Hadoop HDFS

Author: h | 2025-04-25

The objective of this Hadoop HDFS Tutorial is to take you through what is HDFS in Hadoop, what are the different nodes in Hadoop HDFS, how data is stored in HDFS, HDFS There are three components of Hadoop. Hadoop HDFS - Hadoop Distributed File System (HDFS) is the storage unit of Hadoop. Hadoop MapReduce - Hadoop MapReduce is the processing unit of Hadoop. Hadoop

apache/hadoop-hdfs: Mirror of Apache Hadoop HDFS

What is Hadoop HDFS?HDFS ArchitectureNameNodeSecondary NameNodeDataNodeCheckpoint NodeBackup NodeBlocksFeatures of HDFSReplication Management in HDFS ArchitectureWrite OperationRead OperationAdvantages of HDFS ArchitectureDisadvantages of HDFS ArchitectureConclusionAdditional ResourcesHadoop is an open-source framework for distributed storage and processing. It can be used to store large amounts of data in a reliable, scalable, and inexpensive manner. It was created by Yahoo! in 2005 as a means of storing and processing large datasets. Hadoop provides MapReduce for distributed processing, HDFS for storing data, and YARN for managing compute resources. By using Hadoop, you can process huge amounts of data quickly and efficiently. Hadoop can be used to run enterprise applications such as analytics and data mining. HDFS is the core component of Hadoop. It is a distributed file system that provides capacity and reliability for distributed applications. It stores files across multiple machines, enabling high availability and scalability. HDFS is designed to handle large volumes of data across many servers. It also provides fault tolerance through replication and auto-scalability. As a result, HDFS can serve as a reliable source of storage for your application’s data files while providing optimum performance. HDFS is implemented as a distributed file system with multiple data nodes spread across the cluster to store files.What is Hadoop HDFS?Hadoop is a software framework that enables distributed storage and processing of large data sets. It consists of several open source projects, including HDFS, MapReduce, and Yarn. While Hadoop can be used for different purposes, the two most common are Big Data analytics and NoSQL database management. HDFS stands for “Hadoop Distributed File System” and is a decentralized file system that stores data across multiple computers in a cluster. This makes it ideal for large-scale storage as it distributes the load across multiple machines so there’s less pressure on each individual machine. MapReduce is a programming model that allows users to write code once and execute it across many servers. When combined with HDFS, MapReduce can be used to process massive data sets in parallel by dividing work up into smaller chunks and executing them simultaneously.HDFS is an Open source component of the Apache Software Foundation that manages data. HDFS has scalability, availability, and replication as key features. Name nodes, secondary name nodes, data nodes, checkpoint nodes, backup nodes, and blocks all make up the architecture of HDFS. HDFS is fault-tolerant and is replicated. Files are distributed across the cluster systems using the Name node and Data Nodes. The primary difference between Hadoop and Apache HBase is that Apache HBase is a non-relational database and Apache Hadoop is a non-relational data store.Confused about your next job?In 4 simple steps you can find your personalised career roadmap in Software development for FREEExpand in New Tab HDFS is composed of master-slave architecture, which includes the following elements:NameNodeAll the blocks on DataNodes are handled by NameNode, which is known as the master node. It performs the following functions:Monitor and control all the DataNodes instances.Permits the user to access a file.Stores all of the block records on a DataNode instance.EditLogs are 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.

hadoop/hadoop-hdfs-project/hadoop-hdfs/src/main/java/org

AdvertisementApache Hbase is column-oriented distributed datastore. Previously, we have shown installing Apache Accumulo. Accumulo is distributed key/value store, built on Hadoop. In another guide we have shown how to install Apache Cassandra. Cassandra is a column-oriented distributed datastore, inspired by BigTable. We have sown how to install Hadoop on single server instance. We can install Hbase without installing Hadoop.The reason to use Apache HBase instead of conventional Apache Hadoop is mainly to do random reads and writes. When we are using Hadoop we are going to read the whole dataset whenever we want to run a MapReduce job. Hadoop is a distributed file system (HDFS) and MapReduce (a framework for distributed computing). HBase is key-value data store built on top of Hadoop (on top of HDFS).Hadoop comprises of HDFS and Map-Reduce. HDFS is a file system which provides a reliable storage with high fault tolerance using replication by distributing the data across a set of nodes. The Hadoop distributed file system aka HDFS provides multiple jobs for us. It consists of 2 components, NameNode (Where the metadata about the file system is stored.) and datanodes(Where the actual distributed data is stored).Map-Reduce is a set of 2 types of java daemons called Job-Tracker and Task-Tracker. Job-Tracker daemon governs the jobs to be executed, whereas the Task-tracker daemons are the daemons which run on top of the data-nodes in which the data is distributed so that they can compute the program execution logic provided by the user specific to the data within the corresponding data-node.HDFS is the storage component and Map-Reduce is the Execution component. As for the HBase concern, simply we can not connect remotely to HBase without using HDFS because HBase can not create clusters and it has its own local file system. HBase comprises of HMaster (Which consists of. The objective of this Hadoop HDFS Tutorial is to take you through what is HDFS in Hadoop, what are the different nodes in Hadoop HDFS, how data is stored in HDFS, HDFS

