java操作elasticsearch的案例解析

这篇文章主要介绍了java操作elasticsearch的案例解析,文中通过示例代码介绍的非常详细，对大家的学习或者工作具有一定的参考学习价值,需要的朋友可以参考下

到目前为止，我们一直都是使用RESTful风格的 API操作elasticsearch服务，但是通过我们之前的学习知道，elasticsearch提供了很多语言的客户端用于操作elasticsearch服务，例如：java、python、.net、JavaScript、PHP等。而我们此次就学习如何使用java语言来操作elasticsearch服务。在elasticsearch的官网上提供了两种java语言的API，一种是Java Transport Client，一种是Java REST Client。

而Java REST Client又分为Java Low Level REST Client和Java High Level REST Client，Java High Level REST Client是在Java Low Level REST Client的基础上做了封装，使其以更加面向对象和操作更加便利的方式调用elasticsearch服务。

官方推荐使用Java High Level REST Client，因为在实际使用中，Java Transport Client在大并发的情况下会出现连接不稳定的情况。

那接下来我们就来看看elasticsearch提供的Java High Level REST Client（以下简称高级REST客户端）的一些基础的操作，跟多的操作大家自行阅读elasticsearch的官方文档：https://www.elastic.co/guide/en/elasticsearch/client/java-rest/current/java-rest-high.html在官网上已经对高级REST客户端的各种API做了很详细的使用说明，我们这篇文章主要还是翻译官网上的内容，先让大家以更友好的中文文档方式入门，等大家熟悉了这些API之后在查阅官网。

1.基本过滤查询

long start = System.currentTimeMillis();
long end = start - 4 * 60 * 60 * 1000;
RangeQueryBuilder rangeQueryBuilder = QueryBuilders.rangeQuery("timestamp").from(end,true).to(start,true);
QueryBuilder s=QueryBuilders.boolQuery().must(rangeQueryBuilder);
QueryBuilder qb=new MatchAllQueryBuilder();
SearchResponse response= elasticsearchTemplate.getClient().prepareSearch("monitoring-cpu").setTypes("cloud-cpu").setQuery(s).setFrom(0)
.setSize(100).get();
SearchHits searchHits = response.getHits();
for(SearchHit hit:searchHits.getHits()){
System.out.println(hit.getSourceAsString());
}

2.条件过滤，进然后行分组，对组内数据求平均，然后排行查询

//ES中查询所有主机的监控数据
   BoolQueryBuilder uuidsBoolQuery = QueryBuilders.boolQuery();

uuidsBoolQuery.must(QueryBuilders.matchQuery("uuid", uuidStr));

//暂定向前推一天，计算平均
   long end = System.currentTimeMillis();
   long start = end - 24 * 60 * 60 * 1000;
   RangeQueryBuilder rangeQueryBuilder = QueryBuilders.rangeQuery("timestamp").from(start,true).to(end,true);
   QueryBuilder timeFilter = QueryBuilders.boolQuery().must(rangeQueryBuilder);

//开始cputop查询
   //分组字段是id，排序由多个字段排序组成
   TermsAggregationBuilder orderCpu = AggregationBuilders.terms("group-uuid").field("uuid.keyword").order(Terms.Order.compound(
       Terms.Order.aggregation("avg-cpuuse", true)
   ));

//求和字段1
   AvgAggregationBuilder avgCpu = AggregationBuilders.avg("avg-cpuuse").field("usage_idle");

orderCpu.subAggregation(avgCpu);//添加到分组聚合请求中
   orderCpu.size(10);//top10限制

FilterAggregationBuilder cpuAggregationBuilder = AggregationBuilders.filter("uuidFilter", uuidsBoolQuery)
       .subAggregation(AggregationBuilders.filter("timeFilter",timeFilter).subAggregation(orderCpu));

SearchResponse response = elasticsearchTemplate.getClient().prepareSearch("monitoring-cpu").setTypes("cloud-cpu")
       .addAggregation(cpuAggregationBuilder)
       .get();

InternalFilter uuidFilterRe = response.getAggregations().get("uuidFilter");
   InternalFilter timeFilterRe = uuidFilterRe.getAggregations().get("timeFilter");

Terms tms = timeFilterRe.getAggregations().get("group-uuid");
   //遍历每一个分组的key
   for(Terms.Bucket tbb:tms.getBuckets()){
     //获取count的和
     InternalAvg avg = tbb.getAggregations().get("avg-cpuuse");
     for (Map userResource : userResources) {
       Object uuid = userResource.get("uuid");
       if (uuid != null && !"".equals(uuid.toString())){
         if (uuid.equals(tbb.getKey())){
           userResource.put("cupPercent",numberFormat.format(100.0 - avg.getValue()));
           cpuSort.add(userResource);
         }
       }
     }
   }

3.过滤聚合求平均查询

//ES中查询所有主机的监控数据
   BoolQueryBuilder uuidsBoolQuery = QueryBuilders.boolQuery();

uuidsBoolQuery.must(QueryBuilders.matchQuery("uuid", "1,2,4"));

//暂定向前推一天，计算平均
   long end = System.currentTimeMillis();
   long start = end - 24 * 60 * 60 * 1000;
   RangeQueryBuilder rangeQueryBuilder = QueryBuilders.rangeQuery("timestamp").from(start,true).to(end,true);
   QueryBuilder timeFilter = QueryBuilders.boolQuery().must(rangeQueryBuilder);

//开始查询Cpu平均使用率
   FilterAggregationBuilder cpuAggregationBuilder = AggregationBuilders.filter("uuidFilter", uuidsBoolQuery)
       .subAggregation(AggregationBuilders.filter("timeFilter",timeFilter)
           .subAggregation(AggregationBuilders.avg("avgCpu").field("usage_idle")));

SearchResponse response = elasticsearchTemplate.getClient().prepareSearch("monitoring-cpu").setTypes("cloud-cpu")
       .addAggregation(cpuAggregationBuilder)
       .get();

InternalFilter uuidFilterRe = response.getAggregations().get("uuidFilter");
   InternalFilter timeFilterRe = uuidFilterRe.getAggregations().get("timeFilter");
   InternalAvg avgCpuRe = timeFilterRe.getAggregations().get("avgCpu");

String cpupercent = "0.00";
   if (!"NaN".equals(avgCpuRe.getValue() + "")){
     cpupercent = numberFormat.format(100.0 - avgCpuRe.getValue());
   }

来源：https://www.cnblogs.com/kenmeon/p/11750732.html