---

---

1、概述

​ 目前Flink支持使用DataStream API 和SQL API方式实时读取和写入I=ceberg表，建议使用SQL API方式实时读取和写入Iceberg表。

• Iceberg支持的Flink版本为1.11.x版本以上，以下为版本匹配关系：

Flink版本	Iceberg版本	备注
Flink1.11.X	Iceberg0.11.1
Flink1.12.x ~ Flink1.13.x	Iceberg0.12.1	SQL API有Bug
Flink1.14.x	Iceberg0.12.1	SQL API有Bug

​ 本次学习以Flink和Iceberg整合使用Flink版本为1.14.5，Iceberg版本为0.12.1版本。

2、DataStream API

2.1、实时写入Iceberg表

2.1.1、导入依赖

<?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>org.example</groupId><artifactId>flinkiceberg1</artifactId><version>1.0-SNAPSHOT</version><properties><project.build.sourceEncoding>UTF-8</project.build.sourceEncoding><maven.compiler.source>1.8</maven.compiler.source><maven.compiler.target>1.8</maven.compiler.target><!-- flink 1.12.x -1.13.x  版本与Iceberg 0.12.1 版本兼容 ，不能与Flink 1.14 兼容--><flink.version>1.13.5</flink.version><!--<flink.version>1.12.1</flink.version>--><!--<flink.version>1.14.2</flink.version>--><!-- flink 1.11.x 与Iceberg 0.11.1 合适--><!--<flink.version>1.11.6</flink.version>--><hadoop.version>3.1.1</hadoop.version></properties><dependencies><dependency><groupId>com.alibaba.ververica</groupId><artifactId>ververica-connector-iceberg</artifactId><version>1.13-vvr-4.0.7</version><exclusions><exclusion><groupId>com.google.guava</groupId><artifactId>guava-parent</artifactId></exclusion></exclusions></dependency><!-- Flink 操作Iceberg 需要的Iceberg依赖 --><dependency><groupId>org.apache.iceberg</groupId><artifactId>iceberg-flink-runtime</artifactId><version>0.12.1</version><!--<version>0.11.1</version>--></dependency><!-- java开发Flink所需依赖 --><dependency><groupId>org.apache.flink</groupId><artifactId>flink-java</artifactId><version>${flink.version}</version></dependency><dependency><groupId>org.apache.flink</groupId><artifactId>flink-streaming-java_2.11</artifactId><version>${flink.version}</version></dependency><dependency><groupId>org.apache.flink</groupId><artifactId>flink-clients_2.11</artifactId><version>${flink.version}</version></dependency><dependency><groupId>org.apache.flink</groupId><artifactId>flink-streaming-scala_2.11</artifactId><version>${flink.version}</version></dependency><!-- Flink Kafka连接器的依赖 --><dependency><groupId>org.apache.flink</groupId><artifactId>flink-connector-kafka_2.11</artifactId><version>${flink.version}</version></dependency><dependency><groupId>org.apache.flink</groupId><artifactId>flink-connector-base</artifactId><version>${flink.version}</version></dependency><!-- 读取hdfs文件需要jar包--><dependency><groupId>org.apache.hadoop</groupId><artifactId>hadoop-client</artifactId><version>${hadoop.version}</version><exclusions><exclusion><artifactId>guava</artifactId><groupId>com.google.guava</groupId></exclusion></exclusions></dependency><!-- Flink SQL & Table--><dependency><groupId>org.apache.flink</groupId><artifactId>flink-table-runtime-blink_2.11</artifactId><version>${flink.version}</version></dependency><dependency><groupId>org.apache.flink</groupId><artifactId>flink-table</artifactId><version>${flink.version}</version></dependency><dependency><groupId>org.apache.flink</groupId><artifactId>flink-table-common</artifactId><version>${flink.version}</version></dependency><dependency><groupId>org.apache.flink</groupId><artifactId>flink-table-api-java</artifactId><version>${flink.version}</version></dependency><dependency><groupId>org.apache.flink</groupId><artifactId>flink-table-api-java-bridge_2.11</artifactId><version>${flink.version}</version></dependency><dependency><groupId>org.apache.flink</groupId><artifactId>flink-table-planner_2.11</artifactId><version>${flink.version}</version></dependency><dependency><groupId>org.apache.flink</groupId><artifactId>flink-table-planner-blink_2.11</artifactId><version>${flink.version}</version></dependency><dependency><groupId>junit</groupId><artifactId>junit</artifactId><version>4.11</version><scope>test</scope></dependency><!-- log4j 和slf4j 包,如果在控制台不想看到日志，可以将下面的包注释掉--><dependency><groupId>org.slf4j</groupId><artifactId>slf4j-log4j12</artifactId><version>1.7.25</version><scope>test</scope></dependency><dependency><groupId>log4j</groupId><artifactId>log4j</artifactId><version>1.2.17</version></dependency><dependency><groupId>org.slf4j</groupId><artifactId>slf4j-api</artifactId><version>1.7.25</version></dependency><dependency><groupId>org.slf4j</groupId><artifactId>slf4j-nop</artifactId><version>1.7.25</version><scope>test</scope></dependency><dependency><groupId>org.slf4j</groupId><artifactId>slf4j-simple</artifactId><version>1.7.5</version></dependency></dependencies></project>

