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Flink SQL操作Hudi并同步Hive使用总结

发布时间：2025-05-17 05:00:55 作者：益华网络来源：undefined 浏览量（1）点赞（1）

摘要：前言记录总结自己第一次如何使用Flink SQL读写Hudi并同步Hive，以及遇到的问题及解决过程。版本Flink 1.14.3Hudi 0.12.0/0.12.1 本文采用Flink yarn-session模式，不会的可以参考之前的文章。Hudi包下载地址：https://repo1.maven.org/maven2/org/apache/hudi/hudi-flink1.14-bundle/0.12.1/hudi-f

前言

记录总结自己第一次如何使用Flink SQL读写Hudi并同步Hive，以及遇到的问题及解决过程。

版本

Flink 1.14.3Hudi 0.12.0/0.12.1

本文采用Flink yarn-session模式，不会的可以参考之前的文章。

Hudi包

下载地址：https://repo1.maven.org/maven2/org/apache/hudi/hudi-flink1.14-bundle/0.12.1/hudi-flink1.14-bundle-0.12.1.jar

如果想同步Hive的话，就不能使用上面下载的包了，必须使用profileflink-bundle-shade-hive自己打包,这里参考官网：https://hudi.apache.org/cn/docs/syncing_metastore,打包命令

## Hive3

mvn clean package -DskipTests -Drat.skip=true -Pflink-bundle-shade-hive3 -Dflink1.14 -Dscala-2.12

## Hive2

mvn clean package -DskipTests -Drat.skip=true -Pflink-bundle-shade-hive2 -Dflink1.14 -Dscala-2.12

## Hive1

mvn clean package -DskipTests -Drat.skip=true -Pflink-bundle-shade-hive1 -Dflink1.14 -Dscala-2.12

为了避免不必要的麻烦，最好自己修改一下对应的profile中的Hive版本，比如我们环境的Hive版本为HDP的3.1.0.3.1.0.0-78，我们将hive.version对应的值改为3.1.0.3.1.0.0-78再打包就可以了。

方式1、建在内存中、不同步Hive表

这种建表方式，元数据在内存中，退出SQL客户端后，需要重新建表（表数据文件还在）

建表

CREATE TABLE test_hudi_flink1 (

id int PRIMARY KEY NOT ENFORCED,

name VARCHAR(10),

price int,

ts int,

dt VARCHAR(10)

)

PARTITIONED BY (dt)

WITH (

connector = hudi,

path = /tmp/hudi/test_hudi_flink1,

table.type = MERGE_ON_READ,

hoodie.datasource.write.keygenerator.class = org.apache.hudi.keygen.ComplexAvroKeyGenerator,

hoodie.datasource.write.recordkey.field = id,

hoodie.datasource.write.hive_style_partitioning = true

);

PRIMARY KEY和hoodie.datasource.write.recordkey.field作用相同，联合主键时，可以单独放在最后 PRIMARY KEY (id1, id2) NOT ENFORCED

CREATE TABLE test_hudi_flink1 (

id1 int,

id2 int,

name VARCHAR(10),

price int,

ts int,

dt VARCHAR(10),

PRIMARY KEY (id1, id2) NOT ENFORCED

)

Insert

insert into test_hudi_flink1 values (1,hudi,10,100,2022-10-31),(2,hudi,10,100,2022-10-31);

至于upadte和delete可以参考官网：https://hudi.apache.org/cn/docs/flink-quick-start-guide

查询

select * from test_hudi_flink1;

通过Flink查询出来的结果是没有Hudi的元数据字段的

方式2、建在Hive Catalog中、不同步Hive表

这种建表方式，会在对应的Hive中创建表，好处是，当我们退出SQL客户端后，再重新启动一个新的SQL客户端，我们可以直接使用Hive Catalog中的表，进行读写数据。

建表

CREATE CATALOG hive_catalog WITH (

type = hive,

default-database = default,

hive-conf-dir = /usr/hdp/3.1.0.0-78/hive/conf

);

use catalog hive_catalog;

use hudi;

CREATE TABLE test_hudi_flink2 (

id int PRIMARY KEY NOT ENFORCED,

name VARCHAR(10),

price int,

ts int,

dt VARCHAR(10)

)

PARTITIONED BY (dt)

