24、Flink 的table api与sql之Catalogs（java api操作分区与函数、表）-4

Flink 系列文章

1、Flink 部署、概念介绍、source、transformation、sink使用示例、四大基石介绍和示例等系列综合文章链接

13、Flink 的table api与sql的基本概念、通用api介绍及入门示例
14、Flink 的table api与sql之数据类型: 内置数据类型以及它们的属性
15、Flink 的table api与sql之流式概念-详解的介绍了动态表、时间属性配置（如何处理更新结果）、时态表、流上的join、流上的确定性以及查询配置
16、Flink 的table api与sql之连接外部系统: 读写外部系统的连接器和格式以及FileSystem示例（1）
16、Flink 的table api与sql之连接外部系统: 读写外部系统的连接器和格式以及Elasticsearch示例（2）
16、Flink 的table api与sql之连接外部系统: 读写外部系统的连接器和格式以及Apache Kafka示例（3）
16、Flink 的table api与sql之连接外部系统: 读写外部系统的连接器和格式以及JDBC示例（4）

16、Flink 的table api与sql之连接外部系统: 读写外部系统的连接器和格式以及Apache Hive示例（6）

20、Flink SQL之SQL Client: 不用编写代码就可以尝试 Flink SQL，可以直接提交 SQL 任务到集群上

22、Flink 的table api与sql之创建表的DDL
24、Flink 的table api与sql之Catalogs（介绍、类型、java api和sql实现ddl、java api和sql操作catalog）-1
24、Flink 的table api与sql之Catalogs（java api操作数据库、表）-2
24、Flink 的table api与sql之Catalogs（java api操作视图）-3
24、Flink 的table api与sql之Catalogs（java api操作分区与函数）-4

26、Flink 的SQL之概览与入门示例
27、Flink 的SQL之SELECT (select、where、distinct、order by、limit、集合操作和去重)介绍及详细示例（1）
27、Flink 的SQL之SELECT (SQL Hints 和 Joins)介绍及详细示例（2）
27、Flink 的SQL之SELECT (窗口函数)介绍及详细示例（3）
27、Flink 的SQL之SELECT (窗口聚合)介绍及详细示例（4）
27、Flink 的SQL之SELECT (Group Aggregation分组聚合、Over Aggregation Over聚合 和 Window Join 窗口关联)介绍及详细示例（5）
27、Flink 的SQL之SELECT (Top-N、Window Top-N 窗口 Top-N 和 Window Deduplication 窗口去重)介绍及详细示例（6）
27、Flink 的SQL之SELECT (Pattern Recognition 模式检测)介绍及详细示例（7）

29、Flink SQL之DESCRIBE、EXPLAIN、USE、SHOW、LOAD、UNLOAD、SET、RESET、JAR、JOB Statements、UPDATE、DELETE（1）
29、Flink SQL之DESCRIBE、EXPLAIN、USE、SHOW、LOAD、UNLOAD、SET、RESET、JAR、JOB Statements、UPDATE、DELETE（2）
30、Flink SQL之SQL 客户端（通过kafka和filesystem的例子介绍了配置文件使用-表、视图等）
32、Flink table api和SQL 之用户自定义 Sources & Sinks实现及详细示例
41、Flink之Hive 方言介绍及详细示例
42、Flink 的table api与sql之Hive Catalog
43、Flink之Hive 读写及详细验证示例
44、Flink之module模块介绍及使用示例和Flink SQL使用hive内置函数及自定义函数详细示例–网上有些说法好像是错误的

---

---

本文简单介绍了通过java api或者SQL操作分区、函数以及表，特别是创建hive的表，通过6个示例进行说明 。
本文依赖flink和hive、hadoop集群能正常使用。
本文示例java api的实现是通过Flink 1.13.5版本做的示例，hive的版本是3.1.2，hadoop的版本是3.1.4。

五、Catalog API

4、分区操作

1）、官方示例

// create view
catalog.createPartition(
    new ObjectPath("mydb", "mytable"),
    new CatalogPartitionSpec(...),
    new CatalogPartitionImpl(...),
    false);

// drop partition
catalog.dropPartition(new ObjectPath("mydb", "mytable"), new CatalogPartitionSpec(...), false);

// alter partition
catalog.alterPartition(
    new ObjectPath("mydb", "mytable"),
    new CatalogPartitionSpec(...),
    new CatalogPartitionImpl(...),
    false);

// get partition
catalog.getPartition(new ObjectPath("mydb", "mytable"), new CatalogPartitionSpec(...));

// check if a partition exist or not
catalog.partitionExists(new ObjectPath("mydb", "mytable"), new CatalogPartitionSpec(...));

// list partitions of a table
catalog.listPartitions(new ObjectPath("mydb", "mytable"));

// list partitions of a table under a give partition spec
catalog.listPartitions(new ObjectPath("mydb", "mytable"), new CatalogPartitionSpec(...));

// list partitions of a table by expression filter
catalog.listPartitions(new ObjectPath("mydb", "mytable"), Arrays.asList(epr1, ...));

2）、API创建hive分区示例

本示例旨在演示如何使用flink api创建hive的分区表，至于hive的分区表如何使用，请参考hive的相关专题。同时，修改分区、删除分区都比较简单不再赘述。
16、Flink 的table api与sql之连接外部系统: 读写外部系统的连接器和格式以及Apache Hive示例（6）
3、hive的使用示例详解-建表、数据类型详解、内部外部表、分区表、分桶表

1、maven依赖

此处使用的依赖与上示例一致，mainclass变成本示例的类，不再赘述。
具体打包的时候运行主类则需要视自己的运行情况决定是否修改。

2、代码

import java.util.Arrays;
import java.util.Collections;
import java.util.HashMap;
import java.util.List;
import java.util.Map;

import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.DataTypes;
import org.apache.flink.table.api.Schema;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
import org.apache.flink.table.catalog.CatalogDatabase;
import org.apache.flink.table.catalog.CatalogDatabaseImpl;
import org.apache.flink.table.catalog.CatalogPartition;
import org.apache.flink.table.catalog.CatalogPartitionImpl;
import org.apache.flink.table.catalog.CatalogPartitionSpec;
import org.apache.flink.table.catalog.CatalogTable;
import org.apache.flink.table.catalog.Column;
import org.apache.flink.table.catalog.ObjectPath;
import org.apache.flink.table.catalog.ResolvedCatalogTable;
import org.apache.flink.table.catalog.ResolvedSchema;
import org.apache.flink.table.catalog.hive.HiveCatalog;
import org.apache.flink.table.factories.FactoryUtil;
import org.apache.flink.table.module.hive.HiveModule;

/**
 * @author alanchan
 *
 */
public class TestHivePartitionByAPI {
	static final String TEST_COMMENT = "test table comment";
	static String databaseName = "viewtest_db";
	static String tableName1 = "t1";
	static String tableName2 = "t2";
	static boolean isGeneric = false;

	public static void main(String[] args) throws Exception {

		// 0、运行环境
		StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
		StreamTableEnvironment tenv = StreamTableEnvironment.create(env);

		// 1、创建数据库
//		catalog.createDatabase(db1, createDb(), false);
		HiveCatalog hiveCatalog = init(tenv);

		// 2、创建分区表
//		catalog.createTable(path1, createPartitionedTable(), false);
		// 2.1 创建分区表 t1
		ObjectPath path1 = new ObjectPath(databaseName, tableName1);
		hiveCatalog.createTable(path1, createPartitionedTable(), false);

