Skip to main content



Installing single node Hadoop 2.x on Ubuntu

1) Prerequisite
  • Install open-ssh server
  • Install Sun java-7-oracle
2) Add Hadoop Group and User
$ sudo addgroup hadoop

$ sudo adduser --ingroup hadoop hduser

$ sudo adduser hduser sudo



3) Setup SSH Certificate

$ ssh-keygen -t rsa -P '' ... Your identification has been saved in /home/hduser/.ssh/id_rsa. Your public key has been saved in /home/hduser/.ssh/id_rsa.pub. ... $ cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keys $ ssh localhost

4) Disabling IPv6
$ sudo gedit /etc/sysctl.conf Add following lines to the end of file and reboot the machine #disable ipv6 net.ipv6.conf.all.disable_ipv6 = 1 net.ipv6.conf.default.disable_ipv6 = 1 net.ipv6.conf.lo.disable_ipv6 = 1

5) Install/ Setup Hadoop
  • Download the hadoop tar.gz file.
  • Follow below steps on shell
$ sudo tar vxzf hadoop-2.7.1.tar.gz -C /usr/local $ cd /usr/local $ sudo mv hadoop-2.7.1 hadoop $ sudo chown -R hduser:hadoop hadoop

6) Setup environment variable for hadoop
$cd ~
$vi .bashrc
paste following to the end of the file
### #Hadoop variables export JAVA_HOME=/usr/lib/jvm/jdk export HADOOP_INSTALL=/usr/local/hadoop export PATH=$PATH:$HADOOP_INSTALL/bin export PATH=$PATH:$HADOOP_INSTALL/sbin export PATH=$PATH:$JAVA_HOME/bin export HADOOP_MAPRED_HOME=$HADOOP_INSTALL export HADOOP_COMMON_HOME=$HADOOP_INSTALL export HADOOP_HDFS_HOME=$HADOOP_INSTALL export YARN_HOME=$HADOOP_INSTALL export HADOOP_COMMON_LIB_NATIVE_DIR=$HADOOP_INSTALL/lib/native export HADOOP_OPTS="-Djava.library.path=$HADOOP_INSTALL/lib" export YARN_OPTS="$YARN_OPTS -Djava.net.preferIPv4Stack=true" ###end

7) Login using hduser and verify hadoop version
$ hadoop version
Hadoop 2.7.1 Subversion https://git-wip-us.apache.org/repos/asf/hadoop.git -r 15ecc87ccf4a0228f35af08fc56de536e6ce657a Compiled by jenkins on 2015-06-29T06:04Z Compiled with protoc 2.5.0 From source with checksum fc0a1a23fc1868e4d5ee7fa2b28a58a This command was run using /usr/local/hadoop/share/hadoop/common/hadoop-common-2.7.1.jar

8) Configure Hadoop
$ cd /usr/local/hadoop/etc/hadoop $ vi core-site.xml #Paste following between <configuration> <property> <name>fs.default.name</name> <value>hdfs://localhost:9000</value> </property> $ vi yarn-site.xml #Paste following between <configuration> <property> <name>yarn.nodemanager.aux-services</name> <value>mapreduce_shuffle</value> </property> <property> <name>yarn.nodemanager.aux-services.mapreduce.shuffle.class</name> <value>org.apache.hadoop.mapred.ShuffleHandler</value> </property> $ mv mapred-site.xml.template mapred-site.xml $ vi mapred-site.xml #Paste following between <configuration> <property> <name>mapreduce.framework.name</name> <value>yarn</value> </property> $ cd ~ $ mkdir -p mydata/hdfs/namenode $ mkdir -p mydata/hdfs/datanode $ cd /usr/local/hadoop/etc/hadoop $ vi hdfs-site.xml Paste following between <configuration> tag <property> <name>dfs.replication</name> <value>1</value> </property> <property> <name>dfs.namenode.name.dir</name> <value>file:/home/hduser/mydata/hdfs/namenode</value> </property> <property> <name>dfs.datanode.data.dir</name> <value>file:/home/hduser/mydata/hdfs/datanode</value> </property>

 vi /usr/local/hadoop/etc/hadoop/hadoop-env.sh # Set Java home. The java implementation to use. export JAVA_HOME=/usr/lib/jvm/jdk

9) Format Namenode
$ hdfs namenode -format

10) Start Hadoop Service
$ start-dfs.sh
15/12/09 14:39:19 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Starting namenodes on [localhost]
localhost: starting namenode, logging to /usr/local/hadoop/logs/hadoop-hduser-namenode-ubuntu-VirtualBox.out
localhost: starting datanode, logging to /usr/local/hadoop/logs/hadoop-hduser-datanode-ubuntu-VirtualBox.out
Starting secondary namenodes [0.0.0.0]

0.0.0.0: starting secondarynamenode, logging to /usr/local/hadoop/logs/hadoop-hduser-secondarynamenode-ubuntu-VirtualBox.out

 $ start-yarn.sh
starting yarn daemons
starting resourcemanager, logging to /usr/local/hadoop/logs/yarn-hduser-resourcemanager-ubuntu-VirtualBox.out

localhost: starting nodemanager, logging to /usr/local/hadoop/logs/yarn-hduser-nodemanager-ubuntu-VirtualBox.out

$ mr-jobhistory-daemon.sh start historyserver
starting historyserver, logging to /usr/local/hadoop/logs/mapred-hduser-historyserver-ubuntu-VirtualBox.out

$ jps
2511 DataNode
2388 NameNode
3023 NodeManager
3346 JobHistoryServer
3413 Jps
2694 SecondaryNameNode
2894 ResourceManager

