Focused on big data technology research, exploring the Big Data era | [email protected]
This article examines Docker's early container platform, the evolution of Docker Swarm, the birth and architecture of Kubernetes, and compares their features, scalability, storage, and fault‑tolerance to explain why Kubernetes has become the dominant cloud‑native orchestration solution.
This article traces the evolution from early virtualization challenges to modern container technologies, explaining how LXC laid the groundwork for Docker, how Docker differs from virtual machines, and why containers have become essential for cloud‑native development and DevOps workflows.
TCOS 2.0, the container operating system from Transwarp, expands compatibility to Windows, ARM, MIPS, and domestic platforms, adds GPU heterogeneous scheduling, HPA autoscaling, enhanced local storage management, and improved monitoring, providing a robust foundation for big data, AI, and cloud-native applications.
This article explains how kube-scheduler in Kubernetes orchestrates pod placement by applying pre‑selection (predicates) and scoring (priorities) strategies, discusses fairness, resource efficiency, speed, flexibility, details common predicate and priority algorithms, and demonstrates practical scenarios with YAML and command‑line examples.
TDH 5.2.3 introduces a series of stability and performance upgrades—including transaction and compaction optimizations, enhanced error handling, SQL length protection, improved Oracle‑compatible UDFs, default resource pool support, Guardian caching, TxSQL monitoring, and workflow and OLAP engine fixes—aimed at delivering a more reliable big‑data platform.
Transwarp Data Hub 6.0 introduces major upgrades—including Compactor and Profiler for Inceptor, attribute‑based access control for Guardian, a standalone Manager service, enhanced stream‑computing tools, new UDFs, and performance tweaks—offering enterprises a more powerful, secure, and easy‑to‑deploy big‑data platform.
The article details the feature upgrades, performance improvements, bug fixes, and new capabilities introduced in TDH versions 5.2.1 and 5.2.2, covering components such as Inceptor, Slipstream, Rubik, Workflow, Manager, Guardian, and future roadmap for the big‑data platform.
This article examines the evolution of a data‑sharing exchange platform—moving from slow JDBC‑based transfers to storage‑level copying, introducing a two‑stage DistCp workflow, and securing cross‑cluster access with Kerberos‑based trust managed by the Guardian component.
This article examines how a cloud‑native big data platform with multi‑tenant architecture addresses data silos, manual data distribution, and slow transfer speeds, using a real‑world banking case to illustrate functional requirements, design patterns, and optimization strategies.
Milano, a distributed log collection and analysis platform built on the ELK stack, leverages Filebeat, Kafka, Logstash, Elasticsearch, and Kibana to provide high‑throughput, low‑latency, secure, and visual log management for massive clusters, addressing the challenges of traditional manual log inspection.