		// 2.21 创建分区表 t2，只有表名称不一致，体现不使用方法化的创建方式
		ObjectPath path2 = new ObjectPath(databaseName, tableName2);
		ResolvedSchema resolvedSchema = new ResolvedSchema(
				Arrays.asList(Column.physical("id", DataTypes.INT()), Column.physical("name", DataTypes.STRING()), Column.physical("age", DataTypes.INT())),
				Collections.emptyList(), null);

//		   Schema schema,
//        @Nullable String comment,
//        List partitionKeys,
//        Map options
		CatalogTable origin = CatalogTable.of(Schema.newBuilder().fromResolvedSchema(resolvedSchema).build(), TEST_COMMENT, Arrays.asList("name", "age"),
				new HashMap<String, String>() {
					{
						put("streaming", "false");
						putAll(getGenericFlag(isGeneric));
					}
				});
		CatalogTable catalogTable = new ResolvedCatalogTable(origin, resolvedSchema);
		hiveCatalog.createTable(path2, catalogTable, false);

		// 3、断言
//		assertThat(catalog.listPartitions(path1)).isEmpty();
		// 3、创建分区
		// 3.1 创建分区方式1
//		catalog.createPartition(path1, createPartitionSpec(), createPartition(), false);
//		   ObjectPath tablePath,
//        CatalogPartitionSpec partitionSpec,
//        CatalogPartition partition,
//        boolean ignoreIfExists
		hiveCatalog.createPartition(path1, createPartitionSpec(), createPartition(), false);

		// 3.21 创建分区方式2
		hiveCatalog.createPartition(path2, new CatalogPartitionSpec(new HashMap<String, String>() {
			{
				put("name", "alan");
				put("age", "20");
			}
		}), new CatalogPartitionImpl(new HashMap<String, String>() {
			{
				put("streaming", "false");
				putAll(getGenericFlag(isGeneric));
			}
		}, TEST_COMMENT), false);

		System.out.println("path1 listPartitions:"+hiveCatalog.listPartitions(path1));
		System.out.println("path2 listPartitions:"+hiveCatalog.listPartitions(path2));

		System.out.println("path1 listPartitions:"+hiveCatalog.listPartitions(path1, createPartitionSpecSubset()));
		System.out.println("path2 listPartitions:"+hiveCatalog.listPartitions(path2, createPartitionSpecSubset()));

//		assertThat(hiveCatalog.listPartitions(path1)).containsExactly(createPartitionSpec());
//		assertThat(catalog.listPartitions(path1, createPartitionSpecSubset())).containsExactly(createPartitionSpec());

		// 4、检查分区
//		CatalogTestUtil.checkEquals(createPartition(), catalog.getPartition(path1, createPartitionSpec()));
		
		//5、删除测试数据库
//		tenv.executeSql("drop database " + databaseName + " cascade");
	}

	private static HiveCatalog init(StreamTableEnvironment tenv) throws Exception {

		String moduleName = "myhive";
		String hiveVersion = "3.1.2";
		tenv.loadModule(moduleName, new HiveModule(hiveVersion));

		String name = "alan_hive";
		String defaultDatabase = "default";
		String hiveConfDir = "/usr/local/bigdata/apache-hive-3.1.2-bin/conf";

		HiveCatalog hiveCatalog = new HiveCatalog(name, defaultDatabase, hiveConfDir);
		tenv.registerCatalog(name, hiveCatalog);
		tenv.useCatalog(name);
		tenv.listDatabases();
		hiveCatalog.createDatabase(databaseName, new CatalogDatabaseImpl(new HashMap(), hiveConfDir) {
		}, true);

//	tenv.executeSql("create database "+databaseName);

		tenv.useDatabase(databaseName);
		return hiveCatalog;
	}

	CatalogDatabase createDb() {
		return new CatalogDatabaseImpl(new HashMap<String, String>() {
			{
				put("k1", "v1");
			}
		}, TEST_COMMENT);
	}

	static CatalogTable createPartitionedTable() {
		final ResolvedSchema resolvedSchema = createSchema();
		final CatalogTable origin = CatalogTable.of(Schema.newBuilder().fromResolvedSchema(resolvedSchema).build(), TEST_COMMENT, createPartitionKeys(), getBatchTableProperties());
		return new ResolvedCatalogTable(origin, resolvedSchema);
	}

	static ResolvedSchema createSchema() {
		return new ResolvedSchema(
				Arrays.asList(Column.physical("id", DataTypes.INT()), Column.physical("name", DataTypes.STRING()), Column.physical("age", DataTypes.INT())),
				Collections.emptyList(), null);
	}

	static List<String> createPartitionKeys() {
		return Arrays.asList("name", "age");
	}

	static Map<String, String> getBatchTableProperties() {
		return new HashMap<String, String>() {
			{
				put("streaming", "false");
				putAll(getGenericFlag(isGeneric));
			}
		};
	}

	static Map<String, String> getGenericFlag(boolean isGeneric) {
		return new HashMap<String, String>() {
			{
				String connector = isGeneric ? "COLLECTION" : "hive";
				put(FactoryUtil.CONNECTOR.key(), connector);
			}
		};
	}

	static CatalogPartitionSpec createPartitionSpec() {
		return new CatalogPartitionSpec(new HashMap<String, String>() {
			{
				put("name", "alan");
				put("age", "20");
			}
		});
	}

	static CatalogPartitionSpec createPartitionSpecSubset() {
		return new CatalogPartitionSpec(new HashMap<String, String>() {
			{
				put("name", "alan");
			}
		});
	}

	static CatalogPartition createPartition() {
		return new CatalogPartitionImpl(getBatchTableProperties(), TEST_COMMENT);
	}
}

3、运行结果

• flink 运行结果

[alanchan@server2 bin]$ flink run  /usr/local/bigdata/flink-1.13.5/examples/table/table_sql-0.0.4-SNAPSHOT.jar

path1 listPartitions:[CatalogPartitionSpec{{name=alan, age=20}}]
path2 listPartitions:[CatalogPartitionSpec{{name=alan, age=20}}]
path1 listPartitions:[CatalogPartitionSpec{{name=alan, age=20}}]
path2 listPartitions:[CatalogPartitionSpec{{name=alan, age=20}}]

