本文介紹通過Druid連接池連接并訪問時序引擎的方法。
背景信息
使用Java開發訪問時序引擎的應用程序時，通常建議通過JDBC Driver連接Lindorm時序引擎。JDBC訪問邏輯的基礎是需要創建Connection對象，如果每次連接時序引擎都創建一個新的Connection對象，會帶來很大的開銷。為了降低開銷，可以使用連接池來執行連接的創建和管理。
前提條件
已安裝Java環境，要求安裝JDK 1.8及以上版本。
已將客戶端IP地址添加至Lindorm白名單，具體操作，請參見設置白名單。
操作步驟
以開源連接池Alibaba Druid為例。
配置連接池Druid的參數。在Maven項目的
src/main/resources目錄中新建druid.properties文件，并在文件中添加以下內容。# 驅動類名，無需替換 driverClassName=com.aliyun.lindorm.table.client.Driver # url、username、password需要替換為業務實際的內容，可以在Lindorm控制臺上獲取 url=jdbc:lindorm:tsdb:url=http://ld-bp12pt80qr38p****-proxy-tsdb-pub.lindorm.rds.aliyuncs.com:8242 username=root password=root # 連接屬性，指定要連接的database，需根據實際情況將****替換為實際的內容 connectionProperties=database=default # 初始化連接池即創建連接，建議保持不變 init=true # 初始化連接池時建立連接的個數，可以根據實際情況調整 initialSize=10 # 連接池中允許的最小空閑連接數量，可以根據實際情況調整 minIdle=10 # 連接池中允許的最大連接數量，可以根據實際情況調整 maxActive=20 # 獲取連接最大等待時間，單位毫秒，建議保持不變 maxWait=30000 # 連接保活配置項，建議保持不變，否則可能出現連接斷開 # 異常ConnectionDisconnectedException druid.keepAlive=true druid.keepAliveBetweenTimeMillis=30000 minEvictableIdleTimeMillis=600000 maxEvictableIdleTimeMillis=900000 timeBetweenEvictionRunsMillis=5000 # 連接驗證配置項，建議保持不變 validationQuery=SELECT 1 testWhileIdle=true testOnBorrow=false testOnReturn=false # PreparedStatement緩存配置項，這里配置為關閉緩存，建議保持不變 # 否則運行時可能會出現NoSuchStatement異常 poolPreparedStatements=false maxOpenPreparedStatements=-1 druid.maxPoolPreparedStatementPerConnectionSize=-1說明url為時序引擎的JDBC連接地址，相關參數說明，請參見JDBC連接地址說明。
默認用戶名及密碼均為
root。
在Maven項目中添加Druid連接池的依賴。打開客戶端，創建Project并在pom.xml中配置Maven依賴。示例代碼如下：
<dependency> <groupId>com.alibaba</groupId> <artifactId>druid</artifactId> <version>1.2.6</version> </dependency>通過以下兩種方式安裝Lindorm JDBC Driver依賴。
手動安裝。
在本地自行下載JAR包集成JDBC Driver，下載鏈接為：Lindorm-all-client。選擇需要安裝的版本，以2.1.5為例，下載lindorm-all-client-2.1.5.jar包。
通過Maven下載。
如果在Maven項目中集成JDBC Driver，創建Project并在pom.xml中添加以下依賴配置，具體內容如下：
<dependency> <groupId>com.aliyun.lindorm</groupId> <artifactId>lindorm-all-client</artifactId> <version>2.2.1.3</version> </dependency>說明lindorm-all-client的版本號根據需求填寫。
加載連接池Druid的參數并初始化連接池Druid。
// 加載參數 Properties properties = new Properties(); InputStream inputStream = tsdb.class.getClassLoader().getResourceAsStream("druid.properties"); properties.load(inputStream); // 初始化連接池 DataSource dataSource = DruidDataSourceFactory.createDataSource(properties);通過連接池Druid獲取JDBC的連接信息并訪問Lindorm時序引擎。
// 創建表 try (Connection connection = dataSource.getConnection()) { try (Statement statement = connection.createStatement()) { String sql = "CREATE TABLE sensor (device_id VARCHAR TAG,region VARCHAR TAG,time TIMESTAMP,temperature DOUBLE,humidity DOUBLE,PRIMARY KEY(device_id))"; int ret = statement.executeUpdate(sql); System.out.println(ret); } } // 插入數據 try (Connection connection = dataSource.getConnection()) { try (Statement stmt = connection.createStatement()) { stmt.addBatch("INSERT INTO sensor(device_id, region, time, temperature, humidity) values('F07A1260','north-cn','2021-04-22 15:33:00',12.1,45)"); stmt.addBatch("INSERT INTO sensor(device_id, region, time, temperature, humidity) values('F07A1260','north-cn','2021-04-22 15:33:10',13.2,47)"); stmt.addBatch("INSERT INTO sensor(device_id, region, time, temperature, humidity) values('F07A1260','north-cn','2021-04-22 15:33:20',10.6,46)"); stmt.addBatch("INSERT INTO sensor(device_id, region, time, temperature, humidity) values('F07A1261','south-cn','2021-04-22 15:33:00',18.1,44)"); stmt.addBatch("INSERT INTO sensor(device_id, region, time, temperature, humidity) values('F07A1261','south-cn','2021-04-22 15:33:10',19.7,44)"); stmt.executeBatch(); } } // 查詢數據 // 使用綁定參數的方式查詢數據 // 強烈建議指定時間范圍減少數據掃描 try (Connection connection = dataSource.getConnection()) { try (PreparedStatement pstmt = connection.prepareStatement("SELECT device_id, region,time,temperature,humidity FROM sensor WHERE time >= ? and time <= ?")) { Timestamp startTime = Timestamp.valueOf("2021-04-22 15:33:00"); Timestamp endTime = Timestamp.valueOf("2021-04-22 15:33:20"); pstmt.setTimestamp(1, startTime); pstmt.setTimestamp(2, endTime); try (ResultSet rs = pstmt.executeQuery()) { while (rs.next()) { String device_id = rs.getString("device_id"); String region = rs.getString("region"); Timestamp time = rs.getTimestamp("time"); Double temperature = rs.getDouble("temperature"); Double humidity = rs.getDouble("humidity"); System.out.printf("%s %s %s %f %f\n", device_id, region, time, temperature, humidity); } } } }
完整示例
import com.alibaba.druid.pool.DruidDataSourceFactory;
import javax.sql.DataSource;
import java.io.IOException;
import java.io.InputStream;
import java.sql.*;
import java.util.Properties;
public class tsdb{
public static void main(String[] args) throws Exception {
// 加載參數
Properties properties = new Properties();
InputStream inputStream = tsdb.class.getClassLoader().getResourceAsStream("druid.properties");
properties.load(inputStream);
// 初始化連接池
DataSource dataSource = DruidDataSourceFactory.createDataSource(properties);
// 創建表
try (Connection connection = dataSource.getConnection()) {
try (Statement statement = connection.createStatement()) {
String sql = "CREATE TABLE sensor (device_id VARCHAR TAG,region VARCHAR TAG,time TIMESTAMP,temperature DOUBLE,humidity DOUBLE,PRIMARY KEY(device_id))";
int ret = statement.executeUpdate(sql);
System.out.println(ret);
}
}
// 插入數據
try (Connection connection = dataSource.getConnection()) {
try (Statement stmt = connection.createStatement()) {
stmt.addBatch("INSERT INTO sensor(device_id, region, time, temperature, humidity) values('F07A1260','north-cn','2021-04-22 15:33:00',12.1,45)");
stmt.addBatch("INSERT INTO sensor(device_id, region, time, temperature, humidity) values('F07A1260','north-cn','2021-04-22 15:33:10',13.2,47)");
stmt.addBatch("INSERT INTO sensor(device_id, region, time, temperature, humidity) values('F07A1260','north-cn','2021-04-22 15:33:20',10.6,46)");
stmt.addBatch("INSERT INTO sensor(device_id, region, time, temperature, humidity) values('F07A1261','south-cn','2021-04-22 15:33:00',18.1,44)");
stmt.addBatch("INSERT INTO sensor(device_id, region, time, temperature, humidity) values('F07A1261','south-cn','2021-04-22 15:33:10',19.7,44)");
stmt.executeBatch();
}
}
// 查詢數據
// 使用綁定參數的方式查詢數據
// 強烈建議指定時間范圍減少數據掃描
try (Connection connection = dataSource.getConnection()) {
try (PreparedStatement pstmt = connection.prepareStatement("SELECT device_id, region,time,temperature,humidity FROM sensor WHERE time >= ? and time <= ?")) {
Timestamp startTime = Timestamp.valueOf("2021-04-22 15:33:00");
Timestamp endTime = Timestamp.valueOf("2021-04-22 15:33:20");
pstmt.setTimestamp(1, startTime);
pstmt.setTimestamp(2, endTime);
//pstmt.setString(1, "2021-04-22 15:33:00");
// pstmt.setString(2, "2021-04-22 15:33:20");
try (ResultSet rs = pstmt.executeQuery()) {
while (rs.next()) {
String device_id = rs.getString("device_id");
String region = rs.getString("region");
Timestamp time = rs.getTimestamp("time");
Double temperature = rs.getDouble("temperature");
Double humidity = rs.getDouble("humidity");
System.out.printf("%s %s %s %f %f\n", device_id, region, time, temperature, humidity);
}
}
}
}
}
}執行成功預計返回以下結果：
0
F07A1260 north-cn 2021-04-22 15:33:00.0 12.100000 45.000000
F07A1260 north-cn 2021-04-22 15:33:10.0 13.200000 47.000000
F07A1260 north-cn 2021-04-22 15:33:20.0 10.600000 46.000000
F07A1261 south-cn 2021-04-22 15:33:00.0 18.100000 44.000000
F07A1261 south-cn 2021-04-22 15:33:10.0 19.700000 44.000000文檔內容是否對您有幫助？