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This article details the evolution of ByteDance's TLS (Tinder Log Service) from a Loki‑based prototype to an EB‑scale, cloud‑native log system, covering its core properties, data organization, architecture, caching, hybrid storage, private codec, ecosystem compatibility, intelligent features, and real‑world case studies.
This guide explains how to combine traditional keyword search and vector‑based semantic search into a hybrid retrieval system using Volcengine Cloud Search, covering the underlying concepts, required components, workflow steps, and a complete Python implementation for an image search application.
This article explains how A/B testing and feature‑flag experiments enable faster iteration, safer rollouts, and data‑driven optimization for large‑model AI products, offering practical steps and real‑world scenarios to improve quality and efficiency.
With the rise of LLMs, vector search and vector databases have become essential for extending model memory, and this article explains the principles, algorithms, design choices, implementation details, and performance results of ByteHouse’s cloud‑native vector retrieval engine.
VikingDB is a cloud‑native vector database that originated at ByteDance, offering high‑performance ANN search, hybrid dense‑sparse retrieval for Retrieval‑Augmented Generation, extensive scaling and filtering capabilities, and ready‑to‑use SDKs for real‑world AI applications.
SDXL‑Lightning, a new diffusion‑based text‑to‑image model from ByteDance, uses Progressive Adversarial Distillation to cut inference steps to as few as 2 while maintaining high resolution and fidelity, offering ten‑fold speed gains, open‑source access, and compatibility with SDXL, ControlNet, and ComfyUI.
MetaApp replaced ClickHouse with the open‑source cloud‑native data warehouse ByConity, achieving over 50% cost reduction and faster, more stable OLAP queries by separating storage and compute, simplifying scaling, and improving resource utilization across a range of analytics workloads such as deduplication, retention, conversion and point‑lookup.
This article explains how ByteDance’s BMQ, a cloud‑native message engine with a compute‑storage separated architecture, overcomes Kafka’s scalability and operational limits by using Proxy, Broker, Coordinator, and Controller modules, a distributed storage model, and advanced caching to achieve rapid scaling, high throughput, and resilient operations.
This guide walks you through purchasing a Volcengine cloud server, configuring security groups, and installing Palworld on both Linux and Windows instances using scripts or manual commands, enabling you to run a private Palworld server accessible via the public IP and port 8211.
The article details ByteDance’s Gödel Scheduler, a cloud‑native, distributed Kubernetes scheduler that unifies online and offline workloads, describing its architecture, enhanced features, performance gains, roadmap, and open‑source plans, including its multi‑instance design, optimistic concurrency, and rescheduling capabilities for improved throughput and scheduling quality.
