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Elasticsearch7.6學習筆記1 Getting start with Elasticsearch

  • 2020 年 4 月 10 日
  • 筆記

Elasticsearch7.6學習筆記1 Getting start with Elasticsearch

前言

權威指南中文只有2.x, 但現在es已經到7.6. 就安裝最新的來學下.

安裝

這裡是學習安裝, 生產安裝是另一套邏輯.

win

es下載地址:

https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.6.0-windows-x86_64.zip

kibana下載地址:

https://artifacts.elastic.co/downloads/kibana/kibana-7.6.0-windows-x86_64.zip

官方目前最新是7.6.0, 但下載速度慘不忍睹. 使用迅雷下載速度可以到xM.

binelasticsearch.bat  binkibana.bat

雙擊bat啟動.

docker安裝

對於測試學習，直接使用官方提供的docker鏡像更快更方便。

安裝方法見： https://www.cnblogs.com/woshimrf/p/docker-es7.html

以下內容來自:

https://www.elastic.co/guide/en/elasticsearch/reference/7.6/getting-started.html

Index some documents 索引一些文檔

本次測試直接使用kibana, 當然也可以通過curl或者postman訪問localhost:9200.

訪問localhost:5601, 然後點擊Dev Tools.

新建一個客戶索引(index)

PUT /{index-name}/_doc/{id}

PUT /customer/_doc/1  {    "name": "John Doe"  }

put 是http method, 如果es中不存在索引(index) customer, 則創建一個, 並插入一個數據, id為, name=John`.
如果存在則更新. 注意, 更新是覆蓋更新, 即body json是什麼, 最終結果就是什麼.

返回如下:

{    "_index" : "customer",    "_type" : "_doc",    "_id" : "1",    "_version" : 7,    "result" : "updated",    "_shards" : {      "total" : 2,      "successful" : 2,      "failed" : 0    },    "_seq_no" : 6,    "_primary_term" : 1  }
  • _index 是索引名稱
  • _type 唯一為_doc
  • _id 是文檔(document)的主鍵, 也就是一條記錄的pk
  • _version 是該_id的更新次數, 我這裡已經更新了7次
  • _shards 表示分片的結果. 我們這裡一共部署了兩個節點, 都寫入成功了.

在kibana上設置-index manangement里可以查看index的狀態. 比如我們這條記錄有主副兩個分片.

保存記錄成功後可以立馬讀取出來:

GET /customer/_doc/1

返回

{    "_index" : "customer",    "_type" : "_doc",    "_id" : "1",    "_version" : 15,    "_seq_no" : 14,    "_primary_term" : 1,    "found" : true,    "_source" : {      "name" : "John Doe"    }  }
  • _source 就是我們記錄的內容

批量插入

當有多條數據需要插入的時候, 我們可以批量插入. 下載準備好的文檔, 然後通過http請求導入es.

創建一個索引bank: 由於shards(分片)和replicas(副本)創建後就不能修改了，所以要先創建的時候配置shards. 這裡配置了3個shards和2個replicas.

PUT /bank  {    "settings": {      "index": {        "number_of_shards": "3",        "number_of_replicas": "2"      }    }  }

文檔地址: https://gitee.com/mirrors/elasticsearch/raw/master/docs/src/test/resources/accounts.json

下載下來之後, curl命令或者postman 發送文件請求過去

curl -H "Content-Type: application/json" -XPOST "localhost:9200/bank/_bulk?pretty&refresh" --data-binary "@accounts.json"  curl "localhost:9200/_cat/indices?v"

每條記錄格式如下:

{    "_index": "bank",    "_type": "_doc",    "_id": "1",    "_version": 1,    "_score": 0,    "_source": {      "account_number": 1,      "balance": 39225,      "firstname": "Amber",      "lastname": "Duke",      "age": 32,      "gender": "M",      "address": "880 Holmes Lane",      "employer": "Pyrami",      "email": "amberduke@pyrami.com",      "city": "Brogan",      "state": "IL"    }  }

在kibana monitor中選擇self monitor. 然後再indices中找到索引bank。可以看到我們導入的數據分布情況。

可以看到, 有3個shards分在不同的node上, 並且都有2個replicas.

