24、Flink 的table api与sql之Catalogs（java api操作视图）-3

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

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

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本文简单介绍了通过java api操作视图，提供了三个示例，即sql实现和java api的两种实现方式。
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，SQL 如果没有特别说明则是Flink 1.17版本。

五、Catalog API

3、视图操作

1）、官方示例

// create view
catalog.createTable(new ObjectPath("mydb", "myview"), new CatalogViewImpl(...), false);// drop view
catalog.dropTable(new ObjectPath("mydb", "myview"), false);// alter view
catalog.alterTable(new ObjectPath("mydb", "mytable"), new CatalogViewImpl(...), false);// rename view
catalog.renameTable(new ObjectPath("mydb", "myview"), "my_new_view", false);// get view
catalog.getTable("myview");// check if a view exist or not
catalog.tableExists("mytable");// list views in a database
catalog.listViews("mydb");

2）、SQL创建HIVE 视图示例

1、maven依赖

properties><encoding>UTF-8</encoding><project.build.sourceEncoding>UTF-8</project.build.sourceEncoding><maven.compiler.source>1.8</maven.compiler.source><maven.compiler.target>1.8</maven.compiler.target><java.version>1.8</java.version><scala.version>2.12</scala.version><flink.version>1.13.6</flink.version></properties><dependencies><dependency><groupId>org.apache.flink</groupId><artifactId>flink-clients_2.11</artifactId><version>${flink.version}</version></dependency><dependency><groupId>org.apache.flink</groupId><artifactId>flink-scala_2.11</artifactId><version>${flink.version}</version></dependency><dependency><groupId>org.apache.flink</groupId><artifactId>flink-java</artifactId><version>${flink.version}</version><scope>provided</scope> </dependency><dependency><groupId>org.apache.flink</groupId><artifactId>flink-streaming-scala_2.11</artifactId><version>${flink.version}</version></dependency><dependency><groupId>org.apache.flink</groupId><artifactId>flink-streaming-java_2.11</artifactId><version>${flink.version}</version><scope>provided</scope></dependency><dependency><groupId>org.apache.flink</groupId><artifactId>flink-table-api-scala-bridge_2.11</artifactId><version>${flink.version}</version></dependency><dependency><groupId>org.apache.flink</groupId><artifactId>flink-table-api-java-bridge_2.11</artifactId><version>${flink.version}</version></dependency><!-- blink执行计划,1.11+默认的 --><dependency><groupId>org.apache.flink</groupId><artifactId>flink-table-planner-blink_2.11</artifactId><version>${flink.version}</version><scope>provided</scope> </dependency><dependency><groupId>org.apache.flink</groupId><artifactId>flink-table-common</artifactId><version>${flink.version}</version></dependency><!-- flink连接器 --><dependency><groupId>org.apache.flink</groupId><artifactId>flink-connector-kafka_2.12</artifactId><version>${flink.version}</version><!-- <scope>provided</scope> --></dependency><dependency><groupId>org.apache.flink</groupId><artifactId>flink-sql-connector-kafka_2.12</artifactId><version>${flink.version}</version><scope>provided</scope></dependency><dependency><groupId>org.apache.flink</groupId><artifactId>flink-connector-jdbc_2.12</artifactId><version>${flink.version}</version><scope>provided</scope></dependency><dependency><groupId>org.apache.flink</groupId><artifactId>flink-csv</artifactId><version>${flink.version}</version></dependency><dependency><groupId>org.apache.flink</groupId><artifactId>flink-json</artifactId><version>${flink.version}</version></dependency><dependency><groupId>org.apache.flink</groupId><artifactId>flink-connector-hive_2.12</artifactId><version>${flink.version}</version><scope>provided</scope> </dependency><dependency><groupId>org.apache.hive</groupId><artifactId>hive-metastore</artifactId><version>2.1.0</version></dependency><dependency><groupId>org.apache.hive</groupId><artifactId>hive-exec</artifactId><version>3.1.2</version><scope>provided</scope> </dependency><dependency><groupId>org.apache.flink</groupId><artifactId>flink-shaded-hadoop-2-uber</artifactId><version>2.7.5-10.0</version><!-- <scope>provided</scope> --></dependency><dependency><groupId>mysql</groupId><artifactId>mysql-connector-java</artifactId><version>5.1.38</version><scope>provided</scope><!--<version>8.0.20</version> --></dependency><!-- 日志 --><dependency><groupId>org.slf4j</groupId><artifactId>slf4j-log4j12</artifactId><version>1.7.7</version><scope>runtime</scope></dependency><dependency><groupId>log4j</groupId><artifactId>log4j</artifactId><version>1.2.17</version><scope>runtime</scope></dependency><dependency><groupId>com.alibaba</groupId><artifactId>fastjson</artifactId><version>1.2.44</version></dependency><dependency><groupId>org.projectlombok</groupId><artifactId>lombok</artifactId><version>1.18.2</version><!-- <scope>provided</scope> --></dependency></dependencies><build><sourceDirectory>src/main/java</sourceDirectory><plugins><!-- 编译插件 --><plugin><groupId>org.apache.maven.plugins</groupId><artifactId>maven-compiler-plugin</artifactId><version>3.5.1</version><configuration><source>1.8</source><target>1.8</target><!--<encoding>${project.build.sourceEncoding}</encoding> --></configuration></plugin><plugin><groupId>org.apache.maven.plugins</groupId><artifactId>maven-surefire-plugin</artifactId><version>2.18.1</version><configuration><useFile>false</useFile><disableXmlReport>true</disableXmlReport><includes><include>**/*Test.*</include><include>**/*Suite.*</include></includes></configuration></plugin><!-- 打包插件(会包含所有依赖) --><plugin><groupId>org.apache.maven.plugins</groupId><artifactId>maven-shade-plugin</artifactId><version>2.3</version><executions><execution><phase>package</phase><goals><goal>shade</goal></goals><configuration><filters><filter><artifact>*:*</artifact><excludes><!-- zip -d learn_spark.jar META-INF/*.RSA META-INF/*.DSA META-INF/*.SF --><exclude>META-INF/*.SF</exclude><exclude>META-INF/*.DSA</exclude><exclude>META-INF/*.RSA</exclude></excludes></filter></filters><transformers><transformerimplementation="org.apache.maven.plugins.shade.resource.ManifestResourceTransformer"><!-- 设置jar包的入口类(可选) --><mainClass> org.table_sql.TestHiveViewBySQLDemo</mainClass></transformer></transformers></configuration></execution></executions></plugin></plugins></build>

