24、Flink 的table api与sql之Catalogs（java api操作数据库、表）-2
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
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
文章目录
- Flink 系列文章
- 五、Catalog API
-
- 1、数据库操作
-
- 1）、jdbccatalog示例
- 2）、hivecatalog示例-查询指定数据库下的表名称
- 3）、hivecatalog示例-创建database
- 2、表操作
本文简单介绍了通过java api操作数据库、表，分别提供了具体可运行的例子。
本文依赖flink和hive、hadoop集群能正常使用。
本文分为2个部分，即数据库操作、表操作。
本文示例java api的实现是通过Flink 1.13.5版本做的示例，SQL 如果没有特别说明则是Flink 1.17版本。
五、Catalog API
1、数据库操作
下文列出了一般的数据库操作，示例是以jdbccatalog为示例，flink的版本是1.17.0。
// create database
catalog.createDatabase("mydb", new CatalogDatabaseImpl(...), false);
// drop database
catalog.dropDatabase("mydb", false);
// alter database
catalog.alterDatabase("mydb", new CatalogDatabaseImpl(...), false);
// get databse
catalog.getDatabase("mydb");
// check if a database exist
catalog.databaseExists("mydb");
// list databases in a catalog
catalog.listDatabases("mycatalog");
1）、jdbccatalog示例
- pom.xml
<properties>
<encoding>UTF-8encoding>
<project.build.sourceEncoding>UTF-8project.build.sourceEncoding>
<maven.compiler.source>1.8maven.compiler.source>
<maven.compiler.target>1.8maven.compiler.target>
<java.version>1.8java.version>
<scala.version>2.12scala.version>
<flink.version>1.17.0flink.version>
properties>
<dependencies>
<dependency>
<groupId>jdk.toolsgroupId>
<artifactId>jdk.toolsartifactId>
<version>1.8version>
<scope>systemscope>
<systemPath>${JAVA_HOME}/lib/tools.jarsystemPath>
dependency>
<dependency>
<groupId>org.apache.flinkgroupId>
<artifactId>flink-clientsartifactId>
<version>${flink.version}version>
dependency>
<dependency>
<groupId>org.apache.flinkgroupId>
<artifactId>flink-scala_2.12artifactId>
<version>${flink.version}version>
dependency>
<dependency>
<groupId>org.apache.flinkgroupId>
<artifactId>flink-javaartifactId>
<version>${flink.version}version>
dependency>
<dependency>
<groupId>org.apache.flinkgroupId>
<artifactId>flink-streaming-scala_2.12artifactId>
<version>${flink.version}version>
dependency>
<dependency>
<groupId>org.apache.flinkgroupId>
<artifactId>flink-streaming-javaartifactId>
<version>${flink.version}version>
<scope>providedscope>
dependency>
<dependency>
<groupId>org.apache.flinkgroupId>
<artifactId>flink-table-api-scala-bridge_2.12artifactId>
<version>${flink.version}version>
dependency>
<dependency>
<groupId>org.apache.flinkgroupId>
<artifactId>flink-table-api-java-bridgeartifactId>
<version>${flink.version}version>
<scope>providedscope>
dependency>
<dependency>
<groupId>org.apache.flinkgroupId>
<artifactId>flink-table-planner_2.12artifactId>
<version>${flink.version}version>
<scope>testscope>
dependency>
<dependency>
<groupId>org.apache.flinkgroupId>
<artifactId>flink-table-commonartifactId>
<version>${flink.version}version>
dependency>
<dependency>
<groupId>org.apache.flinkgroupId>
<artifactId>flink-connector-jdbcartifactId>
<version>3.1.0-1.17version>
<scope>providedscope>
dependency>
<dependency>
<groupId>org.apache.flinkgroupId>
<artifactId>flink-csvartifactId>
<version>${flink.version}version>
dependency>
<dependency>
<groupId>org.apache.flinkgroupId>
<artifactId>flink-jsonartifactId>
<version>${flink.version}version>
dependency>
<dependency>
<groupId>mysqlgroupId>
<artifactId>mysql-connector-javaartifactId>
<version>5.1.38version>
dependency>
<dependency>
<groupId>org.apache.flinkgroupId>
<artifactId>flink-table-planner_2.12artifactId>
<version>${flink.version}version>
<scope>testscope>
dependency>
<dependency>
<groupId>org.apache.flinkgroupId>
<artifactId>flink-table-planner-loaderartifactId>
<version>${flink.version}version>
<scope>providedscope>
dependency>
<dependency>
<groupId>org.apache.flinkgroupId>
<artifactId>flink-table-runtimeartifactId>
<version>${flink.version}version>
<scope>providedscope>
dependency>
dependencies>
- java
import java.util.List;
