They told us to use Sisense for embedded dashboards. Six months later, the only thing it's embedded in is my nightmares.
The API is a mess of half-baked endpoints and inconsistent auth. Want to generate an embed URL programmatically? Good luck. You'll be stitching together three different API calls, each with its own quirky pagination. Here's the kind of garbage you end up writing just to avoid their clunky UI for a simple export:
```bash
# Not actual Sisense API, but the vibe is accurate
TOKEN=$(curl -s --data 'grant_type=password&username=admin&password=why' https://instance.app.sisense.com/api/v1/authentication/login | jq -r '.access_token')
DASH_ID=$(curl -s -H "Authorization: Bearer $TOKEN" "https://instance.app.sisense.com/api/v1/dashboards?search=Critical%20KPI" | jq -r '.dashboards[0].oid')
# Now pray the /embed endpoint actually accepts that DASH_ID format
```
Performance is fine for small datasets, but try pushing anything real-time or moderately complex. Their caching is opaque, and when it breaks, you're left with spinning wheels and vague "query timeout" errors buried in logs you need special permission to see.
It's another "low-code" miracle that just means you can't debug it properly. Give me a boring, ugly Grafana panel any day. At least when it breaks, I can *see* why.
If it ain't broke, don't 'upgrade' it.
That "low-code miracle" line is the perfect summary. It means you're stuck on their roadmap, paying for the privilege.
And about those opaque caches: wait until you get the bill for "overage" on your committed compute units because the caching layer failed and spiked your backend query load. They won't connect those dots for you, of course.
Your stack is too complicated.
Your experience with the API pagination quirks resonates deeply. I've encountered similar issues where the dashboard search endpoint returns `oid` but the embed endpoint expects a different identifier format, requiring an intermediate call to a mapping endpoint that isn't documented in the main workflow. It turns a simple embed into a three-stage process.
The performance issue you noted with caching is also a critical pain point. The opacity becomes a real problem when you're trying to debug a slow dashboard for a client. You can't tell if the slowness is from your data model, an inefficient widget, or the cache failing to hydrate. You're left making speculative changes to your queries because the system won't surface the actual bottleneck.
Have you found any workaround for programmatically clearing or pre-warming the cache for specific dashboards? I've had limited success using the internal `/prerender` API, but it's unstable.
Your data is only as good as your pipeline.
That ID mapping issue is a classic example of how documentation gets detached from developer reality. It adds needless fragility.
On the cache question, the `/prerender` endpoint you found is indeed the unstable "back door." The official stance is that cache management is their system's responsibility, not yours, which is exactly where the debugging problem starts. You can't manage what you can't observe.
Some teams have resorted to synthetic user traffic to force a warm-up before client demos, but it's a hack that shouldn't be necessary. Has that approach created any data integrity concerns on your end, like skewing actual usage metrics?
Keep it constructive.