RESTful API
design & development
in Long Beach

Developers building against an API need somewhere safe to try things. They want to create test orders, trigger errors, send webhooks and break things without touching real customers or real money. A good sandbox makes integration faster and cheaper for everyone. A poor one pushes developers to test in production.

The sandbox should behave like production. Same endpoints, same authentication, same response formats, same error codes. Differences between the two environments are a constant source of bugs that appear only after launch.

Test data must be realistic. Empty accounts do not help anyone. A sandbox should come with sample records that cover common cases and edge cases: customers with long names, orders with many lines, products with special characters, accounts in different currencies.

Developers need ways to trigger specific outcomes. Payment providers offer test card numbers that always succeed, always decline or require extra verification. The same idea works for any API. Special values that produce known errors let developers test their error handling properly.

Webhooks must work in the sandbox too. Integrators need to receive test events at their own development addresses and check that their handlers respond correctly. A tool that lets them resend a past event or send a specific event on demand saves hours of setup.

Rate limits and quotas should exist, but be clear. Sandbox limits that differ from production confuse developers. If they must differ, the documentation should say so plainly.

Separate credentials prevent accidents. Sandbox keys should look different from production keys, for example with a clear prefix, so a developer can see immediately which environment a request will hit.

Resetting helps. Developers who make a mess of their test data should be able to clear it and start again, without contacting support.

Keep the sandbox current. When production changes, the sandbox should change first or at the same time. A sandbox running the version from last year gives integrators false confidence.

Finally, watch how the sandbox is used. Errors that developers hit repeatedly point to confusing parts of the API or the documentation.

Access to the sandbox should be easy. Requiring a signed contract or a sales call before a developer can try the API slows every evaluation. Instant self service sign-up for sandbox keys, with production access granted after review, lets developers judge the API on its merits.

Documentation and sandbox should work together. Examples in the documentation that can be run against the sandbox with one click, using the test keys of the reader, show exactly how each call behaves.

Clear limits on what the sandbox cannot do help as well. Some operations, such as real bank transfers or physical shipments, can only be simulated. The documentation should say which responses are simulated and how they differ from production, so integrators are not surprised on launch day.

Support channels for sandbox users should be visible. A forum, a chat channel or a named contact during evaluation shortens the time from first call to working integration.

Every API that returns lists will eventually face a list too large to send at once. Orders, customers, events, log entries. Returning everything in one response is slow, uses too much memory and eventually times out. Pagination splits the result into pages, and the way it does this shapes how reliable integrations are.

Offset pagination is the simplest. The client asks for records one to fifty, then fifty one to a hundred, and so on. It is easy to understand and easy to implement. It becomes slow on large tables, because the database must skip over all earlier records each time.

Offset pagination also misbehaves when data changes. If a new record is added while a client is paging through a list, every later page shifts by one. Some records are shown twice, others are skipped. For lists that change often, this is a real problem.

Cursor pagination avoids both issues. Each page includes a token that points to the last record returned. The next request asks for records after that point. The database can find the position quickly, and new records do not shift earlier pages.

The cursor should be opaque. Clients should treat it as a string and pass it back unchanged. That leaves the server free to change how cursors work internally without breaking integrations.

Sorting must be stable. If two records have the same value in the sort field, the order between them must still be fixed, usually by adding the record ID as a second sort key. Without this, records at page boundaries can be lost or repeated.

Filtering reduces the need to page at all. Clients often need only records created after a date, with a certain status or belonging to one account. Supporting these filters on the server saves both sides from moving data that will be thrown away.

Changes since a point in time deserve their own support. Integrations that sync data need records updated since their last run, including deletions. An endpoint that returns changes since a given moment, or a feed of events, is far more efficient than paging through the full list each time.

Limits on page size protect the service. A sensible default, a documented maximum and a clear error for requests above it prevent one client from asking for everything at once.

Document the behaviour with examples. Show how to request the first page, how to continue and how to know when the list is finished.

Totals are expensive. Returning the total number of matching records on every page forces the database to count the whole result set, which can be slow for large tables. Many APIs make the total optional, or return an estimate, and tell clients simply whether more pages exist.

Field selection is a close relative of filtering. Letting clients request only the fields they need reduces the size of each page, which matters for mobile clients and for records with many large fields.