Hadoop, Hadoop Config, HDFS, Hadoop MapReduce

Database Service Node to Run the Examples with Oracle SQL Connector for HDFSYou must configure the co-managed Database service node in order to run the examples, as shown below. See Oracle Big Data Connectors User's Guide, section Installing and Configuring a Hadoop Client on the Oracle Database System for more details. Generate Oracle SQL Connector for HDFS zip file on the cluster node and copy to the database node. Example:cd /opt/oraclezip -r /tmp/orahdfs-.zip orahdfs-/*Unzip the Oracle SQL Connector for HDFS zip file on the database node. Example:mkdir -p /u01/misc_products/bdcunzip orahdfs-.zip -d /u01/misc_products/bdcInstall the Hadoop client on the database node in the /u01/misc_products/ directory.Connect as the sysdba user for the PDB and verify that both OSCH_BIN_PATH and OSCH_DEF_DIR database directories exist and point to valid operating system directories. For example, create or replace directory OSCH_BIN_PATH as '/u01/misc_products/bdc/orahdfs-/bin'; grant read,execute on directory OSCH_BIN_PATH to OHSH_EXAMPLES; where OHSH_EXAMPLES is the user created in Step 2: Create the OHSH_EXAMPLES User, above.create or replace directory OSCH_DEF_DIR as '/u01/misc_products/bdc/xtab_dirs'; grant read,write on directory OSCH_DEF_DIR to OHSH_EXAMPLES; Note: create the xtab_dirs operating system directory if it doesn't exist. Change to your OSCH (Oracle SQL Connector for HDFS) installation directory, and edit the configuration file hdfs_stream. For example,sudo su -l oracle cd /u01/misc_products/bdc/orahdfs- vi bin/hdfs_streamCheck that the following variables are configured correctly. Read the instructions included in the hdfs_stream file for more details.#Include Hadoop client bin directory to the PATH variable export PATH=/u01/misc_products/hadoop-/bin:/usr/bin:/bin export JAVA_HOME=/usr/java/jdk #See explanation below export HADOOP_CONF_DIR=/u01/misc_products/hadoop-conf#Activate the Kerberos configuration for secure clustersexport HADOOP_CLIENT_OPTS="-Djava.security.krb5.conf=/u01/misc_products/krb5.conf"Configure the Hadoop configuration directory (HADOOP_CONF_DIR). If it's not already configured, use Apache Ambari to download the Hadoop Client configuration archive file, as follows:Login to Apache Ambari. the HDFS service, and select the action Download Client Configuration. Extract the files under the HADOOP_CONF_DIR (/u01/misc_products/hadoop-conf) directory. Ensure that the hostnames and ports configured in HADOOP_CONF_DIR/core-site.xml are accessible from your co-managed Database service node (see the steps below). For example, fs.defaultFS hdfs://bdsmyhostmn0.bmbdcsxxx.bmbdcs.myvcn.com:8020 In this example host bdsmyhostmn0.bmbdcsxxx.bmbdcs.myvcn.com and port 8020 must be accessible from your co-managed Database service node. For secure clusters:Copy the Kerberos configuration file from the cluster node to the database node. Example:cp krb5.conf /u01/misc_products/Copy the Kerberos keytab file from the cluster node to the database node. Example:cp /u01/misc_products/Run the following commands to verify that HDFS access is working. #Change to the Hadoop client bin directory cd /u01/misc_products/hadoop-/bin #--config points to your HADOOP_CONF_DIR directory. ./hadoop --config /u01/misc_products/hadoop-conf fs -ls This command should list the HDFS contents. If you get a timeout or "no route to host" or "unknown host" errors, you will need to update your /etc/hosts