Hey everyone,
I've been using Helicone for a few weeks to track LLM costs and latency. Love the concept! But I'm hitting a snag.
When I try to write a custom SQL query against our request logs (we have a few million rows now), the editor feels really sluggish. Typing lags, and running a simple `COUNT` can take 10+ seconds. My query isn't even that complex:
```sql
SELECT model, COUNT(*) as request_count
FROM request
WHERE created_at > NOW() - INTERVAL '7 days'
GROUP BY model;
```
Is this normal for larger datasets, or is my setup maybe the issue? I'm on their default cloud plan. Wondering if anyone else has tips or if I should be summarizing data differently first.
Thanks!
That's a typical scaling wall. I'd check if your `created_at` column has an index, because without one that WHERE clause forces a full table scan on millions of rows. Even with an index, a `COUNT(*)` on a 7-day range might still be heavy if your date filter selects a large portion of the table.
You could test with a narrower window first:
```sql
SELECT model, COUNT(*) as request_count
FROM request
WHERE created_at > NOW() - INTERVAL '1 day'
GROUP BY model;
```
If that's fast, then the issue is primarily data volume. For regular reporting, you're right to consider pre-aggregation. I run a daily materialized view for key metrics and query that instead of the raw logs.
Yeah, the materialized view approach is solid for recurring reports. I've done something similar with a nightly cron job that dumps aggregated counts (by model, status code, user) into a separate reporting table. Queries against that are instant.
One caveat - if you need to drill down into specific anomalous requests, you still need the raw logs. So we keep the detailed data but treat it as a "cold" archive for forensic queries only.
The index check is correct as far as it goes, but on a managed platform like theirs, you're likely at the mercy of their black-box schema design. If they didn't index `created_at` on a logging table, that's a rookie mistake, but you can't fix it.
The real lag typing in the editor, though, that's probably the web UI trying to do some client-side syntax validation or autocomplete on a massive table. I've seen that choke browsers before you even hit 'run'.
Materialized views are the right long-term answer, but you're now talking about managing ETL jobs on top of their platform. At that point, I'd ask if you're better off piping the webhook export to your own data warehouse and cutting out the middleman for analytics entirely.