這篇文章主要介紹了java操作elasticsearch的案例解析,文中通過示例程式碼介紹的非常詳細，對大家的學習或者工作具有一定的參考學習價值,需要的朋友可以參考下

到目前為止，我們一直都是使用RESTful風格的 API操作elasticsearch服務，但是通過我們之前的學習知道，elasticsearch提供了很多語言的客戶端用於操作elasticsearch服務，例如：java、python、.net、JavaScript、PHP等。而我們此次就學習如何使用java語言來操作elasticsearch服務。在elasticsearch的官網上提供了兩種java語言的API，一種是Java Transport Client，一種是Java REST Client。

而Java REST Client又分為Java Low Level REST Client和Java High Level REST Client，Java High Level REST Client是在Java Low Level REST Client的基礎上做了封裝，使其以更加面向物件和操作更加便利的方式呼叫elasticsearch服務。

官方推薦使用Java High Level REST Client，因為在實際使用中，Java Transport Client在大併發的情況下會出現連線不穩定的情況。

那接下來我們就來看看elasticsearch提供的Java High Level REST Client（以下簡稱高階REST客戶端）的一些基礎的操作，跟多的操作大家自行閱讀elasticsearch的官方文件：https://www.elastic.co/guide/en/elasticsearch/client/java-rest/current/java-rest-high.html在官網上已經對高階REST客戶端的各種API做了很詳細的使用說明，我們這篇文章主要還是翻譯官網上的內容，先讓大家以更友好的中文文件方式入門，等大家熟悉了這些API之後在查閱官網。

1.基本過濾查詢

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.條件過濾，進然後行分組，對組內資料求平均，然後排行查詢

//ES中查詢所有主機的監控資料
    BoolQueryBuilder uuidsBoolQuery = QueryBuilders.boolQuery();

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

    //暫定向前推一天，計算平均
    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);

    //開始cputop查詢
    //分組欄位是id，排序由多個欄位排序組成
    TermsAggregationBuilder orderCpu = AggregationBuilders.terms("group-uuid").field("uuid.keyword").order(Terms.Order.compound(
        Terms.Order.aggregation("avg-cpuuse",true)
    ));

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

    orderCpu.subAggregation(avgCpu);//新增到分組聚合請求中
    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");
    //遍歷每一個分組的key
    for(Terms.Bucket tbb:tms.getBuckets()){
      //獲取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.過濾聚合求平均查詢

//ES中查詢所有主機的監控資料
    BoolQueryBuilder uuidsBoolQuery = QueryBuilders.boolQuery();

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

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

    //開始查詢Cpu平均使用率
    FilterAggregationBuilder cpuAggregationBuilder = AggregationBuilders.filter("uuidFilter",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內容，希望對大家的學習有所幫助，也希望大家多多支援我們。