WITH (

connector = hudi,

path = /tmp/hudi/test_hudi_flink2,

hoodie.datasource.write.keygenerator.class = org.apache.hudi.keygen.ComplexAvroKeyGenerator,

hoodie.datasource.write.recordkey.field = id,

hoodie.datasource.write.hive_style_partitioning = true

);

Insert

insert into test_hudi_flink2 values (1,hudi,10,100,2022-10-31),(2,hudi,10,100,2022-10-31);

查询

select * from test_hudi_flink2;

但是同样地也无法查询Hudi的元数据字段，而且在Hive表中查询此表是会有异常的，因为表结构是这样的：

show create table test_hudi_flink2;

+----------------------------------------------------+

| createtab_stmt |

+----------------------------------------------------+

| CREATE TABLE `test_hudi_flink2`( |

| ) |

| ROW FORMAT SERDE |

| org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe |

| STORED AS INPUTFORMAT |

| org.apache.hadoop.mapred.TextInputFormat |

| OUTPUTFORMAT |

| org.apache.hadoop.hive.ql.io.IgnoreKeyTextOutputFormat |

| LOCATION |

| hdfs://cluster1/warehouse/tablespace/managed/hive/hudi.db/test_hudi_flink2 |

| TBLPROPERTIES ( |

| flink.connector=hudi, |

| flink.hoodie.datasource.write.hive_style_partitioning=true, |

| flink.hoodie.datasource.write.recordkey.field=id, |

| flink.partition.keys.0.name=dt, |

| flink.path=/tmp/hudi/test_hudi_flink2, |

| flink.schema.0.data-type=INT NOT NULL, |

| flink.schema.0.name=id, |

| flink.schema.1.data-type=VARCHAR(10), |

| flink.schema.1.name=name, |

| flink.schema.2.data-type=INT, |

| flink.schema.2.name=price, |

| flink.schema.3.data-type=INT, |

| flink.schema.3.name=ts, |

| flink.schema.4.data-type=VARCHAR(10), |

| flink.schema.4.name=dt, |

| flink.schema.primary-key.columns=id, |

| flink.schema.primary-key.name=PK_3386, |

| transient_lastDdlTime=1667129407) |

+----------------------------------------------------+select * from test_hudi_flink2;

Error: Error while compiling statement: FAILED: SemanticException Line 0:-1 Invalid column reference TOK_ALLCOLREF (state=42000,code=40000)方式3、建在内存中、同步Hive表

这样建表的好处是，可以利用同步到Hive中的表,通过Hive SQL和Spark SQL查询，也可以利用Spark进行insert、update等，但是Flink SQL客户端退出后，不能利用Hive中的表进行写数据，需要再重新建表

MOR表

建表

配置环境变量HIVE_CONF_DIR

export HIVE_CONF_DIR=/usr/hdp/3.1.0.0-78/hive/confCREATE TABLE test_hudi_flink3 (

id int PRIMARY KEY NOT ENFORCED,

name VARCHAR(10),

price int,

ts int,

dt VARCHAR(10)

)

PARTITIONED BY (dt)

WITH (

connector = hudi,

path = /tmp/hudi/test_hudi_flink3,

table.type = MERGE_ON_READ,

hoodie.datasource.write.keygenerator.class = org.apache.hudi.keygen.ComplexAvroKeyGenerator,

hoodie.datasource.write.recordkey.field = id,

hoodie.datasource.write.hive_style_partitioning = true,

hive_sync.enable = true,

hive_sync.mode = hms,

hive_sync.metastore.uris = thrift://indata-192-168-44-128.indata.com:9083,

hive_sync.conf.dir=/usr/hdp/3.1.0.0-78/hive/conf,

hive_sync.db = hudi,

hive_sync.table = test_hudi_flink3,

hive_sync.partition_fields = dt,

hive_sync.partition_extractor_class = org.apache.hudi.hive.HiveStylePartitionValueExtractor

);

HIVE_CONF_DIR和hive_sync.conf.dir作用是一样的，如果没有配置hive_sync.conf.dir的话就会取HIVE_CONF_DIR，如果都没有配置，同步会有异常，具体看后面的异常解决。

关于同步Hive的参数，官方文档上说hive_sync.metastore.uris是必须的，但是经过验证，不设置也可以，因为hive_sync.conf.dir下面有hive-site.xml读取里面的配置信息获取即可，Spark SQL同步Hive也是读取hive-site.xml的。其他的参数可以自己去了解

