Latest version of hadoop

Author: e | 2025-04-25

HADOOP- Make the Guava version Hadoop which builds with configurable. Resolved; HADOOP-9991 Fix up Hadoop POMs, roll up JARs to latest versions. Open; HADOOP-

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Hadoop is a distributed computing framework for processing and storing massive datasets. It runs on Ubuntu and offers scalable data storage and parallel processing capabilities.Installing Hadoop enables you to efficiently handle big data challenges and extract valuable insights from your data.To Install Hadoop on Ubuntu, the below steps are required:Install Java.Create a User.Download Hadoop.Configure Environment.Configure Hadoop.Start Hadoop.Access Web Interface.Prerequisites to Install Hadoop on UbuntuComplete Steps to Install Hadoop on UbuntuStep 1: Install Java Development Kit (JDK)Step 2: Create a dedicated user for Hadoop & Configure SSHStep 3: Download the latest stable releaseStep 4: Configure Hadoop Environment VariablesStep 5: Configure Hadoop Environment VariablesStep 6: Start the Hadoop ClusterStep 7: Open the web interfaceWhat is Hadoop and Why Install it on Linux Ubuntu?What are the best Features and Advantages of Hadoop on Ubuntu?What to do after Installing Hadoop on Ubuntu?How to Monitor the Performance of the Hadoop Cluster?Why Hadoop Services are Not starting on Ubuntu?How to Troubleshoot issues with HDFS?Why My MapReduce jobs are failing?ConclusionPrerequisites to Install Hadoop on UbuntuBefore installing Hadoop on Ubuntu, make sure your system is meeting below specifications:A Linux VPS running Ubuntu.A non-root user with sudo privileges.Access to Terminal/Command line.Complete Steps to Install Hadoop on UbuntuOnce you provided the above required options for Hadoop installation Ubuntu including buying Linux VPS, you are ready to follow the steps of this guide.In the end, you will be able to leverage its capabilities to efficiently manage and analyze large datasets.Step 1: Install Java Development Kit (JDK)Since Hadoop requires Java to run, use the following command to install the default JDK and JRE:sudo apt install default-jdk default-jre -yThen, run the command below to Verify the installation by checking the Java version:java -versionOutput:java version "11.0.16" 2021-08-09 LTSOpenJDK 64-Bit Server VM (build 11.0.16+8-Ubuntu-0ubuntu0.22.04.1)As you see, if Java is installed, you’ll see the version information.Step 2: Create a dedicated user for Hadoop & Configure SSHTo create a new user, run the command below and create the Hadoop user:sudo adduser hadoopTo add the user to the sudo group, type:sudo usermod -aG sudo hadoopRun the command below to switch to the Hadoop user:sudo su - hadoopTo install OpenSSH server and client, run:sudo apt install openssh-server openssh-client -yThen, generate SSH keys by running the following command:ssh-keygen -t rsaNotes:Press Enter to save the key to the default location.You can optionally set a passphrase for added security.Now, you can add the public key to authorized_keys:cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keysTo set permissions Included in each version of ODH.Big Data Service Release and Patch VersionsBig Data Service releases software