济南建站价格seo网络推广教程
目录
1.采集流程
2.项目架构
3.resources目录下的log4j.properties文件
4.依赖
5.ODS层——OdsApp
6.环境入口类——CreateEnvUtil
7.kafka工具类——KafkaUtil
8.启动集群项目
这一层要从Mysql读取数据，分为事实数据和维度数据，将不同类型的数据进行不同的ETL处理，发送到kakfa中。
代码
1.采集流程
2.项目架构
3.resources目录下的log4j.properties文件
log4j.rootLogger=error,stdout
log4j.appender.stdout=org.apache.log4j.ConsoleAppender
log4j.appender.stdout.target=System.out
log4j.appender.stdout.layout=org.apache.log4j.PatternLayout
log4j.appender.stdout.layout.ConversionPattern=%d %p [%c] - %m%n4.依赖
<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd"><modelVersion>4.0.0</modelVersion><groupId>com.atguigu.tms.realtime</groupId><artifactId>tms-realtime</artifactId><version>1.0-SNAPSHOT</version><properties><maven.compiler.source>8</maven.compiler.source><maven.compiler.target>8</maven.compiler.target><project.build.sourceEncoding>UTF-8</project.build.sourceEncoding><java.version>1.8</java.version><flink.version>1.17.0</flink.version><hadoop.version>3.3.4</hadoop.version><flink-cdc.version>2.3.0</flink-cdc.version></properties><dependencies><dependency><groupId>org.apache.flink</groupId><artifactId>flink-java</artifactId><version>${flink.version}</version></dependency><dependency><groupId>org.apache.flink</groupId><artifactId>flink-streaming-java</artifactId><version>${flink.version}</version></dependency><dependency><groupId>org.apache.flink</groupId><artifactId>flink-connector-kafka</artifactId><version>${flink.version}</version></dependency><dependency><groupId>com.alibaba</groupId><artifactId>fastjson</artifactId><version>1.2.68</version></dependency><dependency><groupId>org.apache.hadoop</groupId><artifactId>hadoop-client</artifactId><version>${hadoop.version}</version><exclusions><exclusion><groupId>org.slf4j</groupId><artifactId>slf4j-reload4j</artifactId></exclusion></exclusions></dependency><dependency><groupId>org.apache.flink</groupId><artifactId>flink-clients</artifactId><version>${flink.version}</version></dependency><dependency><groupId>org.slf4j</groupId><artifactId>slf4j-api</artifactId><version>1.7.25</version><scope>provided</scope></dependency><dependency><groupId>org.slf4j</groupId><artifactId>slf4j-log4j12</artifactId><version>1.7.25</version><scope>provided</scope></dependency><dependency><groupId>org.apache.logging.log4j</groupId><artifactId>log4j-to-slf4j</artifactId><version>2.14.0</version><scope>provided</scope></dependency><dependency><groupId>com.ververica</groupId><artifactId>flink-connector-mysql-cdc</artifactId><version>${flink-cdc.version}</version></dependency><dependency><groupId>org.apache.flink</groupId><artifactId>flink-table-runtime</artifactId><version>${flink.version}</version><scope>provided</scope></dependency><dependency><groupId>org.apache.flink</groupId><artifactId>flink-table-planner-loader</artifactId><version>${flink.version}</version><scope>provided</scope></dependency><dependency><groupId>org.apache.hbase</groupId><artifactId>hbase-client</artifactId><version>2.4.11</version></dependency><dependency><groupId>org.apache.hadoop</groupId><artifactId>hadoop-auth</artifactId><version>${hadoop.version}</version><exclusions><exclusion><groupId>org.slf4j</groupId><artifactId>slf4j-reload4j</artifactId></exclusion></exclusions></dependency><dependency><groupId>org.projectlombok</groupId><artifactId>lombok</artifactId><version>1.18.20</version></dependency><dependency><groupId>redis.clients</groupId><artifactId>jedis</artifactId><version>3.3.0</version></dependency><dependency><groupId>org.apache.flink</groupId><artifactId>flink-connector-jdbc</artifactId><version>3.1.0-1.17</version></dependency><dependency><groupId>ru.yandex.clickhouse</groupId><artifactId>clickhouse-jdbc</artifactId><version>0.3.2</version><exclusions><exclusion><groupId>com.fasterxml.jackson.core</groupId><artifactId>jackson-databind</artifactId></exclusion><exclusion><groupId>com.fasterxml.jackson.core</groupId><artifactId>jackson-core</artifactId></exclusion></exclusions></dependency></dependencies><build><plugins><plugin><groupId>org.apache.maven.plugins</groupId><artifactId>maven-shade-plugin</artifactId><version>3.1.1</version><executions><execution><phase>package</phase><goals><goal>shade</goal></goals><configuration><artifactSet><excludes><exclude>com.google.code.findbugs:jsr305</exclude><exclude>org.slf4j:*</exclude><exclude>log4j:*</exclude><exclude>org.apache.hadoop:*</exclude></excludes></artifactSet><filters><filter><!-- Do not copy the signatures in the META-INF folder.Otherwise, this might cause SecurityExceptions when using the JAR. --><!-- 打包时不复制META-INF下的签名文件，避免报非法签名文件的SecurityExceptions异常--><artifact>*:*</artifact><excludes><exclude>META-INF/*.SF</exclude><exclude>META-INF/*.DSA</exclude><exclude>META-INF/*.RSA</exclude></excludes></filter></filters><transformers combine.children="append"><!-- The service transformer is needed to merge META-INF/services files --><!-- connector和format依赖的工厂类打包时会相互覆盖，需要使用ServicesResourceTransformer解决--><transformerimplementation="org.apache.maven.plugins.shade.resource.ServicesResourceTransformer"/></transformers></configuration></execution></executions></plugin></plugins></build></project>5.ODS层——OdsApp
package com.atguigu.tms.realtime.app.ods;import com.alibaba.fastjson.JSON;
import com.alibaba.fastjson.JSONObject;
import com.atguigu.tms.realtime.utils.CreateEnvUtil;
import com.atguigu.tms.realtime.utils.KafkaUtil;
import com.esotericsoftware.minlog.Log;
import com.ververica.cdc.connectors.mysql.source.MySqlSource;
import com.ververica.cdc.debezium.JsonDebeziumDeserializationSchema;
import org.apache.flink.api.common.eventtime.WatermarkStrategy;
import org.apache.flink.api.common.restartstrategy.RestartStrategies;
import org.apache.flink.api.common.serialization.SimpleStringSchema;
import org.apache.flink.api.common.time.Time;
import org.apache.flink.api.java.functions.KeySelector;
import org.apache.flink.connector.base.DeliveryGuarantee;
import org.apache.flink.connector.kafka.sink.KafkaRecordSerializationSchema;
import org.apache.flink.connector.kafka.sink.KafkaSink;
import org.apache.flink.runtime.state.hashmap.HashMapStateBackend;
import org.apache.flink.streaming.api.CheckpointingMode;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.datastream.KeyedStream;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.CheckpointConfig;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.ProcessFunction;
import org.apache.flink.util.Collector;/*** ODS数据的采集*/
public class OdsApp {public static void main(String[] args) throws Exception {// TODO 1.获取流处理环境并指定检查点StreamExecutionEnvironment env = CreateEnvUtil.getStreamEnv(args);env.setParallelism(4);// TODO 2.使用FlinkCDC从Mysql中读取数据-事实数据-保存到kafkaString dwdOption = "dwd";String dwdServerId = "6030";String dwdSourceName = "ods_app_dwd_source";mysqlToKafka(dwdOption, dwdServerId, dwdSourceName, env, args);// TODO 3.使用FlinkCDC从Mysql中读取数据-维度数据-保存到kafkaString realtimeOption = "realtime_dim";String realtimeServerId = "6040";String realtimeSourceName = "ods_app_realtimeDim_source";mysqlToKafka(realtimeOption, realtimeServerId, realtimeSourceName, env, args);env.execute();}public static void mysqlToKafka(String option, String serverId, String sourceName, StreamExecutionEnvironment env, String[] args) {// TODO 1.使用FlinkCDC从Mysql中读取数据MySqlSource<String> mysqlSource = CreateEnvUtil.getMysqlSource(option, serverId, args);SingleOutputStreamOperator<String> strDS = env.fromSource(mysqlSource, WatermarkStrategy.noWatermarks(), sourceName).setParallelism(1)// 并行度设置为1的原因是防止乱序.uid(option + sourceName);// TODO 2.