# 安装Java
$ dnf install -y java-11-openjdk-devel

# 下载Hadoop
$ wget https://downloads.apache.org/hadoop/common/hadoop-3.3.4/hadoop-3.3.4.tar.gz
$ tar -xzf hadoop-3.3.4.tar.gz
$ mv hadoop-3.3.4 /opt/hadoop

# 下载Spark
$ wget https://downloads.apache.org/spark/spark-3.3.2/spark-3.3.2-bin-hadoop3.tgz
$ tar -xzf spark-3.3.2-bin-hadoop3.tgz
$ mv spark-3.3.更多视频教程www.fgedu.net.cn2-bin-hadoop3 /opt/spark

# 下载Kafka
$ wget https://downloads.apache.org/kafka/3.3.1/kafka_2.13-3.3.1.tgz
$ tar -xzf kafka_2.13-3.3.1.tgz
$ mv kafka_2.13-3.3.1 /opt/kafka

# 配置Hadoop
$ vi /opt/hadoop/etc/hadoop/hadoop-env.sh
—
export JAVA_HOME=/usr/lib/jvm/java-11-openjdk
—

$ vi /opt/hadoop/etc/hadoop/core-site.xml
—
fs.defaultFS
hdfs://namenode:9000
—

$ vi /opt/hadoop/etc/hadoop/hdfs-site.xml
—
dfs.replication
3 dfs.namenode.name.dir
/data/hadoop/namenode dfs.datanode.data.dir
/data/hadoop/datanode
—

# 配置Spark
$ vi /opt/spark/conf/spark-env.sh
—
export JAVA_HOME=/usr/lib/jvm/java-11-openjdk
export HADOOP_CONF_DIR=/opt/hadoop/etc/hadoop
export SPARK_HOME=/opt/spark
—

# 配置Kafka
$ vi /opt/kafka/config/server.properties
—
broker.id=0
listeners=PLAINTEXT://:9092
log.dirs=/data/kafka/logs
zookeeper.connect=zookeeper:2181
—

# 启动服务
# 启动Zookeeper
$ /opt/kafka/bin/zookeeper-server-start.sh -daemon /opt/kafka/config/zookeeper.properties

# 启动Kafka
$ /opt/kafka/bin/kafka-server-start.sh -daemon /opt/kafka/config/server.properties

# 启动HDFS
$ /opt/hadoop/sbin/start-dfs.sh

# 启动YARN
$ /opt/hadoop/sbin/start-yarn.sh

# 启动Spark
$ /opt/spark/sbin/start-all.sh

# 验证部署
$ jps
$ /opt/hadoop/bin/hdfs dfsadmin -report
$ /opt/kafka/bin/kafka-topics.sh –list –bootstrap-server localhost:9092
$ /opt/spark/bin/spark-submit –version

# 安装Elasticsearch
$ rpm –import https://artifacts.elastic.co/GPG-KEY-elasticsearch
$ vi /etc/yum.repos.d/elasticsearch.repo
—
[elasticsearch-8.x]
name=Elasticsearch repository for 8.x packages
baseurl=https://artifacts.elastic.co/packages/8.x/yum
gpgcheck=1
gpgkey=https://artifacts.elastic.co/GPG-KEY-elasticsearch
enabled=1
autorefresh=1
type=rpm-md
—

$ dnf install -y elasticsearch
$ systemctl start elasticsearch
$ systemctl enable elasticsearch

# 安装Kibana
$ dnf install -y kibana
$ systemctl start kibana
$ systemctl enable kibana

# 安装Flink
$ wget https://downloads.apache.org/flink/flink-1.15.3/flink-1.15.3-bin-scala_2.12.tgz
$ tar -xzf flink-1.15.3-bin-scala_2.12.tgz
$ mv flink-1.15.3 /opt/flink

# 安装Grafana
$ dnf install -y grafana
$ systemctl start grafana-server
$ systemctl enable grafana-server

# 配置Flink
$ vi /opt/flink/conf/flink-conf.yaml
—
jobmanager.rpc.address: localhost
jobmanager.rpc.port: 6123
jobmanager.heap.size: 1学习交流加群风哥QQ113257174024m
taskmanager.heap.size: 2048m
taskmanager.numberOfTaskSlots: 2
parallelism.default: 2
—

# 启动服务
# 启动Flink
$ /opt/flink/bin/start-cluster.sh

# 验证部署
$ systemctl status elasticsearch
$ systemctl status kibana
$ systemctl status grafana-server
$ /opt/flink/bin/flink list

## 1. 配置Kafka
$ vi /opt/kafka/config/server.properties
—
broker.id=0
listeners=PLAINTEXT://:9092
log.dirs=/data/kafka/logs
zookeeper.connect=zookeeper:2181
default.replication.factor=3
min.insync.replicas=2
log.retention.hours=168
log.segment.bytes=1073741824
num.partitions=3
—

