一、流程架构图
二、开源框架及本版选择
本次项目中用到的相关服务有:hadoop、zookeeper、kafka、maxwell、hbase、phoenix、flink
三、服务部署完成后,开发Flink主程序
3.1 结构图如下:
3.2 代码详细内容
3.2.1 pom文件
<?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"> <parent> <artifactId>gamll-realtime-2024</artifactId> <groupId>org.example</groupId> <version>1.0-SNAPSHOT</version> </parent> <modelVersion>4.0.0</modelVersion> <groupId>com.dianyanyuan</groupId> <artifactId>gmall-realtime</artifactId> <properties> <java.version>1.8</java.version> <maven.compiler.source>${java.version}</maven.compiler.source> <maven.compiler.target>${java.version}</maven.compiler.target> <flink.version>1.13.0</flink.version> <scala.version>2.12</scala.version> <hadoop.version>3.1.3</hadoop.version> </properties> <dependencies> <dependency> <groupId>org.apache.flink</groupId> <artifactId>flink-java</artifactId> <version>${flink.version}</version> </dependency> <dependency> <groupId>org.apache.flink</groupId> <artifactId>flink-streaming-java_${scala.version}</artifactId> <version>${flink.version}</version> </dependency> <dependency> <groupId>org.apache.flink</groupId> <artifactId>flink-connector-kafka_${scala.version}</artifactId> <version>${flink.version}</version> </dependency> <dependency> <groupId>org.apache.flink</groupId> <artifactId>flink-clients_${scala.version}</artifactId> <version>${flink.version}</version> </dependency> <dependency> <groupId>org.apache.flink</groupId> <artifactId>flink-json</artifactId> <version>${flink.version}</version> </dependency> <dependency> <groupId>com.alibaba</groupId> <artifactId>fastjson</artifactId> <version>1.2.68</version> </dependency> <!--如果保存检查点到hdfs上,需要引入此依赖--> <dependency> <groupId>org.apache.hadoop</groupId> <artifactId>hadoop-client</artifactId> <version>${hadoop.version}</version> </dependency> <dependency> <groupId>org.projectlombok</groupId> <artifactId>lombok</artifactId> <version>1.18.20</version> </dependency> <!--Flink默认使用的是slf4j记录日志,相当于一个日志的接口,我们这里使用log4j作为具体的日志实现--> <dependency> <groupId>org.slf4j</groupId> <artifactId>slf4j-api</artifactId> <version>1.7.25</version> </dependency> <dependency> <groupId>org.slf4j</groupId> <artifactId>slf4j-log4j12</artifactId> <version>1.7.25</version> </dependency> <dependency> <groupId>org.apache.logging.log4j</groupId> <artifactId>log4j-to-slf4j</artifactId> <version>2.14.0</version> </dependency> <dependency> <groupId>org.apache.flink</groupId> <artifactId>flink-connector-jdbc_${scala.version}</artifactId> <version>${flink.version}</version> </dependency> <dependency> <groupId>com.ververica</groupId> <artifactId>flink-connector-mysql-cdc</artifactId> <version>2.1.0</version> </dependency> <dependency> <groupId>org.apache.phoenix</groupId> <artifactId>phoenix-spark</artifactId> <version>5.0.0-HBase-2.0</version> <exclusions> <exclusion> <groupId>org.glassfish</groupId> <artifactId>javax.el</artifactId> </exclusion> </exclusions> </dependency> <!-- 如果不引入 flink-table 相关依赖,则会报错: Caused by: java.lang.ClassNotFoundException: org.apache.flink.connector.base.source.reader.RecordEmitter 引入以下依赖可以解决这个问题(引入某些其它的 flink-table相关依赖也可) --> <dependency> <groupId>org.apache.flink</groupId> <artifactId>flink-table-api-java-bridge_2.12</artifactId> <version>1.13.0</version> </dependency> <dependency> <groupId>com.alibaba</groupId> <artifactId>druid</artifactId> <version>1.1.16</version> </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--> <transformer implementation="org.apache.maven.plugins.shade.resource.ServicesResourceTransformer"/> </transformers> </configuration> </execution> </executions> </plugin> </plugins> </build> </project>