2.1.2、创建Iceberg表

• 核心：通过Flink创建Iceberg表

-- 1、创建catalogCREATE CATALOG hadoop_catalog WITH (
>   'type'='iceberg',
>   'catalog-type'='hadoop',
>   'warehouse'='hdfs://leidi01:8020/iceberg_catalog',
>   'property-version'='1'
> );-- 2、创建databases
create database flink_iceberg;-- 3、创建Sink表
CREATE TABLE hadoop_catalog.flink_iceberg.icebergdemo1 (id STRING,data STRING
); 

• 运行结果

2.1.3、代码实现

public class FlinkIcebergDemo1 {public static void main(String[] args) throws Exception {StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();//1.必须设置checkpoint ,Flink向Iceberg中写入数据时当checkpoint发生后，才会commit数据。env.enableCheckpointing(5000);//2.读取Kafka 中的topic 数据KafkaSource<String> source = KafkaSource.<String>builder().setBootstrapServers("192.168.6.102:6667").setTopics("json").setGroupId("my-group-id").setStartingOffsets(OffsetsInitializer.latest()).setValueOnlyDeserializer(new SimpleStringSchema()).build();DataStreamSource<String> kafkaSource = env.fromSource(source, WatermarkStrategy.noWatermarks(), "Kafka Source");//3.对数据进行处理，包装成RowData 对象，方便保存到Iceberg表中。SingleOutputStreamOperator<RowData> dataStream = kafkaSource.map(new MapFunction<String, RowData>() {@Overridepublic RowData map(String s) throws Exception {System.out.println("s = "+s);String[] split = s.split(",");GenericRowData row = new GenericRowData(4);row.setField(0, Integer.valueOf(split[0]));row.setField(1, StringData.fromString(split[1]));row.setField(2, Integer.valueOf(split[2]));row.setField(3, StringData.fromString(split[3]));return row;}});//4.创建Hadoop配置、Catalog配置和表的Schema，方便后续向路径写数据时可以找到对应的表Configuration hadoopConf = new Configuration();Catalog catalog = new HadoopCatalog(hadoopConf,"hdfs://leidi01:8020/flinkiceberg/");//配置iceberg 库名和表名TableIdentifier name =TableIdentifier.of("icebergdb", "flink_iceberg_tbl");//创建Icebeng表SchemaSchema schema = new Schema(Types.NestedField.required(1, "id", Types.IntegerType.get()),Types.NestedField.required(2, "nane", Types.StringType.get()),Types.NestedField.required(3, "age", Types.IntegerType.get()),Types.NestedField.required(4, "loc", Types.StringType.get()));//如果有分区指定对应分区，这里“loc”列为分区列，可以指定unpartitioned 方法不设置表分区
//        PartitionSpec spec = PartitionSpec.unpartitioned();PartitionSpec spec = PartitionSpec.builderFor(schema).identity("loc").build();//指定Iceberg表数据格式化为Parquet存储Map<String, String> props =ImmutableMap.of(TableProperties.DEFAULT_FILE_FORMAT, FileFormat.PARQUET.name());Table table = null;// 通过catalog判断表是否存在，不存在就创建，存在就加载if (!catalog.tableExists(name)) {table = catalog.createTable(name, schema, spec, props);}else {table = catalog.loadTable(name);}TableLoader tableLoader = TableLoader.fromHadoopTable("hdfs://leidi01:8020/flinkiceberg//icebergdb/flink_iceberg_tbl", hadoopConf);//5.通过DataStream Api 向Iceberg中写入数据FlinkSink.forRowData(dataStream)//这个 .table 也可以不写，指定tableLoader 对应的路径就可以。.table(table).tableLoader(tableLoader)//默认为false,追加数据。如果设置为true 就是覆盖数据.overwrite(false).build();env.execute("DataStream Api Write Data To Iceberg");}
}

