fix: add deployment usage in java quickstart

Makefile

@@ -148,7 +148,7 @@ thirdparty-fast:
 	    if [ "$$new_zetasql_version" != "$(ZETASQL_VERSION)" ] ; then \
 		echo "[deps]: thirdparty up-to-date. reinstall zetasql from $(ZETASQL_VERSION) to $$new_zetasql_version"; \
 		$(MAKE) thirdparty-configure; \
-		$(CMAKE_PRG) --build $(THIRD_PARTY_BUILD_DIR) --target zetasql; \
+		$(CMAKE_PRG) --build $(THIRD_PARTY_BUILD_DIR) -j $(NPROC) --target zetasql; \
 	    else \
 		echo "[deps]: all up-to-date. zetasql already installed with version: $(ZETASQL_VERSION)"; \
 	    fi; \
@@ -160,7 +160,7 @@ thirdparty-fast:
 
 # third party compiled code install to 'OpenMLDB/.deps/usr', source code install to 'OpenMLDB/thirdsrc'
 thirdparty: thirdparty-configure
-	$(CMAKE_PRG) --build $(THIRD_PARTY_BUILD_DIR)
+	$(CMAKE_PRG) --build $(THIRD_PARTY_BUILD_DIR) -j $(NPROC)
 
 thirdparty-configure:
 	$(CMAKE_PRG) -S third-party -B $(THIRD_PARTY_BUILD_DIR) -DSRC_INSTALL_DIR=$(THIRD_PARTY_SRC_DIR) -DDEPS_INSTALL_DIR=$(THIRD_PARTY_DIR) $(THIRD_PARTY_CMAKE_FLAGS)

demo/docker-compose.test.yml

@@ -16,16 +16,16 @@ services:
       - /bin/bash
       - -cx
       - |
-        # TODO(tobe): Remove this after 0.7.3 is released
-        rm -f /work/openmldb/spark-3.2.1-bin-openmldbspark/jars/curator-* # curator NoSuchMethodError error
+        # # TODO(tobe): Remove this after 0.7.3 is released
+        # rm -f /work/openmldb/spark-3.2.1-bin-openmldbspark/jars/curator-* # curator NoSuchMethodError error
         ./init.sh
         sleep 5
         # quickstart test
         cd /work/quick_start
         /work/openmldb/bin/openmldb --zk_cluster=127.0.0.1:2181 --zk_root_path=/openmldb --role=sql_client < cluster_quickstart.sql
         python3 request_test.py
 
-        # java/python sdk
+        # java/python sdk, no jar in ci, so we should check the result manually
         cd /work/java_quickstart
         java -cp demo-1.0-SNAPSHOT.jar com.openmldb.demo.App
         cd /work/python_quickstart
@@ -46,16 +46,14 @@ services:
         python3 train_and_serve.py
         python3 predict.py || (cat predict.log && return -1)
 
-        # oneflow sql test
+        # oneflow sql test # will be failed before https://github.com/4paradigm/OpenMLDB/issues/3151 fixed
         cd /work/oneflow_demo/sql_scripts
         # 10 min
         cat sql_order | xargs cat | /work/openmldb/bin/openmldb --zk_cluster=127.0.0.1:2181 --zk_root_path=/openmldb --role=sql_client
 
-        python3 /work/job_checker.py
-
         # open it after new diag tool released, or you can test in local by USE_ADD_WHL
-        # cd /work
-        # openmldb_tool status --diff -f /work/openmldb/conf/hosts 
-        # openmldb_tool inspect
-        # openmldb_tool test
-        # openmldb_tool static-check --conf_file=/work/openmldb/conf/hosts -VCL --local
+        cd /work
+        openmldb_tool status --diff -f /work/openmldb/conf/hosts 
+        openmldb_tool inspect
+        openmldb_tool test
+        openmldb_tool static-check --conf_file=/work/openmldb/conf/hosts -VCL --local

demo/java_quickstart/demo/pom.xml

@@ -14,8 +14,8 @@
 
   <properties>
     <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
-    <maven.compiler.source>1.7</maven.compiler.source>
-    <maven.compiler.target>1.7</maven.compiler.target>
+    <maven.compiler.source>8</maven.compiler.source>
+    <maven.compiler.target>8</maven.compiler.target>
   </properties>
 
