February 16, 2020

524 words 3 mins read

Apache Hadoop 3.x state of the union and upgrade guidance

Apache Hadoop 3.x state of the union and upgrade guidance

Wangda Tan and Wei-Chiu Chuang outline the current status of Apache Hadoop community and dive into present and future of Hadoop 3.x. You'll get a peak at new features like erasure coding, GPU support, NameNode federation, Docker, long-running services support, powerful container placement constraints, data node disk balancing, etc. And they walk you through upgrade guidance from 2.x to 3.x.

Talk Title Apache Hadoop 3.x state of the union and upgrade guidance
Speakers Wangda Tan (Cloudera), Wei-Chiu Chuang (Cloudera)
Conference Strata Data Conference
Conf Tag Make Data Work
Location New York, New York
Date September 24-26, 2019
URL Talk Page
Slides Talk Slides

Apache Hadoop YARN is the modern distributed operating system for big data applications. It morphed the Hadoop compute layer to be a common resource-management platform that can host a wide variety of applications. Many organizations leverage YARN in building their applications on top of Hadoop without repeatedly worrying about resource management, isolation, multitenancy issues, etc. The Hadoop Distributed File System (HDFS) is the primary data storage system used by Hadoop applications. It employs a NameNode and DataNode architecture to implement a distributed file system that provides high-performance access to data across highly scalable Hadoop clusters. Wangda Tan and Wei-Chiu Chuang the current status of Apache Hadoop 3.x—how it’s used today in deployments large and small, and they dive into the exciting present and future of Hadoop 3.x—features that further strengthen Hadoop as the primary resource-management platform and the storage system for enterprise data centers. They explore the current status and the future promise of features and initiatives for both YARN and HDFS of Hadoop 3.×. For YARN 3.x, there is powerful container placement, global scheduling, support for machine learning (Spark) and deep learning (TensorFlow) workloads through GPU and field-programmable gate array (FPGA) scheduling and isolation support, extreme scale with YARN federation, containerized apps on YARN, support for long-running services (alongside applications) natively without any changes, seamless application/services upgrades, powerful scheduling features like application priorities, intra-queue preemption across applications, and operational enhancements including insights through Timeline Service v2, a new web UI, better queue management, etc. Also, HDFS 3.0 announced GA for erasure coding, which doubles the storage efficiency of data and thus reduces the cost of storage for enterprise use cases. HDFS added support for multiple standby NameNodes for better availability. For better reliability of metadata and easier operations, Journal nodes have been enhanced to sync the edit log segments to protect against rolling failures. Disk balancing within a DataNode was another important feature added to ensure disks are evenly utilized in a DataNode, which also ensures better aggregate throughput and prevents from lopsided utilization if new disks are added or replaced in a DataNode. The HDFS team is currently driving the Ozone initiative, which lays the foundation of the next generation of storage architecture for HDFS where data blocks are organized in storage containers for higher scale and handling of small objects in HDFS. The Ozone project also includes an object store implementation to support new use cases. And you’ll leave with all the knowledge of how to upgrade painlessly from 2.x to 3.x to get all the benefits.

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