CDH 5.3 on VMware with Local Attached Storage

Executive Summary

This document covers the architecture for running Cloudera Enterprise on VMware vSphere®-based infrastructure with locally attached Storage.

NOTE: This is a work in progress and details will change as software versions and capabilities change.

Audience and Scope

This guide is for IT architects who are responsible for the design and deployment of virtualized infrastructure in the data center, as well as for Hadoop administrators and architects who will be data center architects or engineers and/or collaborate with specialists in that space.

This document describes Cloudera recommendations on the following topics:

  • Data network considerations
  • Virtualization hardware/platform considerations
  • Virtualization strategy for the Cloudera software stack

Glossary of Terms

Term Description
DataNode Worker nodes of the cluster to which the HDFS data is written.
DRS Distributed Resource Scheduler (this is the software that controls movement of VMs and storage on a vSphere cluster)
HBA Host bus adapter. An I/O controller that is used to interface a host with storage devices.
HDD Hard disk drive.
HDFS Hadoop Distributed File System.
High Availability

Configuration that addresses availability issues in a cluster. In a standard configuration, the NameNode is a single point of failure (SPOF). Each cluster has a single NameNode, and if that machine or process became unavailable, the cluster as a whole is unavailable until the NameNode is either restarted or brought up on a new host. The secondary NameNode does not provide failover capability.

High availability enables running two NameNodes in the same cluster: the active NameNode and the standby NameNode. The standby NameNode allows a fast failover to a new NameNode in case of machine crash or planned maintenance.

HVE Hadoop Virtualization Extensions - this is what enables proper placement of data blocks and scheduling of YARN jobs in a Virtualized Environment wherein, multiple copies of a single block of data or YARN jobs (don’t get placed/scheduled on VMs that reside on the same hypervisor host). The YARN component of HVE is still work in progress and won’t be supported in CDH 5.3 (YARN-18). The HDFS component is supported in CDH 5.3.
JBOD Just a Bunch of Disks (this is in contrast to Disks configured via software or hardware RAID with striping and redundancy mechanisms for data protection)
Job History Server Process that archives job metrics and metadata. One per cluster.
LBT Load-based teaming. - this is a teaming (LBT) policy that is traffic-load-aware and ensures physical NIC capacity in a NIC team is optimized.
LUN Logical unit number. Logical units allocated from a storage array to a host. This looks like a SCSI disk to the host, but it is only a logical volume on the storage array side.
NameNode The metadata master of HDFS essential for the integrity and proper functioning of the distributed filesystem.
NIC Network interface card.
NIOC Network I/O Control.
NodeManager The process that starts application processes and manages resources on the DataNodes.
NUMA Non-uniform memory access. Addresses memory access latency in multi-socket servers, where memory that is remote to a core (that is, local to another socket) needs to be accessed. This is typical of SMP (symmetric multiprocessing) systems, and there are several strategies to optimize applications and operating systems. vSphere can be optimized for NUMA. It can also present the NUMA architecture to the virtualized guest OS, which can then leverage it to optimize memory access. This is called vNUMA.
PDU Power distribution unit.

QJM

QJN

Quorum Journal Manager. Provides a fencing mechanism for high availability in a Hadoop cluster. This service is used to distribute HDFS edit logs to multiple hosts (at least three are required) from the active NameNode. The standby NameNode reads the edits from the JournalNodes and constantly applies them to its own namespace. In case of a failover, the standby NameNode applies all of the edits from the JournalNodes before promoting itself to the active state.

Quorum JournalNodes. Nodes on which the journal services are installed.

RDM Raw device mappings. Used to configure storage devices (usually logical unit numbers (LUNs)) directly to virtual machines running on vSphere.
RM ResourceManager. The resource management component of YARN. This initiates application startup and controls scheduling on the DataNodes of the cluster (one instance per cluster).
SAN Storage area network.
SIOC Storage I/O Control.
ToR Top of rack.
VM Virtual machine.
vMotion VMware term for live migration of virtual machines across physical hosts.
ZK Zookeeper. A centralized service for maintaining configuration information, naming, and providing distributed synchronization and group services.

Revision History

Version Author/Editor Comments
1 Dwai Lahiri First draft
     
     

VMware-Based Infrastructure with Direct Attached Storage for HDFS

The storage subsystem described in this section is completely local (direct attached storage) on each vSphere host, and the VMs access the disks by one-to-one mapping of physical disks to vSphere VMFS virtual disks, or by raw device mappings (RDMs).

