Cisco Data Intelligence Platform with Hortonworks and Modernizing with NVMe Hybrid Storage

Deployment Guide for the Cisco UCS Integrated Infrastructure for Hortonworks 3.1.0 with Intel NVMe and HDD in Hybrid Storage

 

Updated: February 27, 2020

Published: December 6, 2019

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Table of Contents

Executive Summary. 6

Solution Overview.. 7

Introduction. 7

Audience. 7

Purpose of this Document 7

What’s New in this Release?. 8

What’s Next?. 8

Solution Summary. 8

Cisco Data Intelligence Platform.. 8

Modernizing Hadoop with NVMe. 9

Reference Architecture. 10

Data Lake Reference Architecture. 10

Technology Overview.. 13

Cisco UCS Integrated Infrastructure for Big Data and Analytics. 13

Cisco UCS Manager 13

Cisco UCS 6300 Series Fabric Interconnects. 13

Cisco UCS C-Series Rack-Mount Servers. 14

Cisco UCS C240 M5 Rack-Mount Server 14

Cisco UCS Virtual Interface Cards (VICs) 15

Intel P4510 Series Data Center NVMe. 16

Hortonworks Data Platform.. 16

Apache Ambari 17

HDP for Data Access. 17

Red Hat Ansible Automation. 18

Solution Design. 19

Requirements. 19

Physical Topology. 19

Port Configuration on Fabric Interconnect 19

Server Configuration and Cabling for Cisco UCS C240 M5. 19

Software Distributions and Versions. 20

Deployment Hardware and Software. 22

Cisco Unified Computing System Configuration. 22

Configure Cisco UCS Fabric Interconnect 22

Configure Fabric Interconnects for a Cluster Setup. 22

Configure Cisco UCS Manager 24

Create Service Profile Template. 53

Install Red Hat Enterprise Linux 7.6. 60

Post OS Install Configuration. 71

Configure /etc/hosts. 71

Set Up Passwordless Login. 72

Create a Red Hat Enterprise Linux (RHEL) 7.6 Local Repository. 73

Create the Red Hat Repository Database. 74

Set Up Ansible. 74

Install httpd. 77

Set Up All Nodes to use the RHEL Repository. 77

Upgrade the Cisco Network Driver for VIC1387. 78

Set Up JAVA. 79

Enable Syslog. 80

Set the ulimit 81

Disable SELinux. 82

Set TCP Retries. 82

Disable the Linux Firewall 83

Disable IPv6 Defaults. 83

Disable Swapping. 83

Disable Memory Overcommit 84

Disable Transparent Huge Pages. 84

NTP Configuration. 85

Install Megaraid StorCLI 86

Configure the Filesystem for NameNodes and DataNodes. 87

Delete Partitions. 89

Cluster Verification. 90

Install HDP 3.1.0. 92

Prerequisites for HDP Installation. 92

Hortonworks Repository. 92

Downgrade Snappy on All Nodes. 95

HDP Installation. 95

Install and Setup Ambari Server on rhel1. 95

Setup Ambari Server On Admin Node (Rhel1) 101

Launch the Ambari Server 103

Create the Cluster 104

Select Version. 104

Select Hosts. 105

Hostname Pattern Expressions. 107

Confirm Hosts. 108

Choose Services. 108

Assign Masters. 109

Assign Slaves and Clients. 110

Customize Services. 111

HDFS. 115

MapReduce2. 117

YARN.. 117

Configure the HDFS NameNode High Availability. 119

HBase. 119

Zookeeper 119

Storm.. 119

Ambari Metrics. 120

Accumulo. 121

Atlas. 121

Kafka. 121

Knox. 121

SmartSense. 122

Spark. 122

Review.. 122

Deploy. 123

Summary of the Installation Process. 124

High Availability for HDFS NameNode and YARN ResourceManager 125

Configure the HDFS NameNode High Availability. 125

Configure the YARN ResourceManger HA. 133

Configure NVMe as YARN Local Directory. 137

Summary. 139

For More Information. 139

Bill of Materials. 140

Appendix. 144

Configure Data Drives on Name Node and Data Nodes. 144

Configure Data Drives on Name Nodes. 145

Configure Data Drives on Data Nodes. 146

About the Authors. 147

Acknowledgements. 147

 

 

Data scientists are constantly searching for newer techniques and methodologies that can unlock the value of big data and distill this data further to identify additional insights which could transform productivity and provide business differentiation.

One such area is Artificial Intelligence/Machine Learning (AI/ML), which has seen tremendous development with bringing in new frameworks and new forms of compute (CPU, GPU and FPGA) to work on data to provide key insights. While data lakes have historically been data intensive workloads, these advancements in technologies have led to a new growing demand of compute intensive workloads to operate on the same data.

While data scientists want to be able to use the latest and greatest advancements in AI/ML software and hardware technologies on their datasets, the IT team is also constantly looking at enabling these data scientists to be able to provide such a platform to a data lake. This has led to architecturally siloed implementations. When data, which is ingested, worked, and processed in a data lake, needs to be further operated by AI/ML frameworks, it often leaves the platform and must be on-boarded to a different platform to be processed. This would be fine if this demand is seen only on a small percentage of workloads. However, AI/ML workloads working closely on the data in a data lake are seeing an increase in adoption. For instance, data lakes in customer environment are seeing deluge of data from new use cases such as IoT, autonomous driving, smart cities, genomics and financials, who are all seeing more and more demand of AI/ML processing of this data.

IT is demanding newer solutions to enable data scientists to operate on both a data lake and an AI/ML platform (or a compute farm) without worrying about the underlying infrastructure. IT also needs this to seamlessly grow to cloud scale while reducing the TCO of this infrastructure and without affecting utilization. Thus, driving a need to plan a data lake along with an AI/ML platform in a systemic fashion.

Seeing this increasing demand by IT, and also envisioning this as a natural extension of a data lake, we announced Cisco Data Intelligence Platform.  Cisco Data Intelligence Platform is discussed in detail here.

This CVD implements Cisco UCS Integrated Infrastructure using Hortonworks 3.1.0 and offers hybrid storage model which includes Intel Non-Volatile Memory Express (NVMe) to host Hadoop temp data along with hard disk drives (HDD) for Hadoop Distributed File System (HDFS). As a result, this reference architecture helps achieve improved performance with fewer nodes and maintain better TCO.

Introduction

Both Big Data and machine learning technology have progressed to the point where they are being implemented in production systems running 24x7. There exists a very clear need for a proven, dependable, high-performance platform for the ingestion, processing, storage and analysis of the data, as well as the seamless dissemination of the output, results and insights of the analysis.

This solution implements the Cisco UCS Integrated Infrastructure for Big Data and Analytics based on Cisco Data Intelligence Platform (CDIP) architecture and Intel NVMe, a world-class platform solution specifically designed for demanding workloads that is both easy to scale and easy to manage, even as the requirements grow to thousands of servers and petabytes of storage; and the Cloudera Enterprise Data Hub, an integrated set of tools designed to enable flexible, fast access to the entire data store.

Many companies, recognizing the immense potential of big data and machine learning technology, are gearing up to leverage these new capabilities, building out departments and increasing hiring. However, these efforts face a new set of challenges:

·         Making the data available to the diverse set of people who need it

·         Enabling access to high-performance computing resources, GPUs, that also scale with the data growth

·         Allowing people to work with the data using the environments in which they are familiar

·         Publishing their results so the organization can make use of it

·         Enabling the automated production of those results

·         Managing the data for compliance and governance

·         Scaling the system as the data grows

·         Managing and administering the system in an efficient, cost-effective way

This solution is based on the Cisco UCS Integrated Infrastructure for Big Data and Analytics and includes computing, storage, connectivity, and unified management capabilities to help companies manage the immense amount of data being collected. It is built on Cisco Unified Computing System (Cisco UCS) infrastructure, using Cisco UCS 6332 Series Fabric Interconnects, and Cisco UCS C-Series Rack Servers with Intel NVMe. This architecture is specifically designed for performance and linear scalability for big data and machine learning workload.

Audience

The intended audience of this document includes sales engineers, field consultants, professional services, IT managers, partner engineering and customers who want to deploy the Hortonworks 3.1.0 on the Cisco UCS Integrated Infrastructure for Big Data and Analytics with Intel NVMe (Cisco UCS M5 Rack-Mount servers).

Purpose of this Document

This document describes the architecture and deployment procedures for Hortonworks 3.1.0 on a 28-node Cisco UCS C240 M5 cluster based on Cisco UCS Integrated Infrastructure for Big Data and Analytics.

This document describes the architecture and step by step guidelines of deployment procedures for Cisco Data Intelligence Platform using Hortonworks 3.1.0 on Cisco UCS C240 M5 with Intel NVMe and Spinning HDDs in hybrid configuration.

What’s New in this Release?

This CVD implements the following:

·         Data Lake with Hortonworks 3.1.0 on Cisco UCS Integrated Infrastructure with hybrid storage model consisting Intel NVMe and HDD for Big Data and Analytics

·         Installation and setup of the above through Hortonworks Ambari

·         Integration of Intel NVMe with CDH for separating Temp or Spark MMap files to NVMe and HDFS to HDD.

What’s Next?

This CVD showcases Cisco UCS Manager (UCSM). This solution can also be deployed using Cisco Intersight. Additional Cisco UCS features will be added to the Appendix in the following months. Some of these include the following:

·         Cisco Boot optimized M.2 Raid Controller for hardware RAID

·         4^th Generation FI

·         All NVMe solution for Cisco UCS Integrated Infrastructure for Big Data and Analytics

Solution Summary

This CVD details the process of installing Hortonworks 3.1.0 and the configuration details of the cluster. The current version of Cisco UCS Integrated Infrastructure for Big Data and Analytics offers the following configurations depending on the compute and storage requirements.

Cisco Data Intelligence Platform

Cisco Data Intelligence Platform (CDIP) is a cloud scale architecture which brings together big data, AI/compute farm, and storage tiers to work together as a single entity while also being able to scale independently to address the IT issues in the modern data center. This architecture allows for:

·         Extremely fast data ingest, and data engineering done at the data lake

·         AI compute farm allowing for different types of AI frameworks and compute types (GPU, CPU, FPGA) to work on this data for further analytics

·         A storage tier, allowing to gradually retire data which has been worked on to a storage dense system with a lower $/TB providing a better TCO

·         Seamlessly scale the architecture to thousands of nodes with a single pane of glass management using Cisco Application Centric Infrastructure (ACI)

Cisco Data Intelligence Platform caters to the evolving architecture bringing together a fully scalable infrastructure with centralized management and fully supported software stack (in partnership with industry leaders in the space) to each of these three independently scalable components of the architecture including data lake, AI/ML and Object stores.

Figure 1        Cisco Data Intelligent Platform

Cisco has developed numerous industry leading Cisco Validated Designs (reference architectures) in the area of Big Data (CVDs with Cloudera, Hortonworks and MapR), compute farm with Kubernetes (CVD with Red Hat OpenShift) and Object store (Scality, SwiftStack, Cloudian, and others).

This Cisco Data Intelligence Platform can be deployed in these variants:

·         CDIP with Cloudera with Data Science Workbench (powered by Kubernetes) and Tiered Storage with Hadoop

·         CDIP with Hortonworks with Apache Hadoop 3.1 and Data Science Workbench (powered by Kubernetes) and Tiered Storage with Hadoop

Figure 2        Cisco Data Intelligence Platform with Hadoop, Kubernetes and Object Store

This architecture can start from a single rack and scale to thousands of nodes with a single pane of glass management with Cisco Application Centric Infrastructure (ACI). More details can be found in Scaling the Solution for Cisco Data Intelligence Platform.