• hive 查看表分区情况

0: jdbc:hive2://server4:10000> desc formatted t1;
+-------------------------------+----------------------------------------------------+-----------------------+
|           col_name            |                     data_type                      |        comment        |
+-------------------------------+----------------------------------------------------+-----------------------+
| # col_name                    | data_type                                          | comment               |
| id                            | int                                                |                       |
|                               | NULL                                               | NULL                  |
| # Partition Information       | NULL                                               | NULL                  |
| # col_name                    | data_type                                          | comment               |
| name                          | string                                             |                       |
| age                           | int                                                |                       |
|                               | NULL                                               | NULL                  |
| # Detailed Table Information  | NULL                                               | NULL                  |
| Database:                     | viewtest_db                                        | NULL                  |
| OwnerType:                    | USER                                               | NULL                  |
| Owner:                        | null                                               | NULL                  |
| CreateTime:                   | Tue Oct 17 10:43:55 CST 2023                       | NULL                  |
| LastAccessTime:               | UNKNOWN                                            | NULL                  |
| Retention:                    | 0                                                  | NULL                  |
| Location:                     | hdfs://HadoopHAcluster/user/hive/warehouse/viewtest_db.db/t1 | NULL                  |
| Table Type:                   | MANAGED_TABLE                                      | NULL                  |
| Table Parameters:             | NULL                                               | NULL                  |
|                               | bucketing_version                                  | 2                     |
|                               | comment                                            | test table comment    |
|                               | numFiles                                           | 0                     |
|                               | numPartitions                                      | 1                     |
|                               | numRows                                            | 0                     |
|                               | rawDataSize                                        | 0                     |
|                               | streaming                                          | false                 |
|                               | totalSize                                          | 0                     |
|                               | transient_lastDdlTime                              | 1697510635            |
|                               | NULL                                               | NULL                  |
| # Storage Information         | NULL                                               | NULL                  |
| SerDe Library:                | org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe | NULL                  |
| InputFormat:                  | org.apache.hadoop.mapred.TextInputFormat           | NULL                  |
| OutputFormat:                 | org.apache.hadoop.hive.ql.io.IgnoreKeyTextOutputFormat | NULL                  |
| Compressed:                   | No                                                 | NULL                  |
| Num Buckets:                  | -1                                                 | NULL                  |
| Bucket Columns:               | []                                                 | NULL                  |
| Sort Columns:                 | []                                                 | NULL                  |
| Storage Desc Params:          | NULL                                               | NULL                  |
|                               | serialization.format                               | 1                     |
+-------------------------------+----------------------------------------------------+-----------------------+

5、函数操作

1）、官方示例

// create function
catalog.createFunction(new ObjectPath("mydb", "myfunc"), new CatalogFunctionImpl(...), false);

// drop function
catalog.dropFunction(new ObjectPath("mydb", "myfunc"), false);

// alter function
catalog.alterFunction(new ObjectPath("mydb", "myfunc"), new CatalogFunctionImpl(...), false);

// get function
catalog.getFunction("myfunc");

// check if a function exist or not
catalog.functionExists("myfunc");

// list functions in a database
catalog.listFunctions("mydb");

2）、API操作Function

通过api来操作函数，比如创建、修改删除以及查询等。

1、maven依赖

此处使用的依赖与上示例一致，mainclass变成本示例的类，不再赘述。
具体打包的时候运行主类则需要视自己的运行情况决定是否修改。

2、代码

import java.util.HashMap;

import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
import org.apache.flink.table.catalog.CatalogDatabaseImpl;
import org.apache.flink.table.catalog.CatalogFunctionImpl;
import org.apache.flink.table.catalog.ObjectPath;
import org.apache.flink.table.catalog.hive.HiveCatalog;
import org.apache.flink.table.module.hive.HiveModule;
import org.apache.hadoop.hive.ql.udf.UDFRand;
import org.apache.hadoop.hive.ql.udf.generic.GenericUDFAbs;

/**
 * @author alanchan
 *
 */
public class TestFunctionByAPI {
	static String databaseName = "viewtest_db";
	static String tableName1 = "t1";

	public static void main(String[] args) throws Exception {
		// 0、环境
		StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
		StreamTableEnvironment tenv = StreamTableEnvironment.create(env);

		// 1、创建数据库
		// catalog.createDatabase(db1, createDb(), false);
		HiveCatalog hiveCatalog = init(tenv);

		// 2、检查function是否存在
		ObjectPath path1 = new ObjectPath(databaseName, tableName1);
		System.out.println("function是否存在 :" + hiveCatalog.functionExists(path1));

		// 3、创建function
		hiveCatalog.createFunction(path1, new CatalogFunctionImpl(GenericUDFAbs.class.getName()), false);
		System.out.println("function是否存在 :" + hiveCatalog.functionExists(path1));
		
		// 4、修改function
		hiveCatalog.alterFunction(path1, new CatalogFunctionImpl(UDFRand.class.getName()), false);
		System.out.println("修改后的function是否存在 :" + hiveCatalog.functionExists(path1));
		
		System.out.println("查询function :" + hiveCatalog.getFunction(path1));
		System.out.println("function 列表 :" + hiveCatalog.listFunctions(databaseName));

		// 5、删除function
		hiveCatalog.dropFunction(path1, false);
		System.out.println("function是否存在 :" + hiveCatalog.functionExists(path1));

		// 6、删除测试数据库
		// tenv.executeSql("drop database " + databaseName + " cascade");
	}

	private static HiveCatalog init(StreamTableEnvironment tenv) throws Exception {
		String moduleName = "myhive";
		String hiveVersion = "3.1.2";
		tenv.loadModule(moduleName, new HiveModule(hiveVersion));

		String name = "alan_hive";
		String defaultDatabase = "default";
		String hiveConfDir = "/usr/local/bigdata/apache-hive-3.1.2-bin/conf";

		HiveCatalog hiveCatalog = new HiveCatalog(name, defaultDatabase, hiveConfDir);
		tenv.registerCatalog(name, hiveCatalog);
		tenv.useCatalog(name);
		tenv.listDatabases();
		hiveCatalog.createDatabase(databaseName, new CatalogDatabaseImpl(new HashMap(), hiveConfDir) {
		}, true);

		tenv.useDatabase(databaseName);
		return hiveCatalog;
	}

}

3、运行结果

[alanchan@server2 bin]$ flink run  /usr/local/bigdata/flink-1.13.5/examples/table/table_sql-0.0.5-SNAPSHOT.jar

function是否存在 :false
function是否存在 :true
修改后的function是否存在 :true
查询function :CatalogFunctionImpl{className='org.apache.hadoop.hive.ql.udf.UDFRand', functionLanguage='JAVA', isGeneric='false'}
function 列表 :[t1]
function是否存在 :false

6、表操作（补充）

1）、官方示例

// create table
catalog.createTable(new ObjectPath("mydb", "mytable"), new CatalogTableImpl(...), false);

// drop table
catalog.dropTable(new ObjectPath("mydb", "mytable"), false);

// alter table
catalog.alterTable(new ObjectPath("mydb", "mytable"), new CatalogTableImpl(...), false);

// rename table
catalog.renameTable(new ObjectPath("mydb", "mytable"), "my_new_table");

// get table
catalog.getTable("mytable");

// check if a table exist or not
catalog.tableExists("mytable");

// list tables in a database
catalog.listTables("mydb");

2）、SQL创建hive表示例

1、maven依赖

此处使用的依赖与上示例一致，mainclass变成本示例的类，不再赘述。
具体打包的时候运行主类则需要视自己的运行情况决定是否修改。

2、代码

import java.util.HashMap;
import java.util.List;

import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.SqlDialect;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
import org.apache.flink.table.catalog.CatalogDatabaseImpl;
import org.apache.flink.table.catalog.hive.HiveCatalog;
import org.apache.flink.table.module.hive.HiveModule;
import org.apache.flink.types.Row;
import org.apache.flink.util.CollectionUtil;

/**
 * @author alanchan
 *
 */
public class TestCreateHiveTableBySQLDemo {
	static String databaseName = "viewtest_db";
	public static final String tableName = "alan_hivecatalog_hivedb_testTable";
	public static final String hive_create_table_sql = "CREATE  TABLE  " + tableName +  " (\n" + 
			  "  id INT,\n" + 
			  "  name STRING,\n" + 
			  "  age INT" + ") " + 
			  "TBLPROPERTIES (\n" + 
			  "  'sink.partition-commit.delay'='5 s',\n" + 
			  "  'sink.partition-commit.trigger'='partition-time',\n" + 
			  "  'sink.partition-commit.policy.kind'='metastore,success-file'" + ")";