2、代码

package org.table_sql;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.CatalogView;
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.flink.types.Row;
import org.apache.flink.util.CollectionUtil;/*** @author alanchan**/
public class TestHiveViewBySQLDemo {public static final String tableName = "viewtest";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 {StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();StreamTableEnvironment tenv = StreamTableEnvironment.create(env);String moduleName = "myhive";String hiveVersion = "3.1.2";tenv.loadModule(moduleName, new HiveModule(hiveVersion));String name = "alan_hive";String defaultDatabase = "default";String databaseName = "viewtest_db";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);// 创建第一个视图viewName_byTableString selectSQL = "select * from " + tableName;String viewName_byTable = "test_view_table_V";String createViewSQL = "create view " + viewName_byTable + " as " + selectSQL;tenv.getConfig().setSqlDialect(SqlDialect.HIVE);tenv.executeSql(hive_create_table_sql);//		tenv.getConfig().setSqlDialect(SqlDialect.DEFAULT);String insertSQL = "insert into " + tableName + " values (1,'alan',18)";tenv.executeSql(insertSQL);tenv.executeSql(createViewSQL);tenv.listViews();CatalogView catalogView = (CatalogView) hiveCatalog.getTable(new ObjectPath(databaseName, viewName_byTable));List<Row> results = CollectionUtil.iteratorToList(tenv.executeSql("select * from " + viewName_byTable).collect());for (Row row : results) {System.out.println("test_view_table_V: " + row.toString());}// 创建第二个视图String viewName_byView = "test_view_view_V";tenv.executeSql("create view " + viewName_byView + " (v2_id,v2_name,v2_age) comment 'test_view_view_V comment' as select * from " + viewName_byTable);catalogView = (CatalogView) hiveCatalog.getTable(new ObjectPath(databaseName, viewName_byView));results = CollectionUtil.iteratorToList(tenv.executeSql("select * from " + viewName_byView).collect());System.out.println("test_view_view_V comment : " + catalogView.getComment());for (Row row : results) {System.out.println("test_view_view_V : " + row.toString());}tenv.executeSql("drop database " + databaseName + " cascade");}}