Modernizing Hadoop with NVMe

NVMe (Non-Volatile Memory express) is a host controller interface and storage protocol created to accelerate the transfer of data and low latency by reducing IO bottleneck to bring performance improvement compare to its predecessors SAS or SATA. This characteristics of NVMe make them perfect candidate to manage huge data streams in parallel at same time minimize latency for data intensive workloads. NVMe helps achieve important business insights by unlocking parallel access to the storage from real time streams whether that is data sent from Internet of Things (IoT) to Big Data lake.

Enterprises built and utilized Big Data Analytics solutions architected with legacy hard disk drives (HDD) to collect, process and run analysis in real-time or batch processing on massive data to make agile business decisions. The cost advantage of traditional HDD over high-performance flash drives and additional speed, efficiency and reduced latency was not perceived as valuable since majority of analytics was batch processed. Technology such as machine learning, IoT devices, autonomous vehicle etc. where data collection and analysis from various sensors and devices needs to be processed in real-time and store in the data lake. As the quantity of the data grows more and more applications and services will be drawn where the data is stored which requires modernizing existing Hadoop architecture.

Traditionally, Hadoop HDFS and YARN temporary files are stored on HDD hence creating contention for running applications and MapReduce or Spark jobs on large datasets creating overlap on multiple data flows generated by YARN. In order to cater growing datasets requirement in enterprises and achieve better performance, this solution separates temporary data for MapReduce or even Spark and MMap files for Spark which is very IO intensive on NVMe from HDFS data.

Reference Architecture

Table 1   lists the reference architecture configuration details for the data lake. AI/ML components of the data lake and tiered storage are explained in detailed in the Cisco Data Intelligence Platform with Hortonworks Data Platform 3.1 and Cloudera Data Science Workbench 1.5 published CVD.

Data Lake Reference Architecture

Table 1  lists the data lake reference architecture configuration details for Cisco UCS Integrated Infrastructure for Big Data and Analytics.

Table 1    Cisco UCS Integrated Infrastructure for Big Data and Analytics Configuration Options

As illustrated in Figure 3, a 28-node cluster with Rack#1 hosting 16 Cisco UCS C240 M5 server and Rack#2 hosting 12 Cisco UCS C240 M5 server. Each link in the figure represents a 40 Gigabit Ethernet link from each of the sixteen servers directly connected to a Fabric Interconnect. Every server is connected to both Fabric Interconnects.

Optionally a 30-node starter cluster with Rack#1 hosting 16 Cisco UCS C240 M5 servers and Rack#2 hosting six Cisco UCS 240 M5 servers for AI/Compute workload and four Cisco UCS S3260 Storage server for tiered storage as shown in Figure 4.

Figure 3        8x Cisco UCS S3260 Chassis with 2 Server Nodes Each (Object Storage Nodes)

Figure 4        Cisco Data Intelligence Platform with Hortonworks – 30 Node Configuration (Optional)

An alternate configuration for cases where more GPU capacity is needed. Four of the Cisco UCS C240 M5 servers from the previous configuration in 0 are replaced with Cisco UCS C480 M5 ML M5 server which support up to eight V100 MXM GPUs.

Cisco UCS Integrated Infrastructure for Big Data and Analytics

The Cisco UCS Integrated Infrastructure for Big Data and Analytics solution for Hortonworks is based on Cisco UCS Integrated Infrastructure for Big Data and Analytics, a highly scalable architecture designed to meet a variety of scale-out application demands with seamless data integration and management integration capabilities built using the components described in this section.

Cisco UCS Manager

Cisco UCS Manager (UCSM) resides within the Cisco UCS Fabric Interconnect. It makes the system self-aware and self-integrating, managing all the system components as a single logical entity. Cisco UCS Manager can be accessed through an intuitive graphical user interface (GUI), a command-line interface (CLI), or an XML application-programming interface (API). Cisco UCS Manager uses service profiles to define the personality, configuration, and connectivity of all resources within Cisco UCS, radically simplifying provisioning of resources so that the process takes minutes instead of days. This simplification allows IT departments to shift their focus from constant maintenance to strategic business initiatives.

Key Features

·         Supports Cisco UCS B-Series Blade and Cisco UCS C-Series Rack Servers, the Cisco UCS C3260 storage server, Cisco UCS Mini, and the Cisco HyperFlex hyperconverged infrastructure.

·         Programmatically controls server, network, and storage resources, with a unified, policy-driven management, so they can be efficiently managed at scale through software.

·         Works with HTML 5, Java, or CLI graphical user interfaces.

·         Can automatically detect, inventory, manage, and provision system components that are added or changed.

·         Facilitates integration with third-party systems management tools.

·         Builds on existing skills and supports collaboration across disciplines through role-based administration.

Cisco UCS 6300 Series Fabric Interconnects

Cisco UCS 6300 Series Fabric Interconnects provide high-bandwidth, low-latency connectivity for servers, with integrated, unified management provided for all connected devices by Cisco UCS Manager. Deployed in redundant pairs, Cisco fabric interconnects offer the full active-active redundancy, performance, and exceptional scalability needed to support the large number of nodes that are typical in clusters serving big data applications. Cisco UCS Manager enables rapid and consistent server configuration using service profiles, automating ongoing system maintenance activities such as firmware updates across the entire cluster as a single operation. Cisco UCS Manager also offers advanced monitoring with options to raise alarms and send notifications about the health of the entire cluster.

The Cisco UCS 6300 series Fabric interconnects are a core part of Cisco UCS, providing low-latency, lossless 10 and 40 Gigabit Ethernet, Fiber Channel over Ethernet (FCoE), and Fiber Channel functions with management capabilities for the entire system. All servers attached to Fabric interconnects become part of a single, highly available management domain.

Figure 5        Cisco UCS 6332 UP 32 -Port Fabric Interconnect

Cisco UCS C-Series Rack-Mount Servers

Cisco UCS C-Series Rack-Mount Servers keep pace with Intel Xeon processor innovation by offering the latest processors with increased processor frequency and improved security and availability features. With the increased performance provided by the Intel Xeon Scalable Family Processors, Cisco UCS C-Series servers offer an improved price-to-performance ratio. They also extend Cisco UCS innovations to an industry-standard rack-mount form factor, including a standards-based unified network fabric, Cisco VN-Link virtualization support, and Cisco Extended Memory Technology.

It is designed to operate both in standalone environments and as part of Cisco UCS managed configuration, these servers enable organizations to deploy systems incrementally—using as many or as few servers as needed—on a schedule that best meets the organization’s timing and budget. Cisco UCS C-Series servers offer investment protection through the capability to deploy them either as standalone servers or as part of Cisco UCS. One compelling reason that many organizations prefer rack-mount servers is the wide range of I/O options available in the form of PCIe adapters. Cisco UCS C-Series servers support a broad range of I/O options, including interfaces supported by Cisco and adapters from third parties.

Cisco UCS C240 M5 Rack-Mount Server

The Cisco UCS C240 M5 Rack-Mount Server (Figure 6) is a 2-socket, 2-Rack-Unit (2RU) rack server offering industry-leading performance and expandability. It supports a wide range of storage and I/O-intensive infrastructure workloads, from big data and analytics to collaboration. Cisco UCS C-Series Rack Servers can be deployed as standalone servers or as part of a Cisco Unified Computing System (Cisco UCS) managed environment to take advantage of Cisco’s standards-based unified computing innovations that help reduce customers’ Total Cost of Ownership (TCO) and increase their business agility.

In response to ever-increasing computing and data-intensive real-time workloads, the enterprise-class Cisco UCS C240 M5 server extends the capabilities of the Cisco UCS portfolio in a 2RU form factor. It incorporates the 2^nd generation Intel® Xeon® Scalable and Intel® Xeon® Scalable processors, supporting up to 20 percent more cores per socket, twice the memory capacity, and five times more Non-Volatile Memory Express (NVMe) PCI Express (PCIe) Solid-State Disks (SSDs) compared to the previous generation of servers. These improvements deliver significant performance and efficiency gains that will improve your application performance. The Cisco UCS C240 M5 delivers outstanding levels of storage expandability with exceptional performance, along with the following:

·         Latest Intel Xeon Scalable CPUs with up to 28 cores per socket

·         Up to 24 DDR4 DIMMs for improved performance

·         Up to 26 hot-swappable Small-Form-Factor (SFF) 2.5-inch drives, including 2 rear hot-swappable SFF drives (up to 10 support NVMe PCIe SSDs on the NVMe-optimized chassis version), or 12 Large-Form- Factor (LFF) 3.5-inch drives plus 2 rear hot-swappable SFF drives

·         Support for 12-Gbps SAS modular RAID controller in a dedicated slot, leaving the remaining PCIe Generation 3.0 slots available for other expansion cards

·         Modular LAN-On-Motherboard (mLOM) slot that can be used to install a Cisco UCS Virtual Interface Card (VIC) without consuming a PCIe slot, supporting dual 10- or 40-Gbps network connectivity

·         Dual embedded Intel x550 10GBASE-T LAN-On-Motherboard (LOM) ports

·         Modular M.2 or Secure Digital (SD) cards that can be used for boot

Figure 6        Cisco UCS C240 M5 Rack-Mount Server

Cisco UCS Virtual Interface Cards (VICs)

Cisco UCS Virtual Interface Cards (VIC) are unique to Cisco. Cisco UCS Virtual Interface Cards incorporate next-generation converged network adapter (CNA) technology from Cisco which reduces the number of network adapters, cables, and switches needed and radically simplifies the network, reducing complexity. Cisco VICs can support 256 Express (PCIe) virtual devices, either virtual Network Interface Cards (vNICs) or virtual Host Bus Adapters (vHBAs), with a high rate of I/O Operations Per Second (IOPS), support for lossless Ethernet, and 10/25/40/100-Gbps connection to servers. The PCIe Generation 3 x16 interface helps ensure optimal bandwidth to the host for network-intensive applications, with a redundant path to the fabric interconnect. Cisco VICs support NIC teaming with fabric failover for increased reliability and availability. In addition, it provides a policy-based, stateless, agile server infrastructure for your data center.

Cisco VIC 1497

The Cisco VIC 1497 (Figure 8) is a dual-port Quad Small Form-Factor (QSFP28) mLOM card designed for the M5 generation of Cisco UCS C-Series Rack Servers. The card supports 40/100-Gbps Ethernet or FCoE. The card can present PCIe standards-compliant interfaces to the host, and these can be dynamically configured as NICs or HBAs.

Figure 7        Cisco VIC 1497

Cisco UCS VIC 1387

The Cisco UCS Virtual Interface Card 1387 offers dual-port Enhanced Quad Small Form-Factor Pluggable (QSFP+) 40 Gigabit Ethernet and Fiber Channel over Ethernet (FCoE) in a modular-LAN-on-motherboard (mLOM) form factor. The mLOM slot can be used to install a Cisco VIC without consuming a PCIe slot providing greater I/O expandability.