	/**
	 * @param args
	 * @throws Exception
	 */
	public static void main(String[] args) throws Exception {
		// 0、运行环境
		StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
		StreamTableEnvironment tenv = StreamTableEnvironment.create(env);

		// 1、创建数据库
		HiveCatalog hiveCatalog = init(tenv);

		// 2、创建表
		tenv.getConfig().setSqlDialect(SqlDialect.HIVE);
		tenv.executeSql(hive_create_table_sql);

		// 3、插入数据
		String insertSQL = "insert into " + tableName + " values (1,'alan',18)";
		tenv.executeSql(insertSQL);

		// 4、查询数据
		List<Row> results = CollectionUtil.iteratorToList(tenv.executeSql("select * from " + tableName).collect());
		for (Row row : results) {
			System.out.println(tableName + ": " + row.toString());
		}

		// 5、删除数据库
		tenv.executeSql("drop database " + databaseName + " cascade");
	}

	private static HiveCatalog init(StreamTableEnvironment tenv) throws Exception {
		String moduleName = "myhive";
		String hiveVersion = "3.1.2";
		tenv.loadModule(moduleName, new HiveModule(hiveVersion));

		String name = "alan_hive";
		String defaultDatabase = "default";
		String hiveConfDir = "/usr/local/bigdata/apache-hive-3.1.2-bin/conf";

		HiveCatalog hiveCatalog = new HiveCatalog(name, defaultDatabase, hiveConfDir);
		tenv.registerCatalog(name, hiveCatalog);
		tenv.useCatalog(name);
		tenv.listDatabases();
		hiveCatalog.createDatabase(databaseName, new CatalogDatabaseImpl(new HashMap(), hiveConfDir) {
		}, true);

		tenv.useDatabase(databaseName);
		return hiveCatalog;
	}
}

3、运行结果

[alanchan@server2 bin]$ flink run  /usr/local/bigdata/flink-1.13.5/examples/table/table_sql-0.0.6-SNAPSHOT.jar

Hive Session ID = eb6579cd-befc-419b-8f95-8fd1e8e287e0
Hive Session ID = be12e47f-d611-4cc4-9be5-8e7628b7c90a
Job has been submitted with JobID 442b113232b8390394587b66b47aebbc
Hive Session ID = b8d772a8-a89d-4630-bbf1-fe5a3e301344
2023-10-17 07:23:31,244 INFO  org.apache.hadoop.mapred.FileInputFormat                     [] - Total input files to process : 0
Job has been submitted with JobID f24c2cc25fa3aba729fc8b27c3edf243
alan_hivecatalog_hivedb_testTable: +I[1, alan, 18]
Hive Session ID = 69fafc9c-f8c0-4f55-b689-5db196a94689

3）、API创建hive表-普通表

1、maven依赖

此处使用的依赖与上示例一致，mainclass变成本示例的类，不再赘述。
具体打包的时候运行主类则需要视自己的运行情况决定是否修改。

2、代码

import java.util.Arrays;
import java.util.Collections;
import java.util.HashMap;
import java.util.List;
import java.util.Map;

import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.DataTypes;
import org.apache.flink.table.api.Schema;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
import org.apache.flink.table.catalog.CatalogBaseTable;
import org.apache.flink.table.catalog.CatalogDatabaseImpl;
import org.apache.flink.table.catalog.CatalogTable;
import org.apache.flink.table.catalog.Column;
import org.apache.flink.table.catalog.ObjectPath;
import org.apache.flink.table.catalog.ResolvedCatalogTable;
import org.apache.flink.table.catalog.ResolvedSchema;
import org.apache.flink.table.catalog.hive.HiveCatalog;
import org.apache.flink.table.factories.FactoryUtil;
import org.apache.flink.table.module.hive.HiveModule;
import org.apache.flink.types.Row;
import org.apache.flink.util.CollectionUtil;

/**
 * @author alanchan
 *
 */
public class TestCreateHiveTableByAPIDemo {
	static String TEST_COMMENT = "test table comment";
	static String databaseName = "hive_db_test";
	static String tableName1 = "t1";
	static String tableName2 = "t2";

	/**
	 * @param args
	 * @throws Exception
	 */
	public static void main(String[] args) throws Exception {
		// 0、运行环境
		StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
		StreamTableEnvironment tenv = StreamTableEnvironment.create(env);

		// 1、创建数据库
		HiveCatalog hiveCatalog = init(tenv);

		// 2、创建表
		ObjectPath path1 = new ObjectPath(databaseName, tableName1);
		ResolvedSchema resolvedSchema = new ResolvedSchema(
				Arrays.asList(
						Column.physical("id", DataTypes.INT()), 
						Column.physical("name", DataTypes.STRING()), 
						Column.physical("age", DataTypes.INT())),
				Collections.emptyList(), null);

		CatalogTable origin = CatalogTable.of(
				Schema.newBuilder().fromResolvedSchema(resolvedSchema).build(), 
				TEST_COMMENT, 
				Collections.emptyList(), 
				new HashMap<String, String>() {
					{
						put("is_streaming", "false");
						putAll(new HashMap<String, String>() {
							{
								put(FactoryUtil.CONNECTOR.key(), "hive");
							}
						});
					}
				});
		CatalogTable catalogTable = new ResolvedCatalogTable(origin, resolvedSchema);

		// 普通表
		hiveCatalog.createTable(path1, catalogTable, false);
		CatalogBaseTable tableCreated = hiveCatalog.getTable(path1);

		List<String> tables = hiveCatalog.listTables(databaseName);
		for (String table : tables) {
			System.out.println(" tableNameList : " + table);
		}

		// 3、插入数据
		String insertSQL = "insert into " + tableName1 + " values (1,'alan',18)";
		tenv.executeSql(insertSQL);

		// 4、查询数据
		List<Row> results = CollectionUtil.iteratorToList(tenv.executeSql("select * from " + tableName1).collect());
		for (Row row : results) {
			System.out.println(tableName1 + ": " + row.toString());
		}

		hiveCatalog.dropTable(path1, false);
		boolean tableExists = hiveCatalog.tableExists(path1);
		System.out.println("表是否drop成功：" + tableExists);

		// 5、删除数据库
		tenv.executeSql("drop database " + databaseName + " cascade");
	}

	private static HiveCatalog init(StreamTableEnvironment tenv) throws Exception {
		String moduleName = "myhive";
		String hiveVersion = "3.1.2";
		tenv.loadModule(moduleName, new HiveModule(hiveVersion));

		String name = "alan_hive";
		String defaultDatabase = "default";
		String hiveConfDir = "/usr/local/bigdata/apache-hive-3.1.2-bin/conf";

		HiveCatalog hiveCatalog = new HiveCatalog(name, defaultDatabase, hiveConfDir);
		tenv.registerCatalog(name, hiveCatalog);
		tenv.useCatalog(name);
		tenv.listDatabases();
		hiveCatalog.createDatabase(databaseName, new CatalogDatabaseImpl(new HashMap(), hiveConfDir) {
		}, true);

		// tenv.executeSql("create database "+databaseName);
		tenv.useDatabase(databaseName);
		return hiveCatalog;
	}

