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AWS engineer Marc Brooker published animations showing how load spikes can trigger persistent performance problems in Postgres and etcd. He says the visuals were generated with Kiro and KiroCrew, while the underlying results came from a test environment capturing real traces.
AWS engineer Marc Brooker has published a set of animations illustrating how ordinary load spikes can trigger metastable failures in systems including Postgres and etcd. Brooker says the animations were created with Kiro and KiroCrew, while their behavior is based on traces captured in a real test environment; the examples aim to make hard-to-see system dynamics easier for engineers to understand.
One animation depicts a Postgres 16 database handling 20 queries per second before a spike in activity. Brooker says the database initially manages the workload because its working set fits in the page cache. During the spike, concurrent queries consume memory, with each query using about 12 MB; as data is served from disk rather than cache, queries slow down. The report describes this as an example of a performance problem that can arise even in a single, non-distributed system.
Brooker also presents an etcd write-load example in which overloaded peers appear unavailable, prompting applications to resend requests. Those resends add demand to an already strained system. A separate etcd animation starts at 500 reads per second and raises the workload to 1,000. Brooker says increased garbage-collection time leaves less CPU for reads, creating a self-reinforcing loop from which the system does not recover without intervention.
The animations are explanatory representations, not the underlying measurements themselves. Brooker says the results came from a real test environment and that traces from that environment back the visuals. He says the animations were produced with Kiro and KiroCrew, mostly using Opus 5.5. The post does not provide a detailed account of the test configuration, trace data, or independent validation.
How Load Spikes Become Persistent
The examples focus on a failure mode that can be difficult to recognize from a brief incident description: an initial spike can change the system’s operating conditions, making the original workload harder to serve even after the spike has passed. In Brooker’s Postgres example, slower disk access follows memory pressure; in the etcd read example, more garbage collection reduces CPU available for the workload. That feedback can turn a temporary disturbance into a lasting performance problem.
For engineers, the practical value is in making these interactions visible. A diagram or animation can help teams reason about queues, retries, memory pressure and recovery behavior, and can give them a concrete scenario to test. Brooker’s material is not a general proof that every deployment will behave this way, but it illustrates why system reliability cannot always be inferred from normal operation or from a workload’s average rate alone.
The post also makes a narrower point about evidence: Brooker says these examples use traces from a test environment rather than only abstract simulations. That makes the visuals relevant to discussions of production-like behavior, while leaving questions about how closely each test matches other deployments.
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From Stability Simulations to Traces
Brooker frames the work as part of his effort to use animations to build intuition about how systems work and fail. In an earlier project, Stability Sim, he explored metastable failures at an abstract level, including how retries and queues can make them easier to trigger. He says some readers questioned whether such dynamics occur in real systems that have been extensively used and operated.
The new post responds with examples drawn from test runs of Postgres 16 and etcd. Brooker says he had seen comparable behavior in settings he could not discuss publicly, and presents the test traces as a way to make the mechanisms more concrete. The post’s framing is his own; it does not report a new production incident or claim that the illustrated failures occurred in a named customer deployment.
Brooker identifies himself as an AWS engineer working on agentic AI, with prior work on EC2, EBS, databases and serverless systems. He states that the views on his blog are his own. The post describes the models and tools used to produce the visuals, but does not give a formal evaluation of whether AI-generated animations improve engineers’ understanding compared with other teaching methods.
“Animations can help us build an intuition for how and why systems work, or how and why they fail.”
— Marc Brooker, AWS engineer, in his blog post
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Limits of the Published Tests
The post does not specify the full hardware, configuration, workload-generation method, duration, or number of test runs behind each animation. It also does not publish the trace files or independent analysis in the material provided. The reported behaviors should therefore be understood as Brooker’s account of test-environment results, not as independently verified findings or evidence that every Postgres or etcd deployment will enter the same state.
Brooker says he believes similar feedback could occur in a runtime without garbage collection because allocator work may rise, but he says he has not reproduced that behavior. The post also does not quantify how often the illustrated conditions occur in production or specify which interventions restore each system. Whether the animations measurably improve learning is not evaluated in the report.
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Further Tests and Reproduction
The post describes no announced release schedule, follow-up study, or planned benchmark. The immediate next step for readers seeking to assess the examples is to compare the described mechanisms with their own systems: reproduce the workload under controlled conditions, inspect traces, and test how recovery responds to spikes and retries. That would help establish whether the same feedback appears under different configurations.
Brooker’s own account leaves one avenue open: testing whether allocator overhead can produce a comparable self-reinforcing effect in a runtime without garbage collection. The post does not say when such a test may be completed. Until additional data or replication is published, the specific examples remain useful illustrations of possible failure dynamics, with their wider frequency and applicability unresolved.
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Key Questions
What did Marc Brooker publish?
He published animations of system behavior, including test examples involving Postgres 16 and etcd, to illustrate how load spikes can lead to persistent performance problems.
Are the animations based on real production incidents?
Brooker says the underlying results came from a real test environment and were backed by captured traces. The post does not identify a customer deployment or say that the examples came from production incidents.
What does metastable failure mean in these examples?
It describes a feedback loop in which a disturbance, such as a load spike, changes system conditions and makes recovery harder. In the etcd read example, Brooker says more garbage collection leaves less CPU for reads, and the system does not recover without intervention.
Which tools were used to make the animations?
Brooker says he used Kiro and KiroCrew, mostly with Opus 5.5. He distinguishes the generated visuals from the test results and traces used to support them.
Has Brooker shown the same effect without garbage collection?
No. He says allocator work might create a similar effect in a runtime without garbage collection, but that he has not reproduced it.
Source: hn
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