import org.apache.flink.connector.jdbc.catalog.JdbcCatalog;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
import org.apache.flink.table.catalog.Catalog;
import org.apache.flink.table.catalog.exceptions.CatalogException;
import org.apache.flink.table.catalog.exceptions.DatabaseNotExistException;
/**
* @author alanchan
*
*/
public class TestJdbcCatalogDemo {
/**
* @param args
* @throws DatabaseNotExistException
* @throws CatalogException
*/
public static void main(String[] args) throws CatalogException, DatabaseNotExistException {
// env
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
StreamTableEnvironment tenv = StreamTableEnvironment.create(env);
// public JdbcCatalog(
// String catalogName,
// String defaultDatabase,
// String username,
// String pwd,
// String baseUrl)
// CREATE CATALOG alan_catalog WITH(
// 'type' = 'jdbc',
// 'default-database' = 'test?useSSL=false',
// 'username' = 'root',
// 'password' = 'root',
// 'base-url' = 'jdbc:mysql://192.168.10.44:3306'
// );
Catalog catalog = new JdbcCatalog("alan_catalog", "test?useSSL=false", "root", "123456", "jdbc:mysql://192.168.10.44:3306");
// Register the catalog
tenv.registerCatalog("alan_catalog", catalog);
List<String> tables = catalog.listTables("test");
// System.out.println("test tables:" + tables
for (String table : tables) {
System.out.println("Database：test tables："+table);
}
}
}
- 运行结果
Database：test tables：allowinsert
Database：test tables：author
Database：test tables：batch_job_execution
Database：test tables：batch_job_execution_context
Database：test tables：batch_job_execution_params
Database：test tables：batch_job_execution_seq
Database：test tables：batch_job_instance
Database：test tables：batch_job_seq
Database：test tables：batch_step_execution
Database：test tables：batch_step_execution_context
Database：test tables：batch_step_execution_seq
Database：test tables：book
Database：test tables：customertest
Database：test tables：datax_user
Database：test tables：dm_sales
Database：test tables：dms_attach_t
Database：test tables：dx_user
Database：test tables：dx_user_copy
Database：test tables：employee
Database：test tables：hibernate_sequence
Database：test tables：permissions
Database：test tables：person
Database：test tables：personinfo
Database：test tables：role
Database：test tables：studenttotalscore
Database：test tables：t_consume
Database：test tables：t_czmx_n
Database：test tables：t_kafka_flink_user
Database：test tables：t_merchants
Database：test tables：t_recharge
Database：test tables：t_user
Database：test tables：t_withdrawal
Database：test tables：updateonly
Database：test tables：user
2）、hivecatalog示例-查询指定数据库下的表名称
本示例需要在有hadoop和hive环境执行，本示例是打包执行jar文件。
关于flink与hive的集成请参考：42、Flink 的table api与sql之Hive Catalog
- pom.xml
<properties>
<encoding>UTF-8encoding>
<project.build.sourceEncoding>UTF-8project.build.sourceEncoding>
<maven.compiler.source>1.8maven.compiler.source>
<maven.compiler.target>1.8maven.compiler.target>
<java.version>1.8java.version>
<scala.version>2.12scala.version>
<flink.version>1.13.6flink.version>
properties>
<dependencies>
<dependency>
<groupId>jdk.toolsgroupId>
<artifactId>jdk.toolsartifactId>
<version>1.8version>
<scope>systemscope>
<systemPath>${JAVA_HOME}/lib/tools.jarsystemPath>
dependency>
<dependency>
<groupId>org.apache.flinkgroupId>
<artifactId>flink-clients_2.12artifactId>
<version>${flink.version}version>
dependency>
<dependency>
<groupId>org.apache.flinkgroupId>
<artifactId>flink-scala_2.12artifactId>
<version>${flink.version}version>
dependency>
<dependency>
<groupId>org.apache.flinkgroupId>
<artifactId>flink-javaartifactId>
<version>${flink.version}version>
dependency>
<dependency>
<groupId>org.apache.flinkgroupId>
<artifactId>flink-streaming-scala_2.12artifactId>
<version>${flink.version}version>
dependency>
<dependency>
<groupId>org.apache.flinkgroupId>
