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MaGe Linux Operations
MaGe Linux Operations
Sep 5, 2025 · Cloud Native

How to Triple Your K8s Cluster Performance with Full‑Stack Node‑to‑Pod Optimization

This article details a systematic, end‑to‑end Kubernetes performance tuning plan—from kernel and container‑runtime tweaks on the node level to resource limits, scheduler policies, and pod‑level configurations—that can triple cluster throughput, cut latency by up to 80%, and dramatically improve stability.

Cluster OptimizationKubernetesNode Configuration
0 likes · 13 min read
How to Triple Your K8s Cluster Performance with Full‑Stack Node‑to‑Pod Optimization
DataFunSummit
DataFunSummit
May 4, 2022 · Big Data

NetEase Big Data Platform: HDFS Optimization and Practices

NetEase’s senior big‑data engineer shares how the company’s large‑scale data platform leverages Hadoop, HDFS, YARN and related technologies, detailing multi‑layer architecture, cross‑cloud deployment, storage optimizations, NameNode performance enhancements, RPC prioritization, and practical lessons from operating petabyte‑scale clusters.

Cluster OptimizationHDFSStorage Management
0 likes · 23 min read
NetEase Big Data Platform: HDFS Optimization and Practices
DataFunTalk
DataFunTalk
Mar 30, 2022 · Big Data

NetEase Big Data Platform: HDFS Optimization and Practice

This article presents NetEase's big data platform architecture, detailing multi‑layer storage and compute design, HDFS deployment challenges, NameNode and NameSpace performance optimizations, cluster scaling strategies, data tiering, hardware upgrades, and real‑world business use cases, illustrating practical large‑scale big data engineering.

Big DataCluster OptimizationData Management
0 likes · 23 min read
NetEase Big Data Platform: HDFS Optimization and Practice
dbaplus Community
dbaplus Community
Mar 16, 2021 · Big Data

How Kuaishou Scales YARN to Tens of Thousands of Nodes with the Kwai Scheduler

This article explains how Kuaishou’s massive offline compute clusters—tens of thousands of machines processing hundreds of petabytes daily—are managed by a heavily customized YARN stack and the home‑grown Kwai Scheduler, detailing architecture, scheduler evolution, multi‑scenario optimizations, and future scaling plans.

Big DataCluster OptimizationKwai Scheduler
0 likes · 14 min read
How Kuaishou Scales YARN to Tens of Thousands of Nodes with the Kwai Scheduler
DataFunTalk
DataFunTalk
Mar 3, 2021 · Big Data

Kwai Scheduler: Scaling YARN for Ultra‑Large Clusters at Kuaishou

This article presents Kuaishou's large‑scale offline computing challenges and describes how the team customized YARN and built the Kwai scheduler to achieve multi‑threaded, pluggable resource scheduling for clusters of tens of thousands of nodes, supporting diverse workloads such as ETL, ad‑hoc queries, machine‑learning training, and real‑time Flink jobs.

Cluster OptimizationKwai SchedulerYARN
0 likes · 15 min read
Kwai Scheduler: Scaling YARN for Ultra‑Large Clusters at Kuaishou
Java Interview Crash Guide
Java Interview Crash Guide
Jan 9, 2021 · Databases

Master Elasticsearch Performance: Practical Tuning Tips for Faster Clusters

This guide consolidates everyday Elasticsearch tuning techniques—covering configuration file tweaks, system‑level settings, and usage‑level optimizations such as memory locking, discovery settings, fault detection, queue sizing, translog handling, bulk indexing, shard management, and disk I/O—to help you build a stable, high‑throughput search cluster.

Cluster OptimizationElasticsearchScalability
0 likes · 18 min read
Master Elasticsearch Performance: Practical Tuning Tips for Faster Clusters
Big Data Technology & Architecture
Big Data Technology & Architecture
Dec 28, 2020 · Big Data

Optimizing OLAP Data Source Integration with SparkSQL: Cluster and Node Tuning, Profiling, and GC

This article details the end‑to‑end process of connecting an OLAP data source to SparkSQL and presents a comprehensive performance‑tuning guide covering cluster‑level resource allocation, single‑node On‑CPU/Off‑CPU analysis, flame‑graph profiling, Java Flight Recorder usage, and garbage‑collection optimization.

Cluster OptimizationOLAPProfiling
0 likes · 16 min read
Optimizing OLAP Data Source Integration with SparkSQL: Cluster and Node Tuning, Profiling, and GC
DataFunTalk
DataFunTalk
Jul 5, 2020 · Big Data

ByteDance’s Optimizations to Hadoop YARN: Enhancing Utilization, Multi‑Load Scenarios, Stability, and Multi‑Region Active‑Active

This article describes ByteDance’s four‑year series of customizations to Hadoop YARN—covering utilization improvements, multi‑load scenario optimizations, stability enhancements, and multi‑region active‑active deployment—along with practical production experiences, architectural details, and future work directions.

ByteDanceCluster OptimizationHadoop
0 likes · 12 min read
ByteDance’s Optimizations to Hadoop YARN: Enhancing Utilization, Multi‑Load Scenarios, Stability, and Multi‑Region Active‑Active
Big Data Technology Architecture
Big Data Technology Architecture
Apr 24, 2020 · Databases

Best Practices for HBase Region Count and Size to Improve Cluster Stability and Performance

The article explains how maintaining an optimal number of HBase regions (typically 20‑200 per RegionServer) and appropriate region size, along with careful MemStore and compaction settings, can prevent memory pressure, reduce GC pauses, and enhance overall cluster stability and throughput.

Cluster OptimizationHBaseRegion Management
0 likes · 5 min read
Best Practices for HBase Region Count and Size to Improve Cluster Stability and Performance
Tencent Cloud Developer
Tencent Cloud Developer
Sep 11, 2019 · Big Data

YARN Practice and Technical Evolution at Kuaishou

Jiaoxiao Fang’s talk details Kuaishou’s YARN deployment, covering its architecture, support for offline, real‑time and ML workloads, and recent enhancements such as event‑handling stability, refined preemption, high‑throughput parallel scheduling, shuffle‑caching for small I/O, plus plans for job protection and multi‑cluster resource utilization.

Big DataCluster OptimizationDistributed Systems
0 likes · 16 min read
YARN Practice and Technical Evolution at Kuaishou