feature updates and patches in a quarterly cadence. The software feature updates and patches can include one or more of ODH (Oracle Distribution for Hadoop) updates including component version updates and bug fixes, CVE (Common Vulnerabilities and Exposures) fixes, OS (Operating System) updates, OS upgrades and OS bug fixes.For the latest releases, see Big Data Servicerelease notes.Big Data Service users are supported if their Big Data Service software version is either the latest Big Data Service release (N), or one version older than the latest Big Data Service release (N-1) or two versions older than the latest Big Data Service release (N-2).The following table lists the Big Data Service release and patch versions for each release.Big Data Service ReleaseODH VersionJDK VersionOS Version3.0.29ODH 2.0.10.22JDK 1.8.0_411OS 1.29.03.0.28ODH 2.0.9.41ODH 1.1.13.21JDK 1.8.0_411OS 1.28.03.0.27ODH 2.0.8.45ODH 1.1.12.16ODH 0.9.10.6JDK 1.8.0_411OS 1.27.03.0.26ODH 2.0.7.11ODH 1.1.11.7 ODH 0.9.9.7JDK 1.8.0_381OS 1.26.03.0.25ODH 2.0.6.5ODH 1.1.10.4ODH 0.9.8.3JDK 1.8.0_381OS 1.25.0ODH 2.x Based on Apache Hadoop 3.3.3The following table lists the components included in ODH and their versions.ComponentVersionApache Ambari2.7.5Apache Flink1.15.2Apache Flume1.10.0Apache Hadoop (HDFS, YARN, MR)3.3.3Apache HBase2.4.13Apache Hive3.1.3Apache Hue4.10.0Apache JupyterHub2.1.1Apache Kafka3.2.0Apache Livy0.7.1Apache Oozie5.2.1Apache Parquet MR1.10Apache Ranger and InfrSolr2.3.0 and 0.1.0Apache Spark3.2.1Apache Sqoop1.4.7Apache Tez0.10.2Apache Zookeeper3.7.1Kerberos1.1-15ODH Utilities1.0Schema Registry1.0.0Trino389Additional value added serviceORAAHincludedODH 1.x Based on Apache Hadoop 3.1The following table lists the components included in ODH 1.x and their versions.ComponentVersionApache Ambari2.7.5Apache Flink1.15.2Apache Flume1.10.0Apache Hadoop (HDFS, YARN, MR)3.1.2Apache HBase2.2.6Apache Hive3.1.2Apache Hue4.10.0Apache JupyterHub2.1.1Apache Kafka3.2.0Apache Livy0.7.1Apache Oozie5.2.0Apache Parquet MR1.10Apache Ranger and InfrSolr2.1.0 and 0.1.0Apache Spark3.0.2Apache Sqoop1.4.7Apache Tez0.10.0Apache Zookeeper3.5.9Kerberos1.1-15ODH Utilities1.0Schema Registry1.0.0Trino360Additional value added serviceORAAHincludedAccessing Big Data ServiceYou access Big Data Service using the Console, OCI CLI, REST APIs, or SDKs. The OCI Console is an easy-to-use, browser-based interface. To access the Console, you must use a supported browser.The OCI CLI provides both quick access and full functionality without the need for programming. Use the Cloud Shell environment to run your CLIs.The REST API documentation provide the most functionality, but require programming expertise. API Reference and Endpoints provide endpoint details and links to the available API reference documents including the Big Data Service API.OCI provides SDKs that interact with Big Data without the need to create a framework.Resource IdentifiersBig Data Service resources, like most types of resources in Oracle Cloud Infrastructure, have a unique, Oracle-assigned identifier called an Oracle Cloud ID (OCID).For information about the OCID format and other ways to identify your resources, see Resource Identifiers.Regions and Availability