• 注意事项：

（1）需要设置Checkpoint，Flink向Iceberg中写入Commit数据时，只有Checkpoint成功之后才会Commit数据，否则后期在Hive中查询不到数据。

（2）读取Kafka数据后需要包装成RowData或者Row对象，才能向Iceberg表中写出数据。写出数据时默认是追加数据，如果指定overwrite就是全部覆盖数据。

（3）在向Iceberg表中写数据之前需要创建对应的Catalog、表Schema,否则写出时只指定对应的路径会报错找不到对应的Iceberg表。

（4）不建议使用DataStream API 向Iceberg中写数据，建议使用SQL API。

2.1.4、Kafka消费者启动

bin/kafka-console-producer.sh --topic json  --broker-list leidi01:6667bin/kafka-console-consumer.sh --bootstrap-server  leidi01:6667 --topic json --from-beginning

• 生产数据

• 运行结果：data中有两个分区

2.1.5、查询表结果

• 说明：在Flink SQL中创建Hadoop Catalog。

-- 1、创建Hadoop Catalog
CREATE CATALOG flinkiceberg WITH ('type'='iceberg','catalog-type'='hadoop','warehouse'='hdfs://leidi01:8020/flinkiceberg/','property-version'='1'
);-- 2、查询表中数据
use catalog flinkiceberg;
use icebergdb;
select * from flink_iceberg_tbl;

• 运行结果

2.2、批量/实时读取Iceberg表

• 核心：DataStream API 读取Iceberg表又分为批量读取和实时读取，通过方法“streaming(true/false)”来控制。

2.2.1、批量读取

• 说明：设置方法“streaming(false)”
• 代码实现

public class FlinkIcebergRead {public static void main(String[] args) throws Exception {StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();//1.配置TableLoaderConfiguration hadoopConf = new Configuration();TableLoader tableLoader = TableLoader.fromHadoopTable("hdfs://leidi01:8020/flinkiceberg//icebergdb/flink_iceberg_tbl", hadoopConf);//2.从Iceberg中读取全量/增量读取数据DataStream<RowData> batchData = FlinkSource.forRowData().env(env).tableLoader(tableLoader)//默认为false,整批次读取，设置为true 为流式读取.streaming(false).build();batchData.map(new MapFunction<RowData, String>() {@Overridepublic String map(RowData rowData) throws Exception {int id = rowData.getInt(0);String name = rowData.getString(1).toString();int age = rowData.getInt(2);String loc = rowData.getString(3).toString();return id+","+name+","+age+","+loc;}}).print();env.execute("DataStream Api Read Data From Iceberg");}
}

• 运行结果

2.2.2、实时读取

• 说明：设置方法“streaming(true)”

• 代码实现

DataStream<RowData> batchData = FlinkSource.forRowData().env(env).tableLoader(tableLoader)//默认为false,整批次读取，设置为true 为流式读取.streaming(true).build();

• Flink SQL插入数据

insert into flink_iceberg_tbl values (5,'s1',30,'guangzhou'),(6,'s2',31,'tianjin');

• 运行结果

2.2.3、指定基于快照实时增量读取数据

• 核心：设置方法StartSnapshotId(快照编号)

（1）查看快照编号

（2）代码实现

//2.从Iceberg中读取全量/增量读取数据
DataStream<RowData> batchData = FlinkSource.forRowData().env(env).tableLoader(tableLoader)//基于某个快照实时增量读取数据，快照需要从元数据中获取.startSnapshotId(1738199999360637062L)//默认为false,整批次读取; 设置为true为流式读取.streaming(true).build();

（3）运行结果

• 说明：*只读取到指定快照往后的数据*

2.2.4、合并Data Flies

• 说明：Iceberg提供Api通过定期提交任务将小文件合并成大文件，可以通过Flink 批任务来执行。

（1）未处理文件

• 说明：Iceberg每提交一次数据都会产生一个Data File。

（2）代码实现

public class RewrietDataFiles {public static void main(String[] args) {ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();// 1、配置TableLoaderConfiguration hadoopConf = new Configuration();//2.创建Hadoop配置、Catalog配置和表的Schema，方便后续向路径写数据时可以找到对应的表Catalog catalog = new HadoopCatalog(hadoopConf,"hdfs://leidi01:8020/flinkiceberg/");//3.配置iceberg 库名和表名并加载表TableIdentifier name = TableIdentifier.of("icebergdb", "flink_iceberg_tbl");Table table = catalog.loadTable(name);//4..合并 data files 小文件RewriteDataFilesActionResult result = Actions.forTable(table).rewriteDataFiles()//默认 512M ，可以手动通过以下指定合并文件大小，与Spark中一样。.targetSizeInBytes(536870912L).execute();}
}

（3）运行结果

3、SQL API

3.1、创建表并插入数据

（1）代码实现

public class SQLAPIWriteIceberg {public static void main(String[] args) {StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();StreamTableEnvironment tblEnv = StreamTableEnvironment.create(env);env.enableCheckpointing(1000);//1.创建CatalogtblEnv.executeSql("CREATE CATALOG hadoop_iceberg WITH (" +"'type'='iceberg'," +"'catalog-type'='hadoop'," +"'warehouse'='hdfs://leidi01:8020/flinkiceberg')");//2.使用当前CatalogtblEnv.useCatalog("hadoop_iceberg");//3.创建数据库tblEnv.executeSql("create database iceberg_db");//4.使用数据库tblEnv.useDatabase("iceberg_db");//5.创建iceberg表 flink_iceberg_tbltblEnv.executeSql("create table hadoop_iceberg.iceberg_db.flink_iceberg_tbl2(id int,name string,age int,loc string) partitioned by (loc)");//6.写入数据到表 flink_iceberg_tbltblEnv.executeSql("insert into hadoop_iceberg.iceberg_db.flink_iceberg_tbl2 values (1,'zs',18,'beijing'),(2,'ls',19,'shanghai'),(3,'ww',20,'guangzhou')");}
}

（2）运行结果

• 说明：通过HDFS查看文件是否生成。

（3）查看数据

• 说明：通过FlinkSQL查看表中数据

-- 1、创建CatalogCREATE CATALOG flinkiceberg WITH (
>     'type'='iceberg',
>     'catalog-type'='hadoop',
>     'warehouse'='hdfs://leidi01:8020/flinkiceberg/',
>     'property-version'='1'
> );-- 2、查询数据
use catalog flinkiceberg
use iceberg_db;
select * from flink_iceberg_tbl2;

• 查看结果

3.2、批量查询表数据

• 说明：SQL API批量查询表中数据，直接查询显示即可

（1）代码逻辑

（2）代码实现

public class SQLAPIReadIceberg {public static void main(String[] args) {StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();StreamTableEnvironment tblEnv = StreamTableEnvironment.create(env);env.enableCheckpointing(1000);//1.创建CatalogtblEnv.executeSql("CREATE CATALOG hadoop_iceberg WITH (" +"'type'='iceberg'," +"'catalog-type'='hadoop'," +"'warehouse'='hdfs://leidi01:8020/flinkiceberg')");
//2.批量读取表数据TableResult tableResult = tblEnv.executeSql("select * from hadoop_iceberg.iceberg_db.flink_iceberg_tbl2 ");tableResult.print();}
}

• 运行结果

3.3、实时查询表数据

• 说明：link SQL API 实时查询Iceberg表数据时需要设置参数**“table.dynamic-table-options.enabled”为true**，以支持SQL语法中的“OPTIONS”选项

