DataScaleFail #13
Welcome back to DataScaleFail where we explore the systems, benchmarks, and architectural decisions that actually hold up under pressure.
If you work close to the metal, databases, distributed systems, or performance engineering, you already know that real-world behavior rarely matches marketing claims. That’s exactly where we focus: cutting through assumptions with data, experiments, and a bit of healthy skepticism.
In this edition, we look at managed PostgreSQL, take a fresh (and slightly uncomfortable) look at Cassandra 5, and share what’s new in our benchmark suite for 2026.
PostgreSQL DBaaS: Benchmarking Beyond the Surface
Managed PostgreSQL promises convenience — but what does that mean for performance consistency and scaling behavior?
In this post, we use the benchANT framework to compare DBaaS offerings under realistic load conditions. Instead of relying on vendor benchmarks, we simulate production-like scenarios to uncover how these systems behave when pushed beyond the comfort zone.
The results highlight surprising differences in latency stability, scaling efficiency, and resource isolation. Some platforms handle burst workloads gracefully; others degrade in less obvious ways.
If you rely on managed PostgreSQL in production, this is a closer look at what you’re really trading for convenience — and where hidden bottlenecks might surface.
Cassandra 5: A Reality Check on Performance Claims
Cassandra 5 introduces improvements that sound promising on paper — but how do they translate into measurable performance gains?
We ran a series of targeted benchmarks to evaluate how the latest version performs under different workloads, including write-heavy and mixed-operation scenarios. The goal: understand not just peak throughput, but consistency and predictability.
The findings are nuanced. While certain optimizations deliver clear benefits, others depend heavily on workload characteristics and tuning decisions. In some cases, expectations and reality diverge more than you might think.
If you’re considering upgrading — or questioning whether you should — this analysis provides a grounded perspective based on actual data, not release notes.
Benchmark Suite Update 2026: What Changed and Why It Matters
Benchmarks are only useful if they evolve alongside real-world systems.
In our latest update to the benchANT benchmark suite, we’ve expanded workload models, refined measurement techniques, and improved reproducibility. The goal is simple: make results more representative of modern production environments.
This update introduces new scenarios that better capture distributed system behavior, along with enhancements to automation and reporting. It also addresses limitations we observed in earlier iterations — because benchmarking tools should be questioned just as much as the systems they test.
If you use benchmarks to guide decisions, this post outlines what’s new — and why it changes how you should interpret results going forward.
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Until next time, keep questioning your results.