<artifactId>flink-streaming-java_2.12artifactId>
<version>${flink.version}version>
dependency>
<dependency>
<groupId>org.apache.flinkgroupId>
<artifactId>flink-table-api-scala-bridge_2.12artifactId>
<version>${flink.version}version>
dependency>
<dependency>
<groupId>org.apache.flinkgroupId>
<artifactId>flink-table-api-java-bridge_2.12artifactId>
<version>${flink.version}version>
dependency>
<dependency>
<groupId>org.apache.flinkgroupId>
<artifactId>flink-table-planner_2.12artifactId>
<version>${flink.version}version>
dependency>
<dependency>
<groupId>org.apache.flinkgroupId>
<artifactId>flink-table-planner-blink_2.12artifactId>
<version>${flink.version}version>
dependency>
<dependency>
<groupId>org.apache.flinkgroupId>
<artifactId>flink-table-commonartifactId>
<version>${flink.version}version>
dependency>
<dependency>
<groupId>org.apache.flinkgroupId>
<artifactId>flink-connector-kafka_2.12artifactId>
<version>${flink.version}version>
dependency>
<dependency>
<groupId>org.apache.flinkgroupId>
<artifactId>flink-sql-connector-kafka_2.12artifactId>
<version>${flink.version}version>
dependency>
<dependency>
<groupId>org.apache.flinkgroupId>
<artifactId>flink-connector-jdbc_2.12artifactId>
<version>${flink.version}version>
dependency>
<dependency>
<groupId>org.apache.flinkgroupId>
<artifactId>flink-csvartifactId>
<version>${flink.version}version>
dependency>
<dependency>
<groupId>org.apache.flinkgroupId>
<artifactId>flink-jsonartifactId>
<version>${flink.version}version>
dependency>
<dependency>
<groupId>org.apache.flinkgroupId>
<artifactId>flink-connector-hive_2.12artifactId>
<version>${flink.version}version>
dependency>
<dependency>
<groupId>org.apache.hivegroupId>
<artifactId>hive-metastoreartifactId>
<version>2.1.0version>
dependency>
<dependency>
<groupId>org.apache.hivegroupId>
<artifactId>hive-execartifactId>
<version>3.1.2version>
dependency>
<dependency>
<groupId>org.apache.flinkgroupId>
<artifactId>flink-shaded-hadoop-2-uberartifactId>
<version>2.7.5-10.0version>
dependency>
<dependency>
<groupId>mysqlgroupId>
<artifactId>mysql-connector-javaartifactId>
<version>5.1.38version>
dependency>
<dependency>
<groupId>io.vertxgroupId>
<artifactId>vertx-coreartifactId>
<version>3.9.0version>
dependency>
<dependency>
<groupId>io.vertxgroupId>
<artifactId>vertx-jdbc-clientartifactId>
<version>3.9.0version>
dependency>
<dependency>
<groupId>io.vertxgroupId>
<artifactId>vertx-redis-clientartifactId>
<version>3.9.0version>
dependency>
<dependency>
<groupId>org.slf4jgroupId>
<artifactId>slf4j-log4j12artifactId>
<version>1.7.7version>
<scope>runtimescope>
dependency>
<dependency>
<groupId>log4jgroupId>
<artifactId>log4jartifactId>
<version>1.2.17version>
<scope>runtimescope>
dependency>
<dependency>
<groupId>com.alibabagroupId>
<artifactId>fastjsonartifactId>
<version>1.2.44version>
dependency>
<dependency>
<groupId>org.projectlombokgroupId>
<artifactId>lombokartifactId>
<version>1.18.2version>
<scope>providedscope>
dependency>
dependencies>
<build>
<sourceDirectory>src/main/javasourceDirectory>
<plugins>
<plugin>
<groupId>org.apache.maven.pluginsgroupId>
<artifactId>maven-compiler-pluginartifactId>
<version>3.5.1version>
<configuration>
<source>1.8source>
<target>1.8target>
configuration>
plugin>
<plugin>
<groupId>org.apache.maven.pluginsgroupId>
<artifactId>maven-surefire-pluginartifactId>
<version>2.18.1version>
<configuration>
<useFile>falseuseFile>
<disableXmlReport>truedisableXmlReport>
<includes>
<include>**/*Test.*include>
<include>**/*Suite.*include>
includes>
configuration>
plugin>
<plugin>
<groupId>org.apache.maven.pluginsgroupId>
<artifactId>maven-shade-pluginartifactId>
<version>2.3version>
<executions>
<execution>
<phase>packagephase>
<goals>
<goal>shadegoal>
goals>
<configuration>
<filters>
<filter>
<artifact>*:*artifact>
<excludes>
