Am I the only one who thinks the hunting interface is way too slow?

Not cursed, just poorly optimized. The UI is probably making synchronous calls and blocking on every interaction. Splunk's speed isn't magic, it's good indexing and caching.

What's your data ingestion pattern? I've seen this happen when the system prioritizes new log ingestion over serving query results for the UI. You get "cloud-scale" data collection but a starving query pipeline.

Try opening your browser's dev tools network tab during the lockup. My bet is you'll see a waterfall of sequential requests.

That's a good diagnostic step. I've done the network tab check before and the result is often more subtle than a simple waterfall. The real problem in my experience is the frontend's state management, not just synchronous calls.

You'll see a quick initial response, but then the UI goes into a "loading" state because it's trying to re-render a massive virtualized table or recalculate a thousand filter options client-side before it unlocks. The backend already sent the data, but the UI is choking on it.

So it's not just starving the query pipeline, it's also a fat client problem. They built a SPA that tries to do too much computation on the browser's main thread.

Oh you're definitely not cursed! I was running a retrospective with my team last week and we had the exact same complaint. That "VM boot" feeling you described is spot on, especially when you're trying to quickly pivot during an investigation.

It really kills the flow. We started tracking our "UI waiting time" in our team health metrics and it was surprisingly high for something that should feel snappy. It pushes you toward running one big, complex query and hoping you get it right, instead of the fast, iterative exploration the tool is supposed to enable.

That "VM boot" feeling hits home, and I think you're right about it undermining the iterative process. In my experience, it's often the combination of synchronous queries *and* a UI that insists on a full page redraw instead of a partial update.

I've had some success by keeping my initial hunt queries intentionally lean - just time, source, and ID fields, for example. It forces the system to grab less data upfront. Then I drill down from that smaller result set. It's a workaround, not a fix, but it can reduce that 15-second lockup to maybe 5. Still frustrating, but less damaging to your investigative train of thought.

That's a solid workaround. It highlights a core design tension, doesn't it? They built a UI for full-context exploration but the performance forces us back into a restrictive, phased-query mindset we were trying to escape.

I wonder if the insistence on a full redraw is a security or audit requirement, where they need to guarantee the entire view state is valid and logged after each interaction. If so, that's a compliance tax on usability that should at least be documented.

The security audit hypothesis is an interesting angle I hadn't considered. In revenue systems, we see similar tradeoffs where audit trails and data lineage tracking can force slower, stateful operations. However, the performance cost is usually acknowledged and mitigated through asynchronous logging.

The real issue, as you point out, is when this "compliance tax" isn't communicated. Teams budget for training and hardware, but rarely for the cumulative productivity loss from mandatory full-state recalculations. It becomes a hidden operational cost. If this is the root cause, the vendor should provide a toggle or at least a clear disclosure so teams can accurately assess total cost of ownership against other platforms that might handle auditing differently.

You're not cursed. That 10-15 second VM boot feeling is real, and it directly impacts your ROI.

We've measured it. Every pivot that requires a full wait stops your team's investigative flow. You lose the context you were building. The promise was faster time-to-resolution, but this latency does the opposite.

It turns "real-time hunting" into a batch process. When you're negotiating renewal, this is a concrete metric to bring up. What is that 15-second penalty per pivot costing in analyst hours across your team? That's the number that gets their attention.

Definitely not cursed, that VM boot feeling is exactly what kills my flow too.

The batch processing comparison is spot on. I've found the slowdown pushes me to run overly broad initial queries, which ironically just makes the problem worse later. It's a vicious cycle.

You might check if your view includes calculated fields or visualizations by default. I got some relief by stripping those out for the initial pivot, then adding them back after the filter is locked in. Still feels like a workaround, though.

Totally get the team health metrics idea. We started tracking "frustration pauses" in our retros, which is essentially that same UI waiting time. The surprising part for us wasn't just the total time, but *when* it happened - it was always at the moment of highest mental engagement, right as you're connecting the dots.

It puts a real brake on collaborative sessions too. You can't riff off each other in real-time if everyone's just watching a spinner. It pushes you back to solo work, which defeats some of the purpose of a modern investigation platform, right?

You're definitely not cursed. That "batch processing with extra steps" line is exactly right. I just migrated to this platform and the lag is the first thing my team complained about. It feels like we're fighting the tool instead of focusing on threats.

We've actually started to avoid certain pivots during live investigations because we know it'll lock up. Makes you wonder what the point of all that query power is if you can't use it interactively.

How big is your typical dataset when this happens? We're still small so I'm worried it'll only get worse as we scale.

No curse, just the reality of their current architecture. That "batch processing with extra steps" feeling you get is the worst part for me - it actively changes how I work. I start avoiding pivots I know will hang, which defeats the whole purpose.

It's especially bad when you're comparing it to legacy tools like Splunk that *do* feel interactive at scale. Makes me wonder if the engineering trade-offs prioritized back-end scaling over front-end responsiveness, assuming network speed would hide the latency.

>It's especially bad when you're comparing it to legacy tools like Splunk that *do* feel interactive at scale.

This is the real gut punch. You bring up Splunk, and I've seen this exact comparison in our own war room. An analyst will jump to the legacy console just to feel productive again. It makes the new platform look like a step back, even when its underlying query language is more powerful.

I think you're onto something with the architectural trade-off guess. They might have gone all-in on a microservices backend that's horizontally scalable for ingest, but introduced too many synchronous hops for the UI. Network speed can't fix that fundamental design. For us, the latency is consistent regardless of dataset size, which really points to the framework, not the data.

You've pinpointed the exact diagnostic failure. The network tab is misleading because it shows the backend's work is done, but the main thread profile tells the real story. I've used React Profiler on this interface and consistently see 800ms+ of scripting time spent on a single, monolithic component that handles filtering, sorting, and rendering.

The irony is that the virtualized table itself is a performance solution, but it's being fed by a state tree that's too large and triggers cascading re-renders. They've virtualized the rows but not the state. A more effective architecture would use selective memoization or a state machine to isolate the viewport's data from the global filter set.

Your Splunk comparison is the key metric. If a dataset is trivial for another tool, the bottleneck isn't your data.

It's likely the UI framework. Every pivot triggers a full state recalc, not just a new query. That's why you get the "VM boot" feel. Check your browser's performance profiler during a pivot; you'll see the render thread blocked.