VMware

VMware vSphere Big Data Extensions 2.2 Release Notes

vSphere Big Data Extensions 2.2 | 4 June 2015 | Build 2774463

Last Updated on 13 March 2016.

Check these release notes for additions and updates.

What's in the Release Notes

These release notes apply to vSphere Big Data Extensions 2.2 and cover the following topics:

What's New in vSphere Big Data Extensions 2.2

Big Data Extensions enables the rapid deployment of Hadoop clusters on a VMware vSphere virtual platform. This release provides the following new features and enhancements.

  • Fully Qualified Domain Name Management. Dynamic DNS (DDNS or DynDNS) is a method of automatically updating a name server in the Domain Name System (DNS) with the active DNS configuration of its configured hostnames, addresses or other information. Big Data Extensions integrates with a Dynamic DNS server in its network through which it provides meaningful host names to the nodes in a Hadoop cluster. The cluster then automatically registers these host names with the DNS server, providing a properly configured DNS for your Big Data Extensions environment.

    Host names provide easier visual identification, as well as allowing you to use services such as Single Sign-On, which requires the use of a properly configured DNS.

  • Centralized User Management.You can specify an Active Directory or LDAP server for user authentication, letting you mange users from a central point. You can use your Active Directory or LDAP server to manage accounts in the Serengeti Management Server and Hadoop nodes generated by Big Data Extensions, as well as specify accounts in your Active Directory or LDAP server to be Hadoop user accounts and/or service accounts.

  • Resize Hadoop Clusters on Demand. You can reduce the number of virtual machines in a running Hadoop cluster, allowing you to manage resources in your VMware ESXi and vCenter Server environments. The virtual machines are deleted, releasing all resources such as memory, CPU, and I/O.

  • Increase Cloning Performance and Resource Usage of Virtual Machines. You can rapidly clone and deploy virtual machines using Instant Clone, a feature of vSphere 6.0. Using Instant Clone, a parent virtual machine is forked, and then a child virtual machine (or instant clone) is created. The child virtual machine leverages the storage and memory of the parent, reducing resource usage.

  • Centralized Logging with vRealize Log Insight or other External Syslog Servers. You can configure Big Data Extensions to use an external (or remote) syslog server, such as VMware vRealize Log Insight. External syslog servers allow you to more easily manage log files, and can be an essential capability if you need to view log files on a compromised machine.

  • Quiesce Big Data Extensions for Backup and Maintenance Procedures. You can quiesce Big Data Extensions so you can safely back-up and restore your environment, or perform other maintenance tasks.

  • Support for the Latest Hadoop Distributions. Big Data Extensions supports Bigtop 0.8, Cloudera CDH 5.3, Hortonworks HDP 2.2, MapR 4.1, and Pivotal PHD 3.0.

  • Support for the Latest Partner Hadoop Management Tools. Big Data Extensions supports Cloudera Manager 5.3 and Ambari 1.7.

  • Ability to Deploy Different Types of Hadoop Clusters. In addition to basic Hadoop clusters, with Big Data Extensions you can deploy HBase clusters, MapReduce clusters, compute-only clusters, data-compute separated clusters, and several variations of customized cluster types to meet your requirements.

  • Support for EMC Isilon OneFS 7.2. Big Data Extensions provides an automated process to deploy and manage compute-only clusters on EMC Isilon OneFS 7.2.

  • International Language Support. Big Data Extensions is localized into six additional languages: Chinese (Simplified), Chinese (Traditional), French, German, Korean, and Japanese. This additional language support provides easy access to a Web interface and documentation that is fully translated.

  • Big Data Extensions Upgrade. You can upgrade Big Data Extensions 2.1 to the current version, Big Data Extensions 2.2, and preserve all the data from the clusters created in Big Data Extensions. All of your existing clusters can be managed by Big Data Extensions 2.2 once the upgrade completes.


Installation Notes for This Release

Read the vSphere Big Data Extensions documentation for step-by-step instructions on installing and configuring Big Data Extensions.

  • If you currently have Big Data Extensions 1.x or 2.0 and want to upgrade to version 2.2, you must first upgrade to version 2.1 and then upgrade to version 2.2. You cannot upgrade directly from Big Data Extensions 1.x or 2.0 to version 2.2.

  • Virtual Update Manager (VUM) is unable to successfully upgrade the Big Data Extensions 2.1 vApp to Big Data Extensions 2.2 vApp. For more information on this issue, see KB #2118702.


  • Resolved Issues

    The following issues have been resolved for Big Data Extensions 2.2.

    • Updated A critical security vulnerability exploiting the stack buffer overflow in the glibc library was disclosed.

      If you are running Big Data Extensions 2.2, your environment is vulnerable to Common Vulnerability and Exposure (CVE) security issue CVE-2015-7547. You should install and apply the patch to address this issue.

      For more information on the patch, and instructions on how to download and install it, see KB #2144423.

    • Big Data Extensions does not support registering more than one vCenter Server.
    • The use of multiple vCenter Servers no longer causes issues with your deployment.

    • Big Data Extensions fails to format VMDKs greater than 2TBs in size.
    • Previously, when deploying a VMDK that is greater than 2TB in size it would only format to 2TB. You can now format VMDKs greater the 2TBs in size and utilize the increased storage capacity.

    • Unable to configure Hadoop Topology when using Ambari.

      Previously, when using the Ambari application manager, you could not configure your Hadoop cluster's topology. You can now improve workload balance across your cluster nodes, improving performance and throughput, using Ambari to specify how Hadoop virtual machines are placed using topology awareness.

    • Big Data Extensions fails to deploy clusters when vCenter Server contains empty datacenters.

      You can now create a Big Data cluster on a vCenter Server datacenter with empty hosts.

    • The configuration values displayed in the Big Data Extensions graphical user interface may not update to the values specified in the cluster specification file.

      This occurs when you create a custom cluster specification file using the Serengeti CLI, and switch to the Big Data Extensions graphical user interface to deploy the cluster. The configuration values displayed in the graphical user interface may not match those in the cluster specification file.

    • When restarting Big Data Extensions, data disks fail to mount using the Linux noatime mount option.

      A standard Linux mount option, noatime, is specified when the file system is mounted, disabling atime updates on the specified file system. Using noatime to mount data disks in Big Data Extensions provides significant performance improvements.


    Known Issues

    Big Data Extensions 2.2 has the following known issues. If you encounter an issue that is not in this known issues list, search the VMware Knowledge Base, or let us know by contacting VMware Technical Support.

    • Running Apache Pig Jobs Fail When Using Cloudera CDH 5.2.

      When running an Apache Pig job in a cluster created with the Cloudera CDH 5.2 distribution, Pig fails to run with the error message: Please initialize HCAT_HOME.

      Workaround: Replace the Pig RPM in your local CDH 5.2.0 Yum repository with the Pig RPM in the official CDH 5.3.0 Yum repository, and run the createrepo command to re-index the Yum repository.

    • Installation of Big Data Extensions fails if the user name of the logged-in user contains non-ASCII characters
      If the user name of the user who is currently logged in to Big Data Extensions contains non-ASCII characters, the installation of Big Data Extensions fails with the error message: An internal error has occurred - Error #1009.

      Workaround: Log in with a user name that does not contain non-ASCII characters and retry the installation.

    • The German and French versions of the Serengeti Command-Line Client display non-ASCII characters as questions marks.

      When running the German and French versions of the Serengeti CLI from a Windows command console, non-ASCII characters display as questions marks.

      Workaround: Use only ASCII characters for user names, object labels, and configuration values within your Big Data Extensions Environment when using the German and French versions of the Serengeti CLI.