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On the authorized_keys file, run:sudo chmod 640 ~/.ssh/authorized_keysFinally, you are ready to test SSH configuration:ssh localhostNotes:If you didn’t set a passphrase, you should be logged in automatically.If you set a passphrase, you’ll be prompted to enter it.Step 3: Download the latest stable releaseTo download Apache Hadoop, visit the Apache Hadoop download page. Find the latest stable release (e.g., 3.3.4) and copy the download link.Also, you can download the release using wget command:wget extract the downloaded file:tar -xvzf hadoop-3.3.4.tar.gzTo move the extracted directory, run:sudo mv hadoop-3.3.4 /usr/local/hadoopUse the command below to create a directory for logs:sudo mkdir /usr/local/hadoop/logsNow, you need to change ownership of the Hadoop directory. So, use:sudo chown -R hadoop:hadoop /usr/local/hadoopStep 4: Configure Hadoop Environment VariablesEdit the .bashrc file using the command below:sudo nano ~/.bashrcAdd environment variables to the end of the file by running the following command:export HADOOP_HOME=/usr/local/hadoopexport HADOOP_INSTALL=$HADOOP_HOMEexport HADOOP_MAPRED_HOME=$HADOOP_HOMEexport HADOOP_COMMON_HOME=$HADOOP_HOMEexport HADOOP_HDFS_HOME=$HADOOP_HOMEexport YARN_HOME=$HADOOP_HOMEexport HADOOP_COMMON_LIB_NATIVE_DIR=$HADOOP_HOME/lib/nativeexport PATH=$PATH:$HADOOP_HOME/sbin:$HADOOP_HOME/binexport HADOOP_OPTS="-Djava.library.path=$HADOOP_HOME/lib/native"To save changes and source the .bashrc file, type:source ~/.bashrcWhen you are finished, you are ready for Ubuntu Hadoop setup.Step 5: Configure Hadoop Environment VariablesFirst, edit the hadoop-env.sh file by running the command below:sudo nano $HADOOP_HOME/etc/hadoop/hadoop-env.shNow, you must add the path to Java. If you haven’t already added the JAVA_HOME variable in your .bashrc file, include it here:export JAVA_HOME=/usr/lib/jvm/java-11-openjdk-amd64export HADOOP_CLASSPATH+=" $HADOOP_HOME/lib/*.jar"Save changes and exit when you are done.Then, change your current working directory to /usr/local/hadoop/lib:cd /usr/local/hadoop/libThe below command lets you download the javax activation file:sudo wget you are finished, you can check the Hadoop version:hadoop versionIf you have passed the steps correctly, you can now configure Hadoop Core Site. To edit the core-site.xml file, run:sudo nano $HADOOP_HOME/etc/hadoop/core-site.xmlAdd the default filesystem URI: fs.default.name hdfs://0.0.0.0:9000 The default file system URI Save changes and exit.Use the following command to create directories for NameNode and DataNode:sudo mkdir -p /home/hadoop/hdfs/{namenode,datanode}Then, change ownership of the directories:sudo chown -R hadoop:hadoop /home/hadoop/hdfsTo change the ownership of the created directory to the hadoop user:sudo chown -R hadoop:hadoop /home/hadoop/hdfsTo edit the hdfs-site.xml file, first run:sudo nano $HADOOP_HOME/etc/hadoop/hdfs-site.xmlThen, paste the following line to set the replication factor: dfs.replication 1 Save changes and exit.At this point, you can configure MapReduce. Run the command below to edit the mapred-site.xml file:sudo nano $HADOOP_HOME/etc/hadoop/mapred-site.xmlTo set the MapReduce framework, paste the following line: mapreduce.framework.name yarn Save changes and exit.To configure YARN, run the command below and edit the yarn-site.xml file:sudo nano $HADOOP_HOME/etc/hadoop/yarn-site.xmlPaste the following to enable the MapReduce shuffle service: yarn.nodemanager.aux-services mapreduce_shuffle Save changes and exit.Format the NameNode by. HADOOP- Make the Guava version Hadoop which builds with configurable. Resolved; HADOOP-9991 Fix up Hadoop POMs, roll up JARs to latest versions. Open; HADOOP-

