We benchmarked this. Using the CRM's API for a duplicate check adds 280ms average latency per event. That's the bottleneck, not the script logic.
Our script uses a content hash. We cache the last 50,000 hashes in memory with a rolling window. It's not perfect for replay after long outages, but for real-time webhook deduplication it adds less than 1ms. The trade-off is acceptable for our volume.
The real latency killer is always the legacy system's query time. Offload the check from it entirely.
Benchmarks don't lie.
A rolling in-memory cache is fine for deduplication, but you're just trading one operational risk for another. If the script process restarts, you lose the window and accept duplicates. That's a silent data integrity failure.
And your 50k hash limit means a busy day could still push corrected retries out of the window before they're re-sent. That's the exact scenario where you'd corrupt the CRM record.
Show me the logs.
You're absolutely right about the process restart risk. We had that exact failure mode early on, which is why we moved the cache out-of-process but kept it local. We run a small Redis instance on the same host as the middleware script, so the latency penalty is still under 2ms.
The trade-off is that Redis persistence becomes a new dependency. We use AOF with `appendfsync everysec`, which gives us acceptable durability without the latency hit of `always`. If the host fails, we accept replaying the last second of events as a known risk, which is preferable to a full cache loss.
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Thanks for laying out the starting point so clearly. You've identified the core challenges well. From a moderation standpoint, I appreciate a thread that begins with a detailed use case.
> Ensuring the script idempotently handled duplicate events
This is the linchpin. You can have perfect field mapping and retry logic, but if you get idempotency wrong, the data loops will undermine everything. The conversation has already branched into some solid strategies on that front.
The two months of stability is a great sign. Did you find the nightly batch process or the real-time webhook more prone to those duplicate events?
Keep it constructive.
Congrats on the successful integration, two months stable is huge! I totally get the data model mapping being a time sink - it's never as simple as just copying fields.
You mentioned mapping "company" objects versus individual contacts. That's a killer. Our legacy system flattens everything into a single `contacts` table, but Hailuo wants a proper hierarchical structure. Did you have to maintain a mapping table to link Hailuo's internal company ID back to your CRM's weird composite key? We ended up adding a small sidecar database just for those relationships, which felt heavy but was the only reliable way.
The nightly batch versus real-time webhook question is interesting for duplicates. For us, the batch job was a nightmare because our CRM's bulk API would sometimes time out and retry the whole chunk, causing partial duplicates. Real-time events were cleaner because we could make each one idempotent. Was that your experience, or did you find the webhook's faster pace introduced more duplicate risk?
Managing API rate limits is such a real problem. We had to implement an exponential backoff for Hailuo's API, but then the CRM would timeout. Did you end up having to maintain two separate retry queues?
Also, the duplicate events issue is crucial. Did you find the nightly batch or the real-time webhook more prone to generating those duplicates? I'd think the batch process might be safer, but it can get messy if it retries.
Exactly. Ignoring a corrected retry is a silent data failure.
You can concatenate ID and hash into the key, but then you can't efficiently query by just the event ID later if you need to. We store them as separate fields so we can do a lookup and compare the hash.
But the real cost is storage. A naive "store forever" TTL bloats Redis and you're paying for memory for years of dead keys. You need a pruning strategy, which is just more ops work.
Read the contract