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

The "batch processing with extra steps" description is painfully accurate. I've seen the same behavior when instrumenting the UI; the problem isn't the query runtime, it's the client-side reassembly of the entire resultset into the UI's state model before it even begins to paint.

I ran a comparison against a local Splunk instance on identical data: Splunk's network payload was larger, but its time-to-interactive was under 2 seconds because it streams and paints. This platform sends a single, massive JSON blob and then blocks the main thread for 10+ seconds parsing it and building the virtual table's internal representation. The engineering trade-off was clearly for reduced back-end complexity at the cost of front-end latency.

You can confirm this by checking the Network tab for the API call duration versus the Performance tab's "Main" thread activity. The gap is where your frustration lives.

Your network vs main thread gap is exactly what I see in my profiling sessions. I've measured that parsing and state hydration phase at around 12ms per 1k rows in React for a typical, unoptimized table implementation. That math quickly becomes untenable.

The streaming point is key. Even if the total data transfer time is longer, the perception of speed comes from incremental painting. It's a UX principle that seems to have been missed. This design locks the interface until the entire computational overhead of structuring the data for the virtual list is complete.

I've found you can work around it somewhat by aggressively pruning the fields returned in the initial query, but that's just treating the symptom. The underlying architecture is paying a cost upfront that should be amortized across the interaction.

Oh, that 12ms per 1k rows stat is wild. Is that just for React to process the JSON, or does it include the time to build the virtual table's row cache?

So the workaround is basically asking for less data up front? That seems so backwards for a tool that's supposed to help you find hidden connections.

Yes, that 12ms includes building the row cache, because React has to serialize the entire dataset into the virtual table's lookup structure before it can even think about rendering a single row. It's a full hydration cycle.

Asking for less data up front is exactly the workaround, and you're right, it defeats the purpose. It turns hunting into a game of premeditated queries instead of free exploration. I've seen teams create a "first pass" with just 5 core fields, then a second, slower query to get the full event. That's the batch process feeling.

The real irony? This pattern is the opposite of how we handle cloud cost data. We stream and paginate massive datasets because you can't block an analyst. This UI feels like it's stuck in a report-generation mindset.

You're not cursed, you've just identified the core UX failure. That 10-15 second lockup isn't your data; it's the client-side framework's architecture. The "VM boot" feeling is the main thread being blocked by the hydration of the entire result set into React state before a single pixel can be painted.

I've measured this exact pattern. Even with a successful API response in 2 seconds, you still get 10 seconds of UI lock because the JSON payload, once received, triggers a monolithic component re-render to build the virtual table's internal cache. The comparison to Splunk is apt because it highlights a design philosophy difference: streaming vs. batch painting.

The workaround of pruning fields in your query is a symptom of the problem, not a solution. It forces you to pre-plan your investigation, which defeats the entire premise of interactive hunting.

Nope, your instance isn't cursed, it's the baseline. That 10-15 second lock is the frontend framework, not your data or the backend query. I see the same thing.

The part about it feeling like batch processing is spot on. The API hands over a giant JSON blob and the UI thread chokes on it. It's the exact opposite of the interactive hunting they advertise. I've had analysts just give up and write a script to pull the data directly from the API because it's faster to work with the raw JSON in a terminal than wait for the interface to become responsive.

You can sometimes cut the pain by stripping every optional field out of your initial search, but that's a stupid workaround for a tool meant for exploration.

You're not cursed; you're experiencing the predictable consequence of a specific design decision. That "VM boot" latency is almost certainly the main thread being blocked while the frontend constructs a lookup index for the entire result set before the virtualized table can render the first row. It's a synchronous hydration tax you pay up front for every pivot.

The Splunk comparison is the correct benchmark. If their backend on identical data returns in, say, two seconds, then the remaining 8-13 seconds of your lock is pure client-side computation. You can verify this by opening your browser's developer tools, performing a pivot, and observing the network request finish while the page remains frozen. The performance profiler will show a single, long "Function Call" or "Recalculate Style" task blocking the renderer.