（1）代码逻辑

（2）代码实现

public class SQLStreamReadIceberg {public static void main(String[] args) {StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();StreamTableEnvironment tblEnv = StreamTableEnvironment.create(env);env.enableCheckpointing(1000);Configuration configuration = tblEnv.getConfig().getConfiguration();// 支持SQL语法中的 OPTIONS 选项configuration.setBoolean("table.dynamic-table-options.enabled", true);//1.创建CatalogtblEnv.executeSql("CREATE CATALOG hadoop_iceberg WITH (" +"'type'='iceberg'," +"'catalog-type'='hadoop'," +"'warehouse'='hdfs://leidi01:8020/flinkiceberg')");//2.从Iceberg表当前快照读取所有数据，并继续增量读取数据// streaming指定为true支持实时读取数据，monitor_interval 监控数据的间隔，默认1sTableResult tableResult = tblEnv.executeSql("select * from hadoop_iceberg.iceberg_db.flink_iceberg_tbl2 /*+ OPTIONS('streaming'='true', 'monitor-interval'='1s')*/");tableResult.print();}
}

• 运行结果：

（3）测试验证

• FlinkSQL插入数据

insert into flink_iceberg_tbl2 values (5,'s1',30,'guangzhou'),(6,'s2',31,'tianjin');

• 运行结果：在IDEA的控制台可以看到新增数据

3.4、基于快照实时增量读取数据

• 说明：基于某个snapshot-id来继续实时获取数据

（1）代码逻辑

（2）代码实现

• FlinkSQL插入数据

insert into flink_iceberg_tbl2 values (7,'s11',30,'beijing'),(8,'s22',31,'beijing');

• snapshot-id如下：

• 代码实现

public class SQLSnapshotReadIceberg {public static void main(String[] args) {StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();StreamTableEnvironment tblEnv = StreamTableEnvironment.create(env);env.enableCheckpointing(1000);Configuration configuration = tblEnv.getConfig().getConfiguration();// 支持SQL语法中的 OPTIONS 选项configuration.setBoolean("table.dynamic-table-options.enabled", true);//1.创建CatalogtblEnv.executeSql("CREATE CATALOG hadoop_iceberg WITH (" +"'type'='iceberg'," +"'catalog-type'='hadoop'," +"'warehouse'='hdfs://leidi01:8020/flinkiceberg')");//2.从Iceberg 指定的快照继续实时读取数据，快照ID从对应的元数据中获取//start-snapshot-id :快照IDTableResult tableResult2 = tblEnv.executeSql("SELECT * FROM hadoop_iceberg.iceberg_db.flink_iceberg_tbl2 /*+ OPTIONS('streaming'='true', 'monitor-interval'='1s', 'start-snapshot-id'='8334669420406375204')*/");tableResult2.print();}
}

（3）运行结果

4、常见报错

4.1、window远程连接hadoop环境变量找不到

• 报错日志

HADOOP_HOME and hadoop.home.dir are unset.

• 报错原因：本地远程连接Hadoop系统时需要在本地配置相关的Hadoop变量，主要包括hadoop.dll 与 winutils.exe 等。

winutils：由于hadoop主要基于linux编写，**winutil.exe主要用于模拟linux下的目录环境**。当Hadoop在windows下运行或调用远程Hadoop集群的时候，需要该辅助程序才能运行。winutils是Windows中的二进制文件，适用于不同版本的Hadoop系统并构建在Windows VM上，该VM用以在Windows系统中测试Hadoop相关的应用程序。

• 解决方案：

（1）下载hadoop集群对应winutils版本

• 注意事项：如果你安装的hadoop版本是：3.1.2或者3.2.0 就用winutils-master里面的hadoop-3.0.0配置环境变量吧！

https://github.com/steveloughran/winutils

（2）将环境变量%HADOOP_HOME%设置为指向包含WINUTILS.EXE的BIN目录上方的目录

4.2、guava包版本冲突

• 报错日志

com.google.common.base.Preconditions.checkArgument(ZLjava/lang/String;Ljava/lang/Object;)V