開始查詢

批量插入了一些數據後, 我們就可以開始學習查詢了. 上文知道, 數據是銀行職員表, 我們查詢所有用戶,並根據帳號排序.

類似 sql

select * from bank order by  account_number asc limit 3

Query DSL

  GET /bank/_search  {    "query": { "match_all": {} },    "sort": [      { "account_number": "asc" }    ],    "size": 3,    "from": 2  }
  • _search 表示查詢
  • query 是查詢條件, 這裡是所有
  • size 表示每次查詢的條數, 分頁的條數. 如果不傳, 默認是10條. 在返回結果的hits中顯示.
  • from表示從第幾個開始

返回:

  {    "took" : 1,    "timed_out" : false,    "_shards" : {      "total" : 1,      "successful" : 1,      "skipped" : 0,      "failed" : 0    },    "hits" : {      "total" : {        "value" : 1000,        "relation" : "eq"      },      "max_score" : null,      "hits" : [        {          "_index" : "bank",          "_type" : "_doc",          "_id" : "2",          "_score" : null,          "_source" : {            "account_number" : 2,            "balance" : 28838,            "firstname" : "Roberta",            "lastname" : "Bender",            "age" : 22,            "gender" : "F",            "address" : "560 Kingsway Place",            "employer" : "Chillium",            "email" : "robertabender@chillium.com",            "city" : "Bennett",            "state" : "LA"          },          "sort" : [            2          ]        },        {          "_index" : "bank",          "_type" : "_doc",          "_id" : "3",          "_score" : null,          "_source" : {            "account_number" : 3,            "balance" : 44947,            "firstname" : "Levine",            "lastname" : "Burks",            "age" : 26,            "gender" : "F",            "address" : "328 Wilson Avenue",            "employer" : "Amtap",            "email" : "levineburks@amtap.com",            "city" : "Cochranville",            "state" : "HI"          },          "sort" : [            3          ]        },        {          "_index" : "bank",          "_type" : "_doc",          "_id" : "4",          "_score" : null,          "_source" : {            "account_number" : 4,            "balance" : 27658,            "firstname" : "Rodriquez",            "lastname" : "Flores",            "age" : 31,            "gender" : "F",            "address" : "986 Wyckoff Avenue",            "employer" : "Tourmania",            "email" : "rodriquezflores@tourmania.com",            "city" : "Eastvale",            "state" : "HI"          },          "sort" : [            4          ]        }      ]    }  }

返回結果提供了如下資訊

  • took es查詢時間, 單位是毫秒(milliseconds)
  • timed_out search是否超時了
  • _shards 我們搜索了多少shards, 成功了多少, 失敗了多少, 跳過了多少. 關於shard, 簡單理解為數據分片, 即一個index里的數據分成了幾片，可以理解為按id進行分表。
  • max_score 最相關的記錄(document)的分數

接下來可可以嘗試帶條件的查詢。

分詞查詢

查詢address中帶mill和lane的地址。

GET /bank/_search  {    "query": { "match": { "address": "mill lane" } },    "size": 2  }

返回

{    "took" : 3,    "timed_out" : false,    "_shards" : {      "total" : 1,      "successful" : 1,      "skipped" : 0,      "failed" : 0    },    "hits" : {      "total" : {        "value" : 19,        "relation" : "eq"      },      "max_score" : 9.507477,      "hits" : [        {          "_index" : "bank",          "_type" : "_doc",          "_id" : "136",          "_score" : 9.507477,          "_source" : {            "account_number" : 136,            "balance" : 45801,            "firstname" : "Winnie",            "lastname" : "Holland",            "age" : 38,            "gender" : "M",            "address" : "198 Mill Lane",            "employer" : "Neteria",            "email" : "winnieholland@neteria.com",            "city" : "Urie",            "state" : "IL"          }        },        {          "_index" : "bank",          "_type" : "_doc",          "_id" : "970",          "_score" : 5.4032025,          "_source" : {            "account_number" : 970,            "balance" : 19648,            "firstname" : "Forbes",            "lastname" : "Wallace",            "age" : 28,            "gender" : "M",            "address" : "990 Mill Road",            "employer" : "Pheast",            "email" : "forbeswallace@pheast.com",            "city" : "Lopezo",            "state" : "AK"          }        }      ]    }  }
  • 我設置了返回2個，但實際上命中的有19個

