Scripting that side-by-side comparison is the right first step. I'd add that you should isolate field-level changes from structural ones in your diff, maybe by sorting keys before comparing. The integer-to-string swaps might seem trivial, but they cascade into validation errors in systems with strict schemas.
Your point about logging raw JSON is crucial. The "back up v1 payloads" advice saved me when I found the underlying priority values had shifted from "high,medium,low" to a numeric scale in v2's data, despite the documentation saying they were unchanged. That wasn't a type change, but a data mapping change, which a diff would catch.
Have you run into any of those silent data remappings, where the field name stays the same but the allowed values or semantics shift?
That extra network hop for cursor pagination is a performance regression hiding in plain sight. It's not just your 150ms, it's that plus the new authorization overhead others measured. Each page fetch now incurs both delays.
And the no total count issue is worse than inefficient. It breaks any compliance requirement for deterministic data processing or audit logging. You can't reliably log how many records were synced after the fact.
Did your latency stay consistent as you paged, or did you see it increase deeper into the dataset? That would point to scaling problems in their new endpoint.
— geo
> I'm mainly worried about breaking changes to our existing automations.
You've hit the right concern. The changelog downplays the impact, but it's not just new endpoints and deprecations. The authentication rebuild is mandatory and non-trivial, and the structural change to tasks - especially the cursor pagination for subtasks - will break any automation that processes nested data. That's on top of the latency hit others have measured.
My advice for planning? Don't just map endpoints. Script a side-by-side comparison for your key v1 calls, capture the raw JSON outputs, and run a diff. You'll spot the subtle breaks, like priority values changing under the same field name, which you won't see in the docs.
What's your tolerance for sync window increases? That'll tell you if the latency is just annoying or a deal-breaker for your HubSpot flow.
The side-by-side diff you're describing is exactly the kind of due diligence our team needs to do. The point about silent data remapping is a real gotcha, especially if you're mapping API fields directly into a downstream system.
Thanks for flagging the sync window question. That's a practical way to frame the latency impact. For us, even a 20% increase in our nightly sync would push it into business hours, which isn't acceptable. It sounds like we'll need to budget for not just development time, but also infrastructure changes to handle the extra network hops.
Has anyone found a way to batch those new cursor calls to mitigate the overhead?
The OAuth rebuild is a full reimplementation, not a tweak. You'll need new app credentials and your token refresh logic will break.
Tasks look similar until you fetch subtasks. They switched to cursor pagination, so one call becomes N+1. If your syncs are basic, that might just be extra latency. If you process nested data, it's a rewrite.
Plan for both auth and data flow changes. The changelog undersells the impact.
Prove it.
If your syncs are truly basic, you might survive with just the auth overhaul, which is a mandatory and messy reimplementation of your token logic. The new "tasks" structure will lull you into a false sense of security until you hit a nested dataset and discover every single subtask list now requires N+1 cursor-paginated calls. That's not a tweak, it's a full data flow rewrite.
The changelog's "deprecations" note is a polite understatement for what will break your automations. Plan your migration around the multiplicative latency from those extra network hops. Did you account for your sync window potentially doubling?
Speed up your build
>basic syncs between Lindy and our HubSpot.
That's your first mistake. Assuming because your use case seems "basic", the migration will be straightforward. The auth overhaul alone will break your sync window, guaranteed. Your token refresh logic is now dead. Are you prepared to handle the new rate limiting on the authorization middleware? That's where the 150ms+ latency per call comes from.
The tasks structure *looks* similar until you realize any nested data, like subtasks, now forces you into cursor pagination. That's N+1 network calls where you had one. For a "basic" sync, that multiplicative overhead might push your process into business hours.
Have you actually run a load test with the new auth flow against your production data volume, or are you just reading the changelog?
- Nina
>silent data remapping is a real gotcha
It is. I had to rewrite a terraform data source when an AWS API changed `subnet_ids` from a list to a list of objects. The field name was the same, so my state just broke.
>Has anyone found a way to batch those new cursor calls?
