Testing
Payment API calls in unit tests create slow, flaky tests with real rate limits and network dependencies. The solution is mocking at the HTTP layer — stripe-mock (a local Stripe API server), nock (Node.js HTTP interceptor), or VCR cassettes (recorded responses replayed in tests). Each approach has trade-offs. This guide
Testing
PayPal and Braintree both provide sandbox environments that mirror their production APIs. PayPal Sandbox uses test buyer/seller accounts. Braintree Sandbox provides test nonce values for different payment scenarios. Both support webhook testing for order completion and payment events. This guide covers setup, test credentials, automation patterns, and common gotchas.
Testing
Stripe subscription testing requires simulating billing cycles, plan changes, and edge cases that only appear over time. Stripe's test mode lets you fast-forward time, trigger immediate renewals, and test proration calculations without waiting for real billing cycles. This guide covers how to test subscription creation, upgrades, cancellations, trial
Testing
Stripe's test mode lets you run complete payment flows without real money. Test card numbers like 4242 4242 4242 4242 simulate successful payments, while other numbers simulate declines, insufficient funds, and 3D Secure authentication. This guide covers how to test the full checkout flow using Playwright, handle authentication
Testing
Stripe webhooks are HTTP POST requests Stripe sends to your server when payment events occur. Testing them requires replaying signed events against your handler without depending on real Stripe infrastructure. This guide covers stripe-cli for local forwarding, constructing test payloads in pytest, verifying webhook signatures, and running webhook tests in
SRE
SLI, SLO, and error budget are the vocabulary of reliability engineering. They give you a rigorous way to answer "is our service reliable enough?" and "how much risk can we take with this deployment?" without relying on gut feeling or incident retrospectives. This guide covers how
SRE
Synthetic monitoring runs scripted tests against your application continuously — from multiple geographic locations, using real browsers or API clients — to detect issues before users report them. Combined with canary deployments, it becomes your automated deployment safety net. Synthetic Monitoring Fundamentals Synthetic monitors are different from real user monitoring (RUM): Synthetic
SRE
When an incident happens at 3 AM, the quality of your response depends on two things: the quality of your runbooks and how many times your team has practiced them. Testing runbook automation and running incident drills converts theory into muscle memory. Automated Runbooks vs. Reference Runbooks Reference runbooks are
SRE
High availability is a promise — that your system will remain operational when components fail. Multi-region failover is how you keep that promise when an entire cloud region becomes unavailable. Testing your HA architecture requires deliberately causing the failures your system claims to handle. HA Architecture Patterns Active-passive (warm standby): Traffic
SRE
Chaos engineering is the practice of deliberately introducing failures into a system to discover weaknesses before they cause incidents. Netflix coined the term with Chaos Monkey — a tool that randomly terminates EC2 instances to force engineers to build resilient services. For Kubernetes workloads, Litmus Chaos has become the most widely
Machine Learning
scikit-learn pipelines combine preprocessing steps and models into a single object. They're elegant for training and inference, but they introduce testing challenges: how do you test a transformer in isolation? How do you verify that the pipeline doesn't leak information? How do you catch regressions when
Machine Learning
Feature stores solve the problem of feature reuse and training/serving consistency in ML systems. They also introduce a new category of bugs: features that compute correctly in isolation but produce wrong values when served at inference time due to point-in-time leakage, online/offline discrepancies, or stale data. Testing a