Shadow Rebuilds AI Video Render Queue on PostgreSQL to Fix Memory and Frame-Drop Issues

Engineering team at Shadow replaced a Redis/RabbitMQ-based job queue with a PostgreSQL-native system after discovering that queue starvation — not GPU availability — was the primary bottleneck in their AI video rendering pipeline. The new architecture uses PostgreSQL's SKIP LOCKED feature and a zero-idle-RAM contract to ensure workers only hold tensors for their current frame batch, preventing memory spikes. The pipeline processes video generated by MiniMax's Hailuo H3 model, which maintains a continuous skeleton state across frames to produce temporally coherent motion at 24fps. To meet cinema standards, the team also implemented synthetic shutter blur derived directly from H3's joint velocity estimates, avoiding a costly optical flow pass. The redesigned system satisfies three core requirements: job persistence through worker crashes, bounded memory usage, and fair dispatch between long and short rendering tasks.
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