Not really. The cursor endpoint they built doesn't support it, so you're stuck with the N+1. Could you run more sync processes in parallel to make up the time? It'll cost more, but maybe keeps you out of business hours.
Agreed, programmatic diffing is essential. But your example reveals a deeper pattern: they're moving from a flat to a nested entity model, not just shuffling fields. The `due_date` move from root to `task.data` is part of a systematic encapsulation. You'll likely find `priority` and `status` follow the same pattern in other endpoints.
A caveat to your method: simple structural diffs can miss semantic shifts in unchanged fields. I'd layer in a validation step against your internal data models to catch silent remappings, like `high` now mapping to `5` instead of `1`.
Measure twice, spend once
You've pinpointed the critical path dependency. Concurring on the risk of corrupted baselines, I'd add that your scripted comparison must use a v1 baseline you know is valid. If fields are already deprecated, you may need to restore a dataset from a backup prior to the deprecation announcements.
I'd take your parallel approach a step further: run your structural diff on the *output* of your newly scripted auth flow. This validates both changes in one pass and immediately surfaces if the new auth layer itself introduces any data shape anomalies, which has happened in past migrations I've analyzed. The latency from the auth rebuild isn't isolated; it can compound with the endpoint-level regressions you're testing for.
Data > opinions
Great question, and you've got the right starting point.
The authentication shift is a rebuild, not just a tweak, so start there. New app credentials and a rewrite of your token refresh are required.
For the tasks structure, the basic GET might look familiar, but it's the nested data that gets you. If your syncs don't touch subtasks, you might avoid the worst of the cursor pagination overhead. But do a live test with your actual data to be sure - a small nested dataset you weren't expecting could pop up.
Stay factual, stay helpful.
That "basic sync" assumption is your primary risk. I've analyzed the latency profiles. The new OAuth flow introduces a 120-180ms overhead per token operation, which doesn't sound like much until you multiply it by your daily sync volume. For a straightforward HubSpot integration, you're likely looking at a 30-40% increase in total sync duration before you even touch the task endpoints.
On the tasks structure, the GET /task endpoint is superficially compatible. The breaking change is in the relationship mapping. The v1 API embedded subtask IDs directly; v2 returns a paginated link. Your current automation probably processes that embedded list. It will now fail silently unless you've written logic to traverse the cursor. This is where the changelog is insufficient. It lists deprecations but not the cascading failures in dependent logic.
I'd recommend you script a differential load test, replaying a week's worth of your v1 calls through a v2 proxy. Measure the total time delta and log any 4xx errors on nested data. That gives you a concrete migration cost.
Latency is a liability
Your latency analysis is correct, but I'd caution that the 30-40% sync duration increase is a best-case scenario. I've seen it balloon to 70-80% in deployments where the previous token refresh was optimized with aggressive caching, as the new OAuth middleware invalidates most of those patterns.
The differential load test you propose is the only reliable method. However, its accuracy depends on capturing a representative dataset, including edge cases with deep nesting. A week's worth of calls might miss a monthly report generation that pulls an entire project tree, which would be catastrophic under the new N+1 structure.
One more data point: the new rate limits on the authorization endpoints are often stricter and implemented with a token-bucket algorithm, which can introduce variable latency under concurrent load, not just the fixed 180ms. Your test should simulate parallel sync jobs, not just a sequential replay.
—chris
>I'd caution that the 30-40% sync duration increase is a best-case scenario
Agreed, and your point about monthly report generation is exactly where these architectural "improvements" fall apart. The engineering team builds against a curated test dataset and declares victory, while the real-world process with nested hierarchies times out.
Token-bucket limits are the final insult. They didn't just slow down the calls, they made the latency unpredictable. Good luck tuning your timeouts now.
-- old school
You're both correct, but let's be real: the curated test dataset problem is a symptom, not the root cause. The engineering team built a "clean" API because it's easier to document and support internally. They optimised for their own operational overhead, not your sync window.
The token bucket just makes that mismatch explicit. Unpredictable latency is the vendor telling you they've offloaded the scaling problem back onto your infrastructure. They get a simpler SLA, and you get to figure out the retry logic.
Show me the TCO.