   <dependencies>
@@ -34,7 +34,8 @@
     <dependency>
       <groupId>org.testng</groupId>
       <artifactId>testng</artifactId>
-      <version>7.7.0</version>
+      <version>6.14.3</version>
+      <!-- <scope>test</scope> we use it in main-->
     </dependency>
   </dependencies>
 

demo/java_quickstart/demo/src/main/java/com/openmldb/demo/App.java

@@ -21,6 +21,7 @@
 import java.sql.ResultSet;
 import java.sql.ResultSetMetaData;
 import java.sql.SQLException;
+import java.sql.Statement;
 import java.sql.Timestamp;
 import java.sql.Types;
 import java.util.ArrayList;
@@ -39,14 +40,20 @@ public class App {
     private String zkPath = "/openmldb";
     private String db = "mydb16";
     private String table = "trans";
+    private String deploymentName = "d1";
 
     public static void main(String[] args) {
+        // 如果中途出现异常，将无法清理掉创建的数据库和表，需要手动清理，否则重试将会与预期不符
         App demo = new App();
         try {
-            // 初始化构造SqlExecutor
+            // 初始化构造 SqlExecutor
             demo.init();
             demo.createDatabase();
+            // set online and default db in SqlExecutor, no need to set in the below sql
+            demo.setOnlineAndDB();
+
             demo.createTable();
+            demo.createDeployment();
             // 通过insert语句插入
             demo.insertWithoutPlaceholder();
             // 通过placeholder的方式插入。placeholder方式不会重复编译sql, 在性能上会比直接insert好很多
@@ -55,6 +62,9 @@ public static void main(String[] args) {
             demo.select();
             // 在request模式下执行sql
             demo.requestSelect();
+            // 执行 deployment
+            demo.executeDeployment();
+            demo.dropDeployment();
             // 删除表
             demo.dropTable();
             // 删除数据库
@@ -93,6 +103,16 @@ private void dropDatabase() {
         }
     }
 
+    private void setOnlineAndDB() {
+        java.sql.Statement state = sqlExecutor.getStatement();
+        try {
+            state.execute("SET @@execute_mode='online';");
+            state.execute("use " + db + ";");
+        } catch (Exception e) {
+            e.printStackTrace();
+        }
+    }
+
     private void createTable() {
         String createTableSql = "create table " + table + "(c1 string,\n" +
                 "                   c3 int,\n" +
@@ -104,7 +124,6 @@ private void createTable() {
                 "                   index(key=c1, ts=c7));";
         java.sql.Statement state = sqlExecutor.getStatement();
         try {
-            state.execute("use " + db + ";");
             state.execute(createTableSql);
         } catch (Exception e) {
             e.printStackTrace();
@@ -120,6 +139,30 @@ private void dropTable() {
         }
     }
 
+    private void createDeployment() {
+        java.sql.Statement state = sqlExecutor.getStatement();
+        try {
+            String selectSql = String.format("SELECT c1, c3, sum(c4) OVER w1 as w1_c4_sum FROM %s WINDOW w1 AS " +
+                    "(PARTITION BY %s.c1 ORDER BY %s.c7 ROWS_RANGE BETWEEN 2d PRECEDING AND CURRENT ROW);", table,
+                    table, table);
+            // 上线一个Deployment
+            String deploySql = String.format("DEPLOY %s %s", deploymentName, selectSql);
+            // set return null rs, don't check the returned value, it's false
+            state.execute(deploySql);
+        } catch (Exception e) {
+            e.printStackTrace();
+        }
+    }
+
+    private void dropDeployment() {
+        java.sql.Statement state = sqlExecutor.getStatement();
+        try {
+            state.execute("DROP DEPLOYMENT " + deploymentName + ";");
+        } catch (Exception e) {
+            e.printStackTrace();
+        }
+    }
+
     private void insertWithoutPlaceholder() {
         String insertSql = String.format("insert into %s values(\"aa\",23,33,1.4,2.4,1590738993000,\"2020-05-04\");",
                 table);
@@ -171,7 +214,6 @@ private void select() {
         int num = 0;
         java.sql.Statement state = sqlExecutor.getStatement();
         try {
-            state.execute("SET @@execute_mode='online';");
             boolean ret = state.execute(selectSql);
             Assert.assertTrue(ret);
             result = state.getResultSet();
@@ -234,6 +276,50 @@ private void requestSelect() {
         }
     }
 
