elasticsearch api client使用

一、基本概念 1、Node 与 Cluster

Elastic 本质上是一个分布式数据库，允许多台服务器协同工作，每台服务器可以运行多个 Elastic 实例。

单个 Elastic 实例称为一个节点（node）。一组节点构成一个集群（cluster）。

2、Index

Elastic 会索引所有字段，经过处理后写入一个反向索引（Inverted Index）。查找数据的时候，直接查找该索引。

所以，Elastic 数据管理的顶层单位就叫做 Index（索引）。它是单个数据库的同义词。每个 Index （即数据库）的名字必须是小写。

下面的命令可以查看当前节点的所有 Index。

$ curl -X GET 'http://localhost:9200/_cat/indices?v'

3、Document

Index 里面单条的记录称为 Document（文档）。许多条 Document 构成了一个 Index。

Document 使用 JSON 格式表示，下面是一个例子。

{
    "user": "张三",
    "title": "工程师",
    "desc": "数据库管理"
}

同一个 Index 里面的 Document，不要求有相同的结构（scheme），但是最好保持相同，这样有利于提高搜索效率。

4、Type

Document 可以分组，比如weather这个 Index 里面，可以按城市分组（北京和上海），也可以按气候分组（晴天和雨天）。这种分组就叫做 Type，它是虚拟的逻辑分组，用来过滤 Document。

不同的 Type 应该有相似的结构（schema），举例来说，id字段不能在这个组是字符串，在另一个组是数值。这是与关系型数据库的表的一个区别。性质完全不同的数据（比如products和logs）应该存成两个 Index，而不是一个 Index 里面的两个 Type（虽然可以做到）。

下面的命令可以列出每个 Index 所包含的 Type。

 $ curl 'localhost:9200/_mapping?pretty=true'

根据规划，Elastic 6.x 版只允许每个 Index 包含一个 Type，7.x 版将会彻底移除 Type。

以上部分摘自：http://www.ruanyifeng.com/blog/2017/08/elasticsearch.html

二、通过Http请求排序 1、数据格式

{
    "_scroll_id": "DnF1ZXJ5VGhlbkZldGNoAwAAAAAATaBwFklfYTRhdy0wVHJxQUNpcm5sWVBHeHcAAAAAAEvhqhYwNTgtVi1xT1FUNlkxMl9CVldWM1lnAAAAAACXzBgWVlhBRnRfd2xRd09HdlduY2tRNXpmQQ==",
    "took": 3,
    "timed_out": false,
    "_shards": {
        "total": 3,
        "successful": 3,
        "failed": 0
    },
    "hits": {
        "total": 9564,
        "max_score": 1,
        "hits": [
            {
                "_index": "alert-201712s",
                "_type": "HISTORY",
                "_id": "000E94E15DA381A680F9C0E0C14F1E7F-1513323398",
                "_score": 1,
                "_source": {
                    "duration": 120,
                    "times": 2,
                    "status": "resolve",
                    "level": "warning",
                    "project": "AAAA"                 
                }
            },
            {
                "_index": "alert-201712s",
                "_type": "HISTORY",
                "_id": "00A70A194DCF6DE937BC97610715DDCE-1513320277",
                "_score": 1,
                "_source": {
                    "duration": 120,
                    "times": 54,
                    "level": "critical",
                    "project": "BBBB"
                }
            },
            ..........
        ]
    }
}

想要先按照project聚合，再按照level聚合，再把聚合后的各个项目、各个level的duration求和（类似与sql中的select sum(duration) ….group by project,level）

2、聚合排序

通过postman请求： 请求方式：Post url：

ip:9200/index名称/Type名称/_search

此处应该是：

localhost:9200/alert-201712s/HISTORY/_search

body参数：

{
    "size": 0,
    "query": {
        "bool": {
            "filter": {
                "terms": {
                    "project": ["AAAA", "BBBB"] }
            }
        }
    },
    "aggs": {
        "projects": {
            "terms": {
                "field": "project",
                "size": 10000
            },
            "aggs": {
                "levels": {
                    "terms": { "field": "level" },
                    "aggs": { "durations": { "sum": { "field": "duration" } } } }
            }
        }
    }
}