Hadoop Version - Understanding Hadoop Versions

That all users must take into account is the manufacturing cost. Hadoop is hardware that requires at least one server room, which does not only equal high electricity costs. It also means Hadoop users must spend a lot of money updating and fixing the machines. All in all, Hadoop requires a lot of money to run properly. Working in real-timeOne major limitation of Hadoop is its lack of real-time responses. That applies to both operational support and data processing. If a Hadoop user needs assistance with operating the Hadoop software on their server room machines, that assistance will not be provided to them in real time.They have to wait for a response, which can impact their work. Similarly, if a Hadoop user needs to analyze some data to make a data-driven decision quickly, they can’t. In Hadoop, there is no data processing in real time. That can pose a challenge in high-paced environments where decisions need to be made without much notice.ScalingHadoop can also be challenging to scale. Because Hadoop is a monolithic technology, organizations will often be stuck with the version of Hadoop they started out with. Even when they grow and deal with larger amounts of data. If they want an upgraded version of Hadoop, they have to replace their entire setup, which is expensive.They either have to replace their entire setup or decide to run a new version of Hadoop on an older machine, which requires more computing power as well as the business to maintain these August 30, 2016 3 minute read My latest Pluralsight course is out now: Hadoop for .NET DevelopersIt takes you through running Hadoop on Windows and using .NET to write MapReduce queries - proving that you can do Big Data on the Microsoft stack.The course has five modules, starting with the architecture of Hadoop and working through a proof-of-concept approach, evaluating different options for running Hadoop and integrating it with .NET.1. Introducing HadoopHadoop is the core technology in Big Data problems - it provides scalable, reliable storage for huge quantities of data, and scalable, reliable compute for querying that data. To start the course I cover HDFS and YARN - how they work and how they work together. I use a 600MB public dataset (from the 2011 UK census), upload it to HDFS and demonstrate a simple Java MapReduce query. Unlike my other Pluralsight course, Real World Big Data in Azure, there are word counts in this course - to focus on the technology, I keep the queries simple for this one.2. Running Hadoop on WindowsHadoop is a Java technology, so you can run it on any system with a compatible JVM. You don’t need to run Hadoop from the JAR files though, there are packaged options which make it easy to run Hadoop on Windows. I cover four options: Hadoop in Docker - using my Hadoop with .NET Core Docker image to run a Dockerized Hadoop cluster; Hortonworks Data Platform, a packaged Hadoop distribution which is available for Linux and Windows; Syncfusion’s Big Data Platform, a new Windows-only Hadoop distribution which has a friendly UI; Azure HDInsight, Microsoft’s managed Hadoop platform in the cloud. If you’re starting out with Hadoop, the Big Data Platform is a great place to start - it’s a simple two-click install, and it comes with lots of sample code.3. Working with Hadoop in .NETJava is the native programming language for MapReduce queries, but Hadoop provides integration for any language with the Hadoop Streaming API. I walk through building a MapReduce program with the full .NET Framework, then using .NET Core, and compare those options with Microsoft’s Hadoop SDK for .NET (spoiler: the SDK is a nice framework, but hasn’t seen much activity for a while). Using .NET Core for MapReduce jobs gives you the option to write queries in C# and run them on Linux or Windows clusters, as I blogged about in Hadoop and .NET Core - A Match Made in Docker.4. Querying Data with MapReduceBasic MapReduce jobs are easy with .NET and .NET Core, but in this module we look at more advanced functionality and see how to write performant, reliable .NET MapReduce jobs. In this module I extend the .NET queries to

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To other applications and databases for data pipelines and workflows.How to Monitor the Performance of the Hadoop Cluster?Use the Hadoop web interface to monitor resource usage, job execution, and other metrics.You can also use tools like Ganglia or Nagios for more advanced monitoring.Why Hadoop Services are Not starting on Ubuntu?There could be several reasons for this. To troubleshoot, consider:Configuration errors: Verify that your configuration files (core-site.xml, hdfs-site.xml, etc.) are correct and contain the necessary properties.NameNode format: Ensure that you’ve formatted the NameNode using hdfs namenode -format.Port conflicts: Check if other applications are using the ports specified in your Hadoop configuration (e.g., 9000 for NameNode).Firewall issues: Make sure your firewall is configured to allow Hadoop services to communicate.How to Troubleshoot issues with HDFS?Use the hdfs dfs -ls command to list files and directories in HDFS.If you encounter errors, check the logs for clues. You can also use the hdfs dfs -tail command to view the latest lines of a log file.Why My MapReduce jobs are failing?There could be several reasons for job failures, including:Input/output errors: Ensure that your input and output paths are correct and that the data format is compatible with your MapReduce job.Job configuration issues: Check your job configuration for errors or inconsistencies.Resource limitations: If your cluster is under heavy load, your job might fail due to insufficient resources.Programming errors: Review your MapReduce code for logical errors or bugs.ConclusionThe steps of this guide help you to successfully install and configure Hadoop, enabling you to efficiently process and store massive datasets. By successfully following the steps outlined in this tutorial, you’ve unlocked the potential of Hadoop on your Ubuntu system.To optimize Hadoop performance, consider tuning your Hadoop configuration based on your specific workload and hardware.

