flume+kafka+flink+mysql实现nginx数据统计与分析

https://juejin.cn/post/6937988947498762270

morganEngineer

6761人浏览 · 2022-04-20 20:08:49

morganEngineer · 2022-04-20 20:08:49 发布

时序图

在这里插入图片描述

架构图

在这里插入图片描述

环境准备

jdk： 1.8
zookeeper版本：3.4.14
kafka版本：kafka_2.12-2.3.1
flume版本：apache-flume-1.8.0-bin.tar.gz
操作系统：CentOS Linux release 7.4.1708 (Core)

安装zookeeper

安装教程：
https://www.runoob.com/w3cnote/zookeeper-setup.html

安装配置kafka

修改/etc/hosts，配置zookeeper域名解析
修改安装目录的应用配置文件/config/server.properties
1. 设置zookeeper地址:
```
zookeeper.connect=zk1:2181
```
host设置为使用hostname而不是ip，避免连接缓慢
2. 设置broker.id(在集群中必须唯一)
```
broker.id=x
```
1. 设置日志文件存储位置（也可使用默认位置）
```
log.dirs=/xxx/xxx
```
1. 配置允许kafka远程访问
```
advertised.listeners=PLAINTEXT://172.23.x.x:9092
```
1. 其余配置
```
#禁止自动创建主题
auto.create.topics.enable=false
#允许删除主题
delete.topic.enable=true
```
1. 配置文档:
  http://kafkadoc.beanmr.com/030_configuration/01_configuration_cn.html#brokerconfigs
在kafka安装目录输入以下命令启动kafka

bin/kafka-server-start.sh config/server.properties &

kafka集群安装好后，在zookeeper安装目录执行以下命令连接zookeeper

./bin/zkCli.sh -server zk1:2181

通过zookeeper节点查看kafka状态
命令

ls /brokers/ids

结果
在这里插入图片描述

创建topic

bin/kafka-topics.sh --create --zookeeper 172.23.x.x:2181 --replication-factor 1 --partitions 2 --topic nginx-log

创建成功后，在当前目录输入命令查看topic分片情况

bin/kafka-topics.sh --describe --zookeeper 172.23.x.x:2181 nginx-log

在这里插入图片描述
topic被存储在2个分区中，并且只有一个leader副本，不做冗余备份。

使用flume

架构图
安装flume
官方配置文档
https://flume.apache.org/releases/content/1.8.0/FlumeUserGuide.html

配置agent，数据输出到终端，以便验证方案是否可行

新建文件/etc/flume/nginx-log.conf（也可从安装目录的配置模板conf/flume-conf.properties.template开始），配置如下。

aNginx1.sources = rNginx1
aNginx1.sinks = kNginx1
aNginx1.channels = cNginx1

#配置source组件aNginx1
aNginx1.sources.rNginx1.type = exec
aNginx1.sources.rNginx1.command = tail -f /var/log/nginx/access.log
# Each sink's type must be defined
aNginx1.sinks.kNginx1.type = logger
# Each channel's type is defined.
# 描述和配置 channel 组件，此处使用是内存缓存的方式
aNginx1.channels.cNginx1.type = memory
# Other config values specific to each type of channel(sink or source)
# can be defined as well
# In this case, it specifies the capacity of the memory channel
aNginx1.channels.cNginx1.capacity = 1000
aNginx1.channels.cNginx1.transactionCapacity = 100
#配置 source、channel、sink 之间的连接关系
aNginx1.sources.rNginx1.channels = cNginx1
aNginx1.sinks.kNginx1.channel = cNginx1

在安装目录执行如下命令启动（将日志输出终端）

bin/flume-ng agent --conf conf --conf-file /etc/flume/nginx-log.conf --name aNginx1 -Dflume.root.logger=INFO,console &

再访问Nginx，观察终端输出效果判断是否抓取到nginx日志。

配置agent，数据输出到kafka

aNginx1.sources = rNginx1
aNginx1.sinks = kNginx1
aNginx1.channels = cNginx1
#配置source组件aNginx1
aNginx1.sources.rNginx1.type = exec
aNginx1.sources.rNginx1.command = tail -f /var/log/nginx/access.log
#调优参数
aNginx1.sources.rNginx1.batchSize = 2000
aNginx1.sources.rNginx1.batchDurationMillis = 1000
aNginx1.sinks.kNginx1.batchSize = 2000
aNginx1.sinks.kNginx1.batchDurationMillis = 1000

# 配置kafka
aNginx1.sinks.kNginx1.type = org.apache.flume.sink.kafka.KafkaSink
aNginx1.sinks.kNginx1.brokerList = 172.23.x.x:9092, 172.23.x.x:9092
aNginx1.sinks.kNginx1.kafka.topic = nginx-log
#压缩
aNginx1.sinks.kNginx1.kafka.producer.compression.type = snappy
#设置序列化方式
aNginx1.sinks.kNginx1.serializer.class = kafka.serializer.StringEncoder

