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AWS engineer Marc Brooker has published animations illustrating how load spikes can trigger persistent performance problems in PostgreSQL and etcd. He says the animations were generated with Kiro and KiroCrew, while the underlying results came from traces captured in a real test environment.
Marc Brooker, an AWS engineer, has published a set of animations showing how ordinary load spikes can lead to persistent failures in systems such as PostgreSQL and etcd. In a blog post dated October 23, he said the animations were made with Kiro and KiroCrew, while the data behind them came from traces captured in a real test environment.
Brooker presents the animations as a way to build intuition about how systems behave under pressure, including metastability: a condition in which an initial disturbance sets off a self-reinforcing cycle that keeps a system degraded. He describes the visuals as explanatory tools, not as substitutes for the traces or test results on which they are based.
In the PostgreSQL example, a database initially handles 20 queries per second with its working set in the page cache. Brooker says a load spike causes queries to consume memory—about 12 MB each in the example—pushing data out of cache. Queries then take longer when they must be served from disk, illustrating how a temporary surge can leave the system in a slower state.
The etcd examples depict different feedback loops. In one, a write-load spike leaves peers appearing unavailable, prompting application-level retries that add more work. In another, a test begins at 500 reads per second before the load rises to 1,000. Brooker says increased garbage-collection time raises average tenure and reduces CPU available to the read workload, creating a loop from which the system does not recover without intervention.
How Load Spikes Become Persistent
The post focuses on a practical reliability problem: systems may not return to normal when the original overload ends. Retries, cache pressure and garbage collection can amplify a disturbance, making the resulting behavior difficult to infer from a static architecture diagram or a single performance figure.
Brooker argues that these effects can occur even in widely used, production-quality software. The examples may help engineers reason about failure scenarios and ask better questions when testing capacity, retry policies and recovery procedures. They do not, by themselves, establish how often these failures occur in production or whether every deployment of the software is vulnerable.
The use of animation also highlights a developing way to communicate systems behavior: pairing visual explanations with measured traces. That distinction matters because a persuasive visualization is useful only insofar as readers can understand what data it represents and what conclusions the data can support.
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From Abstract Models to Traces
Brooker says he previously created Stability Sim to help engineers understand metastable failures. He found that abstract demonstrations could explain how retries and queues trigger such behavior, but said some readers remained doubtful that similar dynamics occurred in real systems.
In the new post, he uses PostgreSQL and etcd test examples to connect the concept to traces. He describes the etcd software as widely used and high quality, and says operational experience alone may not reveal these kinds of failure. His account is based on his tests and observations; the post does not present a broad survey of deployments or independently measured incident rates.
Brooker identifies himself as an AWS engineer working on agentic AI safety and policy, with previous work spanning EC2, EBS, databases and serverless systems. He states that the opinions in his blog are his own. The animations were generated with Kiro and KiroCrew, mostly using Opus 5.5, according to the post.
“Animations can help us build an intuition for how and why systems work, or how and why they fail.”
— Marc Brooker
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Limits of the Test Evidence
The post does not specify enough about the test setups to establish how closely they match particular production deployments. It also gives no frequency estimates, benchmark comparisons or evidence that the illustrated failure patterns affect all PostgreSQL or etcd installations. The examples support the behaviors observed in those tests, not a general claim about every system running the software.
Brooker says he has not reproduced the proposed allocator-related behavior in a runtime without garbage collection. The post also does not describe independent verification of the traces or explain whether the animation tools could introduce omissions or simplifications. Readers should treat the graphics as representations of test results, rather than as direct recordings of production incidents.
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Further Tests and Reproduction
The post does not announce a formal follow-up, release schedule or additional dataset. The next useful step for readers seeking to apply its findings is to examine the underlying test conditions and traces, then determine whether comparable feedback loops appear under their own workloads.
Brooker’s unresolved allocator hypothesis would require a reproducible test before it could be treated as an observed result. More detail on configurations, workload generation and trace access would also help other engineers assess how the examples relate to their systems.
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Key Questions
What is the news?
Marc Brooker published animations explaining system behavior under load, with examples based on test traces from PostgreSQL and etcd.
Were the examples taken from production incidents?
No. Brooker says the results came from a real test environment. The post does not identify them as production incidents.
What does metastability mean in these examples?
It describes a self-reinforcing condition in which an initial load spike triggers effects—such as slower queries or repeated requests—that keep the system degraded after the original disturbance.
How were the animations made?
Brooker says they were generated with Kiro and KiroCrew, mostly using Opus 5.5. He says the underlying results came from captured traces.
Has Brooker confirmed the same effect without garbage collection?
No. He says an allocator-related version of the behavior may occur in runtimes without garbage collection, but that he has not reproduced it.
Source: hn
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