+    private void executeDeployment() {
+        PreparedStatement pstmt = null;
+        ResultSet resultSet = null;
+        try {
+            pstmt = sqlExecutor.getCallablePreparedStmt(db, deploymentName);
+            // 如果是执行deployment, 可以通过名字获取preparedstatement
+            // pstmt = sqlExecutor.getCallablePreparedStmt(db, deploymentName);
+            ResultSetMetaData metaData = pstmt.getMetaData();
+            // 执行request模式需要在RequestPreparedStatement设置一行请求数据
+            setData(pstmt, metaData);
+            // 调用executeQuery会执行这个select sql, 然后将结果放在了resultSet中
+            resultSet = pstmt.executeQuery();
+
+            Assert.assertTrue(resultSet.next());
+            Assert.assertEquals(resultSet.getMetaData().getColumnCount(), 3);
+            Assert.assertEquals(resultSet.getString(1), "bb");
+            Assert.assertEquals(resultSet.getInt(2), 24);
+            Assert.assertEquals(resultSet.getLong(3), 34);
+            Assert.assertFalse(resultSet.next());
+
+            // reuse way
+            for (int i = 0; i < 5; i++) {
+                setData(pstmt, metaData);
+                pstmt.executeQuery();
+                // skip result check
+            }
+        } catch (SQLException e) {
+            e.printStackTrace();
+            Assert.fail();
+        } finally {
+            try {
+                if (resultSet != null) {
+                    // result用完之后需要close
+                    resultSet.close();
+                }
+                if (pstmt != null) {
+                    pstmt.close();
+                }
+            } catch (SQLException throwables) {
+                throwables.printStackTrace();
+            }
+        }
+    }
+
     private void batchRequestSelect() {
         String selectSql = String.format("SELECT c1, c3, sum(c4) OVER w1 as w1_c4_sum FROM %s WINDOW w1 AS " +
                 "(PARTITION BY %s.c1 ORDER BY %s.c7 ROWS BETWEEN 2 PRECEDING AND CURRENT ROW);", table, table, table);

docs/zh/quickstart/sdk/java_sdk.md

@@ -38,7 +38,7 @@
 
 注意：由于 openmldb-native 中包含了 OpenMLDB 编译的 C++ 静态库，默认是 Linux 静态库，macOS 上需将上述 openmldb-native 的 version 改成 `0.7.3-macos`，openmldb-jdbc 的版本保持不变。
 
-openmldb-native 的 macOS 版本只支持 macOS 12，如需在 macOS 11 或 macOS 10.15上运行，需在相应 OS 上源码编译 openmldb-native 包，详细编译方法见[并发编译 Java SDK](https://openmldb.ai/docs/zh/main/deploy/compile.html#java-sdk)。
+openmldb-native 的 macOS 版本只支持 macOS 12，如需在 macOS 11 或 macOS 10.15上运行，需在相应 OS 上源码编译 openmldb-native 包，详细编译方法见[并发编译 Java SDK](https://openmldb.ai/docs/zh/main/deploy/compile.html#java-sdk)。使用自编译的 openmldb-native 包，推荐使用`mvn install`安装到本地仓库，然后在 pom 中引用本地仓库的 openmldb-native 包，不建议用`scope=system`的方式引用。
 
