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());
}
以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持脚本之家。
