Below you will find pages that utilize the taxonomy term “Reliability”
Python Requests for API Calls with Sessions, Timeouts and Error Handling
The curl lesson showed what an API exchange looks like by hand. This one moves the same work into Python with the requests library. By the end you’ll have a small client function that sets a timeout on every call, reuses one connection, and turns every way a call can fail into one clear error your code can act on.
You need Python 3.10 or newer, which is the minimum for the current requests release (2.34.2). Everything runs against a test server on your own machine, so no account or API key is involved.
API Error Handling: HTTP Status Codes, Error Bodies, and Retry Logic
Errors are not edge cases in API development. They are a primary output. Every API call that can fail will fail — due to invalid input, authentication problems, resource conflicts, rate limits, or infrastructure issues — and how an API communicates those failures determines whether integrators can handle them gracefully or are left guessing. An API that returns clear, consistent, actionable errors is a well-designed API. Everything else is guesswork at scale.
Batch Operations in APIs: Designing for Bulk Without Breaking Everything
The standard REST pattern is one resource per request. Create one user, update one order, fetch one product. This works until an integrator needs to create ten thousand users, update five hundred orders, or sync an entire product catalog. Making ten thousand individual API calls is slow, expensive in terms of rate limit quota, and brittle — a network failure on request 7,432 leaves the integrator with a partially completed operation and no clean recovery path.
Idempotency in APIs: Building Operations That Are Safe to Retry
Networks fail. Connections drop. Load balancers time out. Servers restart mid-request. In a distributed system, any request that travels over a network can be sent but not confirmed, leaving the caller uncertain whether the operation completed. This is not a rare edge case — it is a routine condition that well-designed APIs must handle. Idempotency is the mechanism that makes retrying safe when certainty is unavailable.
What Idempotency Means
An operation is idempotent if performing it multiple times produces the same result as performing it once. The outcome is identical whether you call it one time or ten times; subsequent calls do not change anything that the first call already changed.