Figure 8        Cisco UCS VIC 1387

Intel P4510 Series Data Center NVMe

The Intel® SSD DC P4510 Series drives built on NVMe specification 1.2 PCIe with the increased density of Intel 64-layer 3D NAND and enhanced firmware features. The 8TB DC P4510 part of the reference architecture as shown in figure 3, is built to handle read-intensive workloads and beyond which supports optimized storage efficiency while enabling data center to do more per server and minimize service disruptions. The DC P4510 creates greater Quality of Service, bandwidth, and Performance. It significantly increases server agility and utilization and accelerates applications across a wide range of workloads to lead data centers through their evolving transformation.

Key Benefits

·         Optimized for storage efficiency across a range of workloads

·         Manageability to maximize IT efficiency

·         Industry-leading reliability and security

·         Designed for today’s modern data centers

Hortonworks Data Platform

The Hortonworks Data Platform (HDP 3.1.0) delivers essential capabilities in a completely open, integrated and tested platform that is ready for enterprise usage. With Hadoop YARN at its core, HDP provides flexible enterprise data processing across a range of data processing engines, paired with comprehensive enterprise capabilities for governance, security and operations.

All the integration of the entire solution is thoroughly tested and fully documented. By taking the guesswork out of building out a Hadoop deployment, HDP gives a streamlined path to success in solving real business problems.

Hortonworks Data Platform (HDP) 3.0 delivers significant new features, including the ability to launch apps in a matter of minutes and address new use cases for high-performance deep learning and machine learning apps. In addition, this new version of HDP enables enterprises to gain value from their data faster, smarter, in a hybrid environment.

Apache Ambari

Apache Ambari is a completely open source management platform. It performs provisioning, managing, securing, and monitoring Apache Hadoop clusters. Apache Ambari is a part of Hortonworks Data Platform and it allows enterprises to plan and deploy HDP cluster. It also provides ongoing cluster maintenance and management.

Ambari provides an intuitive Web UI as well as an extensive REST API framework which is very useful for automating cluster operations.

The following are the core benefits that Hadoop operators get with Ambari:

·         Simplified Installation, Configuration and Management. Easily and efficiently create, manage and monitor clusters at scale. Takes the guesswork out of configuration with Smart Configs and Cluster Recommendations.  Enables repeatable, automated cluster creation with Ambari Blueprints.

·         Centralized Security Setup. Reduce the complexity to administer and configure cluster security across the entire platform. Helps automate the setup and configuration of advanced cluster security capabilities such as Kerberos and Apache Ranger.

·         Full Visibility into Cluster Health. Ensure your cluster is healthy and available with a holistic approach to monitoring. Configures predefined alerts — based on operational best practices — for cluster monitoring. Captures and visualizes critical operational metrics — using Grafana — for analysis and troubleshooting. Integrated with Hortonworks SmartSense for proactive issue prevention and resolution.

·         Highly Extensible and Customizable. Fit Hadoop seamlessly into your enterprise environment. Highly extensible with Ambari Stacks for bringing custom services under management, and with Ambari Views for customizing the Ambari Web UI.

HDP for Data Access

With YARN at its foundation, HDP provides a range of processing engines that allow users to interact with data in multiple and parallel ways, without the need to stand up individual clusters for each data set/application. Some applications require batch while others require interactive SQL or low-latency access with NoSQL. Other applications require search, streaming or in-memory analytics. Apache Solr, Storm and Spark fulfill those needs respectively.

To function as a true data platform, the YARN-based architecture of HDP enables the widest possible range of access methods to coexist within the same cluster avoiding unnecessary and costly data silos.

As shown in Table 2  , HDP Enterprise natively provides for the following data access types:

·         Batch – Apache MapReduce has served as the default Hadoop processing engine for years. It is tested and relied upon by many existing applications.

·         Interactive SQL Query - Apache Hive is the de facto standard for SQL interactions at petabyte scale within Hadoop. Hive delivers interactive and batch SQL querying across the broadest set of SQL semantics.

·         Search - HDP integrates Apache Solr to provide high-speed indexing and sub-second search times across all your HDFS data.

·         Scripting - Apache Pig is a scripting language for Hadoop that can run on MapReduce or Apache Tez, allowing you to aggregate, join and sort data.

·         Low-latency access via NoSQL - Apache HBase provides extremely fast access to data as a columnar format, NoSQL database. Apache Accumulo also provides high-performance storage and retrieval, but with fine-grained access control to the data.

·         Streaming - Apache Storm processes streams of data in real time and can analyze and take action on data as it flows into HDFS.

Table 2    YARN

Red Hat Ansible Automation

Red Hat Ansible Automation is a powerful IT automation tool. It is capable of provisioning numerous types of resources and deploying applications. It can configure and manage devices and operating system components. Due to its simplicity, extensibility, and portability, this solution extensively utilizes Ansible for performing repetitive deployment steps across the nodes.

Requirements

This CVD describes architecture and deployment procedures for Hortonworks 3.1.0 on a 28-node cluster based on Cisco UCS Integrated Infrastructure for Big Data and Analytics. The solution details how to configure Hortonworks 3.1.0 on the infrastructure and all its dependencies. In addition, it also details the configuration for Hortonworks Dataflow for various use cases.

The cluster configuration consists of the following:

·         Two Cisco UCS 6332UP Fabric Interconnects

·         28 Cisco UCS C240 M5 Rack-Mount servers

·         Two Cisco R42610 standard racks

·         Four Vertical Power distribution units (PDUs) (Country Specific)

Physical Topology

Each rack consists of two vertical PDUs. The first rack consists of two Cisco UCS 6332UP Fabric Interconnects, 16 Cisco UCS C240 M5 Rack Servers connected to each of the vertical PDUs for redundancy; thereby, ensuring availability during power source failure. The second rack consists of 12 Cisco UCS C240 M5 Servers connected to each of the vertical PDUs for redundancy; thereby, ensuring availability during power source failure, like the first rack.

Port Configuration on Fabric Interconnect

Table 3  lists the port configuration on Cisco UCS FI 6332 Fabric Interconnect.

Table 3    Port Configuration on Fabric Interconnect

Server Configuration and Cabling for Cisco UCS C240 M5

The Cisco UCS C240 M5 rack server is equipped with 2 x Intel Xeon 2^nd Gen Scalable Family Processor 6230 (2 x 20 cores, 2.1 GHz), 384 GB of memory, Cisco UCS Virtual Interface Card 1337, Cisco 12-Gbps SAS Modular Raid Controller with 4-GB FBWC, 24 x 2.4 TB 10K rpm SFF SAS HDDs and 2 x 8TB Cisco 2.5" U.2 8TB Intel P4500 NVMe High Perf. Value Endurance, M.2 with 2 x 240-GB SSDs for Boot.

Figure 9 illustrates the port connectivity between the Cisco UCS FI 6332 and Cisco UCS C240 M5 Rack Server. Twenty-two Cisco UCS C240 M5 servers are installed in this configuration.

Figure 9        Fabric topology for Cisco UCS C240 M5 Rack Server

For information on physical connectivity and single-wire management, see:

https://www.cisco.com/c/en/us/td/docs/unified_computing/ucs/c-series_integration/ucsm4-0/b_C-Series-Integration_UCSM4-0/b_C-Series-Integration_UCSM4-0_chapter_01.html

Software Distributions and Versions

The software distributions required versions are listed in Table 4  .

Table 4    Software distribution and Version

Cisco Unified Computing System Configuration

This section details the Cisco Unified Computing System (Cisco UCS) configuration that was done as part of the infrastructure build out. The racking, power, and installation of the Cisco UCS Rack Server is described in the physical topology section earlier in this document. Please refer to the Cisco UCS Manager Getting Started Guide. For more information about each step, see the Cisco UCS Manager - Configuration Guides.

Configure Cisco UCS Fabric Interconnect

This document assumes you are using Cisco UCS Manager Software version 4.0(4c). To upgrade the Cisco UCS Manager software and the Cisco UCS 6332 Fabric Interconnect software to a higher version of the firmware, see the Cisco UCS Manager Install and Upgrade Guides.

Alternatively, if you intend to clear the existing Cisco UCS Manager configuration, follow these steps:

1.    Connect a console cable to the console port on what will become the primary fabric interconnect.

2.    If the fabric interconnects were previously deployed and you want to erase it to redeploy, follow these steps:

a.     Login with the existing username and password.

#connect local-mgmt

#erase config

#yes (to confirm)

3.    After the fabric interconnect restarts, the out-of-box first time installation prompt appears, type “console” and press Enter.

4.    Follow the Initial Configuration steps as outlined in Cisco UCS Manager Getting Started Guide. When configured, log into UCSM IP Address via the web interface to perform the base Cisco UCS configuration.

Configure Fabric Interconnects for a Cluster Setup

To configure the Cisco UCS Fabric Interconnects, follow this step:

1.    Verify the following physical connections on the fabric interconnect:

-        The management Ethernet port (mgmt0) is connected to an external hub, switch, or router.

-        The L1 ports on both fabric interconnects are directly connected to each other.

-        The L2 ports on both fabric interconnects are directly connected to each other

Configure Fabric Interconnect A

To configure Fabric Interconnect A, follow these steps:

1.    Connect to the console port on the first Cisco UCS 6332 Fabric Interconnect.

2.    Enter the password for the admin user.

3.    Enter the same password again to confirm the password for the admin user.

4.    Enter the cluster name for the system name.

5.    Enter the Mgmt0 IPv4 address.

6.    Enter the Mgmt0 IPv4 netmask.

7.    Enter the IPv4 address of the default gateway.

8.    Enter the cluster IPv4 address.

9.    Enter the DNS IPv4 address.

10.  Enter the default domain name.

11.  Wait for the login prompt to make sure the configuration has been saved.

Configure Fabric Interconnect B

To configure Fabric Interconnect B, follow these steps:

1.    Connect to the console port on the second Cisco UCS 6332 Fabric Interconnect.

2.    Enter the admin password that was configured for the first Fabric Interconnect.

3.    Enter the Mgmt0 IPv4 address.

4.    Answer yes to save the configuration.

5.    Wait for the login prompt to confirm that the configuration has been saved.

For more information about configuring Cisco UCS 6332 Series Fabric Interconnect, go to: https://www.cisco.com/c/en/us/td/docs/unified_computing/ucs/ucs-manager/GUI-User-Guides/Getting-Started/4-0/b_UCSM_Getting_Started_Guide_4_0.html

Log into Cisco UCS Manager

To log into Cisco UCS Manager, follow these steps:

1.    Open a Web browser and navigate to the Cisco UCS 6332 Fabric Interconnect cluster address.

2.    Click the Launch link to download the Cisco UCS Manager software.

3.    If prompted to accept security certificates, accept as necessary.

4.    When prompted, enter admin for the username and enter the administrative password.

5.    Click Login to log in to the Cisco UCS Manager.

Upgrade Cisco UCS Manager Software to Version 4.0(4c)

This document assumes you’re using Cisco UCS 4.0(4c). Refer to the Cisco UCS 4.0 Release  (upgrade Cisco UCS Manager software and Cisco UCS 6332 Fabric Interconnect software to version 4.0(2a) ). Also, make sure the Cisco UCS C-Series version 4.0(4c) software bundles are installed on the Fabric Interconnects.

