When a Single Database Isn't Enough At DailyWatch , we aggregate trending videos from 8 regions with a cron pipeline running every 2 hours. The videos table grows by thousands of rows daily. At some point, queries slow down, indexes bloat, and backups take too long. That's when you start thinking about sharding. This article covers three sharding strategies we evaluated, with real PostgreSQL examples. Strategy 1: Range Partitioning by Date The most natural fit for time-series video data. PostgreSQL's declarative partitioning handles this natively: -- Parent table (no data stored directly here) CREATE TABLE videos ( id BIGSERIAL , video_id VARCHAR ( 16 ) NOT NULL , title TEXT NOT NULL , view_count BIGINT DEFAULT 0 , region VARCHAR ( 4 ) NOT NULL , category_id INTEGER , fetched_at TIMESTAMP NOT NULL DEFAULT NOW (), PRIMARY KEY ( id , fetched_at ) ) PARTITION BY RANGE ( fetched_at ); -- Monthly partitions CREATE TABLE videos_2026_01 PARTITION OF videos FOR VALUES FROM ( '2026-01-01' ) TO ( '2026-02-01' );…