前言
之前其实写过ES查询数据，进行分组聚合统计：
复杂聚合分组统计实现
一、目标场景
- 机房机柜的物联网设备上传环境数据，会存储到ES
- 存到ES的温湿度数据需要查询，进行分组后，再聚合统计求平均值
二、使用步骤
1.引入库
我这里因为ES服务已经升级到8.0.0了，然后ES数据查询分组，我这里需要对时间进行格式化，再聚合avg，所以客户端相关版本用的7.17.4
<dependency>
<groupId>org.elasticsearch.client</groupId>
<artifactId>elasticsearch-rest-client</artifactId>
<version>7.17.4</version>
<exclusions>
<exclusion>
<groupId>org.elasticsearch</groupId>
<artifactId>elasticsearch</artifactId>
</exclusion>
</exclusions>
</dependency>
<dependency>
<groupId>org.elasticsearch.client</groupId>
<artifactId>elasticsearch-rest-high-level-client</artifactId>
<version>7.17.4</version>
<exclusions>
<exclusion>
<groupId>org.elasticsearch</groupId>
<artifactId>elasticsearch</artifactId>
</exclusion>
</exclusions>
</dependency>
<dependency>
<groupId>org.elasticsearch</groupId>
<artifactId>elasticsearch</artifactId>
<version>7.17.4</version>
</dependency>
2.配置类
目前我们就是单服务的，这个配置类够用了。其实我配置类就是要把RestHighLevelClient注入，并交给spring管理。
/**
* ES配置类
* @author zwmac
*/
@Configuration
@Data
public class ElasticSearchConfig {
@Value("${es.host}")
private String host;
@Value("${es.port}")
private int port;
@Value("${es.username}")
private String loginName;
@Value("${es.password}")
private String password;
private RestHighLevelClient client;
@Bean
public RestHighLevelClient client() {
final CredentialsProvider credentialsProvider = new BasicCredentialsProvider();
credentialsProvider.setCredentials(AuthScope.ANY,
new UsernamePasswordCredentials(loginName, password));
HttpHost[] httpHostArray = new HttpHost[1];
httpHostArray[0] = new HttpHost(host, port);
RestClientBuilder restClientBuilder = RestClient.builder(httpHostArray)
.setHttpClientConfigCallback(httpClientBuilder -> {
httpClientBuilder.disableAuthCaching();
return httpClientBuilder.setDefaultCredentialsProvider(credentialsProvider);
});
restClientBuilder.setRequestConfigCallback(requestConfigBuilder -> requestConfigBuilder
.setConnectTimeout(60000)
.setSocketTimeout(150000));
client = new RestHighLevelClient(
restClientBuilder
);
return client;
}
}
3.使用
@Resource
private RestHighLevelClient restHighLevelClient;
/**
* 查询温湿度24小时平均值
* @param deviceCode 设备编码
* @param startTime 开始时间
* @param endTime 结束时间
* @param humName 湿度字段名
* @param tempName 温度字段名
* @return 温湿度24小时平均值
*/
private TreeMap<String, Map<String, Double>> queryTempHumDayAvg(String deviceCode, Date startTime, Date endTime, String humName, String tempName) {
TreeMap<String, Map<String, Double>> treeMap = new TreeMap<>();
//ES查询
String index = EsCalendar.getDeviceFlowIndex(startTime, endTime);
SearchRequest searchRequest = new SearchRequest(index);
SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
//忽略不可用索引，允许索引不不存在，通配符表达式将扩展为打开的索引
searchRequest.indicesOptions(IndicesOptions.fromOptions(true, true, true, false));
String timeFmt = "yyyy-MM-dd";
// 组装ES请求数据
String startTimeStr = DateUtil.format(startTime, DatePattern.NORM_DATETIME_PATTERN);
String endTimeStr = DateUtil.format(endTime, DatePattern.NORM_DATETIME_PATTERN);
QueryBuilder rangeQuery = QueryBuilders.rangeQuery("createTime").lte(endTimeStr).gte(startTimeStr);
BoolQueryBuilder boolQueryBuilder = QueryBuilders.boolQuery();
// 必须为deviceCode
boolQueryBuilder.must(QueryBuilders.termQuery("deviceCode", deviceCode));
rangeQuery = QueryBuilders.boolQuery().must(rangeQuery).must(boolQueryBuilder);
QueryBuilder boolQuery = QueryBuilders.boolQuery().must(rangeQuery);
searchSourceBuilder.query(boolQuery).size(0);
//平均值 温度
//String tempName = "temp_avg";
String tempAvgName = tempName + "_avg";
String tempFactorName = "data." + tempName;