<exclude>META-INF/*.SFexclude>
<exclude>META-INF/*.DSAexclude>
<exclude>META-INF/*.RSAexclude>
excludes>
filter>
filters>
<transformers>
<transformer implementation="org.apache.maven.plugins.shade.resource.ManifestResourceTransformer">
<mainClass> org.table_sql.TestHiveCatalogDemomainClass>
transformer>
transformers>
configuration>
execution>
executions>
plugin>
plugins>
build>
- java
import java.util.List;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
import org.apache.flink.table.catalog.exceptions.CatalogException;
import org.apache.flink.table.catalog.exceptions.DatabaseNotExistException;
import org.apache.flink.table.catalog.hive.HiveCatalog;
/**
* @author alanchan
*
*/
public class TestHiveCatalogDemo {
/**
* @param args
* @throws DatabaseNotExistException
* @throws CatalogException
*/
public static void main(String[] args) throws CatalogException, DatabaseNotExistException {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
StreamTableEnvironment tenv = StreamTableEnvironment.create(env);
String name = "alan_hive";
// testhive 数据库名称
String defaultDatabase = "testhive";
String hiveConfDir = "/usr/local/bigdata/apache-hive-3.1.2-bin/conf";
HiveCatalog hiveCatalog = new HiveCatalog(name, defaultDatabase, hiveConfDir);
tenv.registerCatalog("alan_hive", hiveCatalog);
// 使用注册的catalog
tenv.useCatalog("alan_hive");
List<String> tables = hiveCatalog.listTables(defaultDatabase); // tables should contain "test"
// System.out.println("test tables:" + tables
for (String table : tables) {
System.out.println("Database：testhive tables：" + table);
}
}
}
- 运行结果
################hive查询结果##################
0: jdbc:hive2://server4:10000> use testhive;
No rows affected (0.021 seconds)
0: jdbc:hive2://server4:10000> show tables;
+-----------------------+
| tab_name |
+-----------------------+
| apachelog |
| col2row1 |
| col2row2 |
| cookie_info |
| dual |
| dw_zipper |
| emp |
| employee |
| employee_address |
| employee_connection |
| ods_zipper_update |
| row2col1 |
| row2col2 |
| singer |
| singer2 |
| student |
| student_dept |
| student_from_insert |
| student_hdfs |
| student_hdfs_p |
| student_info |
| student_local |
| student_partition |
| t_all_hero_part_msck |
| t_usa_covid19 |
| t_usa_covid19_p |
| tab1 |
| tb_dept01 |
| tb_dept_bucket |
| tb_emp |
| tb_emp01 |
| tb_emp_bucket |
| tb_json_test1 |
| tb_json_test2 |
| tb_login |
| tb_login_tmp |
| tb_money |
| tb_money_mtn |
| tb_url |
| the_nba_championship |
| tmp_1 |
| tmp_zipper |
| user_dept |
| user_dept_sex |
| users |
| users_bucket_sort |
| website_pv_info |
| website_url_info |
+-----------------------+
48 rows selected (0.027 seconds)
################flink查询结果##################
[alanchan@server2 bin]$ flink run /usr/local/bigdata/flink-1.13.5/examples/table/table_sql-0.0.1-SNAPSHOT.jar
Database：testhive tables：student
Database：testhive tables：user_dept
Database：testhive tables：user_dept_sex
Database：testhive tables：t_all_hero_part_msck
Database：testhive tables：student_local
Database：testhive tables：student_hdfs
Database：testhive tables：student_hdfs_p
Database：testhive tables：tab1
Database：testhive tables：student_from_insert
Database：testhive tables：student_info
Database：testhive tables：student_dept
Database：testhive tables：student_partition
Database：testhive tables：emp
Database：testhive tables：t_usa_covid19
Database：testhive tables：t_usa_covid19_p
Database：testhive tables：employee
Database：testhive tables：employee_address
Database：testhive tables：employee_connection
Database：testhive tables：dual
Database：testhive tables：the_nba_championship
Database：testhive tables：tmp_1
Database：testhive tables：cookie_info
Database：testhive tables：website_pv_info
Database：testhive tables：website_url_info
Database：testhive tables：users
Database：testhive tables：users_bucket_sort
Database：testhive tables：singer