}

3、运行结果

• flink 运行结果

[alanchan@server2 bin]$ flink run  /usr/local/bigdata/flink-1.13.5/examples/table/table_sql-0.0.7-SNAPSHOT.jar

 tableNameList : t1
Job has been submitted with JobID b70b8c76fd3f05b9f949a47583596288
2023-10-17 09:01:19,320 INFO  org.apache.hadoop.mapred.FileInputFormat                     [] - Total input files to process : 0
Job has been submitted with JobID 34650c04d0a6fb32f7336f7ccc8b9090
t1: +I[1, alan, 18]
表是否drop成功：false

• hive 表描述
  下述结果是表和数据库没有删除的时候查询结果，也就是将上述示例中关于删除表和库的语句注释掉了。

0: jdbc:hive2://server4:10000> desc formatted t1;
+-------------------------------+----------------------------------------------------+-----------------------+
|           col_name            |                     data_type                      |        comment        |
+-------------------------------+----------------------------------------------------+-----------------------+
| # col_name                    | data_type                                          | comment               |
| id                            | int                                                |                       |
| name                          | string                                             |                       |
| age                           | int                                                |                       |
|                               | NULL                                               | NULL                  |
| # Detailed Table Information  | NULL                                               | NULL                  |
| Database:                     | hive_db_test                                       | NULL                  |
| OwnerType:                    | USER                                               | NULL                  |
| Owner:                        | null                                               | NULL                  |
| CreateTime:                   | Tue Oct 17 16:55:02 CST 2023                       | NULL                  |
| LastAccessTime:               | UNKNOWN                                            | NULL                  |
| Retention:                    | 0                                                  | NULL                  |
| Location:                     | hdfs://HadoopHAcluster/user/hive/warehouse/hive_db_test.db/t1 | NULL                  |
| Table Type:                   | MANAGED_TABLE                                      | NULL                  |
| Table Parameters:             | NULL                                               | NULL                  |
|                               | bucketing_version                                  | 2                     |
|                               | comment                                            | test table comment    |
|                               | streaming                                          | false                 |
|                               | transient_lastDdlTime                              | 1697532902            |
|                               | NULL                                               | NULL                  |
| # Storage Information         | NULL                                               | NULL                  |
| SerDe Library:                | org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe | NULL                  |
| InputFormat:                  | org.apache.hadoop.mapred.TextInputFormat           | NULL                  |
| OutputFormat:                 | org.apache.hadoop.hive.ql.io.IgnoreKeyTextOutputFormat | NULL                  |
| Compressed:                   | No                                                 | NULL                  |
| Num Buckets:                  | -1                                                 | NULL                  |
| Bucket Columns:               | []                                                 | NULL                  |
| Sort Columns:                 | []                                                 | NULL                  |
| Storage Desc Params:          | NULL                                               | NULL                  |
|                               | serialization.format                               | 1                     |
+-------------------------------+----------------------------------------------------+-----------------------+

4）、API创建hive表-流式表

1、maven依赖

此处使用的依赖与上示例一致，mainclass变成本示例的类，不再赘述。
具体打包的时候运行主类则需要视自己的运行情况决定是否修改。

2、代码

该示例与上述使用API创建hive表功能一样，仅仅表示了方法化和流式表的创建方式，运行结果也一样，不再赘述。

import java.util.Arrays;
import java.util.Collections;
import java.util.HashMap;
import java.util.List;
import java.util.Map;

import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.DataTypes;
import org.apache.flink.table.api.Schema;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
import org.apache.flink.table.catalog.CatalogDatabaseImpl;
import org.apache.flink.table.catalog.CatalogTable;
import org.apache.flink.table.catalog.Column;
import org.apache.flink.table.catalog.ObjectPath;
import org.apache.flink.table.catalog.ResolvedCatalogTable;
import org.apache.flink.table.catalog.ResolvedSchema;
import org.apache.flink.table.catalog.hive.HiveCatalog;
import org.apache.flink.table.factories.FactoryUtil;
import org.apache.flink.table.module.hive.HiveModule;
import org.apache.flink.types.Row;
import org.apache.flink.util.CollectionUtil;

/**
 * @author alanchan
 *
 */
public class TestCreateHiveTableByAPIDemo {
	static String TEST_COMMENT = "test table comment";
	static String databaseName = "hive_db_test";
	static String tableName1 = "t1";
	static String tableName2 = "t2";
	static ObjectPath path1 = new ObjectPath(databaseName, tableName1);

	/**
	 * @param args
	 * @throws Exception
	 */
	public static void main(String[] args) throws Exception {
		// 0、运行环境
		StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
		StreamTableEnvironment tenv = StreamTableEnvironment.create(env);

		// 1、创建数据库
		HiveCatalog hiveCatalog = init(tenv);

		// 2、创建表
		// 2.1、创建批处理表
//		testCreateTable_Batch(hiveCatalog);
		// 2.2、创建流式表
		testCreateTable_Streaming(hiveCatalog);
		
		
		// 3、插入数据
		String insertSQL = "insert into " + tableName1 + " values (1,'alan',18)";
		tenv.executeSql(insertSQL);

		// 4、查询数据
		List<Row> results = CollectionUtil.iteratorToList(tenv.executeSql("select * from " + tableName1).collect());
		for (Row row : results) {
			System.out.println(tableName1 + ": " + row.toString());
		}

		hiveCatalog.dropTable(path1, false);
		boolean tableExists = hiveCatalog.tableExists(path1);
		System.out.println("表是否drop成功：" + tableExists);

		// 5、删除数据库
		tenv.executeSql("drop database " + databaseName + " cascade");
	}

	/**
	 * 初始化hivecatalog
	 * 
	 * @param tenv
	 * @return
	 * @throws Exception
	 */
	private static HiveCatalog init(StreamTableEnvironment tenv) throws Exception {
		String moduleName = "myhive";
		String hiveVersion = "3.1.2";
		tenv.loadModule(moduleName, new HiveModule(hiveVersion));

		String name = "alan_hive";
		String defaultDatabase = "default";
		String hiveConfDir = "/usr/local/bigdata/apache-hive-3.1.2-bin/conf";

		HiveCatalog hiveCatalog = new HiveCatalog(name, defaultDatabase, hiveConfDir);
		tenv.registerCatalog(name, hiveCatalog);
		tenv.useCatalog(name);
		tenv.listDatabases();
		// tenv.executeSql("create database "+databaseName);
		hiveCatalog.createDatabase(databaseName, new CatalogDatabaseImpl(new HashMap(), hiveConfDir) {
		}, true);

		tenv.useDatabase(databaseName);
		return hiveCatalog;
	}

	/**
	 * 创建流式表
	 * 
	 * @param catalog
	 * @throws Exception
	 */
	static void testCreateTable_Streaming(HiveCatalog catalog) throws Exception {
		CatalogTable table = createStreamingTable();
		catalog.createTable(path1, table, false);

//		CatalogTestUtil.checkEquals(table, (CatalogTable) catalog.getTable(path1));
	}

	/**
	 * 创建批处理表
	 * 
	 * @param catalog
	 * @throws Exception
	 */
	static void testCreateTable_Batch(HiveCatalog catalog) throws Exception {
		// Non-partitioned table
		CatalogTable table = createBatchTable();
		catalog.createTable(path1, table, false);

//		CatalogBaseTable tableCreated = catalog.getTable(path1);

//		CatalogTestUtil.checkEquals(table, (CatalogTable) tableCreated);
//		assertThat(tableCreated.getDescription().isPresent()).isTrue();
//		assertThat(tableCreated.getDescription().get()).isEqualTo(TEST_COMMENT);

