205 lines
8.9 KiB
Java
205 lines
8.9 KiB
Java
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import com.njcn.influxdb.utils.InfluxDbUtils;
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import org.influxdb.InfluxDB.ConsistencyLevel;
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import org.influxdb.dto.BatchPoints;
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import org.influxdb.dto.Point;
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import org.influxdb.dto.QueryResult;
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import java.util.*;
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import java.util.concurrent.TimeUnit;
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import java.util.stream.Collectors;
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/**
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* 类的介绍：
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*
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* @author xuyang
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* @version 1.0.0
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* @createTime 2021/11/16 11:07
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*/
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public class test {
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//查询
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public static QueryResult select(InfluxDbUtils influxDBUtil) {
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long startTime = System.currentTimeMillis();
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QueryResult result = influxDBUtil.query("select * from data_V where phasic_type='A'");
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long endTime = System.currentTimeMillis();
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System.out.println(endTime - startTime);
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return result;
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}
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//处理结果集
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public static void chanelResult(QueryResult result) {
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QueryResult.Result result1 = result.getResults().get(0);
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if (result1.getSeries() != null) {
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List<List<Object>> valueList = result1.getSeries().stream().map(QueryResult.Series::getValues).collect(Collectors.toList()).get(0);
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if (valueList != null && valueList.size() > 0) {
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for (List<Object> value : valueList) {
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Map<String, String> map = new HashMap<String, String>();
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// 数据库中字段1取值
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String field1 = value.get(0) == null ? null : value.get(0).toString();
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System.out.println(field1);
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// 数据库中字段2取值
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String field2 = value.get(1) == null ? null : value.get(1).toString();
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System.out.println(field2);
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// TODO 用取出的字段做你自己的业务逻辑……
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}
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}
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}
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}
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public static void main(String[] args) {
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InfluxDbUtils influxDBUtil = new InfluxDbUtils("root", "123456789", "http://192.168.1.16:8086", "pqsbase", "");
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insert(influxDBUtil);
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}
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//单条数据插入
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public static void insert(InfluxDbUtils influxDBUtil) {
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Map<String, String> tags = new HashMap<>();
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tags.put("lineid", "1e3b8531483b2a8cbee6747f1f641cf9");
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Map<String, Object> fields = new HashMap<>();
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fields.put("phasic_type","T");
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fields.put("value_type","MAX");
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fields.put("Freq_Dev", 48.6 );
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fields.put("Voltage_Dev", 8.3 );
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fields.put("UBalance", 7.6 );
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fields.put("Flicker", 4.6 );
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fields.put("UAberrance", 6.5 );
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fields.put("I_Neg", 5.6 );
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fields.put("UHarm_2", 6.8 );
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fields.put("UHarm_3", 5.5 );
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fields.put("UHarm_4", 5.4 );
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fields.put("UHarm_5", 7.8 );
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fields.put("UHarm_6", 6.2 );
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fields.put("UHarm_7", 2.5 );
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fields.put("UHarm_8", 8.3 );
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fields.put("UHarm_9", 6.2 );
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fields.put("UHarm_10", 12.8 );
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fields.put("UHarm_11", 2.8 );
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fields.put("UHarm_12", 8.4 );
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fields.put("UHarm_13", 5.6 );
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fields.put("UHarm_14", 5.2 );
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fields.put("UHarm_15", 9.5 );
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fields.put("UHarm_16", 8.3 );
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fields.put("UHarm_17", 7.8 );
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fields.put("UHarm_18", 6.2 );
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fields.put("UHarm_19", 2.5);
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fields.put("UHarm_20", 4.5 );
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fields.put("UHarm_21", 4.5 );
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fields.put("UHarm_22", 6.5 );
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fields.put("UHarm_23", 5.9 );
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fields.put("UHarm_24", 9.2 );
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fields.put("UHarm_25", 5.8 );
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fields.put("IHarm_2", 12.8 );
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fields.put("IHarm_3", 5.4 );
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fields.put("IHarm_4", 6.2 );
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fields.put("IHarm_5", 3.2 );
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fields.put("IHarm_6", 5.2 );
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fields.put("IHarm_7", 5.2 );
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fields.put("IHarm_8", 5.5 );
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fields.put("IHarm_9", 4.8 );
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fields.put("IHarm_10", 8.2 );
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fields.put("IHarm_11", 2.5 );
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fields.put("IHarm_12", 8.6 );
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fields.put("IHarm_13", 5.8 );
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fields.put("IHarm_14", 3.5 );
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fields.put("IHarm_15", 2.4 );
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fields.put("IHarm_16", 5.2 );
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fields.put("IHarm_17", 2.5 );
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fields.put("IHarm_18", 9.2 );
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fields.put("IHarm_19", 8.5);
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fields.put("IHarm_20", 8.5 );