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To configure bds-database-create-bundle.sh to download the Hadoop, Hive, and HBase tarballs, you must supply an URL to each these parameters:--hive-client-ws--hadoop-client-ws--hbase-client-ws To get the information needed to provide the correct URL, first check the content management service (CM or Ambari) and find the version of the Hadoop, Hive, and HBase services running on the Hadoop cluster. The compatible clients are of the same versions. In each case, the client tarball filename includes a version string segment that matches the version of the service installed on the cluster. In the case of CDH, you can then browse the public repository and find the URL to the client that matches the service version. For the HDP repository this would require a tool that can browse Amazon S3 storage. However you can also compose the correct URL using the known URL pattern along with information that you can acquire from Ambari, as described in this section. For CDH (Both Oracle Big Data Appliance and Commodity CDH Systems): Log on to Cloudera Manager and go to the Hosts menu. Select All Hosts , then Inspect All Hosts. When the inspection is finished, select either Show Inspector Results (on the screen) or Download Result Data (to a JSON file). In either case, scan the result set and find the service versions. In JSON version of the inspector results, there is a componentInfo section for each cluster that shows the versions of software installed on that cluster. For example: "componentInfo": [ ... { "cdhVersion": "CDH5", "componentRelease": "1.cdh5.11.1.p0.6", "componentVersion": "2.6.0+cdh5.11.1+2400", "name": "hadoop" }, ... Go to Look in the ”hadoop,” hive,” and “hbase” subdirectories of the CDH5 section of the archive. In the listings, you should find the client packages for the versions of the services installed on the cluster. Copy the URLs and use them as the parameter values supplied to bds-database-create-bundle.sh. For example: See Also:Search for “Host Inspector” on Cloudera website if you need more help using this tool to determine installed software versions. For HDP: Log on to Ambari. Go to Admin, then Stack and Versions. On the Stack tab, locate the entries for the HDFS, Hive, and HBase services and note down the version number of each as the “service version.” Click the Versions tab. Note down the version of HDP that is running on the cluster as the “HDP version base.” Click Show Details to display a pop-up window that shows the full version string for the installed HDP release. Note this down as the “HDP full version” The last piece of information needed is the Linux version (“centos5,” “centos6,” or “centos7”). Note this down as “OS version.” To search though the HDP repository in Amazon S3 storage to find the correct client. HADOOP- Make the Guava version Hadoop which builds with configurable. Resolved; HADOOP-9991 Fix up Hadoop POMs, roll up JARs to latest versions. Open; HADOOP-

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URLs using this information acquired in this steps, you would need an S3 browser, browser extension, or command line tool. As alternative, you can piece together the correct URLs, using these strings. For HDP 2.5 and earlier, the URLs pattern is as follows. version>/2.x/updates/HDP version base>/tars/{hadoop|apache-hive|hbase}-service version>.HDP full version>.tar.gzHere are some examples. Note that the pattern of the gzip filename is slightly different for Hive. There is an extra “-bin” segment in the name. For HDP 2.5 and later releases, the pattern is almost the same except that there is an additional hadoop, hive, or hbase directory under the tar directory: Alternative Method for HDP: You can get the required software versions from the command line instead of using Ambari. # hdp-select versions Copy and save the numbers to the left of the dash as the “HDP version base”. # hadoop version # beeline --version # hbase versionUse the output from these commands to formulate the service version>.HDP full version> segment for each URL.