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This article explains how ByteHouse leverages a native Bitmap data type and dictionary encoding to accelerate real‑time audience segmentation queries in advertising scenarios, achieving up to 50× performance gains over traditional array‑based models.
ByteDance’s multi‑cloud evolution, driven by rapid business growth, cost control, and compliance needs, showcases a distributed cloud‑native platform built on open‑source orchestration, unified resource management, and advanced data‑lake solutions, while addressing operational complexity, interoperability, and emerging AI‑driven challenges.
ByteDance’s streaming computing team members Fang Yong and Hu Weihua share their path from early Flink adopters to Apache Flink Committers, detailing their contributions to Runtime Coordinator and Streaming Warehouse, the challenges of open‑source involvement, and practical advice for developers seeking to engage with the Flink community.
This guide shows how to quickly create a complete image-and-text search solution using Volcano Engine's ESCloud, the CLIP model for feature extraction, and Python, covering data preparation, environment setup, index mapping, bulk indexing, and both text-to-image and image-to-image queries.
This guide explains how to create a domain‑specific intelligent Q&A system by leveraging prompt‑tuning, Volcano Engine Cloud Search vector store, and LangChain, covering model selection, data embedding, vector indexing, retrieval, and LLM integration with full code examples.
Kelemetry, an open‑source tracing system from ByteDance, visualizes Kubernetes control‑plane events by treating each object as a span, linking audit logs, events, and component interactions to provide a unified, searchable view that simplifies debugging, performance analysis, and multi‑cluster observability.
Since Kubernetes became the de‑facto standard, ByteDance faced scaling limits with single‑cluster setups, prompting the adoption of KubeFed V2 and later the development of KubeAdmiral, a next‑generation multi‑cluster federation system that enhances scheduling, resource efficiency, native API support, and dynamic scaling across clouds.
This tutorial walks you through preparing a VKE cluster, pulling the Langchain‑ChatGLM container image, creating the necessary Deployment and Service resources, and adding a local knowledge base, enabling you to run a Langchain‑based ChatGLM service with GPU support on Volcengine’s cloud‑native platform.
Large-model offline (batch) inference, which processes massive data on billion-parameter models, faces GPU memory and distributed scheduling challenges; this article explains how Ray's cloud-native framework, model parallelism, and Ray Datasets pipelines address these issues, improve throughput, and enable elastic, efficient GPU utilization.
Learn how to deploy your own Stable Diffusion text‑to‑image model on Volcengine Cloud by setting up a VKE Kubernetes cluster, configuring storage, GPU resources, container images, and exposing the service via ALB or API Gateway, while leveraging mGPU sharing and serverless GPU options.