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fields.put("IHarm_21", 6.2 );
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fields.put("IHarm_22", 5.2 );
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fields.put("IHarm_23", 8.5 );
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fields.put("IHarm_24", 5.2 );
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fields.put("IHarm_25", 8.4 );
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fields.put("InUHARM_1", 8.2 );
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fields.put("InUHARM_2", 5.2 );
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fields.put("InUHARM_3", 6.2 );
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fields.put("InUHARM_4", 4.2 );
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fields.put("InUHARM_5", 2.3 );
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fields.put("InUHARM_6", 6.2 );
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fields.put("InUHARM_7", 5.2 );
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fields.put("InUHARM_8", 10.2 );
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fields.put("InUHARM_9", 2.3 );
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fields.put("InUHARM_10", 4.2 );
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fields.put("InUHARM_11", 3.5 );
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fields.put("InUHARM_12", 3.6 );
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fields.put("InUHARM_13", 2.3 );
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fields.put("InUHARM_14", 7.2 );
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fields.put("InUHARM_15", 5.6 );
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fields.put("InUHARM_16", 5.6 );
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influxDBUtil.insert("PQS_AbnormalData", tags, fields, System.currentTimeMillis(), TimeUnit.MILLISECONDS);
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}
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//循环写入数据库
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public static void batchInsertOne(InfluxDbUtils influxDBUtil) {
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Map<String, String> tags1 = new HashMap<>();
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tags1.put("LineID", "8");
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tags1.put("Phasic_Type", "A");
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Map<String, Object> fields1 = new HashMap<>();
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fields1.put("RMS", 2);
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fields1.put("RMS_AB", 2);
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fields1.put("RMS_BC", 2);
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fields1.put("RMS_CA", 2);
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Map<String, String> tags2 = new HashMap<>();
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tags2.put("LineID", "9");
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tags2.put("Phasic_Type", "A");
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Map<String, Object> fields2 = new HashMap<>();
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fields2.put("RMS", 4);
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fields2.put("RMS_AB", 4);
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fields2.put("RMS_BC", 4);
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fields2.put("RMS_CA", 4);
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// 一条记录值
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Point point1 = influxDBUtil.pointBuilder("test", System.currentTimeMillis(), TimeUnit.MILLISECONDS, tags1, fields1);
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Point point2 = influxDBUtil.pointBuilder("test", System.currentTimeMillis(), TimeUnit.MILLISECONDS, tags2, fields2);
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// 将两条记录添加到batchPoints中
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BatchPoints batchPoints1 = BatchPoints.database("test").tag("LineID", "8").tag("Phasic_Type", "A").retentionPolicy("")
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.consistency(ConsistencyLevel.ALL).build();
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BatchPoints batchPoints2 = BatchPoints.database("test").tag("LineID", "9").tag("Phasic_Type", "A").retentionPolicy("")
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.consistency(ConsistencyLevel.ALL).build();
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batchPoints1.point(point1);
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batchPoints2.point(point2);
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// 将两条数据批量插入到数据库中
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influxDBUtil.batchInsert(batchPoints1, TimeUnit.MILLISECONDS);
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influxDBUtil.batchInsert(batchPoints2, TimeUnit.MILLISECONDS);
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}
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//批量插入数据
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public static void batchInsert(InfluxDbUtils influxDBUtil) {
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Map<String, String> tags1 = new HashMap<>();
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tags1.put("LineID", "4");
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tags1.put("Phasic_Type", "A");
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Map<String, Object> fields1 = new HashMap<>();
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fields1.put("RMS", 4.1111);
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fields1.put("RMS_AB", 4.1111);
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fields1.put("RMS_BC", 4.1111);
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fields1.put("RMS_CA", 4.1111);
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Map<String, String> tags2 = new HashMap<>();
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tags2.put("LineID", "5");
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tags2.put("Phasic_Type", "A");
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Map<String, Object> fields2 = new HashMap<>();
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fields2.put("RMS", 5.1111);
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fields2.put("RMS_AB", 5.1111);
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fields2.put("RMS_BC", 5.1111);
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fields2.put("RMS_CA", 5.1111);
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// 一条记录值。（注意：生产环境不要用System.currentTimeMillis()，因为数据量大会产生重复时间戳，导致数据丢失，要用数据自己的时间戳，这里只做演示）
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Point point1 = influxDBUtil.pointBuilder("Data_v", System.currentTimeMillis(), TimeUnit.MILLISECONDS, tags1, fields1);
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Point point2 = influxDBUtil.pointBuilder("Data_v", System.currentTimeMillis(), TimeUnit.MILLISECONDS, tags2, fields2);
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// BatchPoints batchPoints1 = BatchPoints.database("Data_v").tag("LineID", "4").tag("Phasic_Type","A").retentionPolicy("").consistency(ConsistencyLevel.ALL).precision(TimeUnit.MILLISECONDS).build();
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BatchPoints batchPoints1 = BatchPoints.database("test").tag("LineID", "4").tag("Phasic_Type", "A").retentionPolicy("").consistency(ConsistencyLevel.ALL).build();
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batchPoints1.point(point1);
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BatchPoints batchPoints2 = BatchPoints.database("test").tag("LineID", "5").tag("Phasic_Type", "A").retentionPolicy("").consistency(ConsistencyLevel.ALL).build();
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// 将两条记录添加到batchPoints中
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batchPoints2.point(point2);
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// 将不同的batchPoints序列化后，一次性写入数据库，提高写入速度
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List<String> records = new ArrayList<String>();
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records.add(batchPoints1.lineProtocol());
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records.add(batchPoints2.lineProtocol());
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// 将两条数据批量插入到数据库中
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influxDBUtil.batchInsert("test", "", ConsistencyLevel.ALL, TimeUnit.MILLISECONDS, records);
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}
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}
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