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://pound( 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()); }

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