Configure Cisco UCS Manager

The following are the high-level steps involved for a Cisco UCS Manager configuration:

1.    Configure Fabric Interconnects for a Cluster Setup.

2.    Set Fabric Interconnects to Fibre Channel End Host Mode.

3.    Synchronize Cisco UCS to NTP.

4.    Configure Fabric Interconnects for Rack or Chassis and Blade Server Discovery.

5.    Configure Global Policies.

6.    Configure Server Ports.

7.    Configure LAN on Cisco UCS Manager.

8.    Configure Ethernet LAN Uplink Ports.

9.    Set QoS system class and Jumbo Frames in both the Cisco Fabric Interconnect.

10.  Create Uplink Port Channels to Cisco Nexus Switches.

11.  Configure FC SAN Uplink Ports

12.  Configure VLAN

13.  Configure IP, UUID, Server, MAC Pool and policy:

a.     IP Pool Creation

b.     UUID Suffix Pool Creation

c.     Server Pool Creation

d.     Configure Server BIOS Policy.

e.     Create Adapter Policy.

f.      Configure Default Maintenance Policy.

g.     Configure vNIC Template

h.     Create Server Boot Policy

Details for each step are discussed in the following sections.

Synchronize Cisco UCSM to NTP

To synchronize the Cisco UCS environment to the NTP server, follow these steps:

1.    In Cisco UCS Manager, in the navigation pane, click the Admin tab.

2.    Select All > Time zone Management.

3.    In the Properties pane, select the appropriate time zone in the Time zone menu.

4.    Click Save Changes and then click OK.

5.    Click Add NTP Server.

6.    Enter the NTP server IP address and click OK.

7.    Click OK to finish.

8.    Click Save Changes.

Figure 10     Synchronize Cisco UCS Manager to NTP

Configure Global Policies

The rack server and chassis discovery policy determine how the system reacts when you add a new rack server or chassis. We recommend using the platform max value as shown. Using platform max helps ensure that Cisco UCS Manager uses the maximum number of IOM uplinks available.

To configure the global policies, follow this step:

1.    In Cisco UCS Manager; Go to Equipment > Policies (right pane) > Global Policies as shown in Figure 11.

Figure 11     Global Policies in UCSM

Configure Server Ports

Configure Server Ports to initiate Chassis and Blade discovery. To configure server ports, follow these steps:

1.    Go to Equipment > Fabric Interconnects > Fabric Interconnect A > Fixed Module > Ethernet Ports.

2.    Select the ports (for this solution ports are 1-28) which are connected to the Cisco UCS VIC 1387 on Cisco UCS C240 M5 rack server.

3.    Right-click and select Configure as Server Port.

Figure 12     Configure Server Port on Cisco UCS Manager Fabric Interconnect for Server/Chassis Discovery

Figure 13     Ports Status after the Server Discovery

Configure Uplink Ports

Configure Network Ports to connect to the datacenter network switch.

To configure Network ports, follow these steps:

1.    Go to Equipment > Fabric Interconnects > Fabric Interconnect A > Fixed Module > Ethernet Ports.

2.    Select the ports (for this solution ports are 29-32) which are connected to the Cisco Nexus 9000 series switch for northbound network connectivity.

3.    Right-click and select Configure as Network Port.

Figure 14     Configure Network Port on Cisco UCS Manager Fabric Interconnect

After the Server port and network port configuration on Cisco UCS FI 6332, Ports 1-30 are utilized for server management and data traffic and 31-32 will be a Network Port.

Create New Organization

To configure the necessary Organization for the Cisco UCS environment, follow these steps:

1.    In Cisco UCS Manager, click the Servers tab in the navigation pane.

2.    Select root > Sub-Organization.

3.    Right-click Sub-Organization.

4.    Enter the name of the Organization.

5.    Click OK.

Figure 15     Create New Organization

Configure IP, UUID, Server and MAC Pools

IP Pool Creation

An IP address pool on the out of band management network must be created to facilitate KVM access to each compute node in the Cisco UCS domain. To create a block of IP addresses for server KVM access in the Cisco UCS environment, follow these steps:

1.    In Cisco UCS Manager, in the navigation pane, click the LAN tab.

2.    Select Pools > root > Sub-Organizations > UCS-HDP > IP Pools > click Create IP Pool.

3.    Enter name for the IP Pool, select option Sequential to assign IP in sequential order then click Next.

4.    Click Add IPv4 Block.

5.    Enter the starting IP address of the block and the number of IP addresses required, and the subnet and gateway information as shown below.

UUID Suffix Pool Creation

To configure the necessary universally unique identifier (UUID) suffix pool for the Cisco UCS environment, follow these steps:

1.    In Cisco UCS Manager, click the Servers tab in the navigation pane.

2.    Select Pools > root > Sub-Organization > UCS-HDP.

3.    Right-click UUID Suffix Pools and then select Create UUID Suffix Pool.

4.    Enter the name of the UUID name.

5.    Optional: Enter a description for the UUID pool.

6.    Keep the prefix at the derived option and select Sequential in as Assignment Order then click Next.

Figure 16     UUID Suffix Pool Creation

Figure 17     Create a Block of UUID Suffixes

Server Pool Creation

To configure the necessary server pool for the Cisco UCS environment, follow these steps:

1.    In Cisco UCS Manager, click the Servers tab in the navigation pane.

2.    Select Pools > root > Sub-Organization > UCS-HDP> right-click Server Pools > Select Create Server Pool.

3.    Enter name of the server pool.

4.    Optional: Enter a description for the server pool then click Next.

Figure 18     Create Server Pool

5.    Select servers to be used for the deployment and click > to add them to the server pool. In our case we added thirty servers in this server pool.

6.    Click Finish and then click OK.

Figure 19     Add Server in the Server Pool

7.    Once the added Servers are in the Pooled servers, click Finish.

MAC Pool Creation

To configure the necessary MAC address pools for the Cisco UCS environment, follow these steps:

1.    In Cisco UCS Manager, click the LAN tab in the navigation pane.

2.    Select Pools > root > Sub-Organization > UCS-HDP > right-click MAC Pools under the root organization.

3.    Select Create MAC Pool to create the MAC address pool.

4.    Enter name for MAC pool. Select Assignment Order as “Sequential”.

5.    Enter the seed MAC address and provide the number of MAC addresses to be provisioned.

6.    Click OK and then click Finish.

7.    In the confirmation message, click OK.

Figure 20     Creating a Block of MAC Addresses

 

Configure VLAN

To configure the necessary virtual local area networks (VLANs) for the Cisco UCS environment, follow these steps:

1.    In Cisco UCS Manager, click the LAN tab in the navigation pane.

2.    Select LAN > LAN Cloud.

3.    Right-click VLANs.

4.    Select Create VLANs.

5.    Enter Public_Traffic as the name of the VLAN to be used for Public Network Traffic.

6.    Keep the Common/Global option selected for the scope of the VLAN.

7.    Enter <VLAN Number> as the ID of the VLAN ID.

8.    Keep the Sharing Type as None.

Figure 21     Create VLAN

The NIC will carry the data traffic from VLAN13. A single vNIC is used in this configuration and the Fabric Failover feature in Fabric Interconnects will take care of any physical port down issues. It will be a seamless transition from an application perspective.

Figure 22     Create VLANs

Set System Class QoS and Jumbo Frame in Both Cisco Fabric Interconnects

To configure jumbo frames and enable quality of service in the Cisco UCS fabric, follow these steps:

1.    In Cisco UCS Manager, click the LAN tab in the navigation pane.

2.    Select LAN > LAN Cloud > QoS System Class.

3.    In the right pane, click the General tab.

4.    On the Platinum row, enter 9216 in the box under the MTU column.

5.    Click Save Changes.

6.    Click OK.

Figure 23     Configure System Class QoS on Cisco UCS Fabric Interconnects

Create QoS Policies

To create the QoS policy to assign priority based on the class using the Cisco UCS Manager GUI, follow these steps:

1.    Select LAN tab in the left pane in the Cisco UCS Manager GUI.

2.    Select LAN > Policies > root > UCS-HDP > QoS Policies.

3.    Right-click QoS Policies.

4.    Select Create QoS Policy.

Figure 24     Create QoS Policy

Figure 25     Platinum QoS Policy

Create vNIC Templates

To create multiple virtual network interface card (vNIC) templates for the Cisco UCS environment, follow these steps:

1.    In Cisco UCS Manager, click the LAN tab in the navigation pane.

2.    Select Policies > root > Sub-Organization > UCS-HDP > vNIC Template.

3.    Right-click vNIC Templates.

4.    Select Create vNIC Template.

5.    Enter name for vNIC template.

6.    Keep Fabric A selected. Select the Enable Failover checkbox.

7.    Select Updating Template as the Template Type.

8.    Under VLANs, select the checkboxes for desired VLANs to add as part of the vNIC Template.

9.    Set Native-VLAN as the native VLAN.

10.  For MTU, enter 9000.

11.  In the MAC Pool list, select MAC Pool configured.

12.  Select Network Control Policy.

13.  Click OK to create the vNIC template.

Figure 26     Create the vNIC Template

Create Host Firmware Package

Firmware management policies allow the administrator to select the corresponding packages for a given server configuration. These policies often include packages for adapter, BIOS, board controller, FC adapters, host bus adapter (HBA) option ROM, and storage controller properties.

To create a firmware management policy for a given server configuration in the Cisco UCS environment, follow these steps:

1.    In Cisco UCS Manager, click the Servers tab in the navigation pane.

2.    Select root > Sub-Organization > UCS-HDP > Host Firmware Packages.

3.    Right-click Host Firmware Packages.

4.    Select Create Host Firmware Package.

5.    Enter name of the host firmware package.

6.    Leave Simple selected.

7.    Select the version.

8.    Click OK to create the host firmware package.

Figure 27     Host Firmware Package

Create Power Control Policy

To create a power control policy for the Cisco UCS environment, follow these steps:

1.    In Cisco UCS Manager, click the Servers tab in the navigation pane.

2.    Select Policies > root > Sub-Organization > UCS-HDP > Power Control Policies.

3.    Right-click Power Control Policies.

4.    Select Create Power Control Policy.

5.    Select Fan Speed Policy as “Max Power”.

6.    Enter NoPowerCap as the power control policy name.

7.    Change the power capping setting to No Cap.

8.    Click OK to create the power control policy.

Figure 28     Create Power Control Policy

Create Server BIOS Policy

To create a server BIOS policy for the Cisco UCS environment, follow these steps:

1.    In Cisco UCS Manager, click the Servers tab in the navigation pane.

2.    Select Policies > root > Sub-Organization > UCS-HDP > BIOS Policies.

3.    Right-click BIOS Policies.

4.    Select Create BIOS Policy.

5.    Enter C240M5-BIOS as the BIOS policy name.

Figure 29     BIOS Configuration

 

Configure Maintenance Policy

To update the default Maintenance Policy, follow these steps:

1.    In Cisco UCS Manager, click the Servers tab in the navigation pane.

2.    Select Policies > root > Sub-Organization > UCS-HDP > Maintenance Policies.

3.    Right-click Maintenance Policies to create a new policy.

4.    Enter name for Maintenance Policy

5.    Change the Reboot Policy to User Ack.

6.    Click Save Changes.

7.    Click OK to accept the change.

Figure 30     Create Server Maintenance Policy

Create the Local Disk Configuration Policy

To create local disk configuration in the Cisco UCS Manager GUI, follow these steps:

1.    Select the Servers tab on the left pane in the Cisco UCS Manager GUI.

2.    Select Policies > root > Sub-Organization > UCS-HDP > Local Disk Config Policies.

3.    Right-click Local Disk Config Policies and Select Create Local Disk Config Policies.

4.    Enter UCS-Boot as the local disk configuration policy name.

5.    Change the Mode to Any Configuration. Check the Protect Configuration box.

6.    Keep the FlexFlash State field as default (Disable).

7.    Keep the FlexFlash RAID Reporting State field as default (Disable).

8.    Click OK to complete the creation of the Local Disk Configuration Policy.

9.    Click OK.

Figure 31     Create the Local Disk Configuration Policy

Create Boot Policy

To create boot policies within the Cisco UCS Manager GUI, follow these steps:

1.    Select the Servers tab in the left pane in the Cisco UCS Manager GUI.

2.    Select Policies > root.

3.    Right-click the Boot Policies.

4.    Select Create Boot Policy.

5.    Enter ucs for the boot policy name.

6.    (Optional) enter a description for the boot policy.

7.    Keep the Reboot on Boot Order Change check box unchecked.

8.    Keep Enforce vNIC/vHBA/iSCSI Name check box checked.

9.    Keep Boot Mode Default (Legacy).

10.  Expand Local Devices > Add CD/DVD and select Add Local CD/DVD.

11.  Expand Local Devices and select Add Local Disk.

12.  Expand vNICs and select Add LAN Boot and enter eth0.

13.  Click OK to add the Boot Policy.

14.  Click OK.

Figure 32     Create Boot Policy for Cisco UCS Server(s)

Create Storage Profile for Individual RAID0

To create the storage profile for the individual RAIDP, follow these steps:

1.    On the UCSM navigation page on the left-hand side select Storage tab.

2.    From the Storage Profiles drop-down list, right-click and select Create Storage Profile.

Figure 33     Create Storage Profile

3.    Enter a name for the Storage Profile and click the LUN Set tab.

4.    Click Add.

5.    Select the properties for the LUN set:

a.     Enter a name for LUN set.

b.     Disk Slot Range – 1 – 24/26/56 (Depends on number of drives installed in a server).

c.     Enter Virtual Drive configuration:

                        i.   Strip Size(kb) – 1024KB

                       ii.   Access Policy – Read Write

                       iii.   Read Policy – Read Ahead

                      iv.   Write Cache Policy – Write Back Good Bbu

                       v.   IO Policy – Direct

                      vi.   Drive Cache – Disable

Figure 34     Set JBOD Disks to Unconfigured Good

Create Storage Policy and Storage Profile

To create a Storage Profile with multiple RAID LUNs, create Storage Policies and attach them to a Storage Profile.

To create a Storage Policy and attach them to a Storage Profile, follow these steps:

1.    Go to the Storage tab on the left side panel selection, select “Storage Policies”.

2.    From the Storage Policies drop-down list, select and right-click “Disk Group Policies”. Select “Create Disk Group Policy”.

3.    Enter name for Disk Group Policy, Select RAID level.

4.    Select “Disk Group Configuration” (Automatic/Manual).

5.    Disk Group Configuration.

6.    Virtual Drive Configuration.

7.    Select Storage Profiles, right-click and select Create Storage Profile.

8.    Enter a name for the Storage profile and click Add.

9.    Enter a Local LUN name and select Auto Deploy.

10.  Check the box for Expand to Available and from the drop-down list select the storage policy you want to attach with the Storage Profile. Click OK.

Figure 35     Storage Policy to Configure Boot LUN for S3260 Server Node(s)

Figure 36     Storage Profile to Configure Individual RAID 0 on Server Node 1: Disk Slot 1-28

Figure 37     Storage Profile to Configure Individual RAID 0 on Server Node 1: Disk Slot 29-56

Create Service Profile Template

To create a service profile template, follow these steps:

1.    In the Cisco UCS Manager, go to Servers > Service Profile Templates > root Sub Organization > FlashStack-CVD > and right-click “Create Service Profile Template” as shown below.

2.    Enter the Service Profile Template name, Updating Template as type of template and select the UUID Pool that was created earlier. Click Next.

3.    Select Local Disk Configuration Policy tab and select Local Storage policy from the drop-down list.

4.    On Storage Profile Policy; select Storage Profile to attach with the server.

5.    In the networking window, select Expert and click Add to create vNICs. Add one or more vNICs that the server should use to connect to the LAN.

6.    In the create vNIC menu as vNIC name.

7.    Select vNIC Template as vNIC0 and Adapter Policy as Linux.

8.    In the SAN Connectivity menu, select no vHBAs.

9.    Click Next on the Zoning tab.

10.  Select Let System Perform Placement for vNIC/vHBA Placement. Click Next.

11.  Click Next on the vMedia Policy tab.

12.  Select Boot Policy in the Server Boot Order tab.

13.  Select UserAck maintenance policy, which requires user acknowledgement prior rebooting server when making changes to policy or pool configuration tied to a service profile.

14.  Select the Server Pool policy to automatically assign a service profile to a server that meets the requirements for server qualification based on the pool configuration. Select Power state when the Service Profile is associated to server.

15.  On the same page you can configure “Host firmware Package Policy” which helps to keep the firmware in sync when associated to server.

16.  Click Finish to create the Service Profile template.

Create Service Profiles from Template

To create a Service Profile from a template, follow these steps:

1.    Right-click the Service Profile Template and select Create Service profile from Template.

Figure 38     Create Service Profile from Template

2.    Repeat the steps above to create service profile template(s) and service profile(s) for Cisco UCS S3260, Cisco C240 M5 according to different deployment scenario.

Install Red Hat Enterprise Linux 7.6

This section provides detailed procedures for installing Red Hat Enterprise Linux Server using Software RAID (OS based Mirroring) on Cisco UCS C240 M5 servers. There are multiple ways to install the RHEL operating system. The installation procedure described in this deployment guide uses KVM console and virtual media from Cisco UCS Manager.

To install the Red Hat Enterprise Linux 7.6 operating system, follow these steps:

1.    Log into the Cisco UCS Manager.

2.    Select the Equipment tab.

3.    In the navigation pane expand Rack-Mounts and then Servers.

4.    Right-click the server and select KVM console.

5.    In the right pane, click the KVM Console >>

6.    Click the link to launch the KVM console.

7.    Point the cursor over the top right corner and select the Virtual Media tab.

8.    Click the Activate Virtual Devices found in Virtual Media tab.

9.    Click the Virtual Media tab to select CD/DVD.

10.  Select Map Drive in the Virtual Disk Management windows.

11.  Browse to the Red Hat Enterprise Linux 7.6 installer ISO image file.

12.  Click Open to add the image to the list of virtual media.

13.  Select the Installation option from Red Hat Enterprise Linux 7.6.

14.  Select the language for the installation and click Continue.

15.  Select date and time, which pops up another window as shown below.

16.  Select the location on the map, set the time, and click Done.

17.  Click Installation Destination.

18.  This opens a new window with the boot disks. Make the selection and choose “I will configure partitioning”. Click Done. We selected two M.2 SATA SSDs.

19.  This opens a window to create the partitions. Click the + sign to add a new partition as shown below with a boot partition size 2048 MB.

20.  Click Add Mount Point to add the partition.

21.  Change the device type to RAID and make sure the RAID level is RAID1 (redundancy) and click Update Settings to save the changes.

22.  Click the + sign to create the swap partition of size 2048 MB. Click Add Mount Point.

23.  Change the Device type to RAID and RAID level to RAID1 (Redundancy) and click Update Settings.

24.  Click + to add the / partition. The size can be left empty so it will use the remaining capacity. Click Add Mountpoint.

25.  Change the Device type to RAID and RAID level to RAID1 (Redundancy). Click Update Settings.

26.  Click Done to go back to the main screen and continue the Installation.

27.  Click Software Selection.

28.  Select Infrastructure Server and select the Add-Ons as noted below then click Done:

a.     Network File System Client

b.     Performance Tools

c.     Compatibility Libraries

d.     Development Tools

e.     Security Tools

29.  Click Network and Hostname and configure Hostname and Networking for the Host.

30.  Type in the hostname as shown below.

31.  Click Configure to open the Network Connectivity window. Click IPv4 Settings.

32.  Change the Method to Manual and click Add to enter the IP Address, Netmask and Gateway details.

33.  Click Save, update the hostname, and turn Ethernet ON. Click Done to return to the main menu.

34.  Click Begin Installation in the main menu.

35.  Select Root Password in the User Settings.

36.  Enter the Root Password and click Done.

37.  Once the installation is complete reboot the system.

38.  Repeat steps 1 to 37 to install Red Hat Enterprise Linux 7.6 on Servers 2 through 30.

The hostnames and their corresponding IP addresses are shown in Table 5  .

Table 5    Hostname and IP address

Post OS Install Configuration

Choose one of the nodes of the cluster or a separate node as the Admin Node for management, such as CDH installation, Ansible, creating a local Red Hat repo, and others. In this document, we used rhel01 for this purpose.

Configure /etc/hosts

Setup /etc/hosts on the Admin node; this is a pre-configuration to setup DNS as shown in the next section.

Below are the sample A records for DNS configuration within Linux environment:

To create the host file on the admin node, follow these steps:

1.    Log into the Admin Node (rhel01).

#ssh 10.15.1.31

2.    Populate the host file with IP addresses and corresponding hostnames on the Admin node (rhel01) and other nodes as follows:

3.    On Admin Node (rhel01):

Set Up Passwordless Login

To manage all the nodes in a cluster from the admin node password-less login needs to be setup. It assists in automating common tasks with Ansible, and shell-scripts without having to use passwords.

To enable password-less login across all the nodes when Red Hat Linux is installed across all the nodes in the cluster, follow these steps:

1.    Log into the Admin Node (rhel01).

2.    Run the ssh-keygen command to create both public and private keys on the admin node.

Figure 39     ssh-keygen

3.    Run the following command from the admin node to copy the public key id_rsa.pub to all the nodes of the cluster.  ssh-copy-id appends the keys to the remote-hosts .ssh/authorized_keys.

4.    Enter yes for Are you sure you want to continue connecting (yes/no)?

5.    Enter the password of the remote host.

Create a Red Hat Enterprise Linux (RHEL) 7.6 Local Repository

To create a repository using RHEL DVD or ISO on the admin node (in this deployment rhel01 is used for this purpose), create a directory with all the required RPMs, run the “createrepo” command and then publish the resulting repository.

To create a RHEL 7.6 local repository, follow these steps:

1.    Log into rhel01. Create a directory that would contain the repository.

2.    Copy the contents of the Red Hat DVD to /var/www/html/rhelrepo

3.    Alternatively, if you have access to a Red Hat ISO Image, Copy the ISO file to rhel01.

4.    Log back into rhel01 and create the mount directory.

5.    Copy the contents of the ISO to the /var/www/html/rhelrepo directory.

6.    On rhel01 create a .repo file to enable the use of the yum command.

7.    Copy rheliso.repo file from /var/www/html/rhelrepo to /etc/yum.repos.d on rhel01.

8.    To make use of repository files on rhel01 without httpd, edit the baseurl of repo file /etc/yum.repos.d/rheliso.repo to point repository location in the file system.

Create the Red Hat Repository Database

To create the Red Hat repository database, follow these steps:

1.    Install the “createrepo” package on admin node (rhel01). Use it to regenerate the repository database(s) for the local copy of the RHEL DVD contents.

2.    Run “createrepo” on the RHEL repository to create the repo database on admin node.

Figure 40     createrepo

 

Set Up Ansible

To set up Ansible, follow these steps:

1.    Download Ansible rpm from the following link: https://releases.ansible.com/ansible/rpm/release/epel-7-x86_64/ansible-2.7.11-1.el7.ans.noarch.rpm

2.    Run the following command to install ansible:

3.    Verify Ansible installation by running the following commands:

4.    Prepare the host inventory file for Ansible as shown below. Various host groups have been created based on any specific installation requirements of certain hosts.

