hadoop3.4.0生态集群部署教程

This document describes the complete process of expanding storage space and updating YARN configuration in a Hadoop cluster. By expanding LV capacity, restarting services, replacing the yarn-site.xml configuration file, distributing and restarting each node, the YARN cluster finally runs normally. Verify that the ResourceManager and HA status are confirmed through jps and yarn node-list commands.

This document describes how to install and configure Flink in an environment where Hadoop and ZooKeeper are deployed, and the detailed steps for submitting Flink jobs through YARN. It includes downloading Flink, configuring environment variables, configuring Hadoop and Flink, creating HDFS directory, and using two job submission models (Per-Job and Application models). It also provides YARN and Flink Web UI access methods to verify job running status.

This article introduces the deployment process of Mahout clusters, including downloading, extracting Mahout compressed packages, configuring environment variables, and adding relevant application settings to the configuration file. Finally, the deployment is verified through startup tests. The expected status indicates that the deployment is complete.

This article describes in detail the installation and configuration of Flume in a cluster environment, including the basic environment construction, the deployment and testing of two cluster architectures: failover mode and multi-layer data flow mode. The failover mode achieves high availability of data reception through active and standby Collector nodes and supports automatic switching; the multi-level data flow mode uses multi-level Agents and Collector nodes to build complex data collection and transmission links to realize log collection and transmission across nodes. Finally, the paper verifies the normal operation and failover functions of the data flow in the two modes through practical tests.

This article introduces the deployment process of the Kafka cluster, including shutting down the firewall, configuring system resource limits, installing and configuring Kafka server parameters, synchronizing configuration files on multiple nodes, starting ZooKeeper and Kafka services, verifying cluster status, and testing message interoperability between producers and consumers, and finally confirming that the Kafka cluster is running normally.

This article describes Hive's deployment process in detail, including the installation and configuration of MySQL metadata, Hive environment variable setting, modification of core configuration files (hive-env.sh and hive-site.xml), the integration of Spark as an execution engine, HDFS directory creation and permission setting, the installation of MySQL JDBC drivers, Hadoop agent user rights configuration, JAR package version conflict handling, and the initialization and final verification of Hive metadata. Through a series of steps, Hive has achieved seamless integration with MySQL, Hadoop, and Spark, ensuring that Hive can operate stably and support efficient data query and management.

This article describes the deployment process of HBase in detail, including environment configuration, file modification, Hadoop configuration file replication, configuration distribution between nodes, cluster startup and testing steps, and provides precautions for error troubleshooting and normal shutdown to ensure the stable operation of HBase clusters. You can verify that the deployment was successful by checking the logs and terminal output.

This article describes the deployment process of Spark clusters in detail, including the steps of downloading and decompressing Spark, configuring environment files, distributing configuration, starting the cluster, and adapting Spark to YARN. By modifying the spark-env.sh and yarn-site.xml configuration files, and distributing and restarting the YARN service on each node, the cooperative operation of Spark and YARN is finally realized. After the verification is successful, the Spark UI interface can be accessed through the browser.

This article describes the deployment process of Hadoop in detail, including downloading and decompressing, configuring core configuration files (such as hadoop-env.sh, core-site.xml, hdfs-site.xml, mapred-site.xml, yarn-site.xml and workers files), setting environment variables, distributing configuration on three servers, initializing the HDFS file system, starting various components (ZooKeeper, JournalNode, NameNode, ZKFC, JobHistory), and verifying whether each service is running normally through a browser. The entire process emphasizes the accuracy of configuration and special operations at first startup.

This article introduces the installation, configuration and running process of ZooKeeper. This includes downloading, setting environment variables, editing configuration files, creating data directories and configuring myid files, and synchronizing configuration and launching services on multiple nodes. By checking port occupation, empowerment and running commands, it is finally verified that ZooKeeper starts up normally and completes the confirmation of the election mechanism.

This article describes in detail how to configure the network, users, host names, SSH-free login and Java environment of the three servers, including basic network settings, user rights allocation, hosts file modification, SSH service deployment, directory creation and permission setting, Java environment installation and distribution, as well as tool package installation and other operations to ensure normal communication between the three servers and achieve secret-free access. All configurations are based on the actual IP address and host name, emphasizing configuration consistency and security.

Summary of the core content of the article: In a production environment, it is recommended to use Hadoop 3.4.0 as a stable version, paired with JDK 8 and compatible ecological components (such as Spark 3.4.3, Hive 3.1.3, etc.) to ensure system stability and functional integrity. The article details the hardware configuration, system environment settings, node role allocation, key configuration file adjustment and deployment process of the three-node cluster, and emphasizes production environment considerations such as data balancing, log monitoring and regular maintenance, which provides a complete guide for building an efficient and reliable Hadoop cluster.