Sharing ByteDance's cloud-native technologies, technical practices, and developer events.
This article analyzes a recent surge of TransactionTooLargeException crashes in the Douyin Android app, explains how oversized Bundles exceed Binder limits during Activity stop, and presents a hook‑based solution that compresses Bundle data to prevent binder transaction failures.
This article explains how TikTok’s cloud‑native platform leverages elastic scaling, monitoring, and quota systems to dynamically share resources between online, latency‑sensitive services and offline, batch workloads, improving utilization while preserving service stability across tidal traffic patterns.
This article explains the challenges of memory management in mixed Kubernetes workloads, introduces ByteDance's open‑source Katalyst Memory Advisor, details native allocation and reclamation mechanisms, outlines its architecture and plugins, and describes interference detection and multi‑level mitigation strategies to improve memory utilization and service quality.
This article introduces the CDSBen model, a machine‑learning‑based benchmark that translates real database transaction patterns into storage‑layer I/O workloads, enabling precise, isolated performance testing of the cloud‑native veDB storage system.
Katalyst v0.3.0 introduces major upgrades including enhanced KCNR API bandwidth isolation, a more extensible task and async execution framework, advanced mixed‑deployment controls, load‑aware resource prediction, and concurrent unit testing, all aimed at improving cloud‑native resource management efficiency.
This article explains Volcano Engine's next‑generation GPU sharing scheduling technology, detailing the two‑layer scheduler, card‑level bin‑pack/spread strategies, system architecture, API definitions, and optimization algorithms that together increase GPU deployment density over 500% and improve utilization by more than 50% for AI workloads.
Kelemetry, an open‑source tracing system from ByteDance, links Kubernetes control‑plane components by treating each object as a span, aggregating audit logs and events into unified traces that are visualized as trees or timelines, supporting multi‑cluster monitoring and custom conversion pipelines.
This article explains the challenges of large‑model offline (batch) inference, such as GPU memory limits and distributed scheduling, and shows how Ray’s cloud‑native architecture, model partitioning, and Ray Datasets can be used to build efficient, elastic inference frameworks deployed with KubeRay.
ByConity is a cloud‑native, storage‑compute separated data warehouse engine that supports multi‑tenant isolation, high performance, and elastic scaling; this guide explains its three‑layer architecture, hardware requirements, Helm‑based Kubernetes deployment, dynamic scaling, and practical SQL testing steps.
At KubeCon+CloudNativeCon 2023 in Amsterdam, Volcano Engine and ByteDance presented two technical sessions covering Dragonfly's P2P image distribution best practices and large‑scale Kubernetes batch processing strategies, offering deep insights and real‑world implementations for cloud‑native developers.