Physical Cluster Topology



Physical Cluster Component List

Component Configuration Description Quantity
Physical servers Two-socket, 6-10 cores per socket > 2 GHz; minimally 256GB RAM. ESX/VMware hosts that house the various VMs/guests. TBD (based on cluster design)
NICs

Dual-port 10 Gbps Ethernet NICs.

The connector type depends on the network design; could be SFP+ or Twinax.

Provide the data network services for the VMware cluster. At least two per physical server.
Internal HDDs Standard OS sizes. The vSphere hypervisor requires little storage, so size is not important. These ensure continuity of service on server resets. The number of drives depends on the server form factor and internal HDD form factor. 12-24 per physical server (including OS disks).
Ethernet ToR/leaf switches Minimally 10 Gbps switches with sufficient port density to accommodate the VMware cluster. These require enough ports to create a realistic spine-leaf topology providing ISL bandwidth above a 1:4 oversubscription ratio (preferably 1:1). Although most enterprises have mature data network practices, consider building a dedicated data network for the Hadoop cluster. At least two per rack.
Ethernet spine switches Minimally 10 Gbps switches with sufficient port density to accommodate incoming ISL links and ensure required throughput over the spine (for inter-rack traffic). Same considerations as for ToR switches. Depends on the number of racks.

Logical Cluster Topology

Logical VM Diagram



Do not allow more than one replica of an HDFS block on any particular physical node. This is enabled with configuring the Hadoop Virtualization Extensions (HVE).

The minimum requirements to build out the cluster are:

  • 3x Master Nodes (VMs)
  • 5x DataNodes (VMs)

The DataNode count depends on the size of HDFS storage to deploy. For simplicity, ensure that DataNodes cohabitate with YARN NodeManager roles. The following table identifies service roles for different node types.

Care must be taken to ensure that CPU and Memory resources are not overcommitted while provisioning these node instances on the virtualized infrastructure.

Craft Distributed Resource Scheduler (DRS) rules so that there is strong negative affinity between the master node VMs. This ensure that no two master nodes are provisioned or migrated to the same physical vSphere host. Alternately, this can also be achieved fairly easily when provisioning through vSphere Big Data Extensions by specifying "instancePerHost=1" which asserts that any host server should have at most one instance of a MasterNode VM (reference the BDE CLI guide for more details)

Care should also be taken to ensure automated movement of VMs is disabled. There should be no DRS or vMotion of VMs allowed in this deployment model. This is critical as VMs are tied to physical disks and movement of VMs within the cluster will result in data loss.

  Master Node Master Node Master Node YARN NodeManager Nodes /HDFS Data Nodes 1..n
ZooKeeper ZooKeeper ZooKeeper ZooKeeper  
HDFS NameNode, Quorum Journal Node NameNode, Quorum JournalNode Quorum JournalNode DataNode
YARN Resource Manager Resource Manager History Server NodeManager
Hive     MetaStore, WebHCat, HiveServer2  
Management (misc.) Cloudera Agent Cloudera Agent Cloudera Agent, Oozie, Cloudera Manager, Management Services Cloudera Agent
Navigator     Navigator, Key Management Services  
HUE     HUE  
SOLR Out of scope Out of scope Out of scope Out of scope
HBASE HMaster HMaster HMaster RegionServer

The following table provides size recommendations for the VMs. This depends on the size of the physical hardware provisioned, as well as the amount of HDFS storage and the services running on the cluster.

Component Configuration Description Quantity

Master Nodes:

two-socket with 6-10 cores/socket > 2GHz; minimally 128 GB RAM; 4-6 disks

Can be VMs or bare metal. If VMs, do not house the active NameNode/RM node and the standby in the same chassis (if using blades) or in the same rack (if using rackmount servers). These nodes house the Cloudera Master services and serve as the gateway/edge device that connects the rest of the customer’s network to the Cloudera cluster. Three (for scaling up to 100 cluster nodes).

YARN NodeManagers/HDFS Data nodes:

two-socket with 6-10 cores/socket > 2GHz; minimally 128 GB RAM;

no. of disks = no. of cores/2

These are VMs that can be deployed as needed on the vSphere cluster, without over-subscription of either CPU or Memory resources. It is also recommended to not provision more VMs per physical node than the number of sockets (NUMA cells) in the node.

These nodes house the HDFS DataNode roles and YARN node managers, as well as additional required services.

