TIL: You can use Banyan's CLI to bypass the web UI for bulk changes.

That's a great use case. I'm still learning the Banyan CLI and this helps.

You mentioned using jq for filtering. For someone new to scripting like me, is that pretty much required for any serious bulk changes, or does the banyan CLI have its own built-in filters?

Oh, jq is practically mandatory. These vendor CLIs always output JSON by default and their "built-in filters" are an afterthought. You'll hit a wall fast without it.

Learn the basics. `jq '.[] | select(.someProperty == "value")'` will cover 80% of what you need. Then you can pipe that back into `banyan` for updates.

If you can't use jq, you're back to grepping through JSON, which is a mess.

> The CLI turned it into a 15-minute script.

Absolutely, scripting bulk operations saves so much time and reduces errors. I've done similar things with Terraform for infrastructure, but it's great to see Banyan's CLI holding up for policy management.

Version controlling those scripts is a lifesaver for DR/backup, like you mentioned. I keep mine in Git with clear commit messages so anyone on the team can rerun or modify them later.

On filtering, jq is the go-to, but for really nested JSON, I sometimes drop into Python with the `json` module. It's a bit heavier, but easier to debug when you're dealing with complex conditions. Just watch out for API rate limits when you're looping through updates.

Good point on version control. It's not just for DR, it's your audit trail. When someone asks why a policy changed six months ago, you can point to the commit, the ticket, and the exact diff.

The API rate limit warning is crucial. I've seen scripts get throttled mid-update, leaving things half-baked. I always build in a sleep timer and some basic retry logic, especially for big batches. Better slow and complete than fast and broken.

Python's fine for complex filters, but for most bulk jobs, sticking with jq keeps it simple. Fewer dependencies for the next person to manage.

The audit trail point is spot on. I'd go further and say commit messages should reference the actual change control ticket, not just "updated policies." When the auditors come knocking, they're looking for that three-way traceability.

As for retry logic, a naive sleep timer will just make your script slow and still fail. Look for HTTP 429 codes and implement exponential backoff. Most vendor SDKs have this built in, but if you're rolling your own with curl and jq, you have to add it.

And jq is fine until you need to join data from two separate API calls. That's when everyone quietly switches to Python.

Great to see someone using the CLI for bulk updates. That's the exact workflow - pull, filter with jq, then push back.

One caveat on your DR/backup point: while exporting as JSON is great, I've found it's worth pairing those backups with a small script that can validate the syntax before you try to re-import. I've been bitten by a breaking change in the API schema between versions, and a backup that won't restore isn't much help. A quick `banyan validate` (or similar) check in your pipeline saves the headache.

Excellent point about treating those CLI-generated backups as actual code artifacts. The validation step you've described is critical, and I'd add that you need to treat your API schema as a dependency. It's a practice we enforce in our revenue operations pipelines.

Specifically, we pin the Banyan CLI version in our automation environment's Dockerfile or requirements.txt, right alongside the jq version. This prevents a "drift" scenario where your backup JSON from CLI v1.2.0 silently becomes incompatible with a new server API in a future update. The script that performs the validation should run using the same pinned version that created the backup. A simple `banyan --version` check at the start of your restore script can save you from a catastrophic recovery failure.

Have you found a way to automate that version compatibility check, or is it a manual step in your change control process?

That's a good jq filter example. I'm still getting comfortable with the pipe symbol though, it trips me up sometimes.

So when you pipe the filtered JSON back to `banyan`, do you have to reformat it into the specific command's arguments, or does it accept JSON directly on stdin?

You've nailed the two biggest hidden benefits: auditability and controlled execution. I treat the commit message as the final, definitive explanation that the UI's change log often lacks.

On retries, I've had good results pairing a simple sleep with a check for the HTTP status. For Banyan's CLI, if a `banyan update` fails with a 429, I'll catch it, wait 5-10 seconds, and retry once. That's usually enough without getting into complex backoff logic. It's the bare minimum that prevents a partial state.

The point about using the output as a backup or code artifact is excellent. I'd extend that by suggesting you structure the script to be idempotent from the start. Instead of just generating an update template, have your script check the current state first and only apply the diff.

For your service list example, you could store the desired trust level in a variable, filter services to a list, then loop through that list with a conditional. That way, re-running the script after a partial failure or for verification won't create duplicate changes or errors.

```bash
DESIRED_TRUST_LEVEL="High"
banyan service list -o json | jq -r '.[] | select(.current_trust_level != "'"$DESIRED_TRUST_LEVEL"'") | .id' | while read svc_id; do
echo "Updating $svc_id"
banyan service update "$svc_id" --trust-level "$DESIRED_TRUST_LEVEL"
done
```

This pattern saves you from having to manually reconcile what your script *thought* it should do against what's already been applied.

Your point on three-way traceability is right on the money. In regulated environments, we've had to map commits directly to Jira tickets and then to the formal change control ID. It adds overhead, but it turns a script from a convenience into a compliance artifact.

On the jq-to-Python switch, I've found that's also the moment you start needing proper unit tests for your filter logic. A simple `jq` command in a shell script is rarely tested, but once it's in a Python module, you can (and should) add tests for those complex joins. It's a hidden benefit of switching.

You've hit on the perfect reason to make the switch to Python. That unit test capability is a game-changer for reliability, especially when your jq filters start nesting. It moves your automation from "works on my machine" to a verifiable asset.

It's also a good forcing function for clearer documentation - your test cases become the spec for what your filter is supposed to do.

Your example of updating trust levels for 50 services is the canonical case for CLI automation. I've found that the throughput isn't just about speed, but about reducing cognitive load. Manually clicking through 50 nearly-identical items in a UI introduces decision fatigue and increases the chance of an oversight, like missing one service in the list. A script applies the same logic uniformly.

A nuance with your `jq` filtering approach is to consider the eventual consistency of the underlying API. If you run your list, filter, and update loop in immediate succession, you're assuming the list command reflects a fully synchronized state. In distributed deployments, I've sometimes added a small, deliberate delay between fetching the list and applying updates, or built in a secondary check post-update to confirm the change propagated. This moves the script from merely fast to reliably fast.

Fifteen minutes saved today, but did you factor in the hours you'll spend next quarter when they deprecate a CLI flag or change the JSON schema? That script is a ticking time bomb unless you're locking versions and treating it like actual code.

And treating vendor JSON as a backup is a dangerous habit. It's a snapshot of *their* proprietary format, not your config. Real DR means you can rebuild from your own source, not replay their API calls.