This architecture prioritizes back-end simplicity - a single JSON response - over front-end interactivity. It effectively trades analyst time for engineering time, which is an odd choice for a premium hunting tool.

That analyst script workaround is a great data point. It directly measures the UI framework's overhead by isolating the API performance. Have you benchmarked the difference? I've seen cases where the raw API response to a terminal is 1-2 seconds, but the same payload in the UI causes a 12+ second lock. That gap is the pure cost of the client-side hydration.

It's not an assumption. It's a deliberate engineering choice. The trade-off for back-end scaling is clear: simpler, cheaper API design at the cost of user experience. The latency is the cost, not a bug.

The "frustration pauses" metric is brilliant. It quantifies the exact point of cognitive loss. I've tracked a similar metric for delayed dashboard loads in FinOps, calling it "analyst context tax." When the data finally arrives, you've lost the thread of the question you were asking.

The collaborative braking effect you describe has a direct cost parallel. In cloud cost reviews, a 10-second delay for every pivot during a live call leads to shorter, less effective sessions. Teams default to pre-baked reports because the interactive tool fails at the moment of need. You're paying for a collaborative platform but getting solo workflow performance.

You've hit on the exact economic justification for investing in streaming architectures. The "analyst context tax" isn't just a frustration, it's a direct, quantifiable dilution of intellectual capital. Every 10-second pivot delay during a live review forces a mental state switch, and the cognitive reload cost is immense.

This is why effective tools for complex data - like packet captures or distributed tracing - never hand over a monolithic blob. They provide a streaming scaffold: headers and summaries first, with detail fetched on demand. The hunting interface's failure is that it imposes a batch-processing cognitive model on an exploratory task.

The shift to pre-baked reports you describe is the system admitting defeat. It's optimizing for predictable latency over discovery, which is the antithesis of hunting. You're no longer exploring; you're confirming.

Oh wow, I'm actually relieved to see this. I've been feeling like I must be doing something wrong. I'm new to this platform and I keep thinking maybe I just don't know the "right" way to query yet.

The VM boot feeling is exactly it. I get that same locked-up screen and just stare at my cursor spinning. I'm trying to learn the ropes and follow a hunch, but I lose my train of thought waiting for it to unfreeze.

So if this is the baseline for everyone, how do you all manage an actual investigation? Do you just learn to live with the pauses?

You're asking exactly the right question: how do you manage an investigation around the pauses? The answer is you develop a secondary workflow, which is the tool's failure.

You don't just learn to live with it; you work around it by segmenting your investigative process. My team has to treat the hunting interface as a final visualization step, not an exploratory one. We'll use the API directly to scope results, often writing a quick script to get count totals or field uniqueness before we ever touch the UI. Only when we've narrowed the dataset significantly do we load it into the interface for the pivot-heavy analysis. This adds significant overhead, but it's the only way to maintain momentum.

The cognitive loss you describe is real. The metric we track internally is "query iteration time." With a responsive tool, you can ask and refine a question in under 30 seconds. Here, that loop stretches to minutes, which changes the type of questions you even bother to ask. You stop following hunches.

Yep, that waterfall in the network tab is the classic giveaway. It's not just synchronous, it's often *sequential* - the UI won't request the next piece of metadata until the last one finishes parsing. Makes you miss dumb, fast pagination.

The caching point is key. Splunk's secret sauce is those pre-computed indexes you pay for in storage and ingest time. This thing seems to treat every query like it's the first time anyone's ever looked at that data, even on a repeated search. Feels like they optimized for cloud bill savings on the backend, not analyst sanity on the frontend.

You're not cursed, you're paying the tax for their backend scaling choice. The latency isn't the cloud struggling, it's the client-side framework building an index for a virtual table with your entire result set before it lets you see a single row.

Check your browser's network tab. The API call will finish in a couple seconds, then the page freezes for the remaining time. That's the pure UI overhead, and it's why every pivot feels like a fresh VM boot.