Adjust memory sizes based on the number of services, or provision additional capacity to run additional services . Disks can be provisioned either as PRDM or with a 1:1:1 mapping of physical disk:VMFS datastore:VMDK virtual disk.

TBD (based on customer needs).

The following table provides recommendations for storage allocation.

Node/Role Disk Layout Description
Management/Master
  • 1 x 200 GB OS.
  • 4 x 1 TB drives (2+2 RAID 10) for database and HDFS metadata.
  • 2 x 100 GB drives as JBOD for ZK and QJN each.
  • Drive capacity depends on the size of the internal HDDs specified for the platform.
  • Cloudera recommends not fracturing the filesystem layout into multiple smaller filesystems. Instead, keep a separate “/” and “/var”.
HDFS DataNodes
  • 1 x 200 GB OS.
  • n x 2 TB HDDs.
  • The drive capacity depends on the size of the internal HDDs specified for the platform.
  • Avoid fracturing the filesystem layout into multiple smaller filesystems. Instead, keep a separate “/” and “/var”.
  • We recommend more spindles for higher throughput and IOPs.

VMware vSphere Design Considerations

Distributed Network Switch Configuration (VDS)

Use LBT (load-based teaming) and NIOC for QoS—the VMware recommended configuration option.

Various functional components in the vSphere cluster need guaranteed bandwidth and have different traffic patterns and loads.

This requires allocation of shares to various port groups based on role.

When using network-based storage (as with Isilon use cases), you must design appropriate port groups and configure QoS to ensure efficient service).

Disk Multipathing Configuration

Disk multipathing policy uses round robin (RR). The storage array vendor might have specific recommendations. Not every vendor supports RR, so use an appropriate DMP algorithm.

This has little impact on this deployment model.

Storage Group Configuration

Each provisioned disk is either -

  • mapped to one vSphere datastore (which in turn contains one VMDK or virtual disk) or
  • mapped to one raw device mapping (RDM)

Storage Configuration

Set up virtual disks in “independent persistent” mode for optimal performance. Eager Zeroed Thick virtual disks provide the best performance.

Partition alignment at the VMFS layer depends on the storage vendor. Misaligned storage impacts performance.

Disable SIOC, and disable storage DRS.

vSphere Tuning Best Practices

Power Policy is an ESXi parameter. The balanced mode may be the best option. Evaluate your environment and choose accordingly. In some cases, performance might be more important than power optimization.

Avoid memory and CPU over-commitment, and use large pages for Hypervisor.

For network tuning, enable advanced features such as TSO, LRO, scatter gather, and so on

interrupt coalescing.

Guest OS Considerations

Assume that the guest OS is a flavor of Linux. This document focuses on Redhat-flavored OS distributions such as RHEL and CentOS. Other OS-specific configurations and their implementation are subject to your own research.

Special tuning parameters may be needed to optimize performance of the guest OS in a virtualized environment. In general, normal tuning guidelines apply, but specific tuning might be needed depending on the virtualization driver used.

Generic Best Practices

Minimize unnecessary virtual hardware devices. Choose the appropriate virtual hardware version; check the latest version and understand its capabilities.

NIC Driver Type

VMXNET3 is supported in RHEL 6.x and CentOS 6.x with the installation of VMware tools.

  • Tune the MTU size for jumbo frames at the guest level as well as ESXi and switch level.
  • Enable TCP segmentation offload (TSO) at the ESXi level (should be enabled by default). This can be leveraged only by VMXNET3 drivers at the Guest layer.
  • Similarly, other offload features can be leveraged only when using the VMXNET3 driver.
  • Use regular platform tuning parameters, such as ring buffer size. However, RSS and RPS tuning must be specific to the VMXNET3 driver.

HBA Driver Type

Use the PVSCSI storage adapter. This provides the best performance characteristics (reduced CPU utilization and increased throughput), and is optimal for I/O-intensive guests (as with Hadoop).

  • Tune queue depth in the guest OS SCSI driver.
  • Disk partition alignment -- Typically if VMFS is already aligned, this is not necessary (TBD for Linux).

I/O Scheduler

The I/O scheduler used for the OS disks might need to be different if using VMDKS. Instead of using CFQ, use deadline or noop elevators. This varies and must be tested. Any performance gains must be quantified appropriately; for example, 1-2% improvement vs. 10-20% improvement).

Memory Tuning

Disable or minimize anonymous paging by setting vm.swappiness=0 or 1.