file and verify your Big Data Service Console network configuration, as follows:Sign into the Cloud Console, click Big Data, then Clusters, then your_cluster>, then Cluster Details. Under the List of cluster nodes section, get the fully qualified name of all your cluster nodes and all the IP addresses .Edit your co-managed Database service configuration file /etc/hosts, for example: #BDS hostnames xxx.xxx.xxx.xxx bdsmynodemn0.bmbdcsad1.bmbdcs.oraclevcn.com bdsmynodemn0 xxx.xxx.xxx.xxx bdsmynodewn0.bmbdcsad1.bmbdcs.oraclevcn.com bdsmynodewn0 xxx.xxx.xxx.xxx bdsmynodewn2.bmbdcsad1.bmbdcs.oraclevcn.com bdsmynodewn2 xxx.xxx.xxx.xxx bdsmynodewn1.bmbdcsad1.bmbdcs.oraclevcn.com bdsmynodewn1 xxx.xxx.xxx.xxx bdsmynodeun0.bmbdcsad1.bmbdcs.oraclevcn.com bdsmynodeun0 That Hadoop is important.Who Uses Hadoop?Hadoop is a popular big data tool, used by many companies worldwide. Here’s a brief sample of successful Hadoop users:British AirwaysUberThe Bank of ScotlandNetflixThe National Security Agency (NSA), of the United StatesThe UK’s Royal Mail systemExpediaTwitterNow that we have some idea of Hadoop’s popularity, it’s time for a closer look at its components to gain an understanding of what is Hadoop.Components of HadoopHadoop is a framework that uses distributed storage and parallel processing to store and manage Big Data. It is the most commonly used software to handle Big Data. There are three components of Hadoop.Hadoop HDFS - Hadoop Distributed File System (HDFS) is the storage unit of Hadoop.Hadoop MapReduce - Hadoop MapReduce is the processing unit of Hadoop.Hadoop YARN - Hadoop YARN is a resource management unit of Hadoop.Let us take a detailed look at Hadoop HDFS in this part of the What is Hadoop article.Hadoop HDFSData is stored in a distributed manner in HDFS. There are two components of HDFS - name node and data node. While there is only one name node, there can be multiple data nodes.HDFS is specially designed for storing huge datasets in commodity hardware. An enterprise version of a server costs roughly $10,000 per terabyte for the full processor. In case you need to buy 100 of these enterprise version servers, it will go up to a million dollars.Hadoop enables you to use commodity machines as your data nodes. This way, you don’t have to spend millions of dollars just on your data nodes. However, the name node is always an enterprise server.Features of HDFSProvides distributed storageCan be implemented on commodity hardwareProvides data securityHighly fault-tolerant - If one machine goes down, the data from that machine goes to the next machineMaster and Slave NodesMaster and slave nodes form the HDFS cluster. The name node is called the master, and the data nodes are called the slaves.The name node is responsible for the workings of the data nodes. It also stores the metadata.The data nodes read, write, process, and replicate the data. They also send signals, known as heartbeats, to the name node. These heartbeats show the status of the data node.Consider that 30TB of data is loaded into the name node. The name node distributes it across the data nodes, and this data is replicated among the data notes. You can see in the image above that the blue, grey, and red data are replicated among the three data nodes.Replication of the data is performed three times by default. It is done this way, so if a commodity machine fails, you can replace it with a new machine that has the same data.Let us focus on Hadoop MapReduce

