Performance Optimization of Dada Delivery Mini-Program: Static Resources, Interface Aggregation, and Startup Page Adjustments

Background : Dada Delivery's mini‑program is a major ToC entry with services like order pickup, delivery, and queueing; performance is critical for user retention and order volume.

Problems Identified : Low open rates due to large code packages, unmet performance benchmarks, fragmented optimization efforts, and difficulty maintaining metrics across iterations.

Key Actions :

Static resource optimization (package size control, pre‑loading, compression, lazy loading).

Interface aggregation and sequencing improvements (caching low‑frequency data, parallel requests, pre‑fetching, merging APIs).

Startup page module adjustments (pre‑fetching key assets, caching location data, moving heavy SDKs to async sub‑packages, backend‑driven ad logic).

Best‑practice consolidation and knowledge transfer.

Static Resource Optimization : Enforced platform limits (total sub‑package ≤20 MB, each package ≤2 MB), removed unused files, applied lazy code loading, and used component placeholders for async sub‑package loading.

Image Handling : Compressed images (<100 KB), used double‑resolution images, replaced small icons with IconFont/base64, served large images via CDN with WebP conversion, and implemented lazy loading.

Interface Aggregation & Sequencing : Cached low‑real‑time data, parallelized independent requests with Promise.all , pre‑fetched first‑screen data, and merged APIs, reducing request count from 21 to 17 and cutting total latency by ~500 ms on Alipay.

Startup Page Adjustments : Pre‑loaded banner and ad resources, cached location data, split large SDKs into async sub‑packages, and shifted ad‑logic to backend to reduce front‑end code.

Best Practices & Monitoring : Established metric monitoring (package size, image size, launch time, evaluation scores), standardized release cadence (twice weekly), and codified strategies for static resources, image processing, and setData optimization.

Results :

WeChat: launch time reduced by 20%, open rate increased by 0.5%.

Alipay: launch time reduced by 40%, SCI score reached full marks.

Future Plan : Build a self‑contained monitoring system to continuously track and alert on performance regressions, ensuring sustained excellence across all mini‑programs.