# 创建Kafka主题
$ /opt/kafka/bin/kafka-topics.sh –create –topic data-topic –partitions 3 –replication-factor 3 –bootstrap-server localhost:9092

## 2. 配置Spark
$ vi /opt/spark/conf/spark-de学习交流加群风哥微信: itpux-comfaults.conf
—
spark.master yarn
spark.eventLog.enabled true
spark.eventLog.dir hdfs://namenode:9000/spark-logs
spark.executor.memory 2g
spark.driver.memory 1g
spark.yarn.executor.memoryOverhead 512
spark.yarn.driver.memoryOverhead 512
—

## 3. 配置Flink
$ vi /opt/flink/conf/flink-conf.yaml
—
jobmanager.rpc.address: localhost
jobmanager.rpc.port: 6123
jobmanager.heap.size: 2048m
taskmanager.heap.size: 4096m
taskmanager.numberOfTaskSlots: 4
parallelism.default: 4
state.backend: rocksdb
state.checkpoints.dir: hdfs://namenode:9000/flink-checkpoints
—

## 4. 配置Elasticsearch
$ vi /etc/elasticsearch/elasticsearch.更多学习教程公众号风哥教程itpux_comyml
—
cluster.name: big-data-cluster
node.name: node-1
path.data: /data/elasticsearch
path.logs: /var/log/elasticsearch
network.host: 0.0.0.0
http.port: 9200
discovery.type: single-node
—

# 验证系统大数据处理平台进阶策略
# 检查Kafka配置
$ /opt/kafka/bin/kafka-topics.sh –describe –topic data-topic –bootstrap-server localhost:9092

# 检查Spark配置
$ /opt/spark/bin/spark-submit –conf spark.executor.memory=2g –version

# 检查Flink配置
$ /opt/flink/bin/flink run –help

# 检查Elasticsearch配置
$ curl -X GET “localhost:9200/_cluster/health”

## 1. 创建Jenkinsfile
$ vi Jenkinsfile
—
pipeline {
agent any

stages {
stage(‘Checkout’) {
steps {
git branch: ‘main’, url: ‘https://github.com/example/big-data-processing.git’
}
}

stage(‘Build’) {
steps {
sh ‘mvn clean package -DskipTests’
}
}

stage(‘Deploy’) {
steps {
sh ‘ansible-playbook playbooks/deploy-big-data.yml’
}
}

stage(‘Test’) {
steps {
sh ‘ansible-playbook playbooks/test-big-data.yml’
}
}

stage(‘Verify’) {
steps {
sh ‘ansible-playbook playbooks/verify-big-data.yml’
}
}
}

post {
always {
archiveArtifacts artifacts: ‘**/target/*.jar’, fingerprint: true
}
success {
echo ‘Big data processing pipeline completed successfully!’
}
failure {
echo ‘Big data processing pipeline failed!’
mail to: ‘admin@fgedu.net.cn’, subject: ‘Big data processing pipeline failure’, body: ‘Big data processing pipeline failed in Jenkins’
}
}
}
—

# 集成系统大数据处理平台进阶与监控系统
# 配置Prometheus + Grafana

## 1. 配置Prometheus监控
$ vi /etc/prometheus/prometheus.yml
—
global:
scrape_interval: 15s

scrape_configs:
– job_name: ‘hadoop’
static_configs:
– targets: [‘namenode:9100’]

– job_name: ‘kafka’
static_configs:
– targets: [‘kafka:9308’]

– job_name: ‘spark’
static_configs:
– targets: [‘spark:8080’]

– job_name: ‘flink’
static_configs:
– targets: [‘flink:8081’]

– job_name: ‘elasticsearch’
static_configs:
– targets: [‘elasticsearch:9100’]
—

## 2. 配置Grafana仪表盘
# 导入大数据处理监控仪表盘

# 集成系统大数据处理平台进阶与容器编排系统
# 配置Kubernetes

## 1. 配置部署
$ vi big-data-deployment.yaml
—
apiVersion: apps/v1
kind: Deployment
metadata:
name: kafka
namespace: big-data
spec:
replicas: 3
selector:
matchLabels:
app: kafka
template:
metadata:
labels:
app: kafka
spec:
containers:
– name: kafka
image: bitnami/kafka:latest
ports:
– containerPort: 9092
env:
– name: KAFKA_CFG_ZOOKEEPER_CONNECT
value: zookeeper:2181
– name: KAFKA_CFG_ADVERTISED_LISTENERS
value: PLAINTEXT://kafka:9092
—

$ kubectl apply -f big-data-deployment.yaml

# 验证系统大数据处理平台进阶集成
# 检查CI/CD集成
# 运行Jenkins Pipeline

# 检查监控集成
$ curl http://prometheus-server:9090/targets

# 检查容器编排集成
$ kubectl get pods -n big-data
风哥提示：