Which approach best handles API pagination and rate limiting in CDX integrations?

Which approach best handles API pagination and rate limiting in CDX integrations?

• A. Hard-code page size to 100 and never retry.
• B. Disable pagination to simplify.
• C. Use pagination tokens or cursors; implement backoff strategies, and respect rate limits with throttling and retries.
• D. Use global rate limit exceeding.

Answer: (C)

The key idea is building a resilient data-fetching flow that works smoothly with APIs that paginate and enforce rate limits. Using a pagination token or cursor lets you request the next page without guessing sizes, which makes the process robust to changes in data volume and avoids skipping or duplicating records. Pair that with backoff strategies so when you hit temporary outages or 429 responses you back off and retry safely, ideally with exponential backoff and some randomness to spread retries. Respecting the API’s rate limits through throttling and controlled retries keeps you within allowed usage and prevents getting blocked. Together, these practices create a reliable, scalable integration that can handle large datasets and imperfect network conditions. Why the other approaches aren’t as solid: hard-coding a page size and never retrying makes the integration fragile—changes in data size or transient errors can cause gaps or failures. Disabling pagination risks pulling enormous payloads at once, which can overwhelm memory, cause timeouts, or waste resources. Trying to exceed rate limits is unsafe and unsustainable, likely leading to blocks or bans and data access interruptions.

The key idea is building a resilient data-fetching flow that works smoothly with APIs that paginate and enforce rate limits. Using a pagination token or cursor lets you request the next page without guessing sizes, which makes the process robust to changes in data volume and avoids skipping or duplicating records. Pair that with backoff strategies so when you hit temporary outages or 429 responses you back off and retry safely, ideally with exponential backoff and some randomness to spread retries. Respecting the API’s rate limits through throttling and controlled retries keeps you within allowed usage and prevents getting blocked. Together, these practices create a reliable, scalable integration that can handle large datasets and imperfect network conditions.

Why the other approaches aren’t as solid: hard-coding a page size and never retrying makes the integration fragile—changes in data size or transient errors can cause gaps or failures. Disabling pagination risks pulling enormous payloads at once, which can overwhelm memory, cause timeouts, or waste resources. Trying to exceed rate limits is unsafe and unsustainable, likely leading to blocks or bans and data access interruptions.