11) Run Hadoop Example
$ hadoop jar /usr/local/hadoop/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.1.jar pi 2 7
WARNING: Use "yarn jar" to launch YARN applications.
Number of Maps  = 2
Samples per Map = 7
15/12/09 14:42:57 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Wrote input for Map #0
Wrote input for Map #1
Starting Job
15/12/09 14:43:02 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032

15/12/09 14:43:04 INFO input.FileInputFormat: Total input paths to process : 2
15/12/09 14:43:04 INFO mapreduce.JobSubmitter: number of splits:2
15/12/09 14:43:05 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1449652253965_0001
15/12/09 14:43:07 INFO impl.YarnClientImpl: Submitted application application_1449652253965_0001
15/12/09 14:43:07 INFO mapreduce.Job: The url to track the job: http://ubuntu-VirtualBox:8088/proxy/application_1449652253965_0001/
15/12/09 14:43:07 INFO mapreduce.Job: Running job: job_1449652253965_0001
...
...
Job Finished in 57.921 seconds
Estimated value of Pi is 3.71428571428571428571

Comments

Post a Comment

Popular posts

Spring MongoDB Rename field with derived Value of another field

Input Collection -  [ { 'k' : 'Troubleshooting' , 'hour' : '2024-10-10T16' , 'v' : [ 'WebPage, Login' ] }, { 'k' : 'TroubleshootingMe' , 'hour' : '2024-10-07T01' , 'v' : [ 'Accounts, etc' ] }  ] Expected Output -  [ { 'hour' : '2024-10-10T16' , 'Troubleshooting' : [ 'WebPage, Login' ] }, { 'hour' : '2024-10-07T01' , 'TroubleshootingMe' : [ 'Accounts, etc' ] }  ]   Above Can be achieved by  $replaceRoot / $replaceWith as follows - { $replaceWith : { $mergeObjects : [ { hour : "$hour" }, { "$arrayToObject" : [ [ { k : "$k" , v : "$v" } ] ] } ] } } or { $replaceRoo...
Post a Comment
Read more




Spark MongoDB Connector Not leading to correct count or data while reading

  We are using Scala 2.11 , Spark 2.4 and Spark MongoDB Connector 2.4.4 Use Case 1 - We wanted to read a Shareded Mongo Collection and copy its data to another Mongo Collection. We noticed that after Spark Job successful completion. Output MongoDB did not had many records. Use Case 2 -  We read a MongoDB collection and doing count on dataframe lead to different count on each execution. Analysis,  We realized that MongoDB Spark Connector is missing data on bulk read as a dataframe. We tried various partitioner, listed on page -  https://www.mongodb.com/docs/spark-connector/v2.4/configuration/  But, none of them worked for us. Finally, we tried  MongoShardedPartitioner  this lead to constant count on each execution. But, it was greater than the actual count of records on the collection. This seems to be limitation with MongoDB Spark Connector. But,  MongoShardedPartitioner  seemed closest possible solution to this kind of situation. But, it per...
Post a Comment
Read more




Experience with MongoDB and Optimizations

  Experience with MongoDB and Optimizations Before reading below. I would like to point out that this  experience  is related to version  6.0.14-ent, having 6 shards, each shard having 3 machines, each machine is VM with 140 GB RAM and 2TB SSD. And, we had been hosting almost 36 TB of data. MongoDB is not good with Big Data Joins and/ or Big Data OLAP processing. It is mainly meant for OLTP purposes.  Instead of joining millions of keys between 2 collections. It is better to lookup data of one key from one collection then lookup it in other collection. Thus, merging data from 2 collection for same key. Its better to keep De-normalized data in one document.  Updating a document later is cumbersome.  MongoDB crash if data is overloaded. And, it has long downtime if crashed unlike other databases which fails write to database if disk space achieves certain limit. Thus, keeping database active and running for read traffic. MongoDB needs indexes for fast qu...
Post a Comment
Read more




Spring MongoDB Log Connection Pool Details - Active, Used, Waiting

  We couldn't find any direct way to log Mongo Connection pool Size. So, we did implement an indirect way as below.  This may be incorrect at times when dealing with Sharded MongoDB having Primaty & Secondary nodes. Because, connection may be used based on read prefrence - Primary, primaryPreferred, Secondary, etc. But, this gives an understanding if connections are used efficiently and there is no wait to acquire connections from pool. This can be further enhanced to log correct connection pool statistics.  1) Implement  MyConnectionPoolListener  as below -  import java.util.concurrent.atomic.AtomicInteger; import org.slf4j.Logger; import org.slf4j.LoggerFactory; import com.mongodb.event.ConnectionCheckOutFailedEvent; import com.mongodb.event.ConnectionCheckOutStartedEvent; import com.mongodb.event.ConnectionCheckedInEvent; import com.mongodb.event.ConnectionCheckedOutEvent; import com.mongodb.event.ConnectionClosedEvent; import com.mongodb.event.Conne...
Post a Comment
Read more




Spark Streaming with Kafka Leading to increase in Open File Descriptors ( Kafka )

  Open File Descriptors w.r.t Kafka brokers relates with following -  number of file descriptors to just track log segment files. Additional file descriptors to communicate via network sockets with external parties (such as clients, other brokers, Zookeeper, and Kerberos). For # 1 this is formula -  (number of partitions)*(partition size / segment size) Reference -  https://docs.cloudera.com/cdp-private-cloud-base/7.1.6/kafka-performance-tuning/topics/kafka-tune-broker-syslevel-file-descriptors.html For #2, every connection made my consumer or producer or zookeeper or  Kerberos  opens file descriptors. Note that each TCP connection creates 2 file descriptors. These connections can be for internal communication of heartbeat, or  security handshake , or data transfer to or from client (producer or consumer) When we run a Spark application integrating it with  Kafka . And, if it is not stable, meaning -  Streaming window for micro batches is les...
Post a Comment
Read more