Download hadoop-hdfs-0.21.0.jar : hadoop hdfs h - Java2s

You can use Oracle Big Data Connectors and Oracle Copy to Hadoop (a feature of Big Data SQL) to load data from an Big Data Service cluster into an Oracle Cloud database instance and to copy from an Oracle Cloud database instance to a Big Data Service cluster. The database can be an Oracle Autonomous Database or a co-managed Oracle Database service, as shown in the following table: Type of DatabaseFeatures Supported for Copying DataOracle Autonomous DatabaseYou can use the following with Oracle Shell for Hadoop Loaders (OHSH) to copy data between a Big Data Service cluster and an Autonomous Database instance.Oracle Loader for Hadoop (OLH)The Copy to Hadoop (CP2HADOOP) feature of Oracle Big Data SQLCo-managed Oracle DatabaseYou can use the following with Oracle Shell for Hadoop Loaders to copy data between a Big Data Service cluster and a co-managed Oracle Database instance.Oracle SQL Connector for HDFS (OSCH) Oracle Loader for HadoopThe Copy to Hadoop feature of Oracle Big Data SQL FeaturesBig Data connectors and features are pre-installed on your Big Data Service clusters. The Copy to Hadoop feature of Oracle Big Data SQL is also already installed on your cluster.The following features are pre-installed on every node of your cluster:Oracle Shell for Hadoop LoadersOracle Shell for Hadoop Loaders (OHSH) is a helper shell that provides a simple-to-use command line interface to Oracle Loader for Hadoop, Oracle SQL Connector for HDFS, and Copy to Hadoop.Copy to HadoopCopy to Hadoop (CP2HADOOP) is a feature of Oracle Big Data SQL, for copying data from an Oracle database to HDFS.Oracle Loader for HadoopOracle Loader for Hadoop (OLH) is a high-performance loader for loading data from a Hadoop cluster into a table in an Oracle database. Oracle SQL Connector for Hadoop Distributed File System (HDFS)Oracle SQL Connector for HDFS (OSCH) enables an Oracle external table to access data stored in HDFS files or in a table in Apache Hive. Use this connector only for loading data into a co-managed Oracle Database service. Note Oracle SQL Connector for HDFS is supported only for connecting to a co-managed Oracle Database service. It is not supported for connecting to Oracle Autonomous Database. Oracle Instant Client for LinuxOracle Instant Client enables development and deployment of applications that connect to Oracle Database.Set TNS Settings for Connecting to a DatabaseConfigure TNS for Autonomous DatabaseDownload Client Credentials from the Autonomous Database console and unzip it to the /opt/oracle/bdc-test/dbwallet/client directory.Change to the directory where you unzipped the file.cd /opt/oracle/bdc-test/dbwallet/clientEdit sqlnet.ora and change the WALLET_LOCATION parameter to the path /opt/oracle/bdc-test/dbwallet/client.For example: WALLET_LOCATION = (SOURCE = (METHOD = file) (METHOD_DATA = (DIRECTORY="/opt/oracle/bdc-test/dbwallet/client"))) Create a file called connection.properties in this directory and include the following properties:javax.net.ssl.trustStore=/opt/oracle/bdc-test/dbwallet/client/cwallet.ssojavax.net.ssl.trustStoreType=SSOjavax.net.ssl.keyStore=/opt/oracle/bdc-test/dbwallet/client/cwallet.sso javax.net.ssl.keyStoreType=SSOTest to verify database connectivity using your Autonomous Database wallet configuration, as follows:Get tns names from tnsnames.ora. For example: myuseradw_high = ( -- configuration )Run the following commands and enter the admin password when prompted:sqlplus admin@For example:export TNS_ADMIN=/opt/oracle/bdc-test/dbwallet/client/sqlplus admin@myuseradw_highConfigure TNS for a Co-Managed Oracle Database ServiceDownload the tnsnames.ora file for a co-managed Oracle Database service and copy it to

Apache Hadoop HDFS - An Introduction to HDFS - DataFlair

The sudden increase in the volume of data from the order of gigabytes to zettabytes has created the need for a more organized file system for storage and processing of data. The demand stemming from the data market has brought Hadoop in the limelight making it one of biggest players in the industry. Hadoop Distributed File System (HDFS), the commonly known file system of Hadoop and Hbase (Hadoop’s database) are the most topical and advanced data storage and management systems available in the market.What are HDFS and HBase?HDFS is fault-tolerant by design and supports rapid data transfer between nodes even during system failures. HBase is a non-relational and open source Not-Only-SQL database that runs on top of Hadoop. HBase comes under CP type of CAP (Consistency, Availability, and Partition Tolerance) theorem.HDFS is most suitable for performing batch analytics. However, one of its biggest drawbacks is its inability to perform real-time analysis, the trending requirement of the IT industry. HBase, on the other hand, can handle large data sets and is not appropriate for batch analytics. Instead, it is used to write/read data from Hadoop in real-time.Both HDFS and HBase are capable of processing structured, semi-structured as well as un-structured data. HDFS lacks an in-memory processing engine slowing down the process of data analysis; as it is using plain old MapReduce to do it. HBase, on the contrary, boasts of an in-memory processing engine that drastically increases the speed of read/write.HDFS is very transparent in its execution of data analysis. HBase, on the other hand, being a NoSQL database in tabular format, fetches values by sorting them under different key values.Enhanced Understanding with Use Cases for HDFS & HBaseUse Case 1 – Cloudera optimization for European bank using HBaseHBase is ideally suited for real-time environments and this can be best demonstrated. The objective of this Hadoop HDFS Tutorial is to take you through what is HDFS in Hadoop, what are the different nodes in Hadoop HDFS, how data is stored in HDFS, HDFS

hadoop Tutorial = Load data into hadoop hdfs

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