3、运行结果

前提是flink的集群可用。使用maven打包成jar。

[alanchan@server2 bin]$ flink run  /usr/local/bigdata/flink-1.13.5/examples/table/table_sql-0.0.2-SNAPSHOT.jarHive Session ID = ed6d5c9b-e00f-4881-840d-24c72aba6db7
Hive Session ID = 14445dc8-1f08-4f0f-bb45-aba8c6f52174
Job has been submitted with JobID bff7b59367bd5de6e778b442c4cc4404
Hive Session ID = 4c16f4fc-4c10-4353-b322-e6633e3ebe3d
Hive Session ID = 57949f09-bdcb-497f-a85c-ed9766fc4ce3
2023-10-13 02:42:24,891 INFO  org.apache.hadoop.mapred.FileInputFormat                     [] - Total input files to process : 0
Job has been submitted with JobID 80e48bb76e3d580412fdcdc434a8a979
test_view_table_V: +I[1, alan, 18]
Hive Session ID = a73d5b93-2129-4159-ad5e-0814df77e987
Hive Session ID = e4ae1a79-4d5e-4835-81de-ebc2041eedf9
2023-10-13 02:42:33,648 INFO  org.apache.hadoop.mapred.FileInputFormat                     [] - Total input files to process : 1
Job has been submitted with JobID c228d9ce3bdce91dc68bff75d14db1e5
test_view_view_V comment : test_view_view_V comment
test_view_view_V : +I[1, alan, 18]
Hive Session ID = e4a38393-d760-4bd3-8d8b-864cbe0daba7

3）、API创建Hive 视图示例

通过api创建视图相对比较麻烦，且存在版本更新的过期方法情况。
通过TableSchema和CatalogViewImpl创建视图则已过期，当前推荐使用通过CatalogView和ResolvedSchema来创建视图。
另外需要注意的是下面两个参数的区别
String originalQuery,原始的sql
String expandedQuery,带有数据库名称的表，甚至包含hivecatalog

例如：如果使用default作为默认的数据库，查询语句为select * from test1，则
originalQuery = ”select name,value from test1“即可，
expandedQuery = “selecttest1.name, test1.value from default.test1”

修改、删除视图等操作比较简单，不再赘述。

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。

2、代码

import static org.apache.flink.util.Preconditions.checkNotNull;import java.util.ArrayList;
import java.util.Arrays;
import java.util.Collections;
import java.util.HashMap;
import java.util.List;
import org.apache.flink.api.common.typeinfo.Types;
import org.apache.flink.api.common.typeinfo.TypeInformation;
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.SqlDialect;
import org.apache.flink.table.api.TableSchema;
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.CatalogView;
import org.apache.flink.table.catalog.CatalogViewImpl;
import org.apache.flink.table.catalog.ObjectPath;
import org.apache.flink.table.catalog.ResolvedCatalogView;
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.module.hive.HiveModule;
import org.apache.flink.types.Row;
import org.apache.flink.util.CollectionUtil;
import org.apache.flink.table.catalog.CatalogBaseTable;
import org.apache.flink.table.catalog.Column;/*** @author alanchan**/
public class TestHiveViewByAPIDemo {public static final String tableName = "viewtest";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 {StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();StreamTableEnvironment tenv = StreamTableEnvironment.create(env);System.setProperty("HADOOP_USER_NAME", "alanchan");String moduleName = "myhive";String hiveVersion = "3.1.2";tenv.loadModule(moduleName, new HiveModule(hiveVersion));String catalogName = "alan_hive";String defaultDatabase = "default";String databaseName = "viewtest_db";String hiveConfDir = "/usr/local/bigdata/apache-hive-3.1.2-bin/conf";HiveCatalog hiveCatalog = new HiveCatalog(catalogName, defaultDatabase, hiveConfDir);tenv.registerCatalog(catalogName, hiveCatalog);tenv.useCatalog(catalogName);tenv.listDatabases();hiveCatalog.createDatabase(databaseName, new CatalogDatabaseImpl(new HashMap(), hiveConfDir) {}, true);//		tenv.executeSql("create database "+databaseName);tenv.useDatabase(databaseName);tenv.getConfig().setSqlDialect(SqlDialect.HIVE);tenv.executeSql(hive_create_table_sql);String insertSQL = "insert into " + tableName + " values (1,'alan',18)";String insertSQL2 = "insert into " + tableName + " values (2,'alan2',19)";String insertSQL3 = "insert into " + tableName + " values (3,'alan3',20)";tenv.executeSql(insertSQL);tenv.executeSql(insertSQL2);tenv.executeSql(insertSQL3);tenv.getConfig().setSqlDialect(SqlDialect.DEFAULT);String viewName1 = "test_view_table_V";String viewName2 = "test_view_table_V2";ObjectPath path1= new ObjectPath(databaseName, viewName1);//ObjectPath.fromString("viewtest_db.test_view_table_V2")ObjectPath path2= new ObjectPath(databaseName, viewName2);String originalQuery = "SELECT id, name, age FROM "+tableName+" WHERE id >=1 ";
//		String originalQuery = String.format("select * from %s",tableName+" WHERE id >=1 ");System.out.println("originalQuery:"+originalQuery);String expandedQuery = "SELECT  id, name, age FROM "+databaseName+"."+tableName+"  WHERE id >=1 ";		
//		String expandedQuery = String.format("select * from %s.%s", catalogName, path1.getFullName());System.out.println("expandedQuery:"+expandedQuery);String comment = "this is a comment";// 创建视图，第一种方式(通过TableSchema和CatalogViewImpl)，已声明过期	createView1(originalQuery,expandedQuery,comment,hiveCatalog,path1);// 查询视图List<Row> results = CollectionUtil.iteratorToList( tenv.executeSql("select * from " + viewName1).collect());for (Row row : results) {System.out.println("test_view_table_V: " + row.toString());}// 创建视图，第二种方式（通过Schema和ResolvedSchema）createView2(originalQuery,expandedQuery,comment,hiveCatalog,path2);List<Row> results2 = CollectionUtil.iteratorToList( tenv.executeSql("select * from viewtest_db.test_view_table_V2").collect());for (Row row : results2) {System.out.println("test_view_table_V2: " + row.toString());}tenv.executeSql("drop database " + databaseName + " cascade");}static void createView1(String originalQuery,String expandedQuery,String comment,HiveCatalog hiveCatalog,ObjectPath path) throws Exception {TableSchema viewSchema = new TableSchema(new String[]{"id", "name","age"}, new TypeInformation[]{Types.INT, Types.STRING,Types.INT});CatalogBaseTable viewTable = new CatalogViewImpl(originalQuery,expandedQuery,viewSchema, new HashMap(),comment);hiveCatalog.createTable(path, viewTable, false);}static void createView2(String originalQuery,String expandedQuery,String comment,HiveCatalog hiveCatalog,ObjectPath path) throws Exception {ResolvedSchema resolvedSchema = new ResolvedSchema(Arrays.asList(Column.physical("id", DataTypes.INT()),Column.physical("name", DataTypes.STRING()),Column.physical("age", DataTypes.INT())),Collections.emptyList(),null);CatalogView origin =  CatalogView.of(Schema.newBuilder().fromResolvedSchema(resolvedSchema).build(),comment,
//	                        String.format("select * from tt"),
//	                        String.format("select * from %s.%s", TEST_CATALOG_NAME, path1.getFullName()),originalQuery,expandedQuery,Collections.emptyMap());CatalogView view = new ResolvedCatalogView(origin, resolvedSchema);
//			ObjectPath.fromString("viewtest_db.test_view_table_V2")hiveCatalog.createTable(path, view, false);}}