3.2.2 log4.properties文件
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{yyyy-MM-dd HH:mm:ss} %10p (%c:%M) - %m%n log4j.rootLogger=error,stdout
3.2.3 hbase配置文件 (这个可以直接复制服务器上hbase服务conf文件夹中的hbase-site.xml文件)
<?xml version="1.0"?> <?xml-stylesheet type="text/xsl" href="configuration.xsl"?> <!-- /** * * Licensed to the Apache Software Foundation (ASF) under one * or more contributor license agreements. See the NOTICE file * distributed with this work for additional information * regarding copyright ownership. The ASF licenses this file * to you under the Apache License, Version 2.0 (the * "License"); you may not use this file except in compliance * with the License. You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. */ --> <configuration> <property> <name>hbase.rootdir</name> <value>hdfs://hadoop101:8020/hbase</value> </property> <property> <name>hbase.cluster.distributed</name> <value>true</value> </property> <property> <name>hbase.zookeeper.quorum</name> <value>hadoop101,hadoop102,hadoop103</value> </property> <property> <name>phoenix.schema.isNamespaceMappingEnabled</name> <value>true</value> </property> <property> <name>phoenix.schema.mapSystemTablesToNamespace</name> <value>true</value> </property> </configuration>
3.2.4 工具类-druid
package com.dianyan.utils; import com.alibaba.druid.pool.DruidDataSource; import com.dianyan.common.GmallConfig; public class DruidDSUtil { private static DruidDataSource druidDataSource = null; public static DruidDataSource createDataSource() { // 创建连接池 druidDataSource = new DruidDataSource(); // 设置驱动全类名 druidDataSource.setDriverClassName(GmallConfig.PHOENIX_DRIVER); // 设置连接 url druidDataSource.setUrl(GmallConfig.PHOENIX_SERVER); // 设置初始化连接池时池中连接的数量 druidDataSource.setInitialSize(5); // 设置同时活跃的最大连接数 druidDataSource.setMaxActive(20); // 设置空闲时的最小连接数,必须介于 0 和最大连接数之间,默认为 0 druidDataSource.setMinIdle(1); // 设置没有空余连接时的等待时间,超时抛出异常,-1 表示一直等待 druidDataSource.setMaxWait(-1); // 验证连接是否可用使用的 SQL 语句 druidDataSource.setValidationQuery("select 1"); // 指明连接是否被空闲连接回收器(如果有)进行检验,如果检测失败,则连接将被从池中去除 // 注意,默认值为 true,如果没有设置 validationQuery,则报错 // testWhileIdle is true, validationQuery not set druidDataSource.setTestWhileIdle(true); // 借出连接时,是否测试,设置为 false,不测试,否则很影响性能 druidDataSource.setTestOnBorrow(false); // 归还连接时,是否测试 druidDataSource.setTestOnReturn(false); // 设置空闲连接回收器每隔 30s 运行一次 druidDataSource.setTimeBetweenEvictionRunsMillis(30 * 1000L); // 设置池中连接空闲 30min 被回收,默认值即为 30 min druidDataSource.setMinEvictableIdleTimeMillis(30 * 60 * 1000L); return druidDataSource; } }
3.2.5 工具类-kafka
package com.dianyan.utils; import org.apache.flink.api.common.serialization.SimpleStringSchema; import org.apache.flink.api.common.typeinfo.BasicTypeInfo; import org.apache.flink.api.common.typeinfo.TypeInformation; import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer; import org.apache.flink.streaming.connectors.kafka.KafkaDeserializationSchema; import org.apache.kafka.clients.consumer.ConsumerConfig; import org.apache.kafka.clients.consumer.ConsumerRecord; import java.util.Properties; public class