Observed Signal · Jul 5, 2026 · Technical Release · Source: DEV Community · Impact: 2/5 · Sentiment: Neutral
93-Day Postgres Streak Engine for 28K Users
A developer describes how they built a scalable streak-tracking engine for Wishyze, an AI-powered daily ritual platform, supporting 28,547 users and a longest active streak of 93 days. The implementation uses Supabase (Postgres) with a users table and ritual_logs table (storing completed_at and a date-friendly completed_date), an index on (user_id, completed_date DESC), and a gaps-and-islands SQL pattern (ROW_NUMBER subtraction) within a 120-day window to compute current streaks. To avoid timezone errors, the application computes and stores each user's local completed_date at write time. Current_streak and longest_streak are denormalized on the users row and updated at write time to make reads (leaderboards, dashboards) cheap. The article also outlines a phase model for behavioral context and lists operational lessons and optimizations for scale.
Practical engineering guide for scalable engagement/retention mechanics; useful for product and data teams but not industry-shifting.
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Key Takeaways & Evidence Grounding
- Wishyze is an AI-powered daily ritual platform tracking streaks across 28,547 users with a longest active streak of 93 days.
- Implementation uses Supabase (Postgres) with two core tables: users and ritual_logs; ritual_logs stores completed_at (TIMESTAMPTZ) and a completed_date (DATE) plus an index on (user_id, completed_date DESC).
- Current streaks are computed with a gaps-and-islands SQL pattern (ROW_NUMBER() over ordered dates) scoped to a 120-day window to limit scanned rows.
- To handle time zones, the application computes the user's local completed_date at insert time and stores it explicitly rather than relying on server CURRENT_DATE.
- Streak counters (current_streak, longest_streak) are denormalized on the users row and updated during write-time computation to optimize read performance (leaderboards, dashboards).
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