• 报错原因：guava包版本冲突
• 解决方案：使用Maven Helper插件解决冲突

①第一步：在pom界面点击Dependency Analyzer

②第二步：查看Dependency Analyzer功能界面

Ⅰ、显示冲突的jar包

Ⅱ、以列表形式显示所有依赖

Ⅲ、以数的形式显示所有依赖

③第三步：逐个解决conflicts列表中的jar包冲突问题，以guava为例：

​ 点击guava，找到右侧部分红色字体，即依赖冲突的地方，下图显示当前guava版本是24.0，但是有两个依赖的guava版本分别是27.0.0.1和16.0.1。

④将低版本依赖都排除掉

选中红色字体显示的内容->右键->Exclude，完成上述步骤结果如下：

⑤重新加载依赖配置

-------------------------------------------------------------------分割线-------------------------------------------------------------------------------

以上guava包冲突解决后依旧报错，将Hadoop版本从3.2.2降低到3.1.1不报错。

​ 注意hive-3.1.2依赖的Hadoop版本是3.1.0 [3]，一般不建议runtime的Hadoop版本高于hive依赖的版本。

Ⅰ、解决方法一是在hive-exec里对guava做迁移，这个需要自己手动给hive-exec重新打包。

Ⅱ、解决方法二是降低Hadoop版本，这里不一定要降低集群的Hadoop版本，而只是降低flink和hive这边用到的Hadoop版本，相对于用老的Hadoop客户端去访问新的Hadoop服务器，这个小版本的包容性一般来说是没有问题的。

<hadoop.version>3.2.2</hadoop.version>
<!-->将hadoop版本由3.2.2版本降低为3.1.1<-->
<hadoop.version>3.1.1</hadoop.version>

4.4、log4j2配置文件报错

• 报错日志

ERROR StatusLogger No log4j2 configuration file found. Using default configuration: logging only errors to the console. Set system property 'org.apache.logging.log4j.simplelog.StatusLogger.level' to TRACE to show Log4j2 internal initialization logging.

• 报错原因：没有发现log4j2配置文件
• 解决方案：添加配置log4j2.xml文件，对应org.apache.logging.log4j.Logger

<?xml version="1.0" encoding="UTF-8"?>
<Configuration status="WARN"><Properties><property name="log_level" value="info" /><Property name="log_dir" value="log" /><property name="log_pattern"value="[%d{yyyy-MM-dd HH:mm:ss.SSS}] [%p] - [%t] %logger - %m%n" /><property name="file_name" value="test" /><property name="every_file_size" value="100 MB" /></Properties><Appenders><Console name="Console" target="SYSTEM_OUT"><PatternLayout pattern="${log_pattern}" /></Console><RollingFile name="RollingFile"filename="${log_dir}/${file_name}.log"filepattern="${log_dir}/$${date:yyyy-MM}/${file_name}-%d{yyyy-MM-dd}-%i.log"><ThresholdFilter level="DEBUG" onMatch="ACCEPT"onMismatch="DENY" /><PatternLayout pattern="${log_pattern}" /><Policies><SizeBasedTriggeringPolicysize="${every_file_size}" /><TimeBasedTriggeringPolicy modulate="true"interval="1" /></Policies><DefaultRolloverStrategy max="20" /></RollingFile><RollingFile name="RollingFileErr"fileName="${log_dir}/${file_name}-warnerr.log"filePattern="${log_dir}/$${date:yyyy-MM}/${file_name}-%d{yyyy-MM-dd}-warnerr-%i.log"><ThresholdFilter level="WARN" onMatch="ACCEPT"onMismatch="DENY" /><PatternLayout pattern="${log_pattern}" /><Policies><SizeBasedTriggeringPolicysize="${every_file_size}" /><TimeBasedTriggeringPolicy modulate="true"interval="1" /></Policies></RollingFile></Appenders><Loggers><Root level="${log_level}"><AppenderRef ref="Console" /><AppenderRef ref="RollingFile" /><appender-ref ref="RollingFileErr" /></Root></Loggers>
</Configuration>

4.5、Flink Hive Catalog报错

• 报错日志

Exception in thread "main" java.lang.NoSuchMethodError: org.apache.calcite.sql.parser.SqlParser.config()Lorg/apache/calcite/sql/parser/SqlParser$Config;

• 报错原因：依赖报错

• 解决方案：将所有依赖切换到2.12，切换flink-table-api-java-bridge到flink-table-api-scala-bridge_2.12。