简单的ETLSingleOutputStreamOperator<String> processDS = strDS.process(new ProcessFunction<String, String>() {@Overridepublic void processElement(String jsonStr, ProcessFunction<String, String>.Context ctx, Collector<String> out) throws Exception {try {// 将json字符串转为json对象JSONObject jsonObj = JSON.parseObject(jsonStr);// after属性不为空，并且不是删除if (jsonObj.getJSONObject("after") != null && !"d".equals(jsonObj.getString("op"))) {// 为了防止歧义，将ts_ms字段改为tsLong tsMs = jsonObj.getLong("ts_ms");jsonObj.put("ts", tsMs);jsonObj.remove("ts_ms");// 移除原来的ts_ms字段// 符合条件以后，向下传递之前先将json对象转为json字符串out.collect(jsonObj.toJSONString());}} catch (Exception e) {e.printStackTrace();Log.error("从Flink-CDC得到的数据不是一个标准的json格式");}}}).setParallelism(1);// 防止乱序// TODO 3.按照主键进行分许，避免出现乱序，主键就是after下的id字段KeyedStream<String, String> keyedDS = processDS.keyBy(new KeySelector<String, String>() {@Overridepublic String getKey(String jsonStr) throws Exception {// 获取当前的key// 流中的字符串转为json对象JSONObject jsonObj = JSON.parseObject(jsonStr);return jsonObj.getJSONObject("after").getString("id");}});// TODO 4.将数据写到kafka主题中keyedDS.sinkTo(KafkaUtil.getKafkaSink("tms_ods", sourceName + "_transPre", args)).uid(option + "_ods_app_sink");}
}6.环境入口类——CreateEnvUtil
package com.atguigu.tms.realtime.utils;import com.esotericsoftware.minlog.Log;
import com.ververica.cdc.connectors.mysql.source.MySqlSource;
import com.ververica.cdc.connectors.mysql.source.MySqlSourceBuilder;
import com.ververica.cdc.connectors.mysql.table.StartupOptions;
import com.ververica.cdc.debezium.JsonDebeziumDeserializationSchema;
import org.apache.flink.api.common.restartstrategy.RestartStrategies;
import org.apache.flink.api.common.time.Time;
import org.apache.flink.api.java.utils.ParameterTool;
import org.apache.flink.runtime.state.hashmap.HashMapStateBackend;
import org.apache.flink.streaming.api.CheckpointingMode;
import org.apache.flink.streaming.api.environment.CheckpointConfig;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.hadoop.yarn.webapp.hamlet2.Hamlet;
import org.apache.kafka.connect.json.DecimalFormat;
import org.apache.kafka.connect.json.JsonConverterConfig;import java.util.HashMap;/*** 获取执行环境* flinkCDC读取mysqlSource的原因是将自己伪装成从机*/
public class CreateEnvUtil {//获取流处理环境public static StreamExecutionEnvironment getStreamEnv(String[] args) {//TODO 1.基本环境准备//1.1 指定流处理环境StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();//TODO 2.检查点相关的设置//2.1 开启检查点env.enableCheckpointing(60000L, CheckpointingMode.EXACTLY_ONCE);//2.2 设置检查点的超时时间env.getCheckpointConfig().setCheckpointTimeout(120000L);//2.3 设置job取消之后 检查点是否保留env.getCheckpointConfig().setExternalizedCheckpointCleanup(CheckpointConfig.ExternalizedCheckpointCleanup.RETAIN_ON_CANCELLATION);//2.4 设置两个检查点之间的最小时间间隔env.getCheckpointConfig().setMinPauseBetweenCheckpoints(30000L);//2.5 设置重启策略env.setRestartStrategy(RestartStrategies.failureRateRestart(3, Time.days(1), Time.seconds(3)));//2.6 设置状态后端env.setStateBackend(new HashMapStateBackend());env.getCheckpointConfig().setCheckpointStorage("hdfs://hadoop102:8020/tms/ck");//2.7 设置操作hdfs的用户//获取命令行参数ParameterTool parameterTool = ParameterTool.fromArgs(args);String hdfsUserName = parameterTool.get("hadoop-user-name", "atguigu");System.setProperty("HADOOP_USER_NAME", hdfsUserName);return env;}//获取MySqlSourcepublic static MySqlSource<String> getMysqlSource(String option, String serverId, String[] args) {ParameterTool parameterTool = ParameterTool.fromArgs(args);String mysqlHostname = parameterTool.get("mysql-hostname", "hadoop102");int mysqlPort = Integer.valueOf(parameterTool.get("mysql-port", "3306"));String mysqlUsername = parameterTool.get("mysql-username", "root");String mysqlPasswd = parameterTool.get("mysql-passwd", "root");option = parameterTool.get("start-up-options", option);// serverId是对服务器节点进行标记serverId = parameterTool.get("server-id", serverId);// 创建配置信息 Map 集合，将 Decimal 数据类型的解析格式配置 k-v 置于其中HashMap config = new HashMap<>();config.put(JsonConverterConfig.DECIMAL_FORMAT_CONFIG, DecimalFormat.NUMERIC.name());// 将前述 Map 集合中的配置信息传递给 JSON 解析 Schema，该 Schema 将用于 MysqlSource 的初始化JsonDebeziumDeserializationSchema jsonDebeziumDeserializationSchema =new JsonDebeziumDeserializationSchema(false, config);MySqlSourceBuilder<String> builder = MySqlSource.<String>builder().hostname(mysqlHostname).port(mysqlPort).username(mysqlUsername).password(mysqlPasswd).deserializer(jsonDebeziumDeserializationSchema);// 读取的数据可能是维度或事实，需要通过标记来区分，从而对不同类型的数据进不同的处理switch (option) {// 读取事实数据case "dwd":String[] dwdTables = new String[]{"tms.order_info","tms.order_cargo","tms.transport_task","tms.order_org_bound"};// 只读取这4个事实表return builder.databaseList("tms").tableList(dwdTables).startupOptions(StartupOptions.latest())// 表示从mysql的binlog最新位置读取最新的数据.serverId(serverId).build();// 读取维度数据case "realtime_dim":String[] realtimeDimTables = new String[]{"tms.user_info","tms.user_address","tms.base_complex","tms.base_dic","tms.base_region_info","tms.base_organ","tms.express_courier","tms.express_courier_complex","tms.employee_info","tms.line_base_shift","tms.line_base_info","tms.truck_driver","tms.truck_info","tms.truck_model","tms.truck_team"};// 读取维度数据表15张return builder.databaseList("tms").tableList(realtimeDimTables).startupOptions(StartupOptions.initial())// 表示在第一次启动时对监控的数据库表执行初始快照，并继续读取最新的binlog。.serverId(serverId).build();case "config_dim":return builder.databaseList("tms_config").tableList("tms_config.tms_config_dim").startupOptions(StartupOptions.initial()).serverId(serverId).build();}Log.error("不支持的操作类型！");return null;}
}7.kafka工具类——KafkaUtil
package com.atguigu.tms.realtime.utils;import org.apache.flink.api.common.serialization.DeserializationSchema;
import org.apache.flink.api.common.serialization.SimpleStringSchema;
import org.apache.flink.api.common.typeinfo.TypeInformation;
import org.apache.flink.api.java.utils.ParameterTool;
import org.apache.flink.connector.base.DeliveryGuarantee;
import org.apache.flink.connector.kafka.sink.KafkaRecordSerializationSchema;
import org.apache.flink.connector.kafka.sink.KafkaSink;
import org.apache.flink.connector.kafka.source.KafkaSource;
import org.apache.flink.connector.kafka.source.enumerator.initializer.OffsetsInitializer;
import org.apache.kafka.clients.consumer.OffsetResetStrategy;
import org.apache.kafka.clients.producer.ProducerConfig;import java.io.IOException;/*** 操作Kafka的工具类*/
public class KafkaUtil {private static final String KAFKA_SERVER = "hadoop102:9092,hadoop103:9092,hadoop104:9092";// 获取kafkaSink的方法 事务id的前缀public static KafkaSink<String> getKafkaSink(String topic, String transIdPrefix, String[] args) {// 使用args参数的原因是为了从外部获取参数。在Java中，args是一个命令行参数数组，当你在命令行中运行Java程序时，你可以通过在命令行中输入参数来传递数据给程序。// 将命令行参数对象封装为 ParameterTool 类对象ParameterTool parameterTool = ParameterTool.fromArgs(args);// 提取命令行传入的 key 为 topic 的配置信息，并将默认值指定为方法参数 topic// 当命令行没有指定 topic 时，会采用默认值topic = parameterTool.get("topic", topic);// 如果命令行没有指定主题名称且默认值为 null 则抛出异常if (topic == null) {throw new IllegalArgumentException("主题名不可为空：命令行传参为空且没有默认值!");}// 获取命令行传入的 key 为 bootstrap-servers 的配置信息，并指定默认值String bootstrapServers = parameterTool.get("bootstrap-severs", KAFKA_SERVER);// 获取命令行传入的 key 为 transaction-timeout 的配置信息，并指定默认值String transactionTimeout = parameterTool.get("transaction-timeout", 15 * 60 * 1000 + "");KafkaSink<String> kafkaSink = KafkaSink.<String>builder().setBootstrapServers(bootstrapServers).setRecordSerializer(KafkaRecordSerializationSchema.builder().setTopic(topic).setValueSerializationSchema(new SimpleStringSchema()).build()).setDeliveryGuarantee(DeliveryGuarantee.EXACTLY_ONCE).setTransactionalIdPrefix(transIdPrefix).setProperty(ProducerConfig.TRANSACTION_TIMEOUT_CONFIG, transactionTimeout).build();return kafkaSink;}// 使用这个就只需要传入topic和args即可public static KafkaSink<String> getKafkaSink(String topic, String[] args) {return getKafkaSink(topic, topic + "_trans", args);}
}8.启动集群项目
开启消费者，然后启动java项目即可