body参数注意aggs的嵌套结构（层级） 查询结果：

{
   "took": 3,
    "timed_out": false,
    "_shards": {
        "total": 3,
        "successful": 3,
        "failed": 0
    },
    "hits": {
        "total": 8768,
        "max_score": 0,
        "hits": []
    },
    "aggregations": {
        "types_count": {
            "doc_count_error_upper_bound": 0,
            "sum_other_doc_count": 0,
            "buckets": [
                {
                    "key": "AAA",
                    "doc_count": 2077,
                    "types_count": {
                        "doc_count_error_upper_bound": 0,
                        "sum_other_doc_count": 0,
                        "buckets": [ { "key": "serious", "doc_count": 789, "durations": { "value": 18720 } }, { "key": "null", "doc_count": 456, "durations": { "value": 23 } }, { "key": "warning", "doc_count": 401, "durations": { "value": 234 } }, { "key": "critical", "doc_count": 4, "durations": { "value": 78 } } ] }
                },
                {
                    "key": "BBB",
                    "doc_count": 1225,
                    "types_count": {
                        "doc_count_error_upper_bound": 0,
                        "sum_other_doc_count": 0,
                        "buckets": [ { "key": "serious", "doc_count": 966, "durations": { "value": 56 } }, { "key": "null", "doc_count": 258, "durations": { "value": 34 } }, { "key": "critical", "doc_count": 1, "durations": { "value": 2343 } } ] }
                }
    }
}

三、java http 请求 1、pom依赖

            org.elasticsearch.client
            elasticsearch-rest-high-level-client
            5.6.4
        
        
            org.elasticsearch.client
            transport
            5.1.1
        

2、代码

import com.google.gson.Gson;
import com.google.gson.JsonObject;
import org.apache.http.Header;
import org.apache.http.HttpHost;
import org.apache.http.entity.StringEntity;
import org.apache.http.message.BasicHeader;
import org.apache.http.util.EntityUtils;
import org.elasticsearch.action.index.IndexRequest;
import org.elasticsearch.client.Response;
import org.elasticsearch.client.RestClient;
import org.elasticsearch.client.transport.TransportClient;

import java.util.HashMap;
import java.util.Map;

public class Test {

    private static String es_url = "localhost:9200";
    private TransportClient client;
    private IndexRequest source;

    //将postman中参数直接复制到idea中自动转义的
     private static String str = "{\n" +
            "\t\"size\": 0,\n" +
            "\t\"query\": {\n" +
            "\t\t\"bool\": {\n" +
            "\t\t\t\"filter\": {\n" +
            "\t\t\t\t\"terms\": {\n" +
            "\t\t\t\t\t\"project\": [\"AA\",\n" +
            "\t\t\t\t\t\"BB\"]\n" +
            "\t\t\t\t}\n" +
            "\t\t\t}\n" +
            "\t\t}\n" +
            "\t},\n" +
            "\t\"aggs\": {\n" +
            "\t\t\"projects\": {\n" +
            "\t\t\t\"terms\": {\n" +
            "\t\t\t\t\"field\": \"project\",\n" +
            "\t\t\t\t\"size\": 10000\n" +
            "\t\t\t},\n" +
            "\t\t\t\"aggs\": {\n" +
            "\t\t\t\t\"levels\": {\n" +
            "\t\t\t\t\t\"terms\": {\n" +
            "\t\t\t\t\t\t\"field\": \"level\",\n" +
            "\t\t\t\t\t\t\"size\": 10000\n" +
            "\t\t\t\t\t},\n" +
            "\t\t\t\t\t\"aggs\": {\n" +
            "\t\t\t\t\t\t\"durations\": {\n" +
            "\t\t\t\t\t\t\t\"sum\": {\n" +
            "\t\t\t\t\t\t\t\t\"field\": \"duration\"\n" +
            "\t\t\t\t\t\t\t}\n" +
            "\t\t\t\t\t\t}\n" +
            "\t\t\t\t\t}\n" +
            "\t\t\t\t}\n" +
            "\t\t\t}\n" +
            "\t\t}\n" +
            "\t}\n" +
            "}";


    public static void main(String[] args) throws Exception {
        HttpHost[] hosts = new HttpHost[1];
        hosts[0] = HttpHost.create(es_url);
        //创建ES请求客户端
        RestClient restClient = RestClient.builder(hosts).build();
        String index = "alert-201712s";
        String type = "HISTORY";
        String endpoint = "/" + index + "/" + type + "/_search";
        Map params = new HashMap();
        StringEntity queryBody = new StringEntity(str, "UTF-8");
        Header header = new BasicHeader("content-type", "application/json");

        Response response = restClient.performRequest("GET", endpoint, params, queryBody, header);
        //System.out.println(response);

        String resultJson = EntityUtils.toString(response.getEntity());
        Gson gson = new Gson();
        //获取到返回的数据
        JsonObject resultObj = gson.fromJson(resultJson, JsonObject.class);

    }

}

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作者：jiankunking 出处：http://blog.csdn.net/jiankunking