Technical articles and experience sharing. MXPLAYER is the top-ranked online video content platform in India, and also the world's largest player app, with 100M+ DAU and hundreds of millions of MAU.
This article details how the MX recommendation team rebuilt their Faiss‑based similarity search service using gRPC, covering service selection, multi‑type vector handling, dynamic index updates, deployment strategies, and performance gains that doubled QPS and cut latency by two‑thirds.
This article explains MX Player’s recommendation indexing architecture, detailing batch and incremental build processes, real‑time and Falcon monitoring, Redis storage design, and how these components together improve system stability, reduce latency, and enhance user experience.
This article explains how MX Player replaced a Redis‑based user‑history list with per‑user Bloom Filters, dramatically reducing memory consumption and improving filtering efficiency for its recommendation system.
This article explains the design of a distributed video transcoding platform that leverages AWS Lambda and EC2‑SLAVE to achieve high‑concurrency, low‑latency streaming, detailing architecture, load balancing, health checks, task monitoring, and cost‑saving strategies for scalable cloud‑based video processing.
This article explains MX Player’s recommendation engine architecture, detailing the division between functional and algorithmic components, API communication via Thrift, and the configurable core processing pipeline that handles recall, filtering, scoring, ranking, rule‑based adjustments, packaging, and caching.