Database：testhive tables：apachelog
Database：testhive tables：singer2
Database：testhive tables：tb_url
Database：testhive tables：row2col1
Database：testhive tables：row2col2
Database：testhive tables：col2row1
Database：testhive tables：col2row2
Database：testhive tables：tb_json_test1
Database：testhive tables：tb_json_test2
Database：testhive tables：tb_login
Database：testhive tables：tb_login_tmp
Database：testhive tables：tb_money
Database：testhive tables：tb_money_mtn
Database：testhive tables：tb_emp
Database：testhive tables：dw_zipper
Database：testhive tables：ods_zipper_update
Database：testhive tables：tmp_zipper
Database：testhive tables：tb_emp01
Database：testhive tables：tb_emp_bucket
Database：testhive tables：tb_dept01
Database：testhive tables：tb_dept_bucket
3）、hivecatalog示例-创建database
本示例着重在于演示如何创建database，其如何构造函数来创建database。
- pom.xml
参考示例2 - java
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.bridge.java.StreamTableEnvironment;
import org.apache.flink.table.catalog.CatalogDatabase;
import org.apache.flink.table.catalog.CatalogDatabaseImpl;
import org.apache.flink.table.catalog.exceptions.CatalogException;
import org.apache.flink.table.catalog.exceptions.DatabaseAlreadyExistException;
import org.apache.flink.table.catalog.exceptions.DatabaseNotExistException;
import org.apache.flink.table.catalog.hive.HiveCatalog;
/**
* @author alanchan
*
*/
public class TestHiveCatalogDemo {
/**
* @param args
* @throws DatabaseNotExistException
* @throws CatalogException
* @throws DatabaseAlreadyExistException
*/
public static void main(String[] args) throws CatalogException, DatabaseNotExistException, DatabaseAlreadyExistException {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
StreamTableEnvironment tenv = StreamTableEnvironment.create(env);
String name = "alan_hive";
// testhive 数据库名称
String defaultDatabase = "testhive";
String hiveConfDir = "/usr/local/bigdata/apache-hive-3.1.2-bin/conf";
HiveCatalog hiveCatalog = new HiveCatalog(name, defaultDatabase, hiveConfDir);
tenv.registerCatalog("alan_hive", hiveCatalog);
// 使用注册的catalog
tenv.useCatalog("alan_hive");
List<String> tables = hiveCatalog.listTables(defaultDatabase);
for (String table : tables) {
System.out.println("Database：testhive tables：" + table);
}
// public CatalogDatabaseImpl(Map properties, @Nullable String comment) {
// this.properties = checkNotNull(properties, "properties cannot be null");
// this.comment = comment;
// }
Map<String, String> properties = new HashMap();
CatalogDatabase cd = new CatalogDatabaseImpl(properties, "this is new database,the name is alan_hivecatalog_hivedb");
String newDatabaseName = "alan_hivecatalog_hivedb";
hiveCatalog.createDatabase(newDatabaseName, cd, true);
List<String> newTables = hiveCatalog.listTables(newDatabaseName);
for (String table : newTables) {
System.out.println("Database：alan_hivecatalog_hivedb tables：" + table);
}
}
}
- 运行结果
################## hive查询结果 ############################
#####提交flink创建database前查询结果
0: jdbc:hive2://server4:10000> show databases;
+----------------+
| database_name |
+----------------+
| default |
| test |
| testhive |
+----------------+
3 rows selected (0.03 seconds)
#####提交flink创建database后查询结果
0: jdbc:hive2://server4:10000> show databases;
+--------------------------+
| database_name |
+--------------------------+
| alan_hivecatalog_hivedb |
| default |
| test |
| testhive |
+--------------------------+
4 rows selected (0.023 seconds)
################## flink 查询结果 ############################
#### 由于只创建了database，其下是没有表的，故没有输出。至于testhive库下的表输出详见示例2，不再赘述。
2、表操作
表操作就是指hivecatalog的操作，因为jdbccatalog不能对库、表进行操作，当然查询类是可以的。故以下示例都是以hivecatalog进行说明。本处与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的第三部分相似，具体参考其示例即可。不再赘述。
// 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");
本文简单介绍了通过java api操作数据库、表，分别提供了具体可运行的例子。