同步表

只有在写数据的时候才会触发同步Hive表

insert into test_hudi_flink3 values (1,hudi,10,100,2022-10-31),(2,hudi,10,100,2022-10-31);

然后我们可以看到在Hive库中生成了两张表test_hudi_flink3_ro、test_hudi_flink3_rt,这和我们使用Spark SQL同步的表是一样的，可以用Hive查询，也可以用Spark查询、写数据

MOR表一开始没有生成parquet文件，在Hive里查询为空（RO、RT都为空），我们可以在SparkSQL里再插入几条数据，就可以查询出数据来了

# ro、rt都支持Spark SQL insert

insert into test_hudi_flink3_ro values (3,hudi,10,100,2022-10-31),(4,hudi,10,100,2022-10-31);

insert into test_hudi_flink3_rt values (5,hudi,10,100,2022-10-31),(6,hudi,10,100,2022-10-31);

关于Flink SQL和Spark SQL配置一致性问题：

hoodie.datasource.write.keygenerator.class 这里设置的为ComplexAvroKeyGenerator，也就是复合主键，原因是Fink SQL 默认值为SimpleKey，但是SparkSQL默认值SqlKeyGenerator，它是ComplexKeyGenerator，也就是默认值为复合主键，但是由于ComplexKeyGenerator在hudi-spark-client中，flink模块没有，所以flink中需要设置hudi-client-common中的ComplexAvroKeyGenerator即可保持一致性（如果keygenerator不一致会导致重复数据）hoodie.datasource.write.hive_style_partitioning flink sql默认值为false，但是SparkSQL为true，所以这里设置为truehive_sync.partition_extractor_class SparkSQL中默认值为MultiPartKeysValueExtractor，对于本例中的字符串类型的分区字段是支持的，但是Flink SQL中的默认值为SlashEncodedDayPartitionValueExtractor，它要求分区字段必须是日期格式的，所以这里设置为HiveStylePartitionValueExtractor即可解决hoodie.allow.operation.metadata.field Flink支持这个配置项，当为true时，Hudi元数据字段中会多一个_hoodie_operation，但是目前Spark还不支持，所以对于这种，对于Flink SQL同步的Hive表，不能再通过Spark SQL写数据，不过后面我会提PR支持我们发现对于Flink的很多配置项key值或者默认值都和Spark或者Hudi Common中不一致，这一点如果需要Flink和Spark配合使用的话，就需要注意保持一致性

COW表

我们来看一下COW表会同步哪些表

建表

CREATE TABLE test_hudi_flink4 (

id int PRIMARY KEY NOT ENFORCED,

name VARCHAR(10),

price int,

ts int,

dt VARCHAR(10)

)

PARTITIONED BY (dt)

WITH (

connector = hudi,

path = /tmp/hudi/test_hudi_flink4,

table.type = COPY_ON_WRITE,

hoodie.datasource.write.keygenerator.class = org.apache.hudi.keygen.ComplexAvroKeyGenerator,

hoodie.datasource.write.recordkey.field = id,

hoodie.datasource.write.hive_style_partitioning = true,

hive_sync.enable = true,

hive_sync.mode = hms,

hive_sync.metastore.uris = thrift://indata-192-168-44-128.indata.com:9083,

hive_sync.conf.dir=/usr/hdp/3.1.0.0-78/hive/conf,

hive_sync.db = hudi,

hive_sync.table = test_hudi_flink4,

hive_sync.partition_fields = dt,

hive_sync.partition_extractor_class = org.apache.hudi.hive.HiveStylePartitionValueExtractor

);

同步表

写数据触发同步Hive表

insert into test_hudi_flink4 values (1,hudi,10,100,2022-10-31),(2,hudi,10,100,2022-10-31);