//		List tables = catalog.listTables(databaseName);

//		assertThat(tables).hasSize(1);
//		assertThat(tables.get(0)).isEqualTo(path1.getObjectName());

//		catalog.dropTable(path1, false);
	}

	/**
	 * 创建流式表
	 * 
	 * @return
	 */
	static CatalogTable createStreamingTable() {
		final ResolvedSchema resolvedSchema = createSchema();
		final CatalogTable origin = CatalogTable.of(
				Schema.newBuilder().fromResolvedSchema(resolvedSchema).build(), 
				TEST_COMMENT, 
				Collections.emptyList(),
				getStreamingTableProperties());
		return new ResolvedCatalogTable(origin, resolvedSchema);
	}

	/**
	 * 创建批处理表
	 * 
	 * @return
	 */
	static CatalogTable createBatchTable() {
		final ResolvedSchema resolvedSchema = createSchema();
		final CatalogTable origin = CatalogTable.of(
				Schema.newBuilder().fromResolvedSchema(resolvedSchema).build(), 
				TEST_COMMENT, 
				Collections.emptyList(),
				getBatchTableProperties());
		return new ResolvedCatalogTable(origin, resolvedSchema);
	}

	/**
	 * 设置批处理表的属性
	 * 
	 * @return
	 */
	static Map<String, String> getBatchTableProperties() {
		return new HashMap<String, String>() {
			{
				put("is_streaming", "false");
				putAll(new HashMap<String, String>() {
					{
						put(FactoryUtil.CONNECTOR.key(), "hive");
					}
				});
			}
		};
	}

	/**
	 * 创建流式表的属性
	 * 
	 * @return
	 */
	static Map<String, String> getStreamingTableProperties() {
		return new HashMap<String, String>() {
			{
				put("is_streaming", "true");
				putAll(new HashMap<String, String>() {
					{
						put(FactoryUtil.CONNECTOR.key(), "hive");
					}
				});
			}
		};
	}

	static ResolvedSchema createSchema() {
		return new ResolvedSchema(
				Arrays.asList(
						Column.physical("id", DataTypes.INT()), 
						Column.physical("name", DataTypes.STRING()), 
						Column.physical("age", DataTypes.INT())),
				Collections.emptyList(), null);
	}
}

3、运行结果

运行结果参考上述示例，运行结果一致。

5）、API创建hive表-分区表

1、maven依赖

此处使用的依赖与上示例一致，mainclass变成本示例的类，不再赘述。
具体打包的时候运行主类则需要视自己的运行情况决定是否修改。

2、代码

本示例没有加载数据，仅示例创建的分区表，并且是2重分区表。关于hive分区表的操作，请参考链接：
3、hive的使用示例详解-建表、数据类型详解、内部外部表、分区表、分桶表

import java.util.Arrays;
import java.util.Collections;
import java.util.HashMap;
import java.util.List;
import java.util.Map;

import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.DataTypes;
import org.apache.flink.table.api.Schema;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
import org.apache.flink.table.catalog.CatalogDatabaseImpl;
import org.apache.flink.table.catalog.CatalogPartition;
import org.apache.flink.table.catalog.CatalogPartitionImpl;
import org.apache.flink.table.catalog.CatalogPartitionSpec;
import org.apache.flink.table.catalog.CatalogTable;
import org.apache.flink.table.catalog.Column;
import org.apache.flink.table.catalog.ObjectPath;
import org.apache.flink.table.catalog.ResolvedCatalogTable;
import org.apache.flink.table.catalog.ResolvedSchema;
import org.apache.flink.table.catalog.exceptions.CatalogException;
import org.apache.flink.table.catalog.exceptions.DatabaseNotExistException;
import org.apache.flink.table.catalog.exceptions.TableAlreadyExistException;
import org.apache.flink.table.catalog.hive.HiveCatalog;
import org.apache.flink.table.factories.FactoryUtil;
import org.apache.flink.table.module.hive.HiveModule;
import org.apache.flink.types.Row;
import org.apache.flink.util.CollectionUtil;

/**
 * @author alanchan
 *
 */
public class TestCreateHiveTableByAPIDemo {
	static String TEST_COMMENT = "test table comment";
	static String databaseName = "hive_db_test";
	static String tableName1 = "t1";
	static String tableName2 = "t2";
	static ObjectPath path1 = new ObjectPath(databaseName, tableName1);

	/**
	 * @param args
	 * @throws Exception
	 */
	public static void main(String[] args) throws Exception {
		// 0、运行环境
		StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
		StreamTableEnvironment tenv = StreamTableEnvironment.create(env);

		// 1、创建数据库
		HiveCatalog hiveCatalog = init(tenv);

		// 2、创建表
		// 2.1、创建批处理表
//		testCreateTable_Batch(hiveCatalog);
		// 2.2、创建流式表
//		testCreateTable_Streaming(hiveCatalog);
		// 2.3、创建分区批处理表
		testCreatePartitionTable_Batch(hiveCatalog);

		// 2.4、创建带有hive属性的批处理表

		// 3、插入数据
		// 分区表不能如此操作，具体参考相关内容
//		String insertSQL = "insert into " + tableName1 + " values (1,'alan',18)";
//		tenv.executeSql(insertSQL);

		// 4、查询数据
		List<Row> results = CollectionUtil.iteratorToList(tenv.executeSql("select * from " + tableName1).collect());
		for (Row row : results) {
			System.out.println(tableName1 + ": " + row.toString());
		}

		hiveCatalog.dropTable(path1, false);
		boolean tableExists = hiveCatalog.tableExists(path1);
		System.out.println("表是否drop成功：" + tableExists);

		// 5、删除数据库
		tenv.executeSql("drop database " + databaseName + " cascade");
	}

	/**
	 * 初始化hivecatalog
	 * 
	 * @param tenv
	 * @return
	 * @throws Exception
	 */
	private static HiveCatalog init(StreamTableEnvironment tenv) throws Exception {
		String moduleName = "myhive";
		String hiveVersion = "3.1.2";
		tenv.loadModule(moduleName, new HiveModule(hiveVersion));

		String name = "alan_hive";
		String defaultDatabase = "default";
		String hiveConfDir = "/usr/local/bigdata/apache-hive-3.1.2-bin/conf";

		HiveCatalog hiveCatalog = new HiveCatalog(name, defaultDatabase, hiveConfDir);
		tenv.registerCatalog(name, hiveCatalog);
		tenv.useCatalog(name);
		tenv.listDatabases();
		// tenv.executeSql("create database "+databaseName);
		hiveCatalog.createDatabase(databaseName, new CatalogDatabaseImpl(new HashMap(), hiveConfDir) {
		}, true);

		tenv.useDatabase(databaseName);
		return hiveCatalog;
	}

	/**
	 * 创建流式表
	 * 
	 * @param catalog
	 * @throws Exception
	 */
	static void testCreateTable_Streaming(HiveCatalog catalog) throws Exception {
		CatalogTable table = createStreamingTable();
		catalog.createTable(path1, table, false);

//		CatalogTestUtil.checkEquals(table, (CatalogTable) catalog.getTable(path1));
	}

	/**
	 * 创建批处理表
	 * 
	 * @param catalog
	 * @throws Exception
	 */
	static void testCreateTable_Batch(HiveCatalog catalog) throws Exception {
		// Non-partitioned table
		CatalogTable table = createBatchTable();
		catalog.createTable(path1, table, false);