5.    Verify host group by running the following commands. Figure 41 shows the outcome of the ping command.

Figure 41     Ansible – Ping Hosts

Install httpd

Setting up the RHEL repository on the admin node requires httpd. To set up RHEL repository on the admin node, follow these steps:

1.    Install httpd on the admin node to host repositories:

2.    Add ServerName and make the necessary changes to the server configuration file:

3.    Start httpd:

Set Up All Nodes to use the RHEL Repository

To set up all noes to use the RHEL repository, follow these steps:

1.    Copy the rheliso.repo to all the nodes of the cluster:

2.    Copy the /etc/hosts file to all nodes:

3.    Purge the yum caches:

Upgrade the Cisco Network Driver for VIC1387

To upgrade the Cisco network driver for VIC1387, follow these steps:

The latest Cisco Network driver is required for performance and updates. The latest drivers can be downloaded from the link below:

https://software.cisco.com/download/home/283862063/type/283853158/release/4.0(4)

1.    Copy the rpm on all nodes of the cluster using the following Ansible commands. For this example, the rpm is assumed to be in present working directory of rhel01:

2.    Use the yum module to install the enic driver rpm file on all the nodes through Ansible:

3.    It is recommended to download the kmod-megaraid driver for higher performance. The RPM can be found in the same package at: \Storage\LSI\Cisco_Storage_12G_SAS_RAID_controller\RHEL\RHEL7.6\kmod-megaraid_sas-07.708.03.00_el7.6-2.x86_64.rpm

4.    Copy the rpm on all nodes of the cluster using the following Ansible commands. For this example, the rpm is assumed to be in present working directory of rhel01:

5.    Use the yum module to install the enic driver rpm file on all the nodes through Ansible:

Set Up JAVA

To setup JAVA, follow these steps:

1.    Download jdk-8u211-linux-x64.rpm and src the rpm to admin node (rhel01) from the link: https://www.oracle.com/technetwork/java/javase/downloads/jdk8-downloads-2133151.html.

2.    Copy JDK rpm to all nodes:

3.    Extract and Install JDK on all nodes:

4.    Create the following files java-set-alternatives.sh and java-home.sh on admin node (rhel01):

5.    Make the two java scripts created above executable:

6.    Copying java-set-alternatives.sh to all nodes.

7.    Setup Java Alternatives

8.    Make sure correct java is setup on all nodes (should point to newly installed java path).

9.    Setup JAVA_HOME on all nodes.

10.  Display JAVA_HOME on all nodes.

 

11.  Display current java –version.

 

Enable Syslog

Syslog must be enabled on each node to preserve logs regarding killed processes or failed jobs. Modern versions such as syslog-ng and rsyslog are possible, making it more difficult to be sure that a syslog daemon is present.

Use one of the following commands to confirm that the service is properly configured:

# ansible all -m command -a “rsyslogd –v”

# ansible all -m command -a “service rsyslog status”

Set the ulimit

On each node, ulimit -n specifies the number of inodes that can be opened simultaneously. With the default value of 1024, the system appears to be out of disk space and shows no inodes available. This value should be set to 64000 on every node.

Higher values are unlikely to result in an appreciable performance gain.

To set ulimit, follow these steps:

1.    For setting the ulimit on Red Hat, edit “/etc/security/limits.conf” on admin node rhel01 and add the following lines:

 

2.    Copy the /etc/security/limits.conf file from admin node (rhel01) to all the nodes using the following command:

3.    Make sure that the /etc/pam.d/su file contains the following settings:

Disable SELinux

SELinux can be disabled by editing /etc/selinux/config and changing the SELINUX line to SELINUX=disabled.

To disable SELinux, follow these steps:

1.    The following command will disable SELINUX on all nodes:

2.    Reboot the machine, if needed for SELinux to be disabled in case it does not take effect. It can be checked using the following command:

 

Set TCP Retries

Adjusting the tcp_retries parameter for the system network enables faster detection of failed nodes. Given the advanced networking features of UCS, this is a safe and recommended change (failures observed at the operating system layer are most likely serious rather than transitory).

To set TCP retries, follow these steps:

1.    Edit the file /etc/sysctl.conf and on admin node rhel01 and add the following lines:

2.    Load the settings from default sysctl file /etc/sysctl.conf by running the following command:

Disable the Linux Firewall

Disable IPv6 Defaults

To disable IPv6 defaults, follow these steps:

1.    Run the following command:

2.    Load the settings from default sysctl file /etc/sysctl.conf:

Disable Swapping

To disable swapping, follow these steps:

1.    Run the following on all nodes. Variable vm.swappiness defines how often swap should be used, 60 is default:

2.    Load the settings from default sysctl file /etc/sysctl.conf and verify the content of sysctl.conf:

Disable Memory Overcommit

To disable Memory Overcommit, follow these steps:

1.    Run the following on all nodes. Variable vm.overcommit_memory=0

2.    Load the settings from default sysctl file /etc/sysctl.conf and verify the content of sysctl.conf:

 

Disable Transparent Huge Pages

Disabling Transparent Huge Pages (THP) reduces elevated CPU usage caused by THP.

To disable Transparent Huge Pages, follow these steps:

1.    You must run the following commands for every reboot; copy this command to /etc/rc.local so they are executed automatically for every reboot:

2.    On the Admin node, run the following commands:

3.    Copy file to each node:

4.    Append the content of file thp_disable to /etc/rc.local:

NTP Configuration

The Network Time Protocol (NTP) is used to synchronize the time of all the nodes within the cluster. The Network Time Protocol daemon (ntpd) sets and maintains the system time of day in synchronism with the timeserver located in the admin node (rhel01). Configuring NTP is critical for any Hadoop Cluster. If server clocks in the cluster drift out of sync, serious problems will occur with HBase and other services.

To configure NTP, follow these steps:

1.    Configure /etc/ntp.conf on the admin node only with the following contents:

2.    Create /root/ntp.conf on the admin node and copy it to all nodes:

3.    Copy ntp.conf file from the admin node to /etc of all the nodes by executing the following commands in the admin node (rhel01):

4.    Run the following to syncronize the time and restart NTP daemon on all nodes:

5.    Make sure to restart of NTP daemon across reboots:

6.    Verify NTP is up and running in all nodes by running the following commands:

 

7.    Install the Chrony service:

8.    Activate the Chrony service at boot:

9.    Start the Chrony service:

Install Megaraid StorCLI

This section explains the steps needed to install StorCLI (Storage Command Line Tool) which is a command line interface designed to be easy to use, consistent, and script. For more details, go to: https://docs.broadcom.com/docs/12352476

To install StorCLI, follow these steps:

1.    Download StorCLI:  https://www.broadcom.com/support/download-search/?pg=&pf=&pn=&po=&pa=&dk=storcli.

2.    Extract the .zip file and copy storcli-1.23.02-1.noarch.rpm from the linux directory.

3.    Download StorCLI and its dependencies and transfer to Admin node:

4.    Copy storcli rpm to all the nodes using the following commands:

5.    Run this command to install storcli on all the nodes:

6.    Run this command to copy storcli64 to root directory:

7.    Run this command to check the state of the disks:

Configure the Filesystem for NameNodes and DataNodes

The following script formats and mounts the available volumes on each node whether it is NameNode or Data node. OS boot partition will be skipped. All drives are mounted based on their UUID as /data/disk1, /data/disk2, and so on.

To configure the filesystem for NameNodes and DataNodes, follow these steps:

1.    On the Admin node, create a file containing the following script:

#vi /root/driveconf.sh

2.    To create partition tables and file systems on the local disks supplied to each of the nodes, run the following script as the root user on each node:

3.    Run the following command to copy driveconf.sh to all the nodes:

4.    Run the following command from the admin node to run the script across all data nodes:

5.    Run the following from the admin node to list the partitions and mount points:

6.    On the admin create following scripts to configure NVMe disks partition and filesystem for DataNodes only.

Delete Partitions

To delete a partition, follow these steps:

1.    Run the mount command (‘mount’) to identify which drive is mounted to which device /dev/sd<?>

2.    umount the drive for which partition is to be deleted and run fdisk to delete as shown below.

Cluster Verification

This section explains the steps to create the script cluster_verification.sh that helps to verify the CPU, memory, NIC, and storage adapter settings across the cluster on all nodes. This script also checks additional prerequisites such as NTP status, SELinux status, ulimit settings, JAVA_HOME settings and JDK version, IP address and hostname resolution, Linux version and firewall settings.

To verify a cluster, follow these steps:

1.    Create the script cluster_verification.sh as shown, on the Admin node (rhel01).

2.    Change permissions to executable:

3.    Run the Cluster Verification tool from the admin node. This can be run before starting Hadoop to identify any discrepancies in Post OS Configuration between the servers or during troubleshooting of any cluster / Hadoop issues:

Install HDP 3.1.0

HDP is an enterprise grade, hardened Hadoop distribution. HDP combines Apache Hadoop and its related projects into a single tested and certified package. HPD 3.1.0 components are depicted in below. This section details how to install HDP 3.1.0 on the cluster.

Prerequisites for HDP Installation

This section details the prerequisites for the HDP installation, such as setting up HDP repositories.

Hortonworks Repository

To set up the Hortonworks repository, follow these steps:

1.    From a host connected to the Internet, create a Hortonworks folder and download the Hortonworks repositories as shown below, then transfer it to the admin node:

2.    Download Hortonworks HDP repo:

3.    Download Hortonworks HDP-Utils repo:

4.    Download HDP-GPL repo:

5.    Download the Ambari repo:

6.    Copy the repository directory to the admin node (rhel1):

7.    Extract the tar ball:

8.    Create HDP repo with the following contents:

9.    Create the Ambari repo:

Downgrade Snappy on All Nodes

Downgrade snappy on all data nodes by running this command from admin node:

HDP Installation

To install HDP, complete the following the steps:

Install and Setup Ambari Server on rhel1

1.    Run the following command in rhel1 to install ambary-server:

2.    Log into rhel2.

3.    Install Red Hat Package Manager (RPM) according to the requirements of your operating system:

4.    Install PostgreSQL version 9.5 or later:

5.    Initialize the database as shown in the below figure by running the following command:

6.    Start PostgreSQL:

7.    Open /var/lib/pgsql/9.6/data/postgresql.conf and update to the following:

8.    Update these files on rhel2 in the location chosen to install the databases for Hive, Oozie and Ambari, using the host ip addresses:

9.    Restart PostgreSQL:

10.  Run the following:

Create Database for Ambari

To create the database for Ambari, follow these steps:

1.    Run the following commands mentioned below in bold to create and prepare database for Ambari:

2.    Restart PostgreSQL:

3.    Verify the ambari user by logging into psql:

4.    Load the Ambari Server database schema:

5.    Find the Ambari-DDL-Postgres-CREATE.sql file in the /var/lib/ambari-server/resources/ directory of the Ambari Server host after you have installed Ambari Server.

