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81c84289b8
Reduce the worst-case concurrent ClickHouse memory of the internal platform-analytics route (all 17 CH queries fire in one Promise.all on the shared admin user, against a 9 GB per-user cap). - Use sipHash64(user_id) as the distinct key in the uniqExact/uniqExactIf aggregates (dauSeries, sparkByProject, mauProjects, activeByProject). Exact at this scale (64-bit, negligible collision prob over 1M users), verified byte-identical; -40% to -61% peak memory per query. - Sample the new/retained/reactivated activity split at 1-in-4 users (consistent cityHash bucket on both subqueries, counts scaled x4). The split's window function + all-history scan made it the heaviest query (~1.3 GiB at 1M users / 50M events); sampling cuts it ~78% for a ~0.4% mean error. Dashboard chart now notes it is a sampled estimate. Adds the benchmark/optimization harnesses used to validate these changes (seed isolated bench_pa DBs, measure peak memory, verify result equality). |
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