完全匹配查詢

GET /bank/_search  {    "query": { "match_phrase": { "address": "mill lane" } }  }

這時候查的完全符合的就一個了

{    "took" : 1,    "timed_out" : false,    "_shards" : {      "total" : 1,      "successful" : 1,      "skipped" : 0,      "failed" : 0    },    "hits" : {      "total" : {        "value" : 1,        "relation" : "eq"      },      "max_score" : 9.507477,      "hits" : [        {          "_index" : "bank",          "_type" : "_doc",          "_id" : "136",          "_score" : 9.507477,          "_source" : {            "account_number" : 136,            "balance" : 45801,            "firstname" : "Winnie",            "lastname" : "Holland",            "age" : 38,            "gender" : "M",            "address" : "198 Mill Lane",            "employer" : "Neteria",            "email" : "winnieholland@neteria.com",            "city" : "Urie",            "state" : "IL"          }        }      ]    }  }

多條件查詢

實際查詢中通常是多個條件一起查詢的

GET /bank/_search  {    "query": {      "bool": {        "must": [          { "match": { "age": "40" } }        ],        "must_not": [          { "match": { "state": "ID" } }        ]      }    }  }
  • bool用來合併多個查詢條件
  • must, should, must_not是boolean查詢的子語句， must, should決定相關性的score，結果默認按照score排序
  • must not是作為一個filter，影響查詢的結果，但不影響score，只是從結果中過濾。

還可以顯式地指定任意過濾器，以包括或排除基於結構化數據的文檔。

比如，查詢balance在20000和30000之間的。

GET /bank/_search  {    "query": {      "bool": {        "must": { "match_all": {} },        "filter": {          "range": {            "balance": {              "gte": 20000,              "lte": 30000            }          }        }      }    }  }

聚合運算group by

按照省份統計人數

按sql的寫法可能是

select state AS group_by_state, count(*) from tbl_bank limit 3;

對應es的請求是

  GET /bank/_search  {    "size": 0,    "aggs": {      "group_by_state": {        "terms": {          "field": "state.keyword",          "size": 3        }      }    }  }
  • size=0是限制返回內容， 因為es會返回查詢的記錄， 我們只想要聚合值
  • aggs是聚合的語法詞
  • group_by_state 是一個聚合結果， 名稱自定義
  • terms 查詢的欄位精確匹配, 這裡是需要分組的欄位
  • state.keyword state是text類型, 字元類型需要統計和分組的，類型必須是keyword
  • size=3 限制group by返回的數量，這裡是top3, 默認top10, 系統最大10000，可以通過修改search.max_buckets實現， 注意多個shards會產生精度問題， 後面再深入學習

返回值：

{    "took" : 5,    "timed_out" : false,    "_shards" : {      "total" : 3,      "successful" : 3,      "skipped" : 0,      "failed" : 0    },    "hits" : {      "total" : {        "value" : 1000,        "relation" : "eq"      },      "max_score" : null,      "hits" : [ ]    },    "aggregations" : {      "group_by_state" : {        "doc_count_error_upper_bound" : 26,        "sum_other_doc_count" : 928,        "buckets" : [          {            "key" : "MD",            "doc_count" : 28          },          {            "key" : "ID",            "doc_count" : 23          },          {            "key" : "TX",            "doc_count" : 21          }        ]      }    }  }
  • hits命中查詢條件的記錄，因為設置了size=0， 返回[]. total是本次查詢命中了1000條記錄
  • aggregations 是聚合指標結果
  • group_by_state 是我們查詢中命名的變數名
  • doc_count_error_upper_bound 沒有在這次聚合中返回、但是可能存在的潛在聚合結果.鍵名有「上界」的意思，也就是表示在預估的最壞情況下沒有被算進最終結果的值，當然doc_count_error_upper_bound的值越大，最終數據不準確的可能性越大，能確定的是，它的值為 0 表示數據完全正確，但是它不為 0，不代表這次聚合的數據是錯誤的.
  • sum_other_doc_count 聚合中沒有統計到的文檔數

值得注意的是, top3是否是準確的呢. 我們看到doc_count_error_upper_bound是有錯誤數量的, 即統計結果很可能不準確, 並且得到的top3分別是28,23,21. 我們再來添加另個查詢參數來比較結果:

