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
文章目录
- Flink 系列文章
- 五、Catalog API
-
- 3、视图操作
-
- 1）、官方示例
- 2）、SQL创建HIVE 视图示例
-
- 1、maven依赖
- 2、代码
- 3、运行结果
- 3）、API创建Hive 视图示例
-
- 1、maven依赖
- 2、代码
- 3、运行结果
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-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>
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");
}
}
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
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.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
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。