//		CatalogBaseTable tableCreated = catalog.getTable(path1);

//		CatalogTestUtil.checkEquals(table, (CatalogTable) tableCreated);
//		assertThat(tableCreated.getDescription().isPresent()).isTrue();
//		assertThat(tableCreated.getDescription().get()).isEqualTo(TEST_COMMENT);

//		List tables = catalog.listTables(databaseName);

//		assertThat(tables).hasSize(1);
//		assertThat(tables.get(0)).isEqualTo(path1.getObjectName());

//		catalog.dropTable(path1, false);
	}

	/**
	 * 
	 * @param catalog
	 * @throws DatabaseNotExistException
	 * @throws TableAlreadyExistException
	 * @throws CatalogException
	 */
	static void testCreatePartitionTable_Batch(HiveCatalog catalog) throws Exception {
		CatalogTable table = createPartitionedTable();
		catalog.createTable(path1, table, false);
		// 创建分区
		catalog.createPartition(path1, createPartitionSpec(), createPartition(), false);

	}

	/**
	 * 创建分区表
	 * 
	 * @return
	 */
	static CatalogTable createPartitionedTable() {
		final ResolvedSchema resolvedSchema = createSchema();
		final CatalogTable origin = CatalogTable.of(Schema.newBuilder().fromResolvedSchema(resolvedSchema).build(), TEST_COMMENT, createPartitionKeys(), getBatchTableProperties());
		return new ResolvedCatalogTable(origin, resolvedSchema);
	}

	/**
	 * 创建分区键
	 * 
	 * @return
	 */
	static List<String> createPartitionKeys() {
		return Arrays.asList("name", "age");
	}

	/**
	 * 创建CatalogPartitionSpec。 Represents a partition spec object in catalog.
	 * Partition columns and values are NOT of strict order, and they need to be
	 * re-arranged to the correct order by comparing with a list of strictly ordered
	 * partition keys.
	 * 
	 * @return
	 */
	static CatalogPartitionSpec createPartitionSpec() {
		return new CatalogPartitionSpec(new HashMap<String, String>() {
			{
				put("name", "alan");
				put("age", "20");
			}
		});
	}

	static CatalogPartition createPartition() {
		return new CatalogPartitionImpl(getBatchTableProperties(), TEST_COMMENT);
	}

	/**
	 * 创建流式表
	 * 
	 * @return
	 */
	static CatalogTable createStreamingTable() {
		final ResolvedSchema resolvedSchema = createSchema();
		final CatalogTable origin = CatalogTable.of(Schema.newBuilder().fromResolvedSchema(resolvedSchema).build(), TEST_COMMENT, Collections.emptyList(),
				getStreamingTableProperties());
		return new ResolvedCatalogTable(origin, resolvedSchema);
	}

	/**
	 * 创建批处理表
	 * 
	 * @return
	 */
	static CatalogTable createBatchTable() {
		final ResolvedSchema resolvedSchema = createSchema();
		final CatalogTable origin = CatalogTable.of(Schema.newBuilder().fromResolvedSchema(resolvedSchema).build(), TEST_COMMENT, Collections.emptyList(),
				getBatchTableProperties());
		return new ResolvedCatalogTable(origin, resolvedSchema);
	}

	/**
	 * 设置批处理表的属性
	 * 
	 * @return
	 */
	static Map<String, String> getBatchTableProperties() {
		return new HashMap<String, String>() {
			{
				put("is_streaming", "false");
				putAll(new HashMap<String, String>() {
					{
						put(FactoryUtil.CONNECTOR.key(), "hive");
					}
				});
			}
		};
	}

	/**
	 * 创建流式表的属性
	 * 
	 * @return
	 */
	static Map<String, String> getStreamingTableProperties() {
		return new HashMap<String, String>() {
			{
				put("is_streaming", "true");
				putAll(new HashMap<String, String>() {
					{
						put(FactoryUtil.CONNECTOR.key(), "hive");
					}
				});
			}
		};
	}

	static ResolvedSchema createSchema() {
		return new ResolvedSchema(Arrays.asList(Column.physical("id", DataTypes.INT()), Column.physical("name", DataTypes.STRING()), Column.physical("age", DataTypes.INT())),
				Collections.emptyList(), null);
	}
}

3、运行结果

hdfs上创建的t1表结构如下：

6）、SQL创建hive表-带hive属性的表（分隔符、分区以及ORC存储）

本示例是通过SQL创建的分区ORC存储的表，然后通过源数据插入至目标分区表中。
关于hive的分区表使用，请参考：3、hive的使用示例详解-建表、数据类型详解、内部外部表、分区表、分桶表

1、maven依赖

此处使用的依赖与上示例一致，mainclass变成本示例的类，不再赘述。
具体打包的时候运行主类则需要视自己的运行情况决定是否修改。

2、代码

import java.util.Arrays;
import java.util.HashMap;
import java.util.List;

import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.SqlDialect;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
import org.apache.flink.table.catalog.CatalogDatabaseImpl;
import org.apache.flink.table.catalog.hive.HiveCatalog;
import org.apache.flink.table.module.hive.HiveModule;
import org.apache.flink.types.Row;
import org.apache.flink.util.CollectionUtil;

/**
 * @author alanchan
 *
 */
public class TestCreateHiveTableBySQLDemo2 {
	static String databaseName = "viewtest_db";
	public static final String sourceTableName = "sourceTable";
	public static final String targetPartitionTableName = "targetPartitionTable";
	public static final String hive_create_source_table_sql = 
			"create table "+sourceTableName +"(id int ,name string, age int,province string) \r\n" + 
			"row format delimited fields terminated by  ','\r\n" + 
			"STORED AS ORC ";
	public static final String hive_create_target_partition_table_sql = 
			"create table "+targetPartitionTableName+" (id int ,name string, age int) \r\n" + 
			"partitioned by (province string)\r\n" + 
			"row format delimited fields terminated by  ','\r\n" + 
			"STORED AS ORC "+ 
			  "TBLPROPERTIES (\n" + 
			  "  'sink.partition-commit.delay'='5 s',\n" + 
			  "  'sink.partition-commit.trigger'='partition-time',\n" + 
			  "  'sink.partition-commit.policy.kind'='metastore,success-file'" + ")";
	
	public static void main(String[] args) throws Exception {
		// 0、运行环境
		StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
		StreamTableEnvironment tenv = StreamTableEnvironment.create(env);

		// 1、创建数据库
		HiveCatalog hiveCatalog = init(tenv);

		// 2、创建表
		tenv.getConfig().setSqlDialect(SqlDialect.HIVE);
		tenv.executeSql(hive_create_source_table_sql);
		tenv.executeSql(hive_create_target_partition_table_sql);

		// 3、插入sourceTableName数据
		List<String> insertSQL = Arrays.asList(
				"insert into  "+sourceTableName+"  values(1,'alan',18,'SH')", 
				"insert into  "+sourceTableName+"  values(2,'alanchan',18,'SH')",
				"insert into  "+sourceTableName+"  values(3,'alanchanchn',18,'SH')", 
				"insert into  "+sourceTableName+"  values(4,'alan_chan',18,'BJ')",
				"insert into  "+sourceTableName+"  values(5,'alan_chan_chn',18,'BJ')", 
				"insert into  "+sourceTableName+"  values(6,'alan',18,'TJ')",
				"insert into  "+sourceTableName+"  values(7,'alan',18,'NJ')", 
				"insert into  "+sourceTableName+"  values(8,'alan',18,'HZ')"
				);
		for(String sql :insertSQL) {
			tenv.executeSql(sql);
		}
		