因为COW表只有RT表，所以不会通过_rt来区分，同步的表名和配置的表名一致。这点可以参考我之前总结的文章Hudi查询类型/视图总结

+----------------------------------------------------+

| createtab_stmt |

+----------------------------------------------------+

| CREATE EXTERNAL TABLE `test_hudi_flink4`( |

| `_hoodie_commit_time` string COMMENT , |

| `_hoodie_commit_seqno` string COMMENT , |

| `_hoodie_record_key` string COMMENT , |

| `_hoodie_partition_path` string COMMENT , |

| `_hoodie_file_name` string COMMENT , |

| `id` int COMMENT , |

| `name` string COMMENT , |

| `price` int COMMENT , |

| `ts` int COMMENT ) |

| PARTITIONED BY ( |

| `dt` string COMMENT ) |

| ROW FORMAT SERDE |

| org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe |

| WITH SERDEPROPERTIES ( |

| hoodie.query.as.ro.table=false, |

| path=/tmp/hudi/test_hudi_flink4) |

| STORED AS INPUTFORMAT |

| org.apache.hudi.hadoop.HoodieParquetInputFormat |

| OUTPUTFORMAT |

| org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat |

| LOCATION |

| hdfs://cluster1/tmp/hudi/test_hudi_flink4 |

| TBLPROPERTIES ( |

| last_commit_time_sync=20221030204426056, |

| spark.sql.sources.provider=hudi, |

| spark.sql.sources.schema.numPartCols=1, |

| spark.sql.sources.schema.numParts=1, |

| spark.sql.sources.schema.part.0={"type":"struct","fields":[{"name":"_hoodie_commit_time","type":"string","nullable":true,"metadata":{}},{"name":"_hoodie_commit_seqno","type":"string","nullable":true,"metadata":{}},{"name":"_hoodie_record_key","type":"string","nullable":true,"metadata":{}},{"name":"_hoodie_partition_path","type":"string","nullable":true,"metadata":{}},{"name":"_hoodie_file_name","type":"string","nullable":true,"metadata":{}},{"name":"id","type":"integer","nullable":false,"metadata":{}},{"name":"name","type":"string","nullable":true,"metadata":{}},{"name":"price","type":"integer","nullable":true,"metadata":{}},{"name":"ts","type":"integer","nullable":true,"metadata":{}},{"name":"dt","type":"string","nullable":true,"metadata":{}}]}, |

| spark.sql.sources.schema.partCol.0=dt, |

| transient_lastDdlTime=1667133869) |

+----------------------------------------------------+方式4、建在Hive Catalog中、同步Hive表

这样建表的好处是，我们既可以利用Hive Catalog中的表通过Flink SQL写数据，也可以利用同步的Hive表通过Hive SQL查询、Spark SQL读写

MOR表

建表

配置环境变量HIVE_CONF_DIR

export HIVE_CONF_DIR=/usr/hdp/3.1.0.0-78/hive/confCREATE CATALOG hive_catalog WITH (

type = hive,

default-database = default,

hive-conf-dir = /usr/hdp/3.1.0.0-78/hive/conf

);

use catalog hive_catalog;

use hudi;

CREATE TABLE test_hudi_flink5 (

id int PRIMARY KEY NOT ENFORCED,

name VARCHAR(10),

price int,

ts int,

dt VARCHAR(10)

)

PARTITIONED BY (dt)

WITH (

connector = hudi,

path = /tmp/hudi/test_hudi_flink5,

table.type = MERGE_ON_READ,

hoodie.datasource.write.keygenerator.class = org.apache.hudi.keygen.ComplexAvroKeyGenerator,

hoodie.datasource.write.recordkey.field = id,

hoodie.datasource.write.hive_style_partitioning = true,

hive_sync.enable = true,

hive_sync.mode = hms,

hive_sync.metastore.uris = thrift://indata-192-168-44-128.indata.com:9083,

hive_sync.conf.dir=/usr/hdp/3.1.0.0-78/hive/conf,

hive_sync.db = hudi,

hive_sync.table = test_hudi_flink5,

hive_sync.partition_fields = dt,

hive_sync.partition_extractor_class = org.apache.hudi.hive.HiveStylePartitionValueExtractor

);

同步表

同样写几条数据触发同步Hive

insert into test_hudi_flink5 values (1,hudi,10,100,2022-10-31),(2,hudi,10,100,2022-10-31);

然后我们可以看到在Hive库中生成了三张表test_hudi_flink4、test_hudi_flink4_ro、test_hudi_flink4_rt,其中test_hudi_flink4是Flink格式的，和上面的方式2中的表结构一样，不能用Hive查询，但是可以在Flink中写数据、查询数据，对于test_hudi_flink4_ro、test_hudi_flink4_rt，我们就可以用Hive查询，也可以用Spark查询、写数据。

COW表

但是对于COW表来说因为同步的表名没有_rt也就是和Hive Catalog表名一样，这样就有问题，所以我们需要区分出Hive Catalog表和同步的表名，一种方式是修改hive_sync.table，另一种方式是Hive Catalog表和同步表保存在不同的Hive Database中，比如下面的示例

CREATE CATALOG hive_catalog WITH (

type = hive,

default-database = default,

hive-conf-dir = /usr/hdp/3.1.0.0-78/hive/conf

);

use catalog hive_catalog;

use flink_hudi;

CREATE TABLE test_hudi_flink6 (

id int PRIMARY KEY NOT ENFORCED,

name VARCHAR(10),

price int,

ts int,

dt VARCHAR(10)

)

PARTITIONED BY (dt)

WITH (

connector = hudi,

path = /tmp/hudi/test_hudi_flink6,

table.type = MERGE_ON_READ,

hoodie.datasource.write.keygenerator.class = org.apache.hudi.keygen.ComplexAvroKeyGenerator,

hoodie.datasource.write.recordkey.field = id,

hoodie.datasource.write.hive_style_partitioning = true,

hive_sync.enable = true,

hive_sync.mode = hms,

hive_sync.metastore.uris = thrift://indata-192-168-44-128.indata.com:9083,

hive_sync.conf.dir=/usr/hdp/3.1.0.0-78/hive/conf,

hive_sync.db = hudi,

hive_sync.table = test_hudi_flink6,

hive_sync.partition_fields = dt,

hive_sync.partition_extractor_class = org.apache.hudi.hive.HiveStylePartitionValueExtractor

);

这样Catalog表保存在flink_hudi库中，同步的表保存在hudi库中

insert into test_hudi_flink6 values (1,hudi,10,100,2022-10-31),(2,hudi,10,100,2022-10-31);异常解决

记录异常信息及解决方法，由于没有及时整理，顺序可能有点乱

不同步Hive

最开始使用的在maven里下载的包，在Hive里查询发现没有同步表，后来在官网https://hudi.apache.org/cn/docs/syncing_metastore，发现要使用profileflink-bundle-shade-hive自己打包

mvn clean package -DskipTests -Drat.skip=true -Pflink-bundle-shade-hive3 -Dflink1.14 -Dscala-2.12

但是用自己的打的包依旧不成功，在Flink SQL客户端没有异常，就很费解，后来发现在Flink yarn-session对应的web界面的Job Manager菜单里能看到具体的日志信息，比如写Hudi的Starting Javalin，这样就好办了，根据具体的异常信息对应解决即可。

异常1

2022-10-29 16:02:41,694 WARN hive.metastore [] - set_ugi() not successful, Likely cause: new client talking to old server. Continuing without it.

org.apache.thrift.transport.TTransportException: null

at org.apache.thrift.transport.TIOStreamTransport.read(TIOStreamTransport.java:132) ~[hudi-flink1.14-bundle-0.12.0.jar:0.12.0]

解决方法：配置环境变量HIVE_CONF_DIR或者配置参数hive_sync.conf.dir，这个问题困扰了我一整天，因为关于这个配置网上没有资料，我是在源码中找到的答案：

public static org.apache.hadoop.conf.Configuration getHiveConf(Configuration conf) {

String explicitDir = conf.getString(FlinkOptions.HIVE_SYNC_CONF_DIR, System.getenv("HIVE_CONF_DIR"));

org.apache.hadoop.conf.Configuration hadoopConf = new org.apache.hadoop.conf.Configuration();

if (explicitDir != null) {

hadoopConf.addResource(new Path(explicitDir, "hive-site.xml"));

}

return hadoopConf;

}

// StreamWriteOperatorCoordinator

this.hiveConf = new SerializableConfiguration(HadoopConfigurations.getHiveConf(conf));

异常2

java.lang.NoSuchMethodError: org.apache.parquet.schema.Types$PrimitiveBuilder.as(Lorg/apache/parquet/schema/LogicalTypeAnnotation;)Lorg/apache/parquet/schema/Types$Builder;

at org.apache.parquet.avro.AvroSchemaConverter.convertField(AvroSchemaConverter.java:177) ~[hudi-flink1.14-bundle-0.12.0.jar:0.12.0]

at org.apache.parquet.avro.AvroSchemaConverter.convertUnion(AvroSchemaConverter.java:242) ~[hudi-flink1.14-bundle-0.12.0.jar:0.12.0]

at org.apache.parquet.avro.AvroSchemaConverter.convertField(AvroSchemaConverter.java:199) ~[hudi-flink1.14-bundle-0.12.0.jar:0.12.0]

