Background
- TDengine提供两种读写方式，这里我们使用JNI方式。
 - 这张图是之前用influxdata公司提供的工具测试的
一、最终测试结果
- 有一点需要注意，从后面我贴出的源码也可以看出，写入Influxdb的数据是在计时前准备好的，并且数据都是已经序列化过的数据；而TDengine的数据是边组织边写入，这在一定程度上也拖慢了写入速度，不过开篇我已经说了，我这里旨在大概了解下，不做科学准确的性能对比测试哈（TDengine官方的测试对比更科学可靠）。
- 无论是写入还是查询，Influxdb的cpu负载和内存占用会随着数据量的增加而增加，例如一亿条数要5秒左右出结果，而TDengine查询几乎秒出，cpu负载和内存占用都较低且波动不大。
二、测试基本环境
| k | v |
|---|
| os | Centos7.9 | | cpu | 12 Intel? Core? i7-8700 CPU @ 3.20GHz | | os | Centos7.9 | | 内存 | 16g | | Influxdb | 1.6.1 | | TDengine | 2.4.0.12 |
三、测试源码
<dependency>
<groupId>com.taosdata.jdbc</groupId>
<artifactId>taos-jdbcdriver</artifactId>
<version>2.0.37</version>
</dependency>
package com.cloudansys.test;
import cn.hutool.core.util.NumberUtil;
import cn.hutool.core.util.StrUtil;
import com.taosdata.jdbc.TSDBDriver;
import com.taosdata.jdbc.TSDBPreparedStatement;
import okhttp3.OkHttpClient;
import org.influxdb.InfluxDB;
import org.influxdb.InfluxDBFactory;
import org.influxdb.dto.Point;
import java.sql.*;
import java.util.ArrayList;
import java.util.List;
import java.util.Properties;
import java.util.concurrent.TimeUnit;
public class TestWriteRead {
public static void main(String[] args) throws Exception {
int factor = 2000;
writeToTDengine(factor);
}
private static void writeToTDengine(int factor) throws Exception {
Connection conn = getTdConn();
int numOfTid = 100, ef = factor / 100, numOfRow = ef * 10000;
int bf = 2000, count = numOfRow / bf, left = factor;
String sql = "insert into ? using st_wy tags(?) values(?,?,?,?)";
TSDBPreparedStatement pStmt = conn.prepareStatement(sql).unwrap(TSDBPreparedStatement.class);
long startTime = System.currentTimeMillis();
System.out.println("=======================<写入开始>=======================");
for (int i = 1; i <= numOfTid; i++) {
long current = System.currentTimeMillis();
for (int j = 0; j < count; j++) {
pStmt.setTableName("t_" + i);
pStmt.setTagInt(0, i);
ArrayList<Long> tsList = new ArrayList<>();
for (int k = 0; k < bf; k++) {
tsList.add(current++);
}
pStmt.setTimestamp(0, tsList);
pStmt.setDouble(1, ranValues(bf));
pStmt.setDouble(2, ranValues(bf));
pStmt.setDouble(3, ranValues(bf));
pStmt.columnDataAddBatch();
pStmt.columnDataExecuteBatch();
}
left -= ef;
System.out.print("\r" + factor + "万条数据写入中：" + left + " 万条");
}
System.out.println("\n=======================<写入结束>=======================");
long endTime = System.currentTimeMillis();
System.out.println("总共耗时：" + (endTime - startTime) / 1000 + "s");
conn.close();
}
private static void initSchema(Connection conn) throws Exception {
Statement stmt = conn.createStatement();
stmt.execute("create stable st_wy(ts timestamp, v1 double, v2 double, v3 double) tags(tid int);");
}
private static void queryTDengine() throws Exception {
Connection conn = getTdConn();
Statement stmt = conn.createStatement();
String sql = "select * from t_1 limit 5;";
ResultSet resultSet = stmt.executeQuery(sql);
Timestamp ts;
double v1, v2, v3;
System.out.println("ts-v1-v2-v3");
String template = "{}-{}-{}-{}";
while (resultSet.next()) {
ts = resultSet.getTimestamp(1);
v1 = resultSet.getDouble("v1");
v2 = resultSet.getDouble(3);
v3 = resultSet.getDouble(4);
System.out.println(StrUtil.format(template, ts, v1, v2, v3));
}
conn.close();
}
private static ArrayList<Double> ranValues(int size) {
ArrayList<Double> resList = new ArrayList<>();
for (int i = 0; i < size; i++) {
resList.add(NumberUtil.round(Math.random(), 3).doubleValue());
}
return resList;
}
private static void writeToInfluxDB(int factor) {
List<String> records = getRecords(factor);
InfluxDB influxDB = getInfluxDB();
long startTime = System.currentTimeMillis();
System.out.println("\n=======================<写入开始>=======================");
writeRecords(influxDB, records);
System.out.println("\n=======================<写入结束>=======================");
long endTime = System.currentTimeMillis();
System.out.println("总共耗时：" + (endTime - startTime) / 1000 + "s");
influxDB.close();
}
private static void writeRecords(InfluxDB influxDB, List<String> records) {
List<String> dataList = new ArrayList<>();
int num = 0, batchFactor = 25, factor = records.size() / 10000;
for (String record : records) {
dataList.add(record);
if (dataList.size() == batchFactor * 10000) {
influxDB.write(dataList);
dataList.clear();
System.out.print("\r" + factor + "万条数据写入中：" + (factor - batchFactor * ++num) + " 万条");
}
}
}
private static List<String> getRecords(int factor) {
List<String> records = new ArrayList<>();
Point.Builder pointBuilder = Point.measurement("wy");
long curMillis = System.currentTimeMillis();
int num = factor / 100, count = num * 10000;
for (int i = 1; i <= 100; i++) {
System.out.print("\r" + factor + "万条数据准备中：" + i * num + " 万条");
for (int j = 1; j <= count; j++) {
pointBuilder
.time(curMillis++, TimeUnit.MILLISECONDS)
.tag("tid", String.valueOf(i))
.addField("v1", NumberUtil.round(Math.random(), 3).doubleValue())
.addField("v2", NumberUtil.round(Math.random(), 3).doubleValue())
.addField("v3", NumberUtil.round(Math.random(), 3).doubleValue());
records.add(pointBuilder.build().lineProtocol());
}
}
return records;
}
private static Connection getTdConn() throws Exception {
Class.forName("com.taosdata.jdbc.TSDBDriver");
String jdbcUrl = "jdbc:TAOS://elephant:6030/db_wlf?user=root&password=taosdata";
Properties connProps = new Properties();
connProps.setProperty(TSDBDriver.PROPERTY_KEY_CHARSET, "UTF-8");
connProps.setProperty(TSDBDriver.PROPERTY_KEY_LOCALE, "en_US.UTF-8");
connProps.setProperty(TSDBDriver.PROPERTY_KEY_TIME_ZONE, "UTC-8");
return DriverManager.getConnection(jdbcUrl, connProps);
}
private static InfluxDB getInfluxDB() {
String serverURL = "http://elephant:8086";
String username = "wlf";
String password = "wlf@123";
String database = "db_wlf";
OkHttpClient.Builder clientBuilder = new OkHttpClient.Builder().readTimeout(100, TimeUnit.SECONDS);
int batchSize = 20;
int interval = 1000;
return InfluxDBFactory
.connect(serverURL, username, password, clientBuilder)
.setDatabase(database)
.enableGzip();
}
}
四、TDengineGUI
- TDengineGUI这个工具界面看着挺素的，但是使用起来还是挺方便的。
- 不过没有提供数据的导入和导出功能（官方命令行提供数据的导入和导出）。
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