Discussions

Quick note after skimming the developer discussions and API docs: this forum tends to favor practical, implementation-focused questions (I saw a recent thread about pagination and an API “Best Practices” page), so I’m framing this as a hands-on question + lessons learned to keep it useful and topical.
truckstop

We’re building a reporting tool that queries Search All Loads and occasionally needs to page through thousands of results. After a few iterations I wanted to share what worked (and what didn’t) and ask if others have better patterns.

What I tried

Server-side streaming: kept a persistent worker that fetched pages sequentially and pushed incremental updates to the UI. This reduced latency spikes but required careful retry/backoff logic for partial failures.

Cursor-based checkpoints: store the last-processed cursor and resume from there on restart — much safer than re-requesting the same pages.

Batch deduping: we saw duplicates when clients retried; adding an idempotency layer (small in-memory cache keyed by load ID) solved most of it.

Why some approaches failed for me

Naïve parallel requests: hammering many pages in parallel caused API throttle responses and inconsistent snapshots. That was a painful debugging day.

Overly large page sizes: fewer requests, yes but bigger responses meant occasional timeouts and retry storms.

A small, real-world example: we once needed a nightly report of ~20k rows. Switching to cursor checkpoints + modest parallelism (4 concurrent page fetches) dropped runtime from 45 minutes to ~8 minutes, and made retries deterministic.

Questions / ask for feedback

Does Truckstop recommend a maximum safe concurrency for paging through SOAP endpoints, or is it per-account and best checked via error codes?
truckstop

Are there recommended idempotency strategies you’ve used with the load search results (persisted cursor vs. hashing result sets)?

Any tips for keeping snapshots consistent if the underlying data changes while paging?

Bonus small aside: I helped a friend with an academic project on cold-chain logistics and even some nursing literature review writing tied to vaccine distribution; the real-world constraints there made me extra cautious about data consistency in our pipeline.