MyKafkaUtil { private static final String KAFKA_SERVER = "hadoop101:9092"; public static FlinkKafkaConsumer<String> getFlinkKafkaConsumer(String topic,String groupId){ Properties properties = new Properties(); properties.setProperty(ConsumerConfig.GROUP_ID_CONFIG,groupId); properties.setProperty(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG,KAFKA_SERVER); return new FlinkKafkaConsumer<String>( topic, new KafkaDeserializationSchema<String>() { @Override public boolean isEndOfStream(String s) { return false; } @Override public String deserialize(ConsumerRecord<byte[], byte[]> consumerRecord) throws Exception { if(consumerRecord == null || consumerRecord.value() == null){ return null; }else { return new String(consumerRecord.value()); } } @Override public TypeInformation<String> getProducedType() { return BasicTypeInfo.STRING_TYPE_INFO; } }, properties ); } }
3.2.6 工具类-Phoenix
package com.dianyan.utils; import com.alibaba.druid.pool.DruidPooledConnection; import com.alibaba.fastjson.JSONObject; import com.dianyan.common.GmallConfig; import org.apache.commons.lang3.StringUtils; import java.sql.PreparedStatement; import java.sql.SQLException; import java.util.Collection; import java.util.Set; public class PhoenixUtil { /** * * @param connection Phoenix连接 * @param sinkTable 表名 * @param data 数据 */ public static void upsertValues(DruidPooledConnection connection, String sinkTable, JSONObject data) throws SQLException { //1.拼接SQL语句 upsert into db.table(id,name,sex) values("1001","张三","male") Set<String> columns = data.keySet(); Collection<Object> values = data.values(); String sql = "upsert into " + GmallConfig.HBASE_SCHEMA + "." + sinkTable + "(" + StringUtils.join(columns,",") + ") values ('" + StringUtils.join(values,"','") + "')"; //2.预编译sql PreparedStatement preparedStatement = connection.prepareStatement(sql); //3.执行 preparedStatement.execute(); connection.commit(); //4.释放资源 preparedStatement.close(); } }
3.2.7 数据库驱动
package com.dianyan.common; public class GmallConfig { // Phoenix库名 public static final String HBASE_SCHEMA = "GMALL_REALTIME"; // Phoenix驱动 public static final String PHOENIX_DRIVER = "org.apache.phoenix.jdbc.PhoenixDriver"; // Phoenix连接参数 public static final String PHOENIX_SERVER = "jdbc:phoenix:hadoop101,hadoop102,hadoop103:2181"; }
3.2.8 table配置
package com.dianyan.bean; import lombok.Data; @Data public class TableProcess { //来源表 String sourceTable; //输出表 String sinkTable; //输出字段 String sinkColumns; //主键字段 String sinkPk; //建表扩展 String sinkExtend; }
3.2.9 简单过滤逻辑
package com.dianyan.app.func; import com.alibaba.fastjson.JSON; import com.alibaba.fastjson.JSONObject; import com.dianyan.bean.TableProcess; import com.dianyan.common.GmallConfig; import org.apache.flink.api.common.state.BroadcastState; import org.apache.flink.api.common.state.MapStateDescriptor; import org.apache.flink.api.common.state.ReadOnlyBroadcastState; import org.apache.flink.configuration.Configuration; import org.apache.flink.streaming.api.functions.co.BroadcastProcessFunction; import org.apache.flink.util.Collector; import java.sql.Connection; import java.sql.DriverManager; import java.sql.PreparedStatement; import java.sql.SQLException; import java.util.