# 描述和配置 channel 组件，此处使用是内存缓存的方式
aNginx1.channels.cNginx1.type = memory
# Other config values specific to each type of channel(sink or source)
# can be defined as well
# In this case, it specifies the capacity of the memory channel
aNginx1.channels.cNginx1.capacity = 3000
aNginx1.channels.cNginx1.transactionCapacity = 1000
#配置 source、channel、sink 之间的连接关系
aNginx1.sources.rNginx1.channels = cNginx1
aNginx1.sinks.kNginx1.channel = cNginx1

在安装目录执行如下命令启动(指定日志输出到文件，日志文件配置见安装目录的conf/log4j.properties)

bin/flume-ng agent --conf conf --conf-file /etc/flume/nginx-log.conf --name aNginx1 -Dflume.root.logger=INFO,LOGFILE &

使用flink

安装依赖

<properties>
        <flink.version>1.10.3</flink.version>
        <lombok.version>1.18.10</lombok.version>
        <fastjson.version>1.2.49</fastjson.version>

        <maven.compiler.version>3.5.1</maven.compiler.version>
        <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
        <project.reporting.outputEncoding>UTF-8</project.reporting.outputEncoding>
        <maven.compiler.encoding>UTF-8</maven.compiler.encoding>
    </properties>

    <dependencies>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-java</artifactId>
            <version>${flink.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-clients_2.12</artifactId>
            <version>${flink.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-streaming-java_2.12</artifactId>
            <version>${flink.version}</version>
<!--            <scope>provided</scope>-->
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-jdbc_2.12</artifactId>
            <version>${flink.version}</version>
            <scope>provided</scope>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-connector-kafka_2.12</artifactId>
            <version>${flink.version}</version>
        </dependency>

        <dependency>
            <groupId>org.projectlombok</groupId>
            <artifactId>lombok</artifactId>
            <scope>provided</scope>
            <version>${lombok.version}</version>
        </dependency>
        <dependency>
            <groupId>com.alibaba</groupId>
            <artifactId>fastjson</artifactId>
            <version>${fastjson.version}</version>
        </dependency>
        <dependency>
            <groupId>log4j</groupId>
            <artifactId>log4j</artifactId>
            <version>1.2.17</version>
        </dependency>
        <dependency>
            <groupId>org.slf4j</groupId>
            <artifactId>slf4j-log4j12</artifactId>
            <version>1.7.25</version>
        </dependency>
    </dependencies>

编写flume从kafka抓取nginx日志

// 构建流执行环境
        final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        
        // kafka 配置
        Properties props = new Properties();
        props.put("bootstrap.servers", "172.23.x.x:9092,172.23.x.x:9092");
        props.put("zookeeper.connect", "zk1:2181");
        props.put("group.id", "flink");
        props.put("client.id", "flink-1");
        props.put("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
        props.put("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
        //当各分区下有已提交的offset时，从提交的offset开始消费；无提交的offset时，消费新产生的该分区下的数据
        props.put("auto.offset.reset", "latest");
        props.put("enable.auto.commit", "true");

        DataStreamSource<String> dataStreamSource = env.addSource(
                new FlinkKafkaConsumer<String>(
                        "nginx-log",
                        new SimpleStringSchema(),
                        props))
                //单线程打印，控制台不乱序，不影响结果
                .setParallelism(1);

        //从kafka里读取数据
        dataStreamSource.timeWindowAll( Time.seconds(5L) ).apply(new AllWindowFunction<String, List<String>, TimeWindow>() {
            @Override
            public void apply(TimeWindow window, Iterable<String> iterableValues, Collector<List<String>> out) throws Exception {
                List<String> strList = Lists.newArrayList(iterableValues);
                if ( strList.isEmpty() ){
                    return;
                }
                strList.forEach( System.out::println );
            }
        });
        env.execute("nginx log analyse running");

抓取结果
在这里插入图片描述 4. 持久化到数据库

新增下沉类，用于保存统计结果

/**
 * @function:jdbc持久化器
 */
public class JdbcPersistence extends RichSinkFunction<List<String>> {

    @Override
    public void invoke(List<String> values, Context context) throws Exception {
        //TODO 解析日志
        for (String log : values) {
            System.out.println(log);
        }

        //TODO 构建统计结果

        //TODO 入库
    }
}

挂载下沉类到flume启动器

//从kafka里读取数据
        dataStreamSource.timeWindowAll( Time.seconds(5L) ).apply(new AllWindowFunction<String, List<String>, TimeWindow>() {
            @Override
            public void apply(TimeWindow window, Iterable<String> iterableValues, Collector<List<String>> out) throws Exception {
                List<String> strList = Lists.newArrayList(iterableValues);
                if ( strList.isEmpty() ){
                    return;
                }
                out.collect(strList);
            }
        }).addSink( new JdbcPersistence() );sink 到数据库