 Java SDK 连接 OpenMLDB 服务，可以使用 JDBC 的方式（推荐），也可以通过 SqlClusterExecutor 的方式直连。如果需要使用在线请求模式，只能使用 SqlClusterExecutor 。下面将依次演示两种连接方式。
 
@@ -106,6 +106,8 @@ PreparedStatement insertStatement = connection.prepareStatement("DELETE FROM t1
 
 ## SqlClusterExecutor 方式
 
+SqlClusterExecutor 是最全面的Java SDK连接方式，不仅有JDBC可以使用的增删查功能，还可以使用请求模式等额外功能。
+
 ### 创建 SqlClusterExecutor
 
 首先，进行 OpenMLDB 连接参数配置。
@@ -388,6 +390,74 @@ try {
 }
 ```
 
+### 执行 Deployment
+
+执行 Deployment ，是通过 `SqlClusterExecutor::getCallablePreparedStmt(db, deploymentName)` 接口获取 CallablePreparedStatement 。区别于上文的 SQL 请求式查询，Deployment 在服务端已上线，速度会快于 SQL 请求式查询。
+
+Deployment 使用过程分为两步：
+
+- 上线Deployment
+```java
+// 上线一个Deployment（此处使用上文的selectSql），实际生产环境通常已经上线成功
+java.sql.Statement state = sqlExecutor.getStatement();
+try {
+    String selectSql = String.format("SELECT c1, c3, sum(c4) OVER w1 as w1_c4_sum FROM %s WINDOW w1 AS " +
+            "(PARTITION BY %s.c1 ORDER BY %s.c7 ROWS_RANGE BETWEEN 2d PRECEDING AND CURRENT ROW);", table,
+            table, table);
+    // 上线一个Deployment
+    String deploySql = String.format("DEPLOY %s %s", deploymentName, selectSql);
+    // set return null rs, don't check the returned value, it's false
+    state.execute(deploySql);
+} catch (Exception e) {
+    e.printStackTrace();
+}
+```
+- 执行Deployment。重新创建 CallablePreparedStmt 有一定耗时，建议尽可以复用 CallablePreparedStmt，`executeQuery()`将会自动清除`setXX`的请求行缓存。
+```java
+// 执行Deployment
+PreparedStatement pstmt = null;
+ResultSet resultSet = null;
+try {
+    pstmt = sqlExecutor.getCallablePreparedStmt(db, deploymentName);
+    // 如果是执行deployment, 可以通过名字获取preparedstatement
+    // pstmt = sqlExecutor.getCallablePreparedStmt(db, deploymentName);
+    ResultSetMetaData metaData = pstmt.getMetaData();
+    // 执行request模式需要在RequestPreparedStatement设置一行请求数据
+    setData(pstmt, metaData);
+    // 调用executeQuery会执行这个select sql, 然后将结果放在了resultSet中
+    resultSet = pstmt.executeQuery();
+
+    Assert.assertTrue(resultSet.next());
+    Assert.assertEquals(resultSet.getMetaData().getColumnCount(), 3);
+    Assert.assertEquals(resultSet.getString(1), "bb");
+    Assert.assertEquals(resultSet.getInt(2), 24);
+    Assert.assertEquals(resultSet.getLong(3), 34);
+    Assert.assertFalse(resultSet.next());
+
+    // reuse way
+    for (int i = 0; i < 5; i++) {
+        setData(pstmt, metaData);
+        pstmt.executeQuery();
+        // skip result check
+    }
+} catch (SQLException e) {
+    e.printStackTrace();
+    Assert.fail();
+} finally {
+    try {
+        if (resultSet != null) {
+            // result用完之后需要close
+            resultSet.close();
+        }
+        if (pstmt != null) {
+            pstmt.close();
+        }
+    } catch (SQLException throwables) {
+        throwables.printStackTrace();
+    }
+}
+```
+
 ###  删除指定索引下某个 key 的所有数据
 
 通过 Java SDK 可以有以下两种方式删除数据: