The Apache Hadoop community provides pre-built Hadoop releases for AArch64. They can be downloaded directly from the repository.

Hadoop 3.3.0 is the first release to support ARM architectures. And from version 3.3.1, Hadoop moves to use lz4-java and snappy-java instead of requiring the native libraries of these to be installed on the systems running Hadoop. However, compared to the snappy native library, the snappy java implementation brings about 5% performance penalty.

Hadoop 3.3.0 is selected for this setup.

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wget https://archive.apache.org/dist/hadoop/common/hadoop-3.3.0/hadoop-3.3.0-aarch64.tar.gz
tar zxvf hadoop-3.3.0-aarch64.tar.gz

On Centos, openjdk v1.8, can be installed using yum repository.

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yum install java-1.8.0-openjdk
export JAVA_HOME=$(readlink -f /usr/bin/java | sed "s:bin/java::")

SSH setup is required to do different operations on slave such as starting, stopping distributed daemons.

If you cannot ssh to localhost without a passphrase, execute the following commands to set up a passphrashless ssh access.

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ssh-keygen -t rsa -P '' -f ~/.ssh/id_rsa
cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keys
chmod 0600 ~/.ssh/authorized_keys

The Hadoop configurations are mainly in these config files: hadoop-env.sh, core-site.xml, hdfs-site.xml and mapreduce-site.xml.

Setup environment of Java, Hadoop home directory and users in Hadoop-env.sh. Hadoop home directory is the one uncompressed in step one. Here, simply use a root account. In product setup accounts need to follow a security policy. Contents of Hadoop-env.sh:

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export JAVA_HOME=$(readlink -f /usr/bin/java | sed "s:bin/java::")
export HADOOP_HOME=<path of Hadoop package uncompressed>
export HDFS_NAMENODE_USER=root
export HDFS_DATANODE_USER=root
export HDFS_SECONDARYNAMENODE_USER=root
export YARN_RESOURCEMANAGER_USER=root
export YARN_NODEMANAGER_USER=root

In core-site.xml, the name of the default file system and base for other temporary directories need to be configured. The HDFS daemons will use this property to determine the host and port for the HDFS namenode. Temporary directory should not be missed after node reboot. Contents of the core-site.xml:

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<configuration>
        <property>
                <name>fs.defaultFS</name>
                <value>hdfs://localhost:9000</value>
        </property>
        <property>
                <name>hadoop.tmp.dir</name>
                <value>/data2/tmp </value>
        </property>
</configuration>

In hdfs-site.xml, replication number, default block size for new files, path of name table(fsimage) and data need be configured. The block size can impact split file size and how many map tasks are required. If the path of name table is a comma-delimited list of directories, then the name table is replicated in all directories for redundancy. To reduce IO, there just needs to be one different disk. If path of data block is a comma-delimited list of directories, then data will be stored in all named directories, typically on different devices. Contents of hdfs-site.xml:

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<configuration>
        <property>
                <name>dfs.replication</name>
                <value>1</value>
        </property>
        <property>
                <name>dfs.blocksize</name>
                <value>1g </value>
        </property>
        <property>
                <name>dfs.namenode.name.dir</name>
                <value>file:/data3/name</value>
        </property>
        <property>
                <name>dfs.datanode.data.dir</name>
                <!-- one disk one dir -->
                <value>file:/data1/data,file:/data2/data,…file:/data12/data</value>
        </property>
</configuration>

In mapreduce-site.xml, the runtime framework for executing MapReduce jobs, environment of map and reduce, history server can be configured. Here yarn is used to schedule jobs. The history server can be accessed via port 19888. Below are the contents of mapreduce-site.xml:

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<configuration>
        <property>
                <name>mapreduce.framework.name</name>
                <value>yarn</value>
        </property>
        <property>
                <name>yarn.app.mapreduce.am.env</name>
                <value>HADOOP_MAPRED_HOME=$HADOOP_HOME</value>
        </property>
        <property>
                <name>mapreduce.map.env</name>
                <value>HADOOP_MAPRED_HOME=$HADOOP_HOME</value>
        </property>
        <property>
                <name>mapreduce.reduce.env</name>
                <value>HADOOP_MAPRED_HOME=$HADOOP_HOME</value>
        </property>
        <property>
                <name>mapreduce.jobhistory.address</name>
                <value>localhost:10020</value>
        </property>
        <property>
                <name>mapreduce.jobhistory.webapp.address</name>
                <value>localhost:19888</value>
        </property>
</configuration>

In yarn-site.xml, pseudo-distributed mode also needs configure yarn.nodemanager.aux-services property to mapreduce_shuffle and local dirs. Auxiliary service response to application start and stop events. Local dirs is a list of directories to store localized files, typically on different devices. Contents of yarn-site.xml:

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<configuration>
        <property>
                <name>yarn.nodemanager.aux-services</name>
                <value>mapreduce_shuffle</value>
        </property>
        <property>
                <name>yarn.resourcemanager.hostname</name>
                <value>localhost</value>
        </property>
        <property>
                <name>yarn.nodemanager.local-dirs</name>
                <!-- one disk one dir -->
                <value>file:/data1/yarn/local,…file:/data12/yarn/local</value>
        </property>
</configuration>

The following instructions are to start Hadoop daemons. Namenode is the node in the Hadoop Distributed File System which keeps track of all the data stored in the Datanode. Namenode has metadata related to the data stored on the Datanodes and has information related to the location of the data stored. So, when you run the hadoop namenode -format command, all the information is deleted from the namenode, which means that the system does not know where the data is stored, hence losing all data stored in the Hadoop File System.

• Format the filesystem:

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# bin/hdfs namenode -format

• Start NameNode daemon and DataNode daemon:

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# sbin/start-dfs.sh

• Start ResourceManager daemon and NodeManager daemon:

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# sbin/start-yarn.sh

• Browse the web interface for the NameNode; by default, it is available at:

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http://localhost:9870/

• Or access CLI interface at:

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# bin/hdfs dfs -ls /
Found 1 items
drwx------   - root supergroup          0 2022-09-13 21:28 /tmp
# jps
77771 NameNode
78091 DataNode
82939 Jps
78636 SecondaryNameNode
80300 ResourceManager
80622 NodeManager

• When you’re done, stop the daemons

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# sbin/stop-dfs.ls
# sbin/stop-yarn.ls

Check and adapt the BIOS setting of the Altra/Altra Max System:

• Advanced->ACPI Settings->Enable Max Performance
• Chipset->CPU Configuration->ANC mode->Monolithic(default)
• Chipset->Memory Configuration->Memory Speed 3200MT/s
• SLC policy->Chipset->CPU Configuration->SLC Replacement Policy->Enhanced Least Recently Used

Or reset the BIOS configuration to restore default setting.

Before deploying and running Hadoop, make sure all the systems in the cluster are at good health status. Using tools like stress-ng, multiload, stream, fio to check CPU/Memory/Disk or asking Ampere support for Ampere Health Advisor (AHA) for platform health checking.

Set the frequency governor to performance mode:

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# cpupower frequency-set --governor performance

On Centos/RHEL, use tuned-adm instead of cpupower command:

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# tuned-adm profile throughput-performance

# tuned-adm active
Current active profile: throughput-performance

# cpupower frequency-info
analyzing CPU 0:
 driver: cppc_cpufreq
 CPUs which run at the same hardware frequency: 0
 CPUs which need to have their frequency coordinated by software: 0
 maximum transition latency: cannot determine or is not supported.
 hardware limits: 1000 MHz - 2.80 GHz
 avaialable cpufreq governors: conservative ondemand userspace powersave performance
 current policy: frequency should be within 2.00 GHz and 2.80 GHz.
                         The governor "performance" may decide which speed to use within this range.
 current CPU frequency 2.80 GHz (asserted by call to hardware)

Ext4 or XFS can be used as the filesystem. The noatime and nodiratime option can be added for mount options. And it is recommended not to use LVM and RAID on DataNode.

For SATA disk, the better scheduler option is deadline; For NVME disk, noop is better:

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# cat /sys/block/sdX/queue/scheduler
[mq-deadline] kyber bfq none

Queue depth and number of requests allowed can be adjusted as need to optimize disk IO performance:

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# cat /sys/block/sdb/queue/nr_requests 
256
# cat /sys/block/sdb/device/queue_depth
32

Increase system limits using ulimit command or modify /etc/security/limits.conf:

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# ulimit -u 131072
# ulimit -n 522627

Reducing the value for swappiness reduces the likelihood that the Linux kernel will push application memory from memory into swap space. Virtual Memory Ratios that are too small force frequent IO operations, and those too large to leave too much data stored in volatile memory. Optimizing this ration is a careful balance between optimizing IO operations and reducing risk of data loss. Set these in /etc/sysctl.conf:

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vm.swappiness=0
vm.dirty_background_ratio = 10
vm.dirty_ratio = 40

Disable transparent hugepages. Transparent Huge Pages (THP) is a Linux memory management system that reduces the overhead of Translation Lookaside Buffer (TLB) lookups on machines with large amounts of memory by using larger memory pages. However, the THP feature is known to perform poorly in Hadoop cluster and results in excessively high CPU utilization:

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# echo never > /sys/kernel/mm/transparent_hugepage/defrag
# echo never > /sys/kernel/mm/transparent_hugepage/enabledsbin/stop-yarn.sh

As we saw in the Hadoop architecture picture, an input split is a chunk of input that is processed by a single map. Each map processes a single split. Each split is divided into records, and the map processes each record — a key-value pair — in turn. With default settings, the split size is dfs.blocksize in core-site.xml. So, by default: Number of maps = size of input file / dfs.blocksize

For example, how many maps of 1TB size input file? It is 1,000. The maximum number of simultaneous map tasks per job can be changed in mapred-site.xml, as shown in the configuration below. There is no limit if this value is 0 or negative. Generally, one mapper should get 1 to 1.5 cores. Every map task usually takes 40 seconds to complete execution:

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mapreduce.job.running.map.limit=128 -yarn.sh
mapreduce.map.cpu.vcore=1

The number of reducers is dependent on IO, more reducers generate more small files, this will impact Namenode; less reducers generate less larger files, it may trigger OOM:

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mapreduce.job.reduces=64
mapreduce.job.running.reduce.limit=64
mapreduce.reduce.cpu.vcore=1

More cores of CPU can benefit task, adjust max cores for one task in yarn-site.xml:

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yarn.scheduler.minimum-allocation-vcores=1
yarn.scheduler.maximum-allocation-vcores=16
#Automaticall determined from hardware
yarn.nodemanager.resource.cpu-vcores=-1  

Different JDK versions and sources may cause performance gaps on Java workloads. Please refer to this guide for JDK selection.

Over-allocated heap size and/or improperly configured GC can result in longer full GC runs (during which all other threads in the JVM are paused), badly affecting overall application throughput and ultimately your cluster performance and SLA. Too small heap allocation can cause the JVM to crash due to OOM. Inside mapred-site.xml:

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mapreduce.map.java.opts=-Xmx1536m
mapreduce.reduce.java.opts=-Xmx3072m

And Use default GC of JDK1.8 or Parallel GC for JDK 11.

Disk IO is one of the major performance bottlenecks. One useful method that helps minimize disk is compress output. Inside mapred-site.xml:

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mapreduce.map.output.compress=true
mapreduce.map.output.compress.codec= org.apache.hadoop.io.compress.SnappyCodec
mapreduce.output.fileoutputformat.compress=ture
mapreduce.output.fileoutputformat.compress.type=BLOCK
mapreduce.output.fileoutputformat.compress.codec=org.apache.hadoop.io.compress.SnappyCodec

Use native snappy library to compress output file, this can deduce IO. Performance of native snappy library is better than snapyy-java. Hadoop v3.3.0 is suggested since in Hadoop v3.3.1 snapy-java is introduced. Refer to link: https://archive.apache.org/dist/hadoop/common/hadoop-3.3.1/RELEASENOTES.md

Check whether all native library installed:

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# hadoop checknative -a