		// 4、查询sourceTableName数据
		List<Row> results = CollectionUtil.iteratorToList(tenv.executeSql("select * from " + sourceTableName).collect());
		for (Row row : results) {
			System.out.println(sourceTableName + ": " + row.toString());
		}
		
		// 5、执行动态插入数据命令
		System.out.println("dynamic.partition:["+ hiveCatalog.getHiveConf().get("hive.exec.dynamic.partition")+"]");
		System.out.println("dynamic.partition.mode:["+hiveCatalog.getHiveConf().get("hive.exec.dynamic.partition.mode")+"]");
		hiveCatalog.getHiveConf().setBoolean("hive.exec.dynamic.partition", true);
		hiveCatalog.getHiveConf().set("hive.exec.dynamic.partition.mode", "nonstrict");
		System.out.println("dynamic.partition:["+ hiveCatalog.getHiveConf().get("hive.exec.dynamic.partition")+"]");
		System.out.println("dynamic.partition.mode:["+hiveCatalog.getHiveConf().get("hive.exec.dynamic.partition.mode")+"]");
		
		//6、插入分区表数据
		String insertpartitionsql = 
				"insert into table "+targetPartitionTableName+" partition(province)\r\n" + 
				"select id,name,age,province from "+ sourceTableName;
		tenv.executeSql(insertpartitionsql);
		
		//7、查询分区表数据
		List<Row> partitionResults = CollectionUtil.iteratorToList(tenv.executeSql(
				"select * from " + targetPartitionTableName).collect());
		for (Row row : partitionResults) {
			System.out.println(targetPartitionTableName + " : " + row.toString());
		}
		
		List<Row> partitionResults_SH = CollectionUtil.iteratorToList(tenv.executeSql(
				"select * from " + targetPartitionTableName+" where  province = 'SH'").collect());
		for (Row row : partitionResults_SH) {
			System.out.println(targetPartitionTableName + " SH: " + row.toString());
		}
		
		// 8、删除数据库
//		tenv.executeSql("drop database " + databaseName + " cascade");
	}

	private static HiveCatalog init(StreamTableEnvironment tenv) throws Exception {
		String moduleName = "myhive";
		String hiveVersion = "3.1.2";
		tenv.loadModule(moduleName, new HiveModule(hiveVersion));

		String name = "alan_hive";
		String defaultDatabase = "default";
		String hiveConfDir = "/usr/local/bigdata/apache-hive-3.1.2-bin/conf";

		HiveCatalog hiveCatalog = new HiveCatalog(name, defaultDatabase, hiveConfDir);
		tenv.registerCatalog(name, hiveCatalog);
		tenv.useCatalog(name);
		tenv.listDatabases();
		hiveCatalog.createDatabase(databaseName, new CatalogDatabaseImpl(new HashMap(), hiveConfDir) {
		}, true);

		tenv.useDatabase(databaseName);
		return hiveCatalog;
	}
}

3、运行结果

• flink 任务运行结果

[alanchan@server2 bin]$ flink run  /usr/local/bigdata/flink-1.13.5/examples/table/table_sql-0.0.10-SNAPSHOT.jar

Hive Session ID = ba971dc3-7fa5-4f2c-a872-9200a0396337
Hive Session ID = a3c01c23-9828-4473-96ad-c9dc40b417c0
Hive Session ID = 547668a9-d603-4c1d-ae29-29c4cccd54f0
Job has been submitted with JobID 881de04ddea94f2c7a9f5fb051e1d4af
Hive Session ID = 676c6dfe-11ae-411e-9be7-ddef386fb2ac
Job has been submitted with JobID 0d76f2446d8cdcfd296d82965f9f759b
Hive Session ID = b18c5e00-7da9-4a43-bf50-d6bcb57d45a3
Job has been submitted with JobID 644f094a3c9fadeb0d81b9bcf339a1e7
Hive Session ID = 76f06744-ec5b-444c-a2d3-e22dfb17d83c
Job has been submitted with JobID 1e8d36f0b0961f81a63de4e9f2ce21af
Hive Session ID = 97f14128-1032-437e-b59f-f89a1e331e34
Job has been submitted with JobID 3bbd81cf693279fd8ebe8a889bdb08e3
Hive Session ID = 1456c502-8c30-44c5-94d1-6b2e4bf71bc3
Job has been submitted with JobID 377101faffcc12d3d4638826e004ddc5
Hive Session ID = ef4f659d-735b-44ca-90c0-4e19ba000e37
Job has been submitted with JobID 33d50d9501a83f28068e52f77d0b0f6d
Hive Session ID = fccefaea-5340-422d-b9ed-dd904857346e
Job has been submitted with JobID 4a53753c008f16573ab7c84e8964bc48
Hive Session ID = 5c066f43-57e8-4aba-9c7b-b75caf4f9fe7
2023-10-19 05:49:12,774 INFO  org.apache.hadoop.conf.Configuration.deprecation             [] - mapred.input.dir is deprecated. Instead, use mapreduce.input.fileinputformat.inputdir
Job has been submitted with JobID b44dd095b7460470c23f8e28243fc895
sourceTable: +I[1, alan, 18, SH]
sourceTable: +I[6, alan, 18, TJ]
sourceTable: +I[4, alan_chan, 18, BJ]
sourceTable: +I[2, alanchan, 18, SH]
sourceTable: +I[3, alanchanchn, 18, SH]
sourceTable: +I[5, alan_chan_chn, 18, BJ]
sourceTable: +I[7, alan, 18, NJ]
sourceTable: +I[8, alan, 18, HZ]
dynamic.partition:[true]
dynamic.partition.mode:[nonstrict]
dynamic.partition:[true]
dynamic.partition.mode:[nonstrict]
Hive Session ID = e63fd003-5d5f-458c-a9bf-e7cbfe51fbf8
Job has been submitted with JobID 59e2558aaf8daced29b7943e12a41164
Hive Session ID = 3111db81-a822-4731-a342-ab32cdc48d86
Job has been submitted with JobID 949435047e324bce96a5aa9e5b6f448d
targetPartitionTable : +I[2, alanchan, 18, SH]
targetPartitionTable : +I[7, alan, 18, NJ]
targetPartitionTable : +I[1, alan, 18, SH]
targetPartitionTable : +I[3, alanchanchn, 18, SH]
targetPartitionTable : +I[5, alan_chan_chn, 18, BJ]
targetPartitionTable : +I[4, alan_chan, 18, BJ]
targetPartitionTable : +I[8, alan, 18, HZ]
targetPartitionTable : +I[6, alan, 18, TJ]
Hive Session ID = 0bfbd60b-da1d-4a44-be23-0bde71e1ad59
Job has been submitted with JobID 49b728c8dc7fdc8037ab72bd6f3c5339
targetPartitionTable SH: +I[1, alan, 18, SH]
targetPartitionTable SH: +I[3, alanchanchn, 18, SH]
targetPartitionTable SH: +I[2, alanchan, 18, SH]
Hive Session ID = 68716de6-fceb-486e-91a8-8e4cf734ecfa

• hdfs数据存储情况

以上，介绍了java api/sql操作分区、函数和表，特别是针对表操作使用了6个示例进行说明。