3、运行结果

[alanchan@server2 bin]$ flink run  /usr/local/bigdata/flink-1.13.5/examples/table/table_sql-0.0.3-SNAPSHOT.jarHive Session ID = ab4d159a-b2d3-489e-988f-eebdc43d9517
Hive Session ID = 391de19c-5d5a-4a83-a88c-c43cca71fc63
Job has been submitted with JobID a880510032165523f3f2a559c5ab4ec9
Hive Session ID = cb063c31-eaf2-44e3-8fc0-9e8d2a6a3a5d
Job has been submitted with JobID cb05286c404b561306f8eb3969c3456a
Hive Session ID = 8132b36e-c9e2-41a2-8f42-3fe842e0991f
Job has been submitted with JobID 264aef7da1b17598bda159d946827dea
Hive Session ID = 7657be14-8188-4362-84a9-4c84c596021b
2023-10-16 07:21:19,073 INFO  org.apache.hadoop.mapred.FileInputFormat                     [] - Total input files to process : 3
Job has been submitted with JobID 05c2bb7265b0430cb12e00237f18444b
test_view_table_V: +I[1, alan, 18]
test_view_table_V: +I[2, alan2, 19]
test_view_table_V: +I[3, alan3, 20]
Hive Session ID = 7bb01c0d-03c9-413a-9040-c89676cec3b9
2023-10-16 07:21:27,512 INFO  org.apache.hadoop.mapred.FileInputFormat                     [] - Total input files to process : 3
Job has been submitted with JobID 79130d1fe56d88a784980d16e7f1cfb4
test_view_table_V2: +I[1, alan, 18]
test_view_table_V2: +I[2, alan2, 19]
test_view_table_V2: +I[3, alan3, 20]
Hive Session ID = 6d44ea95-f733-4c56-8da4-e2687a4bf945

本文简单介绍了通过java api操作视图，提供了三个示例，即sql实现和java api的两种实现方式。