6.    Run the following command to launch the Ambari-DDL-Postgres-CREATE.sql script:

7.    Check the table is created by running \dt command:

8.    Restart PostgreSQL:

Create Database for Hive

Run the following command as shown in bold to create and prepare database for Hive:

Create Database for Oozie

Run the following command to create and prepare database for Oozie:

Setup Ambari Server On Admin Node (Rhel1)

To setup the Ambari server, follow these steps:

1.    Install the PostgreSQL JDBC driver:

2.    Configure Ambari server to use the JDBC driver for connectivity to Ambari database in PostgreSQL:

3.    Setup Ambari Server by running the following command:

4.    Start the Ambari Server:

 

5.    To check status of Ambari Server, run the following command:

 

Launch the Ambari Server

When the Ambari service starts, access the Ambari Install Wizard through the browser. To launch the Ambari server, follow these steps:

1.    Point the browser to http://<ip address for rhel1>:8080 or http://rhel1.hdp3.cisco.com:8080

The Ambari Login screen opens.

2.    Log into the Ambari Server using the default username/password: admin/admin. This can be changed later.

3.    When logged in the “Welcome to Apache Ambari” window opens.

Create the Cluster

To create the cluster, follow these steps:

1.    To create a cluster, click “LAUNCH INSTALL WIZARD.”

2.    From the Get started page type “Cisco_HDP” for the name for the cluster.

3.    Click Next.

Select Version

To select the version, follow these steps:

1.    In the Select Version section, choose the HDP 3.1.0. version.

2.    Under Repositories, select “Use Local Repository.”

3.    Update the Red Hat 7 HDP-3.0 URL to http://rhel1.hdp3.cisco.com/Hortonworks/HDP/centos7/3.1.0.0-187/

4.    Update the Red Hat 7 HDP-3.0-GPL URL to http://rhel1.hdp3.cisco.com/Hortonworks/HDP-GPL/centos7/3.1.0.0-187/

5.    Update the Red Hat 7 HDP-UTILS-1.1.0.22 to http://rhel1.hdp3.cisco.com/Hortonworks/HDP-UTILS/centos7/1.1.0.22/

Select Hosts

To build up the cluster, you need to provide the general information about how you want to set up the cluster. This requires providing the Fully Qualified Domain Name (FQDN) of each of the hosts. You also need to provide access to the private key file that was created in Set Up Password-less SSH; this is used to locate all the hosts in the system and to access and interact with them securely.

1.    Use the Target Hosts text box to enter the list of host names, one per line. Ranges inside brackets can also be used to indicate larger sets of hosts.

2.    Select the option Provide your SSH Private Key in the Ambari cluster install wizard:

3.    Click the Register and Confirm to continue.

Figure 42     Install Options

Hostname Pattern Expressions

1.    Click OK on the Host Name Pattern Expressions popup.

Confirm Hosts

Confirm Hosts helps ensure that Ambari has located the correct hosts for the cluster and checks those hosts to make sure they have the correct directories, packages, and processes to continue the install.

To confirm host, follow these steps:

1.    If any host was selected in error, remove it by selecting the appropriate checkboxes and clicking the grey Remove Selected button.

2.    To remove a single host, click the small white Remove button in the Action column.

3.    When the list of hosts is confirmed, click Next.

Choose Services

HDP is made up of several components. Go to Hortonworks Understand the Basics for more information.

To choose services, follow these steps:

1.    Select all to preselect all items.

2.    When you have made your selections, click Next.

Assign Masters

The Ambari installation wizard attempts to assign the master nodes for various services that have been selected to appropriate hosts in the cluster, as listed in Table 6  . The right column shows the current service assignments by host, with the hostname and its number of CPU cores and amount of RAM indicated.

1.    Reconfigure the service assignments to match Table 6  shown below.

Table 6    Reconfigure the Service Assignments

2.    Click Next.

Assign Slaves and Clients

The Ambari install wizard attempts to assign the slave components (DataNodes, NFSGateway, NodeManager, RegionServers, Supervisor, and Client) to appropriate hosts in the cluster.

To assign slaves and clients, follow these steps:

1.    Reconfigure the service assignment to match the values shown in Table 7  .

2.    Assign DataNode, NodeManager, RegionServer, and Supervisor on nodes rhel3- rhel28.

3.    Assign Client to all nodes.

4.    Click Next.

Table 7    Services and Hostnames

Customize Services

This section shows the tabs that manage configuration settings for Hadoop components. The wizard attempts to set reasonable defaults for each of the options here, but this can be modified to meet specific requirements. The following sections provide configuration guidance that should be refined to meet specific use case requirements.

The following changes need to be made:

·         Memory and service level settings for each component and service level tuning.

·         Customize the log locations of all the components to make sure growing logs do not cause the SSDs to run out of space.

Credentials

Specify the credentials as per your organizational policies for services in CREDENTIALS tab as shown below:

Databases

In the DATABASES tab, to configure the database for DRUID, HIVE, OOZIE, and RANGER, follow these steps:

1.    Configure DRUID as shown below:

2.    Change the default log location by finding the Log Dir property and modifying the /var prefix to /data/disk1.

3.    Configure HIVE:

a.     Select Existing PostgreSQL database in the Hive Database drop-down list.

b.     Enter Database Name as hive

c.     Enter Database Username as hive

d.     Enter Database URL as jdbc:postgresql://rhel2.hdp3.cisco.com/hive

e.     Enter Database password (use the password created during hive database setup in earlier steps; for example, bigdata)

f.      Click TEST CONNECTION to verify the connectivity

g.     In Advanced tab, Change the default log location by filtering the Log Dir property and modifying the /var prefix to /data/disk1.

h.     Change the WebHCat log directory by filtering the Log Dir property and modifying the /var prefix to /data/disk1.

4.    Configure OOZIE:

a.     Select Existing PostgreSQL database in the Hive Database drop-down list.

b.     Enter Database Name as oozie.

c.     Enter Database Username as oozie.

d.     Enter Database URL as jdbc:postgresql://rhel2.hdp3.cisco.com/oozie.

e.     Enter Database password (use the password created during hive database setup in earlier steps; for example, bigdata).

f.      Click TEST CONNECTION to verify the connectivity.

g.     In Advanced tab, Change the default log location by filtering the Log Dir property and modifying the /var prefix to /data/disk1.

5.    Configure RANGER:

a.     Select POSTGRES from DB FLAVOR drop-down list.

b.     Provide a Ranger DB name. For example, ranger.

c.     Provide Ranger DB Username such as rangeradmin.

d.     Enter JDBC connect string for a Ranger Database as jdbc:postgresql//rhel2.dhp3.cisco.com:54323/ranger.

e.     Ranger DB Host as rhel2.hdp3.cisco.com.

f.      Enter Ranger DB password. (Ranger database is not previously created. provide password string that would be configured in the DB. For example, bigdata).

g.     Select “Yes” for Setup Database and Database User.

h.     Enter “postgres” in Database Administrator (DBA) username.

i.      Enter Database Administrator (DBA) password.

j.      Enter jdbc:postgresql://rhel2.hdp3.cisco.com:5432/postgres in JDBC connect string for root user.

k.     Update Ranger Admin Log Dir from /var to /data/disk1.

HDFS

1.    In Ambari, select the HDFS Service tab and use the “Search” box to filter for the properties mentioned in Table 8  and update their values.

2.    Update the following HDFS configurations in Ambari.

Table 8    HDFS Configurations in Ambari

3.    Change the default log location by filtering the Log Dir property and modifying the /var prefix to /data/disk1.

MapReduce2

1.    In Ambari, choose the MapReduce Service tab and update the values as shown below.

2.    Under the MapReduce2 tab, change the default log location by finding the Log Dir property and modifying the /var prefix to /data/disk1.

YARN

1.    In Ambari, select the YARN Service, and update the following as shown in Table 9  .

Table 9    YARN Configuration

2.    Under YARN tab, change the default log location by filtering the Log Dir property and modifying the /var prefix to /data/nvme01 and /data/nvme02.

The Ambari web UI provides a wizard-driven user experience that allows to configure high availability of the components in many Hortonworks Data Platform (HDP) stack services. The high availability of the components is achieved by setting up primary and secondary components. If the primary component fails or becomes unresponsive, services failover to secondary component. After configuring the high availability for a service, Ambari enables you to manage and disable (roll back) the high availability of components within that service.

Configure the HDFS NameNode High Availability

The HDFS NameNode high availability feature enables you to run redundant NameNodes in the same cluster in an Active/Passive configuration with a hot standby. This eliminates the NameNode as a potential single point of failure (SPOF) in an HDFS cluster. With the release of Hadoop 3.0, you can configure more than one backup NameNode.

Prior to the release of Hadoop 2.0, the NameNode represented a single point of failure (SPOF) in an HDFS cluster. Each cluster had a single NameNode, and if that machine or process became unavailable, the cluster would be unavailable until the NameNode was either restarted or brought up on a separate machine. This situation impacted the total availability of the HDFS cluster in two major ways:

·         In the case of an unplanned event such as a machine crash, the cluster would be unavailable until an operator restarted the NameNode.

·         Planned maintenance events such as software or hardware upgrades on the NameNode machine would result in periods of cluster downtime.

HDFS NameNode High Availability avoids this by facilitating either a fast failover to one or more backup NameNodes during machine crash, or a graceful administrator-initiated failover during planned maintenance.

HBase

Under the HBase tab, change the default log location by finding the Log Dir property and modifying the /var prefix to /data/disk1.

Zookeeper

Under the Zookeeper tab, change the default log location by filtering the Log Dir property and modifying the /var prefix to /data/disk1.

Storm

Under the Storm tab, change the default log location by finding the Log Dir property and modifying the /var prefix to /data/disk1.

Ambari Metrics

1.    Choose the Ambari Metrics Service and expand the General tab and make the changes shown below.

2.    Enter the Grafana Admin password as per organizational policy.

3.    Change the default log location for Metrics Collector, Metrics Monitor and Metrics Grafana by finding the Log Dir property and modifying the /var prefix to /data/disk1.

4.    Change the default data dir location for Metrics Grafana by finding the data Dir property and modifying the /var prefix to /data/disk1.

Accumulo

Select Accumulo Service and change the default log location by finding the Log Dir property and modifying the /var prefix to /data/disk1.

Atlas

Under the Atlas tab, change the default log location by finding the Log Dir property and modifying the /var prefix to /data/disk1.

Kafka

Under the Kafka tab, change the default log location by finding the Log Dir property and modifying the /var prefix to /data/disk1.

Knox

1.    Select the Knox Service tab and expand the Knox gateway tab and make the changes shown below.

2.    Enter the Knox Master Secret password as per organizational policy.

3.    For Knox, change the gateway port to 8444 to ensure no conflicts with local HTTP server.

SmartSense

The SmartSense account requires the Hortonworks support subscription. Subscribers can populate the properties as shown below:

Spark

Review

The assignments that have been made are displayed. Check to make sure all is correct before clicking the Deploy button. If any changes are necessary, use the left navigation bar to return to the appropriate screen.

Deploy

1.    When the review is complete, click DEPLOY.

2.    Follow the onscreen installation process. Watch for warnings and failures by clicking the link as shown below:

Summary of the Installation Process

1.    On the Summary page click “COMPLETE.”

High Availability for HDFS NameNode and YARN ResourceManager

High availability for NameNode and YARN Resource Manager can be configured using Ambari or also on non-Ambari clusters. This deployment guide covers the configuration of high availability using Ambari – Use the Ambari wizard interface to configure HA of the components.

The Ambari web UI provides a wizard-driven user experience that allows to configure high availability of the components in many Hortonworks Data Platform (HDP) stack services. The high availability of the components is achieved by setting up primary and secondary components. If the primary component fails or becomes unresponsive, services failover to secondary component. After configuring the high availability for a service, Ambari enables you to manage and disable (roll back) the high availability of components within that service.

