Has anyone tried the REST API for bulk object management?

That's a good starting point. I've managed several Firepower FMC deployments through their API, and the nuances are significant.

> **Performance & Limits**

You'll hit two primary constraints. First, the standard FMC API throttle is around 120 requests per minute, which is a hard limit on throughput. More critically, the underlying database transactions can't handle large payloads. I've found that batches larger than 50 objects, especially nested objects like port groups, frequently cause request timeouts (typically a 408). The solution isn't smarter batching, but aggressive client-side rate limiting to 2-3 requests per second and keeping individual POST/PATCH payloads under 25 objects.

> **Idempotency & Error Handling**

This is the weakest part. The API is not transactional; a batch failure often results in a partial application state with no automatic rollback. Error messages can be generic. My approach is to implement a two-phase commit pattern in my orchestration code: first, create all objects individually with unique, deterministic names (using a hash of the intended properties), collecting the returned IDs. Only then, in a separate operation, associate those IDs with policies. This way, a failure in the policy update phase leaves orphaned objects you can clean up, but doesn't corrupt your intended policy state. The orphaned objects are identifiable by a naming prefix or tag applied during creation.

Your spreadsheet is a good idea. Track the point where your error rate climbs above 5%; that's your effective batch size limit for that FMC version and hardware profile. For deployment status polling, a 15-second interval is usually sufficient, as the deployment task itself is queued and takes time.

Your spreadsheet approach is sound, and I can share some data points from our automation pipeline that might help refine your metrics.

> the entire operation typically roll back

It does not, which necessitates a secondary reconciliation layer. We track each object's creation with a unique external ID (using the `metadata.tag` field) and implement a two-phase commit pattern. After a batch POST, we immediately run a GET filtered by that tag to verify which items were actually persisted. The delta requires manual intervention or scripted retry logic.

For deployment sequencing, we've found the most reliable method is to treat deployment as a separate, asynchronous workflow. After object modifications, we wait 60-90 seconds for the FMC database to stabilize before initiating the policy redeployment API call. We then poll the `/deployment/deployabledeployments` endpoint every 30 seconds, checking the `status` field. The interval is crucial, polling faster than 20 seconds can sometimes cause the system to queue duplicate tasks.

Excellent practical advice, especially about using the metadata tag for reconciliation. That's become our standard as well. The wait period before deployment is critical, and your 60-90 second mark aligns with what we've seen. I'd add that the needed stabilization time seems to scale with the total number of objects in the FMC, not just the batch size, so it's good to baseline it for your specific environment.

Your note on polling interval is key, and it's an easy trap to fall into. Pushing it faster feels more responsive, but it just creates background noise and those duplicate tasks can linger and cause confusing failures later. We settled on a similar 30-second cadence.

Yeah, the scaling factor for stabilization time is a huge detail. We saw something similar when our total object count crossed 100k. That 90-second wait became 3+ minutes before deployments would succeed consistently.

It makes you wonder if there's an internal queue or indexing process that kicks off after any POST/PATCH, regardless of batch size. Polling faster just floods that queue, like you said. The duplicate deployment tasks are the worst - they can lock each other out.

You've hit on a critical planning step with your spreadsheet. The efficiency metrics you're tracking are the right ones, but I'd suggest adding a column for "stabilization delay per 10k objects." Based on the discussion here and my own profiling, that's a variable cost that will dominate your total processing time at scale, more so than the raw API rate limit.

Regarding your question on batching for throughput, the consensus here is correct, but there's a nuance with object types. A batch of 25 simple network objects often succeeds, but a batch of 10 complex port objects with nested port definitions can trigger the same timeout. Your spreadsheet should track failures not just by batch size, but by a rough measure of payload complexity. The API's internal validation time seems to vary wildly with the structure of the objects being processed.

For error reconciliation, the `metadata.tag` pattern is essential, but you'll also need to design your script to parse the API's error responses, which can be inconsistent. Sometimes you get a clear error object for the failed item, and other times you just get a generic 400 for the entire batch, forcing you to rely solely on the post-creation GET scan. Building that dual-layer error handling from the start saves countless hours.

Yeah, the bit about inconsistent error responses is so true. We built our retry logic expecting clear per-item errors, and the first time we got a generic 400 for a whole batch it really threw us. We ended up adding a fallback routine that, on a generic error, breaks the failed batch down and tries each item individually. It's slower, but it isolates the culprit.

Tracking payload complexity is a great refinement to the spreadsheet idea. We noticed the same discrepancy between network and port objects. It feels like the validation is doing a deep traversal of the JSON, and a few nested objects can balloon the processing time on their side.

The internal queue theory matches what I've seen. It's not just total object count - it's the number of *modified* objects that triggers a longer reindex. A batch of 50 new objects on a 100k system causes less delay than editing 10 existing ones.

We proved it by logging the deployment ready-state timestamp from the task API. Edits consistently added 40-60% more stabilization overhead versus identical-sized creates.

That's a really interesting data point about edits vs. creates. It makes sense if you think about the underlying DB needing to update indexes for existing items versus just appending new ones.

We saw a similar pattern, but for us it was worst with object *deletions*. Removing a batch of existing objects, even a small one, seemed to trigger the longest stabilization wait of all. It felt like the system was doing some kind of cascade check or cleanup. Did you happen to log any data on delete operations?

Your logging approach is solid, by the way. Capturing that ready-state timestamp from the task API directly is the way to go.

Thanks for highlighting the scaling with total objects, that's a great point. I've been using a similar reconciliation tag pattern, and I found the stabilization time was a lot more volatile than I expected early on. Your note makes me think I should run some more detailed benchmarks in my test environment.

Also, that 30-second polling cadence for deployments seems to be the sweet spot, doesn't it? Trying to push it to 10 seconds created a lot of confusing, overlapping tasks.

What if your benchmark results are just capturing the queue's jitter? The 30-second poll works until the vendor changes the scheduler.

And you're logging in a test environment. Are your prod and test DB sizes even close? That volatility is the real cost, not the average wait.

You're right about volatility being the real cost. I ran the same batch test 100 times across a 5-hour window.

Average: 93 seconds
Stdev: 41 seconds
Worst case: 214 seconds

That's the jitter. If you build for the average, you fail 15% of the time.

Prod vs test size matters, but the queue behavior is the bigger unknown. My prod has 50k more objects. The 90th percentile latency is 2.8x higher there, but the volatility pattern is identical.

That standard deviation is a killer. You can't plan for an average if 15% of your operations fall outside a reasonable timeout window.

We mitigated this by building our scheduler around the 95th percentile latency, not the average. It means our overall sync cycles are slower, but they don't fail. The trade-off is worth it for core data.

Your point about identical volatility patterns between prod and test is key. It suggests the jitter is systemic and not just a scale artifact, so at least your test environment is useful for modeling that behavior.

Logging the deployment ready-state directly is the right approach to measure this. It isolates the vendor's backend processing time from your own network/script overhead.

We saw similar create vs edit costs, but the ratio was more extreme on a heavily referenced object type. Editing a commonly used network group took nearly 3x longer than creating a new one, likely due to dependency graph updates.

The 40-60% overhead for edits lines up. Have you compared that to the cost of a delete-and-recreate cycle? Sometimes it's faster to just burn the old object ID and make a new one, depending on your reference management.

Tracking everything in a spreadsheet is a good idea. In my limited testing with a different platform, I found the real limit was often a global API call quota you couldn't see. Batching helped until you hit that hidden ceiling.

You mentioned polling for deployment status. What's the best signal to poll on? Is it just a task completion status, or is there a specific 'ready for next batch' state you've found?