at org.apache.parquet.avro.AvroSchemaConverter.convertField(AvroSchemaConverter.java:152) ~[hudi-flink1.14-bundle-0.12.0.jar:0.12.0]

at org.apache.parquet.avro.AvroSchemaConverter.convertField(AvroSchemaConverter.java:260) ~[hudi-flink1.14-bundle-0.12.0.jar:0.12.0]

at org.apache.parquet.avro.AvroSchemaConverter.convertFields(AvroSchemaConverter.java:146) ~[hudi-flink1.14-bundle-0.12.0.jar:0.12.0]

at org.apache.parquet.avro.AvroSchemaConverter.convert(AvroSchemaConverter.java:137) ~[hudi-flink1.14-bundle-0.12.0.jar:0.12.0]

at org.apache.hudi.common.table.TableSchemaResolver.readSchemaFromLogFile(TableSchemaResolver.java:485) ~[hudi-flink1.14-bundle-0.12.0.jar:0.12.0]

at org.apache.hudi.common.table.TableSchemaResolver.readSchemaFromLogFile(TableSchemaResolver.java:468) ~[hudi-flink1.14-bundle-0.12.0.jar:0.12.0]

at org.apache.hudi.common.table.TableSchemaResolver.fetchSchemaFromFiles(TableSchemaResolver.java:604) ~[hudi-flink1.14-bundle-0.12.0.jar:0.12.0]

at org.apache.hudi.common.table.TableSchemaResolver.getTableParquetSchemaFromDataFile(TableSchemaResolver.java:251) ~[hudi-flink1.14-bundle-0.12.0.jar:0.12.0]

at org.apache.hudi.common.table.TableSchemaResolver.getTableAvroSchemaFromDataFile(TableSchemaResolver.java:117) ~[hudi-flink1.14-bundle-0.12.0.jar:0.12.0]

at org.apache.hudi.common.table.TableSchemaResolver.hasOperationField(TableSchemaResolver.java:537) ~[hudi-flink1.14-bundle-0.12.0.jar:0.12.0]

at org.apache.hudi.util.Lazy.get(Lazy.java:53) ~[hudi-flink1.14-bundle-0.12.0.jar:0.12.0]

at org.apache.hudi.common.table.TableSchemaResolver.getTableSchemaFromLatestCommitMetadata(TableSchemaResolver.java:208) ~[hudi-flink1.14-bundle-0.12.0.jar:0.12.0]

at org.apache.hudi.common.table.TableSchemaResolver.getTableAvroSchemaInternal(TableSchemaResolver.java:176) ~[hudi-flink1.14-bundle-0.12.0.jar:0.12.0]

at org.apache.hudi.common.table.TableSchemaResolver.getTableAvroSchema(TableSchemaResolver.java:138) ~[hudi-flink1.14-bundle-0.12.0.jar:0.12.0]

at org.apache.hudi.common.table.TableSchemaResolver.getTableParquetSchema(TableSchemaResolver.java:156) ~[hudi-flink1.14-bundle-0.12.0.jar:0.12.0]

at org.apache.hudi.sync.common.HoodieSyncClient.getStorageSchema(HoodieSyncClient.java:103) ~[hudi-flink1.14-bundle-0.12.0.jar:0.12.0]

at org.apache.hudi.hive.HiveSyncTool.syncHoodieTable(HiveSyncTool.java:206) ~[hudi-flink1.14-bundle-0.12.0.jar:0.12.0]

at org.apache.hudi.hive.HiveSyncTool.doSync(HiveSyncTool.java:157) ~[hudi-flink1.14-bundle-0.12.0.jar:0.12.0]

at org.apache.hudi.hive.HiveSyncTool.syncHoodieTable(HiveSyncTool.java:141) ~[hudi-flink1.14-bundle-0.12.0.jar:0.12.0]

at org.apache.hudi.sink.StreamWriteOperatorCoordinator.doSyncHive(StreamWriteOperatorCoordinator.java:335) ~[hudi-flink1.14-bundle-0.12.0.jar:0.12.0]

at org.apache.hudi.sink.StreamWriteOperatorCoordinator.syncHive(StreamWriteOperatorCoordinator.java:326) ~[hudi-flink1.14-bundle-0.12.0.jar:0.12.0]

at org.apache.hudi.sink.StreamWriteOperatorCoordinator.handleEndInputEvent(StreamWriteOperatorCoordinator.java:426) ~[hudi-flink1.14-bundle-0.12.0.jar:0.12.0]