Configure the HDFS NameNode High Availability

The HDFS NameNode high availability feature enables you to run redundant NameNodes in the same cluster in an Active/Passive configuration with a hot standby. This eliminates the NameNode as a potential single point of failure (SPOF) in an HDFS cluster. With the release of Hadoop 3.0, you can configure more than one backup NameNode.

Prior to the release of Hadoop 2.0, the NameNode represented a single point of failure (SPOF) in an HDFS cluster. Each cluster had a single NameNode, and if that machine or process became unavailable, the cluster would be unavailable until the NameNode was either restarted or brought up on a separate machine. This situation impacted the total availability of the HDFS cluster in two major ways:

·         In the case of an unplanned event such as a machine crash, the cluster would be unavailable until an operator restarted the NameNode.

·         Planned maintenance events such as software or hardware upgrades on the NameNode machine would result in periods of cluster downtime.

HDFS NameNode HA avoids this by facilitating either a fast failover to one or more backup NameNodes during machine crash, or a graceful administrator-initiated failover during planned maintenance.

Active NameNode honors all the client requests and the Standby NameNode acts as a backup. The Standby NameNode keeps its state synchronized with Active NameNode through a group of JournalNodes (JNs). When the Active node performs any namespace modification, the Active node durably logs a modification record to a majority of these JNs. The Standby node reads the edits from the JNs and continuously watches the JNs for changes to the edit log.

Prerequisites for NameNode High Availability

The following are the prerequisites for NameNode high availability:

·         NameNode Machine: Hardware for Active and Standby node should be identical.

·         JournalNodes Machine: JournalNode daemon is relatively lightweight, therefore it can be co-located on machines with other Hadoop daemons; it is typically located on the management nodes.

·         There MUST be at least three JournalNodes, because the edit log modifications must be written to a majority of JNs. This allows the system tolerate failure of a single machine. You may also run more than three JournalNodes, but in order to increase the number of failures that the system can tolerate, you must run an odd number of JNs (3, 5, 7, and so on).

·         ZooKeeper Machines: For automatic failover capability, an existing Zookeeper cluster must exist. The Zookeeper service can also co-exist with other Hadoop daemons.

·         In HA Cluster, the Standby NameNode also performs the checkpoints of the namespace state, therefore do not deploy a Secondary NameNode, CheckpointNode, or BackupNode in a high availability cluster.

Deploy the NameNode High Availability Cluster

To deploy the NameNode high availability on an Ambari managed cluster, follow these steps:

1.    Log into Ambari. Click HDFS > CONFIGS. Click the ACTIONS drop-down list and click Enable NameNode HA to launch the wizard.

Figure 43     Enable NameNode HA

2.    Step 1 launches the Enable NameNode HA wizard. On the Get Started page, specify the Nameservice ID as shown below. Click Next.

Figure 44     Enable NameNode HA Wizard – Get Started

3.    On the Select Hosts page, select the Additional NameNode and JournalNode. Click Next.

Figure 45     Enable NameNode HA Wizard – Select Hosts

4.    On the Review page, confirm the selection. To change any values, click Back, or to continue click Next.

Figure 46     Enable NameNode HA Wizard – Review

5.    Create a checkpoint on the NameNode on the linux server (rhel1.hdp3.cisco.com) as shown below:

Figure 47     Enable NameNode HA Wizard – Create Checkpoint

6.    SSH to current NameNode, rhel1.hdp3.cisco.com and run the following commands:

Figure 48     Current NameNode – Safe Mode and Create Checkpoint Command

7.    Return to the Ambari web UI, verify that the Checkpoint was created. Click Next.

8.    See the progress bar on the Configure Components page. When the configuration steps are completed, click Next.

Figure 49     Enable NameNode HA Wizard – Configure Components

9.    Initialize the JournalNodes as shown below:

Figure 50     Enable NameNode HA Wizard – Initialize JournalNodes

10.  SSH to the current NameNode, for example rhel1.hdp3.cisco.com.

11.  Run the following command:

Figure 51     Initialize JournalNodes

12.  Return to the Ambari UI, when Ambari detects success, click Next.

13.  On the Start Components page, when completed, click Next.

Figure 52     Enable NameNode HA Wizard – Start Components

14.  On the Initialize Metadata page, add the information as shown below:

Figure 53     Enable NameNode HA Wizard – Initialize Metadata

15.  SSH to rhel1.hdp3.cisco.com and run the following command:

Figure 54     Initialize the Metadata for NameNode

16.  SSH to an additional NameNode, for example, rhel3.hdp3.cisco.com and run the following command:

Figure 55     Initialize the Metadata for Additional NameNode

17.  Return to the Ambari web UI, click NEXT. Click OK on the confirmation pop-up window. Make sure the initialization of metadata was performed in NameNode and an additional NameNode as mentioned in step 15 and 16.

Figure 56     Enable NameNode HA Wizard – Confirmation for Initialize the Metadata

18.  On the Finalize HA Setup page, you can see the progress bar as the high availability completes.

Figure 57     Enable NameNode HA Wizard – Finalize High Availability Setup

19.  Click COMPLETE when done.

20.  Click HDFS > SUMMARY tab, verify the Active and Standby NameNode. The Quick Links pane also shows that rhel1.hdp3.cisco.com is running the Active NameNode and rhel3.hdp3.cisco.com is running in Standby NameNode.

Figure 58     Ambari – HDFS – Summary Information

Configure the YARN ResourceManger HA

This section provides instructions on setting up the ResourceManager (RM) HA feature in a HDFS cluster. The Active and Standby ResourceManagers embed the ZooKeeper based ActiveStandbyElector to determine which RM should be active.

Prerequisites for ResourceManager HA

The following are the prerequisites for ResourceManager HA:

·         The servers where Active and Standby RMs are run should have identical hardware.

·         For automated failover configurations, there must be an existing Zookeeper cluster available. The ZooKeeper service nodes can be co-located with the other Hadoop daemons.

Deploy the ResourceManager HA

ResourceManager HA can be configured manually or through Ambari. These instructions are based on configuring ResourceManager HA using Ambari.

To setup ResourceManager HA, follow these steps:

1.    From the Ambari web UI, click Services > YARN. Click the ACTIONS drop-down list and select Enable ResourceManager HA.

Figure 59     Services/YARN - Enable ResourceManger HA

2.    This launches the ResourceManager HA wizard as shown below. Click NEXT.

Figure 60     Enable ResourceManager HA Wizard – Get Started

3.    From the Select Host page, select the host for Additional Resource Manager. Click NEXT.

Figure 61     Enable ResourceManger HA Wizard – Select Host

4.    Proceed to the Review page.

Figure 62     Enable ResourceManager HA Wizard - Review

5.    The Configure Components page shows the progress bar as the Additional ResourceManager is being deployed.

Figure 63     Enable ResourceManager HA Wizard – Configure Components

6.    Click COMPLETE when done.

7.    Verify the ResourceManger HA setup by clicking Services > YARN > SUMMARY tab. The Quick Links pane identifies Active and Standby ResourceManager.

Figure 64     Services/YARN/Summary – Verify ResourceManger HA

Configure NVMe as YARN Local Directory

To configure YARN local directory on NVMe disks, follow these steps:

1.    Click on cluster > YARN > Configuration tab, filter properties for dirs.

2.    Modify disk labels specific to NVMe as per the format and partition performed earlier for following properties:

-        yarn.nodemanager.local-dir

-        yarn.nodemanager.log-dirs

When building an infrastructure to enable this modernized architecture which could scale to thousands of nodes, operational efficiency can’t be an afterthought.

To bring in seamless operation of the application at this scale, you need:

·         Infrastructure automation of Cisco UCS servers with service profiles and Cisco Data Center network automation with application profiles with Cisco ACI

·         Centralized Management and Deep telemetry and Simplified granular trouble-shooting capabilities and • Multi-tenancy allowing application workloads including containers, micro-services, with the right level of security and SLA for each workload.

·         Cisco UCS with Cisco Intersight and Cisco ACI can enable this cloud scale architecture deployed and managed with ease

For More Information

For additional information, see the following resources:

·         To find out more about Cisco UCS big data solutions, see http://www.cisco.com/go/bigdata.

·         To find out more about Cisco UCS big data validated designs, see http://www.cisco.com/go/bigdata_design

·         To find out more about Cisco UCS AI/ML solutions, see http://www.cisco.com/go/ai-compute

·         To find out more about Cisco ACI solutions, see http://www.cisco.com/go/aci

·         To find out more about Cisco validated solutions based on Software Defined Storage, see https://www.cisco.com/c/en/us/solutions/data-center-virtualization/software-defined-storage-solutions/index.html

This section provides the BOM for the 16 Nodes Hadoop Base Rack. See Table 10  for the BOM for the Hadoop Base rack, Table 11  for the BOM for Expansion Rack, Table 12  for software components. Table 13  lists Hortonworks SKUs available from Cisco.

Table 10      Bill of Materials for Cisco UCS C240M5SX Hadoop Nodes Base Rack

Table 11      Bill of Materials for Hadoop Nodes Expansion Rack

Table 12      Red Hat Enterprise Linux License

Table 13      Hortonworks SKU’s Available at Cisco

Configure Data Drives on Name Node and Data Nodes

This section describes the steps needed to configure non-OS disk drives as RAID1 using the StorCli command. All drives are part of a single RAID1 volume. This volume can be used for staging any client data to be loaded to HDFS. This volume will not be used for HDFS data.

To configure data drives on Name node and other nodes, if the drive state shows up as JBOD, creating RAID in the subsequent steps will fail with the error “The specified physical disk does not have the appropriate attributes to complete the requested command.”

To configure data drives on Name and Data nodes, follow these steps:

1.    If the drive state shows up as JBOD, it can be converted into Unconfigured Good using Cisco UCSM or storcli64 command. Following steps should be performed if the state is JBOD.

2.    Get the enclosure id as follows:

 

 

3.    Convert to unconfigured good:

4.     Verify status by running the following command:

5.    Run this script as root user on rhel01 to rhel3 to create the virtual drives for the management nodes:

6.    Run the following command to get enclosure id:

7.    Run MegaCli script:

8.    Run the following command. State should change to Online:

9.    State can also be verified in UCSM as show below in Equipment>Rack-Mounts>Servers>Server # under Inventory/Storage/Disk tab:

Configure Data Drives on Name Nodes

To configure non-OS disk drives as RAID 1 volume using storcli command, follow this step:

1.    Run the following script as root user on Rhel01 to Rhel03 (any NameNode) to create the virtual drive for the management node(s).

Configure Data Drives on Data Nodes

To configure non-OS disk drives as individual RAID0 volumes using storcli command, follow this step. These volumes will be used for HDFS Data.

1.    Issue the following command from the admin node to create the virtual drives with individual RAID 0 configurations on all the data nodes:

Yogesh Ramesh, Big Data Solutions Architect, Cisco Systems, Inc.

Yogesh Ramesh is a Big Data Solutions Architect at Computing Systems Product Group. He is part of the solution engineering team focusing on big data infrastructure, solutions, and performance.

Acknowledgements

For their support and contribution to the design, validation, and creation of this Cisco Validated Design, the author would like to thank:

·         Karthik Kulkarni, Architect, Computing Systems Product Group, Cisco Systems, Inc.

·         Muhammad Afzal, Architect, Computing Systems Product Group, Cisco Systems, Inc.

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