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24、Flink 的table api与sql之Catalogs（java api操作视图）-3
Flink 系列文章
一、Flink 专栏
Flink 专栏系统介绍某一知识点，并辅以具体的示例进行说明。
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1、Flink 部署系列
本部分介绍Flink的部署、配置相关基础内容。 -
2、Flink基础系列
本部分介绍Flink 的基础部分，比如术语、架构、编程模型、编程指南、基本的datastream api用法、四大基石等内容。 -
3、Flik Table API和SQL基础系列
本部分介绍Flink Table Api和SQL的基本用法，比如Table API和SQL创建库、表用法、查询、窗口函数、catalog等等内容。 -
4、Flik Table API和SQL提高与应用系列
本部分是table api 和sql的应用部分，和实际的生产应用联系更为密切，以及有一定开发难度的内容。 -
5、Flink 监控系列
本部分和实际的运维、监控工作相关。
二、Flink 示例专栏
Flink 示例专栏是 Flink 专栏的辅助说明，一般不会介绍知识点的信息，更多的是提供一个一个可以具体使用的示例。本专栏不再分目录，通过链接即可看出介绍的内容。
两专栏的所有文章入口点击：Flink 系列文章汇总索引
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");- 1
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2）、SQL创建HIVE 视图示例
1、maven依赖
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>org.apache.flinkgroupId> <artifactId>flink-clients_2.11artifactId> <version>${flink.version}version> dependency> <dependency> <groupId>org.apache.flinkgroupId> <artifactId>flink-scala_2.11artifactId> <version>${flink.version}version> dependency> <dependency> <groupId>org.apache.flinkgroupId> <artifactId>flink-javaartifactId> <version>${flink.version}version> <scope>providedscope> dependency> <dependency> <groupId>org.apache.flinkgroupId> <artifactId>flink-streaming-scala_2.11artifactId> <version>${flink.version}version> dependency> <dependency> <groupId>org.apache.flinkgroupId> <artifactId>flink-streaming-java_2.11artifactId> <version>${flink.version}version> <scope>providedscope> dependency> <dependency> <groupId>org.apache.flinkgroupId> <artifactId>flink-table-api-scala-bridge_2.11artifactId> <version>${flink.version}version> dependency> <dependency> <groupId>org.apache.flinkgroupId> <artifactId>flink-table-api-java-bridge_2.11artifactId> <version>${flink.version}version> dependency> <dependency> <groupId>org.apache.flinkgroupId> <artifactId>flink-table-planner-blink_2.11artifactId> <version>${flink.version}version> <scope>providedscope> 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>${flink.version}version> <scope>providedscope> dependency> <dependency> <groupId>org.apache.flinkgroupId> <artifactId>flink-connector-jdbc_2.12artifactId> <version>${flink.version}version> <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>org.apache.flinkgroupId> <artifactId>flink-connector-hive_2.12artifactId> <version>${flink.version}version> <scope>providedscope> 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> <scope>providedscope> 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> <scope>providedscope> 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> 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.TestHiveViewBySQLDemomainClass> transformer> transformers> configuration> execution> executions> plugin> plugins> build>- 1
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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_byTable String 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"); } }- 1
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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.jar Hive 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- 1
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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.valuefromdefault.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); } }- 1
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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.jar Hive 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- 1
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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。
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- 原文地址：https://blog.csdn.net/chenwewi520feng/article/details/133862678