at org.apache.hudi.sink.StreamWriteOperatorCoordinator.lambda$handleEventFromOperator$3(StreamWriteOperatorCoordinator.java:278) ~[hudi-flink1.14-bundle-0.12.0.jar:0.12.0]

at org.apache.hudi.sink.utils.NonThrownExecutor.lambda$wrapAction$0(NonThrownExecutor.java:130) ~[hudi-flink1.14-bundle-0.12.0.jar:0.12.0]

at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511) [?:1.8.0_181]

at java.util.concurrent.FutureTask.run(FutureTask.java:266) [?:1.8.0_181]

at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) [?:1.8.0_181]

at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) [?:1.8.0_181]

at java.lang.Thread.run(Thread.java:748) [?:1.8.0_181]

原因是jar包冲突，根据异常信息可知hudi包的org.apache.parquet.schema.Types这个类可能和flink环境下面的其他jar包冲突，经排查，发现hive-exec.*jar里也有一样的类名，将该jar包删除，验证问题解决。（在之前的文章中有写到因为缺某些类，才会将hive-exec.*jar放到flink下面，现在验证不缺这个类了，如果还有的话，可以找其他没有冲突的包替代）

异常3

Caused by: org.apache.flink.util.FlinkRuntimeException: Failed to start the operator coordinators

at org.apache.flink.runtime.scheduler.DefaultOperatorCoordinatorHandler.startAllOperatorCoordinators(DefaultOperatorCoordinatorHandler.java:90) ~[flink-dist_2.12-1.14.3.jar:1.14.3]

at org.apache.flink.runtime.scheduler.SchedulerBase.startScheduling(SchedulerBase.java:585) ~[flink-dist_2.12-1.14.3.jar:1.14.3]

at org.apache.flink.runtime.jobmaster.JobMaster.startScheduling(JobMaster.java:965) ~[flink-dist_2.12-1.14.3.jar:1.14.3]

at org.apache.flink.runtime.jobmaster.JobMaster.startJobExecution(JobMaster.java:882) ~[flink-dist_2.12-1.14.3.jar:1.14.3]

at org.apache.flink.runtime.jobmaster.JobMaster.onStart(JobMaster.java:389) ~[flink-dist_2.12-1.14.3.jar:1.14.3]

at org.apache.flink.runtime.rpc.RpcEndpoint.internalCallOnStart(RpcEndpoint.java:181) ~[flink-dist_2.12-1.14.3.jar:1.14.3]

at org.apache.flink.runtime.rpc.akka.AkkaRpcActor$StoppedState.lambda$start$0(AkkaRpcActor.java:624) ~[flink-rpc-akka_0f8ea990-3e27-4639-9ea1-d92b6879facc.jar:1.14.3]

at org.apache.flink.runtime.concurrent.akka.ClassLoadingUtils.runWithContextClassLoader(ClassLoadingUtils.java:68) ~[flink-rpc-akka_0f8ea990-3e27-4639-9ea1-d92b6879facc.jar:1.14.3]

at org.apache.flink.runtime.rpc.akka.AkkaRpcActor$StoppedState.start(AkkaRpcActor.java:623) ~[flink-rpc-akka_0f8ea990-3e27-4639-9ea1-d92b6879facc.jar:1.14.3]

... 20 more

Caused by: java.lang.NoClassDefFoundError: org/apache/hadoop/hive/conf/HiveConf

at org.apache.hudi.sink.StreamWriteOperatorCoordinator.initHiveSync(StreamWriteOperatorCoordinator.java:315) ~[hudi-flink1.14-bundle-0.12.1.jar:0.12.1]

at org.apache.hudi.sink.StreamWriteOperatorCoordinator.start(StreamWriteOperatorCoordinator.java:191) ~[hudi-flink1.14-bundle-0.12.1.jar:0.12.1]

at org.apache.flink.runtime.operators.coordination.OperatorCoordinatorHolder.start(OperatorCoordinatorHolder.java:194) ~[flink-dist_2.12-1.14.3.jar:1.14.3]

at org.apache.flink.runtime.scheduler.DefaultOperatorCoordinatorHandler.startAllOperatorCoordinators(DefaultOperatorCoordinatorHandler.java:85) ~[flink-dist_2.12-1.14.3.jar:1.14.3]