GET /bank/_search  {    "size": 0,    "aggs": {      "group_by_state": {        "terms": {          "field": "state.keyword",          "size": 3,          "shard_size":  60        }      }    }  }  -----------------------------------------    "aggregations" : {      "group_by_state" : {        "doc_count_error_upper_bound" : 0,        "sum_other_doc_count" : 915,        "buckets" : [          {            "key" : "TX",            "doc_count" : 30          },          {            "key" : "MD",            "doc_count" : 28          },          {            "key" : "ID",            "doc_count" : 27          }        ]      }    }
  • shard_size 表示每個分片計算的數量. 因為agg聚合運算是每個分片計算出一個結果,然後最後聚合計算最終結果. 數據在分片分布不均衡, 每個分片的topN並不是一樣的, 就有可能最終聚合結果少算了一部分. 從而導致doc_count_error_upper_bound不為0. es默認shard_size的值是size*1.5+10, size=3對應就是14.5, 驗證shar_size=14.5時返回值確實和不傳一樣. 而設置為60時, error終於為0了, 即, 可以保證這個3個絕對是最多的top3. 也就是說, 聚合運算要設置shard_size儘可能大, 比如size的20倍.

按省份統計人數並計算平均薪酬

我們想要查看每個省的平均薪酬, sql可能是

select    state, avg(balance) AS average_balance, count(*) AS group_by_state  from tbl_bank  group by state  limit 3

在es可以這樣查詢:

GET /bank/_search  {    "size": 0,    "aggs": {      "group_by_state": {        "terms": {          "field": "state.keyword",          "size": 3,          "shard_size":  60        },        "aggs": {          "average_balance": {            "avg": {              "field": "balance"            }          },          "sum_balance": {            "sum": {              "field": "balance"            }          }        }      }    }  }
  • 第二個aggs是計算每個state的聚合指標
  • average_balance 自定義的變數名稱, 值為相同state的balance avg運算
  • sum_balance 自定義的變數名稱, 值為相同state的balancesum運算

結果如下:

{    "took" : 12,    "timed_out" : false,    "_shards" : {      "total" : 3,      "successful" : 3,      "skipped" : 0,      "failed" : 0    },    "hits" : {      "total" : {        "value" : 1000,        "relation" : "eq"      },      "max_score" : null,      "hits" : [ ]    },    "aggregations" : {      "group_by_state" : {        "doc_count_error_upper_bound" : 0,        "sum_other_doc_count" : 915,        "buckets" : [          {            "key" : "TX",            "doc_count" : 30,            "sum_balance" : {              "value" : 782199.0            },            "average_balance" : {              "value" : 26073.3            }          },          {            "key" : "MD",            "doc_count" : 28,            "sum_balance" : {              "value" : 732523.0            },            "average_balance" : {              "value" : 26161.535714285714            }          },          {            "key" : "ID",            "doc_count" : 27,            "sum_balance" : {              "value" : 657957.0            },            "average_balance" : {              "value" : 24368.777777777777            }          }        ]      }    }  }

按省份統計人數並按照平均薪酬排序

agg terms默認排序是count降序, 如果我們想用其他方式, sql可能是這樣:

select    state, avg(balance) AS average_balance, count(*) AS group_by_state  from tbl_bank  group by state  order by average_balance  limit 3

對應es可以這樣查詢:

GET /bank/_search  {    "size": 0,    "aggs": {      "group_by_state": {        "terms": {          "field": "state.keyword",          "order": {            "average_balance": "desc"          },          "size": 3        },        "aggs": {          "average_balance": {            "avg": {              "field": "balance"            }          }        }      }    }  }

返回結果的top3就不是之前的啦:

  "aggregations" : {      "group_by_state" : {        "doc_count_error_upper_bound" : -1,        "sum_other_doc_count" : 983,        "buckets" : [          {            "key" : "DE",            "doc_count" : 2,            "average_balance" : {              "value" : 39040.5            }          },          {            "key" : "RI",            "doc_count" : 5,            "average_balance" : {              "value" : 36035.4            }          },          {            "key" : "NE",            "doc_count" : 10,            "average_balance" : {              "value" : 35648.8            }          }        ]      }    }

參考

VirMach 便宜 VPS

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