Consider using Virtual NUMA (vNUMA). This exposes the NUMA architecture to the guest OS so that the guest OS can be tuned to leverage NUMA during scheduling. Virtual Hardware version 8 or later is required to leverage vNUMA.

Cloudera Software stack

Guidelines for installing the Cloudera stack on this platform are nearly identical to those for bare-metal. This is addressed in various documents on Cloudera’s website.

Enabling Hadoop Virtualization Extensions (HVE)

Referring to the HDFS-side HVE JIRA (https://issues.apache.org/jira/browse/HADOOP-8468), following are considerations for HVE:

Virtualized platform has following genes that shouldn't been ignored by hadoop topology in scheduling tasks, placing replica, do balancing or fetching block for reading:

1. VMs on the same physical host are affected by the same hardware failure. In order to match the reliability of a physical deployment, replication of data across two virtual machines on the same host should be avoided.

2. The network between VMs on the same physical host has higher throughput and lower latency and does not consume any physical switch bandwidth.

Thus, we propose to make hadoop network topology extendable and introduce a new level in the hierarchical topology, a node group level, which maps well onto an infrastructure that is based on a virtualized environment.

The following diagram illustrates the addition of a new layer of abstraction (in red) called NodeGroups. The NodeGroups represent the physical hypervisor on which the nodes (VMs) reside.



All VMs under the same node group run on the same physical host. With awareness of the node group layer, HVE refines the following policies for Hadoop on virtualization:

Replica Placement Policy

  • No duplicated replicas are on the same node or nodes under the same node group.
  • First replica is on the local node or local node group of the writer.
  • Second replica is on a remote rack of the first replica.
  • Third replica is on the same rack as the second replica.
  • The remaining replicas are located randomly across rack and node group for minimum restriction.

Replica Choosing Policy

The HDFS client obtains a list of replicas for a specific block sorted by distance, from nearest to farthest: local node, local node group, local rack, off rack.

Balancer Policy

  • At the node level, the target and source for balancing follows this sequence: local node group, local rack, off rack.
  • At the block level, a replica block is not a good candidate for balancing between source and target node if another replica is on the target node or on the same node group of the target node.

HVE typically supports failure and locality topologies defined from the perspective of virtualization. However, you can use the new extensions to support other failure and locality changes, such as those relating to power supplies, arbitrary sets of physical servers, or collections of servers from the same hardware purchase cycle.

Using Cloudera Manager, configure the following in safety valves:

  • HDFS
    • hdfs core-site.xml:
      <property>
          <name>net.topology.impl</name>
      <value>org.apache.hadoop.net.NetworkTopologyWithNodeGroup</value>
      </property>
      <property>
          <name>net.topology.nodegroup.aware</name>
          <value>true</value>
      </property>
      <property>
          <name>dfs.block.replicator.classname</name>
      <value>org.apache.hadoop.hdfs.server.blockmanagement.BlockPlacementPolicyWithNodeGroup</value>
      </property>
  • In mapred-site.xml, add the following properties and values:
    <property>
        <name>mapred.jobtracker.nodegroup.aware</name>
        <value>true</value>
    </property>
    <property>
        <name>mapred.task.cache.levels </name>
            <value>3</value>
    </property>

Establish the Topology:

  • Create a topology data file; for example:
    192.168.x.1 /rack1/nodegroup1
    192.168.x.2 /rack1/nodegroup1
    192.168.x.3 /rack2/nodegroup1
    192.168.x.4 /rack2/nodegroup2
    192.168.x.5 /rack3/nodegroup1
    192.168.x.6 /rack3/nodegroup2
  • Create a topology script; the following is from http://ofirm.wordpress.com/2014/01/09/exploring-the-hadoop-network-topology/)
    #!/bin/bash
    HADOOP_CONF=/usr/local/hadoop-1.2.1/conf
    echo `date` input: $@ >> $HADOOP_CONF/topology.log
    while [ $# -gt 0 ] ; do
      nodeArg=$1
      exec< ${HADOOP_CONF}/topology.data
      result=""
      while read line ; do
        ar=( $line )
        if [ "${ar[0]}" = "$nodeArg" ] ; then
          result="${ar[1]}"
        fi
      done
      shift
      if [ -z "$result" ] ; then
    #        echo -n "/default/rack "
         echo -n "/rack01"
      else
        echo -n "$result "
      fi
    done
  • Place the topology data file and script in the same location on each node in your cluster. Specify the location using set topology.script.file.name (in conf/hadoop-site.xml).

References