the importance of addressing these issues despite their rarity. He explains why traditional debugging methods are inefficient for concurrency bugs due to the complex nature of concurrent transactions. Menat introduces the concept of schedule diagrams as a tool to visualize and debug concurrency issues effectively. He outlines a practical approach to identifying transactions, constructing schedule diagrams, and testing hypotheses to resolve concurrency bugs. Through examples like debugging lost updates, stale cache issues, and double execution of exclusive operations, Menat demonstrates how schedule diagrams can help in understanding and resolving concurrency bugs. By leveraging imagination and careful analysis of transaction interleavings, developers can effectively tackle concurrency issues in their applications. Menat concludes by highlighting the utility of schedule diagrams in addressing various types of concurrency bugs and encourages developers to adopt this approach in their debugging processes.

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Link Icon 5 comments
By @Groxx - 3 months
It's worth keeping in mind that updates that are not guarded by some kind of barrier are generally not guaranteed to be visible cross-thread / process / server / etc in the order you wrote them. And reorders are rather common in many languages / hardware types / storage systems / log collectors (including stdout because "do thing" and "log that you did it" are evidently not guarded together if there's a race happening), it's not just a theoretical concern.

Generally speaking though: yes, writing it down can help A LOT, and starting with what you can see is one of those obvious-in-retrospect things that are easily forgotten when under pressure. There are often a LOT of possibilities, and getting it out of your head so you can enumerate them more precisely can super duper important. Intuition for problematic sequences to check first will come with time.

By @SillyUsername - 3 months
Looks a lot like a derivate of a truth table, which is often used to debug multiple input combinations and expected output.
By @gamegoblin - 3 months
If you happen to be coding in Rust, for really robust concurrency testing, I cannot recommend enough the AWS Shuttle library (https://github.com/awslabs/shuttle) which can find insanely complicated race conditions.

What the Shuttle library is doing is basically automatically going through all the permutations of the schedule diagrams described int his blog post.

We used it at AWS to verify the custom filesystem we wrote to power AWS S3.

If you're curious, I wrote a little tutorial on it here: https://grantslatton.com/shuttle

By @rand03853 - 3 months
I insert debug macros with random usleep intervals in critical multithreaded code to expose race conditions. In production they ifdef to nothing.
By @ibash - 3 months
As someone who’s spent a lot of time with javascript, debugging concurrency issues is second nature.

No better training than a great spaghetti ball of promise chains.