elasticsearch api client使用

衣舞晨風發表於2017-12-27
ElasticsearchAPIclient

一、基本概念

1、Node 與 Cluster

Elastic 本質上是一個分散式資料庫，允許多臺伺服器協同工作，每臺伺服器可以執行多個 Elastic 例項。

單個 Elastic 例項稱為一個節點（node）。一組節點構成一個叢集（cluster）。

2、Index

Elastic 會索引所有欄位，經過處理後寫入一個反向索引（Inverted Index）。查詢資料的時候，直接查詢該索引。

所以，Elastic 資料管理的頂層單位就叫做 Index（索引）。它是單個資料庫的同義詞。每個 Index （即資料庫）的名字必須是小寫。

下面的命令可以檢視當前節點的所有 Index。

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

這裡寫圖片描述

3、Document

Index 裡面單條的記錄稱為 Document（文件）。許多條 Document 構成了一個 Index。

Document 使用 JSON 格式表示，下面是一個例子。

{
    "user": "張三",
    "title": "工程師",
    "desc": "資料庫管理"
}

同一個 Index 裡面的 Document，不要求有相同的結構（scheme），但是最好保持相同，這樣有利於提高搜尋效率。

4、Type

Document 可以分組，比如weather這個 Index 裡面，可以按城市分組（北京和上海），也可以按氣候分組（晴天和雨天）。這種分組就叫做 Type，它是虛擬的邏輯分組，用來過濾 Document。

不同的 Type 應該有相似的結構（schema），舉例來說，id欄位不能在這個組是字串，在另一個組是數值。這是與關係型資料庫的表的一個區別。性質完全不同的資料（比如products和logs）應該存成兩個 Index，而不是一個 Index 裡面的兩個 Type（雖然可以做到）。

下面的命令可以列出每個 Index 所包含的 Type。

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

根據規劃，Elastic 6.x 版只允許每個 Index 包含一個 Type，7.x 版將會徹底移除 Type。

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

二、通過Http請求排序

1、資料格式

{
    "_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，再把聚合後的各個專案、各個level的duration求和（類似與sql中的select sum(duration) ….group by project,level）

2、聚合排序

通過postman請求：
請求方式：Post
url：

ip:9200/index名稱/Type名稱/_search

此處應該是：

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

body引數：

{
    "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引數注意aggs的巢狀結構（層級）
查詢結果：

{
   "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 請求

1、pom依賴

<dependency>
            <groupId>org.elasticsearch.client</groupId>
            <artifactId>elasticsearch-rest-high-level-client</artifactId>
            <version>5.6.4</version>
        </dependency>
        <dependency>
            <groupId>org.elasticsearch.client</groupId>
            <artifactId>transport</artifactId>
            <version>5.1.1</version>
        </dependency>

2、程式碼

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;

    //將postman中引數直接複製到idea中自動轉義的
     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);
        //建立ES請求客戶端
        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();
        //獲取到返回的資料
        JsonObject resultObj = gson.fromJson(resultJson, JsonObject.class);

    }

}

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這裡寫圖片描述

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

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