*; public class TableProcessFunction extends BroadcastProcessFunction<JSONObject, String, JSONObject> { private Connection connection; private MapStateDescriptor<String, TableProcess> mapStateDescriptor; // 构造器 public TableProcessFunction(MapStateDescriptor<String, TableProcess> mapStateDescriptor) { this.mapStateDescriptor = mapStateDescriptor; } @Override public void open(Configuration parameters) throws Exception { connection = DriverManager.getConnection(GmallConfig.PHOENIX_SERVER); } //{"before":null,"after":{"source_table":"3213","sink_table":"22","sink_columns":"33","sink_pk":"44","sink_extend":"55"},"source":{"version":"1.5.4.Final","connector":"mysql","name": // "mysql_binlog_source","ts_ms":1710926254168,"snapshot":"false","db":"gmall-config","sequence":null,"table":"table_process","server_id":0,"gtid":null,"file":"","pos":0,"row":0, // "thread":null,"query":null},"op":"r","ts_ms":1710926254171,"transaction":null} @Override public void processBroadcastElement(String value, Context context, Collector<JSONObject> out) throws Exception { // 1.获取并解析数据 JSONObject jsonObject = JSON.parseObject(value); TableProcess tableProcess = JSON.parseObject(jsonObject.getString("after"), TableProcess.class); // 2.校验并建表 checkTable(tableProcess.getSinkTable(), tableProcess.getSinkColumns(), tableProcess.getSinkPk(), tableProcess.getSinkExtend()); // 3.写入状态,广播出去 BroadcastState<String, TableProcess> broadcastState = context.getBroadcastState(mapStateDescriptor); broadcastState.put(tableProcess.getSourceTable(),tableProcess); } /** * 校验并建表 : create table if not exists db.table(id varchar primary key ,bb varchar ,cc varchar) xxx * @param sinkTable phoenix表名 * @param sinkColumns phoenix表字段 * @param sinkPk phoenix表主键 * @param sinkExtend phoenix表扩展字段 */ private void checkTable(String sinkTable,String sinkColumns,String sinkPk,String sinkExtend){ PreparedStatement preparedStatement = null; try { // 处理特殊字段,比如字段值为null的情况 if(sinkPk == null || "".equals(sinkPk)){ sinkPk = "id"; } if(sinkExtend == null){ sinkExtend = ""; } //1.拼接SQL create table if not exists db.table(id varchar primary key ,bb varchar ,cc varchar) xxx StringBuilder createTableSql = new StringBuilder("create table if not exists ") .append(GmallConfig.HBASE_SCHEMA) .append(".") .append(sinkTable) .append("("); String[] columns = sinkColumns.split(","); for (int i = 0; i < columns.length; i++) { // 取出字段 String column = columns[i]; // 是否为主键 if(sinkPk.equals(column)){ createTableSql.append(column).append(" varchar primary key"); }else{ createTableSql.append(column).append(" varchar"); } // 判断是否为最后一个字段 if(i < columns.length-1){ createTableSql.append(","); } } createTableSql.append(")").append(sinkExtend); //2.编译SQL System.out.println("建表语句为>>>>>" + createTableSql); // 预编译 preparedStatement = connection.prepareStatement(createTableSql.toString()); //3.执行SQL,建表 preparedStatement.execute(); } catch (SQLException e) { throw new RuntimeException("建表失败:" + sinkTable ); // 把编译时异常转换为运行时异常 } finally { //4.释放资源 if(preparedStatement != null){ try { preparedStatement.close(); } catch (SQLException e) { e.printStackTrace(); } } } } // {"database":"gmall","table":"base_trademark","type":"bootstrap-insert","ts":1710921861,"data":{"id":2,"tm_name":"苹果","logo_url":"/static/default.jpg"}} @Override public void processElement(JSONObject value, ReadOnlyContext readOnlyContext, Collector<JSONObject> collector) throws Exception { //1.获取广播的配置数据 ReadOnlyBroadcastState<String, TableProcess> broadcastState = readOnlyContext.getBroadcastState(mapStateDescriptor); String table = value.getString("table"); TableProcess tableProcess = broadcastState.get(table); if(tableProcess != null){ //2.过滤字段 filterColumn(value.getJSONObject("data"),tableProcess.getSinkColumns()); //3.补充SinkTable并写出到流中 value.put("sinkTable",tableProcess.getSinkTable()); collector.collect(value); }else{ System.out.println("找不到对应的Key:" + table); } } /** * 过滤字段 * @param data {"id":2,"tm_name":"苹果","logo_url":"/static/default.jpg"} * @param sinkColumns "id","tm_name" */ private void filterColumn(JSONObject data, String sinkColumns) { // 切分 String[] columns = sinkColumns.split(","); List<String> columnList = Arrays.asList(columns); Set<Map.Entry<String, Object>> entries = data.entrySet(); // 遍历 Iterator<Map.Entry<String, Object>> iterator = entries.iterator(); // 迭代器 while(iterator.hasNext()){ Map.Entry<String, Object> next = iterator.next(); if(!columnList.contains(next.getKey())){ iterator.remove(); } } } }
3.2.10 sink端
package com.dianyan.app.func; import com.alibaba.druid.pool.DruidDataSource; import com.alibaba.druid.pool.DruidPooledConnection; import com.alibaba.fastjson.JSONObject; import com.dianyan.utils.DruidDSUtil; import com.dianyan.utils.PhoenixUtil; import org.apache.flink.configuration.Configuration; import org.apache.flink.streaming.api.functions.sink.RichSinkFunction; public class DimSinkFunction extends RichSinkFunction<JSONObject> { private DruidDataSource druidDataSource = null; @Override public void open(Configuration parameters) throws Exception { druidDataSource = DruidDSUtil.createDataSource(); } // {"database":"gmall","table":"base_trademark","type":"bootstrap-insert","ts":1710921861,"data":{"id":2,"tm_name":"苹果","logo_url":"/static/default.jpg"},"sinkTable":"dim_xxx"} @Override public void invoke(JSONObject value, Context context) throws Exception { // 获取连接 DruidPooledConnection connection = druidDataSource.getConnection(); // 写出数据 String sinkTable = value.getString("sinkTable"); // 表名 JSONObject data = value.getJSONObject("data"); PhoenixUtil.upsertValues(connection,sinkTable,data); // 归还连接 connection.close(); } }
3.2.11 主程序
package com.dianyan.app.dim; import com.alibaba.fastjson.JSON; import com.alibaba.fastjson.JSONObject; import com.dianyan.app.func.DimSinkFunction; import com.dianyan.app.func.TableProcessFunction; import com.dianyan.bean.TableProcess; import com.dianyan.utils.MyKafkaUtil; import com.ververica.cdc.connectors.mysql.source.MySqlSource; import com.ververica.cdc.connectors.mysql.table.StartupOptions; import com.ververica.cdc.debezium.JsonDebeziumDeserializationSchema; import org.apache.flink.api.common.eventtime.WatermarkStrategy; import org.apache.flink.api.common.functions.FilterFunction; import org.apache.flink.api.common.functions.FlatMapFunction; import org.apache.flink.api.common.restartstrategy.RestartStrategies; import org.apache.flink.api.common.state.MapStateDescriptor; import org.apache.flink.runtime.state.hashmap.HashMapStateBackend; import org.apache.flink.streaming.api.CheckpointingMode; import org.apache.flink.streaming.api.datastream.BroadcastConnectedStream; import org.apache.flink.streaming.api.datastream.BroadcastStream; import org.apache.flink.streaming.api.datastream.DataStreamSource; import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator; import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment; import org.apache.flink.streaming.api.functions.co.BroadcastProcessFunction; import org.apache.flink.util.Collector; public class DimApp { public static void main(String[] args) throws Exception { //TODO 1.获取执行环境 StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment(); env.setParallelism(1); // 并行度 生产环境根据Kafka的分区数 // 1.1 开启checkpoint // env.enableCheckpointing(5 * 6000L, CheckpointingMode.EXACTLY_ONCE); // env.getCheckpointConfig().setCheckpointTimeout(10 * 6000L); // env.getCheckpointConfig().setMaxConcurrentCheckpoints(2); //checkpoint的并行度 // env.setRestartStrategy(RestartStrategies.fixedDelayRestart(3,5000L)); // 如果Flink服务意外挂了,此处配置每隔5秒重新连接一次,一共尝试3次。 // 1.2 设置状态后端 // env.setStateBackend(new HashMapStateBackend()); // env.getCheckpointConfig().setCheckpointStorage("hdfs://hadoop101:8020/dianyan"); // System.setProperty("HADOOP_USER_NAME","linxueze100"); //TODO 2.读取kafka topic=maxwell 主题数据创建主流 String topic = "maxwell"; String groupid = "dim_app_001"; DataStreamSource<String> kafkaDS = env.addSource(MyKafkaUtil.getFlinkKafkaConsumer(topic, groupid)); //TODO 3.过滤掉非JSON数据以及保留新增、变化以及初始化数据 并将数据转换为JSON格式 SingleOutputStreamOperator<JSONObject> filterJsonObjDS = kafkaDS.flatMap(new FlatMapFunction<String, JSONObject>() { @Override public void flatMap(String value, Collector<JSONObject> collector) throws Exception { try { // 将数据转换为JSON格式 JSONObject jsonObject = JSON.parseObject(value); // 获取数据中的操作字段 String type = jsonObject.getString("type"); // 保留新增、变化和初始化的数据 if ("insert".equals(type) || "update".equals(type) || "bootstrap-insert".equals(type)) { collector.collect(jsonObject); } } catch (Exception e) { System.out.println("发现脏数据:》》》》》》" + value); } } }); //TODO 4.使用FlinkCDC读取Mysql配置信息表,创建配置流 MySqlSource<String> mySqlSource = MySqlSource.<String>builder() .hostname("hadoop101") .port(3306) .username("root") .password("123456") .databaseList("gmall-config") .tableList("gmall-config.table_process") .startupOptions(StartupOptions.initial()) // 启动方式 .deserializer(new JsonDebeziumDeserializationSchema()) // 反序列化 binlog二进制 .build(); DataStreamSource<String> mySqlSourceDS = env.fromSource(mySqlSource, WatermarkStrategy.noWatermarks(), "MysqlSource"); //TODO 5.将配置流处理为广播流 MapStateDescriptor<String, TableProcess> mapStateDescriptor = new MapStateDescriptor<>("map-state", String.class, TableProcess.class); BroadcastStream<String> broadcastStream = mySqlSourceDS.broadcast(mapStateDescriptor); //TODO 6.连接主流和配置流 BroadcastConnectedStream<JSONObject, String> connectedStream = filterJsonObjDS.connect(broadcastStream); //TODO 7.处理连接流 根据配置信息 处理主流数据 SingleOutputStreamOperator<JSONObject> dimDS = connectedStream.process(new TableProcessFunction(mapStateDescriptor)); //TODO 8.将数据写入Phoenix dimDS.addSink(new DimSinkFunction()); dimDS.print(">>>>>>>>>>>>>>>"); //TODO 9.启动任务 env.execute("DimApp"); } }
四、实现效果
通过maxwell实时监控并抽取mysql的binlog文件,对数据的insert、update做实时采集并写入kafka对应topic;通过Flink程序消费kafka指定topic中的数据,简单清洗数据并写入hbase中。过程中zk做协同,phoenix做select等便捷查询。
五、写在最后
此篇文章,重在记录调研实时数仓的碎片记忆。很多细节的地方,没有写出来,也是因为时间有限,比如主程序中Flink消费kafka的topic的名称,要和maxwell采集过来写入kafka的topic保持一致,还有maxwell监控mysql的binlog的配置表的一些问题。
标签:数仓,flink,String,Flink,kafka,org,apache,import,com From: https://www.cnblogs.com/lxzcloud/p/18096677