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物流实时数仓：采集通道搭建
物流实时数仓：数仓搭建
文章目录
前言
现在我们开始进行数仓的搭建，我们用Kafka来代替数仓的ods层。
基本流程为使用Flink从MySQL读取数据然后写入Kafka中
一、IDEA环境准备
1.pom.xml
<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>3.2.3</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>${flink.version}</version>
</dependency>
<dependency>
<groupId>org.slf4j</groupId>
<artifactId>slf4j-api</artifactId>
<version>1.7.25</version>
</dependency>
<dependency>
<groupId>org.slf4j</groupId>
<artifactId>slf4j-log4j12</artifactId>
<version>1.7.25</version>
</dependency>
<dependency>
<groupId>org.apache.logging.log4j</groupId>
<artifactId>log4j-to-slf4j</artifactId>
<version>2.14.0</version>
</dependency>
<dependency>
<groupId>com.ververica</groupId>
<artifactId>flink-connector-mysql-cdc</artifactId>
<version>${flink-cdc.version}</version>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-table-runtime</artifactId>
<version>${flink.version}</version>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-table-planner-loader</artifactId>
<version>${flink.version}</version>
</dependency>
</dependencies>
2.目录创建
按照以上二、代码编写
1.log4j.properties
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%n
2.CreateEnvUtil.java
这个文件中有两个方法
创建初始化Flink的env
Flink连接mysql的MySqlSource
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.kafka.connect.json.DecimalFormat;
import org.apache.kafka.connect.json.JsonConverterConfig;
import java.util.HashMap;
public class CreateEnvUtil {
public static StreamExecutionEnvironment getStreamEnv(String[] args) {
// 1.1 指定流处理环境
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
// 2.检查点相关设置
// 2.1 开启检查点
env.enableCheckpointing(6000L, 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;
}
public static MySqlSource<String> getMysqlSource(String option, String serverId, String[] args) {
ParameterTool parameterTool = ParameterTool.fromArgs(args);
String mysqlHostname = parameterTool.get("hadoop-user-name", "hadoop102");
int mysqlPort = Integer.parseInt(parameterTool.get("mysql-port", "3306"));
String mysqlUsername = parameterTool.get("mysql-username", "root");
String mysqlPasswd = parameterTool.get("mysql-passwd", "000000");
option = parameterTool.get("start-up-option", option);
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);
switch (option) {
// 读取实时数据
case "dwd":
String[] dwdTables = new String[]{
"tms.order_info",
"tms.order_cargo",
"tms.transport_task",
"tms.order_org_bound"};
return builder
.databaseList("tms")
.tableList(dwdTables)
.startupOptions(StartupOptions.latest())
.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"};
return builder
.databaseList("tms")
.tableList(realtimeDimTables)
.startupOptions(StartupOptions.initial())
.serverId(serverId)
.build();
}
Log.error("不支持操作类型");
return null;
}
}
3.KafkaUtil.java
该文件中有一个方法，创建Flink连接Kafka需要的Sink
package com.atguigu.tms.realtime.utils;
import org.apache.flink.api.common.serialization.SimpleStringSchema;
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.kafka.clients.producer.ProducerConfig;
public class KafkaUtil {
private static final String KAFKA_SERVER = "hadoop102:9092,hadoop103:9092,hadoop104:9092";
public static KafkaSink<String> getKafkaSink(String topic, String transIdPrefix, String[] args) {
// 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 + "");
return KafkaSink.<String>builder()
.setBootstrapServers(bootstrapServers)
.setRecordSerializer(KafkaRecordSerializationSchema.builder()
.setTopic(topic)
.setValueSerializationSchema(new SimpleStringSchema())
.build()
)
.setDeliveryGuarantee(DeliveryGuarantee.AT_LEAST_ONCE)
.setTransactionalIdPrefix(transIdPrefix)
.setProperty(ProducerConfig.TRANSACTION_TIMEOUT_CONFIG, transactionTimeout)
.build();
}
public static KafkaSink<String> getKafkaSink(String topic, String[] args) {
return getKafkaSink(topic, topic + "_trans", args);
}
}
4.OdsApp.java
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 org.apache.flink.api.common.eventtime.WatermarkStrategy;
import org.apache.flink.api.java.functions.KeySelector;
import org.apache.flink.streaming.api.datastream.KeyedStream;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.ProcessFunction;
import org.apache.flink.util.Collector;
public class OdsApp {
public static void main(String[] args) throws Exception {
// 1.获取流处理环境并指定检查点
StreamExecutionEnvironment env = CreateEnvUtil.getStreamEnv(args);
env.setParallelism(4);
// 2 使用FlinkCDC从MySQL中读取数据-事实数据
String dwdOption = "dwd";
String dwdServerId = "6030";
String dwdsourceName = "ods_app_dwd_source";
mysqlToKafka(dwdOption, dwdServerId, dwdsourceName, env, args);
// 3 使用FlinkCDC从MySQL中读取数据-维度数据
String realtimeDimOption = "realtime_dim";
String realtimeDimServerId = "6040";
String realtimeDimsourceName = "ods_app_realtimeDim_source";
mysqlToKafka(realtimeDimOption, realtimeDimServerId, realtimeDimsourceName, env, args);
env.execute();
}
public static void mysqlToKafka(String option, String serverId, String sourceName, StreamExecutionEnvironment env, String[] args) {
MySqlSource<String> MySqlSource = CreateEnvUtil.getMysqlSource(option, serverId, args);
SingleOutputStreamOperator<String> dwdStrDS = env.fromSource(MySqlSource, WatermarkStrategy.noWatermarks(), sourceName)
.setParallelism(1)
.uid(option + sourceName);
// 3 简单ETL
SingleOutputStreamOperator<String> processDS = dwdStrDS.process(
new ProcessFunction<String, String>() {
@Override
public void processElement(String jsonStr, ProcessFunction<String, String>.Context ctx, Collector<String> out) {
try {
JSONObject jsonObj = JSONObject.parseObject(jsonStr);
if (jsonObj.getJSONObject("after") != null && !"d".equals(jsonObj.getString("op"))) {
// System.out.println(jsonObj);
Long tsMs = jsonObj.getLong("ts_ms");
jsonObj.put("ts", tsMs);
jsonObj.remove("ts_ms");
String jsonString = jsonObj.toJSONString();
out.collect(jsonString);
}
} catch (Exception e) {
Log.error("从Flink-CDC得到的数据不是一个标准的json格式",e);
}
}
}
).setParallelism(1);
// 4 按照主键进行分组，避免出现乱序
KeyedStream<String, String> keyedDS = processDS.keyBy((KeySelector<String, String>) jsonStr -> {
JSONObject jsonObj = JSON.parseObject(jsonStr);
return jsonObj.getJSONObject("after").getString("id");
});
//将数据写入Kafka
keyedDS.sinkTo(KafkaUtil.getKafkaSink("tms_ods", sourceName + "_transPre", args))
.uid(option + "_ods_app_sink");
}
}
三、代码测试
在虚拟机启动我们需要的组件，目前需要hadoop、zk、kafka和MySQL。
先开一个消费者进行消费。
bin/kafka-console-consumer.sh --bootstrap-server hadoop102:9092 --topic tms_ods
然后运行OdsApp.java
他会先读取维度数据，因为维度数据需要全量更新之前的数据。
当他消费结束后，我们运行jar包，获取事实数据。
java -jar tms-mock-2023-01-06.jar
如果能消费到新数据，代表通道没问题，ODS层创建完成。
总结
至此ODS搭建完成。
原文地址:https://blog.csdn.net/weixin_50835854/article/details/134594285
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