AvgAggregationBuilder tempAvgAggregationBuilder = AggregationBuilders.avg(tempAvgName).field(tempFactorName);
//平均值 湿度
//String humName = "hygrometer_avg";
String humAvgName = humName + "_avg";
String humFactorName = "data." + humName;
AvgAggregationBuilder humAvgAggregationBuilder = AggregationBuilders.avg(humAvgName).field(humFactorName);
String createTimeGroup = "createTimeGroup";
DateHistogramAggregationBuilder aggregation = AggregationBuilders.dateHistogram(createTimeGroup)
.field("createTime").fixedInterval(DateHistogramInterval.DAY)
.format(timeFmt)
//过滤掉count为0的数据
.minDocCount(1).subAggregation(tempAvgAggregationBuilder).subAggregation(humAvgAggregationBuilder);
//分组条件
searchSourceBuilder.aggregation(aggregation);
searchRequest.source(searchSourceBuilder);
// 按照因子列表查询
searchRequest.source(searchSourceBuilder);
SearchResponse searchResponse = null;
Map<String, Map<String, Double>> mp = new HashMap<>();
try {
log.info("方法getCabinetTempHum24HourAvg查询ES请求数据：" + searchRequest);
searchResponse = restHighLevelClient.search(searchRequest, RequestOptions.DEFAULT);
log.info("方法getCabinetTempHum24HourAvg查询ES响应数据：" + searchResponse.toString());
Aggregations aggregations = searchResponse.getAggregations();
if (aggregations != null) {
//组织出参数
aggregations.forEach(agg -> {
ParsedDateHistogram parsedDateHistogram = (ParsedDateHistogram) agg;
List buckets = parsedDateHistogram.getBuckets();
if (CollectionUtil.isNotEmpty(buckets)) {
buckets.forEach(bucket -> {
ParsedDateHistogram.ParsedBucket timeGroupTerm = (ParsedDateHistogram.ParsedBucket) bucket;
String timeStr = timeGroupTerm.getKeyAsString();
Aggregations subAggregations = timeGroupTerm.getAggregations();
if (subAggregations != null) {
Map<String, Double> tempHumMap = new HashMap<>();
Map<String, Aggregation> subAggMap = subAggregations.asMap();
if (subAggMap != null) {
Aggregation tempAgg = subAggMap.get(tempAvgName);
if (tempAgg != null) {
ParsedAvg tempAggPdh = (ParsedAvg) tempAgg;
tempHumMap.put(tempName, tempAggPdh.getValue());
}
Aggregation humAgg = subAggMap.get(humAvgName);
if (humAgg != null) {
ParsedAvg humAggPdh = (ParsedAvg) humAgg;
tempHumMap.put(humName, humAggPdh.getValue());
}
}
mp.put(timeStr, tempHumMap);
}
});
}
});
}
//数据补全
List<DateTime> dateTimeList = DateUtil.rangeToList(startTime, DateUtil.offsetHour(endTime, -1), DateField.HOUR_OF_DAY);
if (CollectionUtil.isNotEmpty(dateTimeList)) {
String finTempName = "temp_avg";
String finHumName = "hum_avg";
dateTimeList.forEach(dateTime -> {
String timeStr = DateUtil.format(dateTime, timeFmt);
Map<String, Double> finTempHumMap = new HashMap<>();
Map<String, Double> tempHumMap = mp.get(timeStr);
if (tempHumMap == null) {
finTempHumMap.put(finTempName, 0.0);
finTempHumMap.put(finHumName, 0.0);
} else {
Double tempAvg = tempHumMap.get(tempName);
Double humAvg = tempHumMap.get(humName);
finTempHumMap.put(finTempName, tempAvg);
finTempHumMap.put(finHumName, humAvg);
}
treeMap.put(timeStr, finTempHumMap);
});
}
} catch (Exception e) {
log.error("方法countByEs查询ES异常", e);
}
return treeMap;
}
关键点注意：
-
QueryBuilders.rangeQuery传入的时间精度，需要yyyy-MM-dd HH:mm:ss,否则会报错
-
这里对时间格式化分组，使用的是DateHistogramAggregationBuilder
这个在EsApi7+就废弃了calendarInterval，替换新的fixedInterval -
分组再聚合，注意嵌套关系，各位自己理解下subAggregation
-
最后数据查询出来后，迭代解析，注意理解ParsedDateHistogram取值、parsedDateHistogram.getBuckets()、迭代解析
总结
- gs一直用老版本的ES6，这次终于被逼的更新了吧，真好。（之前一直建议、希望，都。。。。）
- 本来很想引入EasyEs用用，但是总有同事不认可，算了
- 之前也建议给ES装上sql-package插件，让DBeaver可以连接，试过一阵子，新版本又没装，算了
- 其他就没啥好说的了，唯一就是restHighLevelClient现在在7+也被标记为过时了，下次有机会，这个再改改。
- 希望能帮到大家，uping！
