1. 建立索引
建立測試資料資料來源官方文檔：
PUT /megacorp/employee/1
{
"first_name" : "John",
"last_name" : "Smith",
"age" : 25,
"about" : "I love to go rock climbing",
"interests": [ "sports", "music" ]
}
PUT /megacorp/employee/2
{
"first_name" : "Jane",
"last_name" : "Smith",
"age" : 32,
"about" : "I like to collect rock albums",
"interests": [ "music" ]
}
PUT /megacorp/employee/3
{
"first_name" : "Douglas",
"last_name" : "Fir",
"age" : 35,
"about": "I like to build cabinets",
"interests": [ "forestry" ]
}
2. 搜尋
2.1 搜尋全部員工資料
GET /megacorp/employee/_search 搜尋全部員工 資料存在hits 數組下面
{
"took": 1,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"failed": 0
},
"hits": {
"total": 3,
"max_score": 1,
"hits": [
{
"_index": "megacorp",
"_type": "employee",
"_id": "2",
"_score": 1,
"_source": {
"first_name": "Jane",
"last_name": "Smith",
"age": 32,
"about": "I like to collect rock albums",
"interests": [
"music"
]
}
},
{
"_index": "megacorp",
"_type": "employee",
"_id": "1",
"_score": 1,
"_source": {
"first_name": "John",
"last_name": "Smith",
"age": 25,
"about": "I love to go rock climbing",
"interests": [
"sports",
"music"
]
}
},
{
"_index": "megacorp",
"_type": "employee",
"_id": "3",
"_score": 1,
"_source": {
"first_name": "Douglas",
"last_name": "Fir",
"age": 35,
"about": "I like to build cabinets",
"interests": [
"forestry"
]
}
}
]
}
}
2.2 搜尋單條員工資訊
GET /megacorp/employee/1 1号員工，原始json 文檔存在在_source字段裡面
{
"_index": "megacorp",
"_type": "employee",
"_id": "1",
"_version": 1,
"found": true,
"_source": {
"first_name": "John",
"last_name": "Smith",
"age": 25,
"about": "I love to go rock climbing",
"interests": [
"sports",
"music"
]
}
}
2.3 條件查找
GET /megacorp/employee/_search?q=last_name:Smith get參數模式加上q=字段名：value ，查找last name是Smith 的員工
{
"took": 16,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"failed": 0
},
"hits": {
"total": 2,
"max_score": 0.2876821,
"hits": [
{
"_index": "megacorp",
"_type": "employee",
"_id": "2",
"_score": 0.2876821,
"_source": {
"first_name": "Jane",
"last_name": "Smith",
"age": 32,
"about": "I like to collect rock albums",
"interests": [
"music"
]
}
},
{
"_index": "megacorp",
"_type": "employee",
"_id": "1",
"_score": 0.2876821,
"_source": {
"first_name": "John",
"last_name": "Smith",
"age": 25,
"about": "I love to go rock climbing",
"interests": [
"sports",
"music"
]
}
}
]
}
}
3. 使用DSL搜尋
查詢字元串搜尋便于通過指令行完成特定(ad hoc)的搜尋，但是它也有局限性（參閱簡單搜尋章節）。Elasticsearch提供豐富且靈活的查詢語言叫做DSL查詢(Query DSL),它允許你建構更加複雜、強大的查詢。
DSL(Domain Specific Language特定領域語言)以JSON請求體的形式出現。我們可以這樣表示之前關于“Smith”的查詢:
GET /megacorp/employee/_search
{
"query" : {
"match" : {
"last_name" : "Smith"
}
}
}
以上是使用DSL搜尋形式，這會傳回與之前查詢相同的結果。你可以看到有些東西改變了，我們不再使用查詢字元串(query string)做為參數，而是使用請求體代替
---------------------------------------------------------------------
{
"took": 101,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"failed": 0
},
"hits": {
"total": 2,
"max_score": 0.2876821,
"hits": [
{
"_index": "megacorp",
"_type": "employee",
"_id": "2",
"_score": 0.2876821,
"_source": {
"first_name": "Jane",
"last_name": "Smith",
"age": 32,
"about": "I like to collect rock albums",
"interests": [
"music"
]
}
},
{
"_index": "megacorp",
"_type": "employee",
"_id": "1",
"_score": 0.2876821,
"_source": {
"first_name": "John",
"last_name": "Smith",
"age": 25,
"about": "I love to go rock climbing",
"interests": [
"sports",
"music"
]
}
}
]
}
}
3.1 複雜查詢
GET /megacorp/employee/_search
{
"query": {
"bool": {
"must": { "match": { "last_name": "Smith" }},
"filter":
{ "range": { "age": { "gte": "30" }}}
}
}
}
--------------------------------------------------------------
{
"took": 1,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"failed": 0
},
"hits": {
"total": 1,
"max_score": 0.2876821,
"hits": [
{
"_index": "megacorp",
"_type": "employee",
"_id": "2",
"_score": 0.2876821,
"_source": {
"first_name": "Jane",
"last_name": "Smith",
"age": 32,
"about": "I like to collect rock albums",
"interests": [
"music"
]
}
}
]
}
}
可以用 bool 查詢來實作你的需求。這種查詢将多查詢組合在一起，成為使用者自己想要的布爾查詢。它接收以下參數：
must 文檔 必須 比對這些條件才能被包含進來。 must_not 文檔 必須不 比對這些條件才能被包含進來。 should 如果滿足這些語句中的任意語句，将增加 _score ，否則，無任何影響。它們主要用于修正每個文檔的相關性得分。 filter 必須 比對，但它以不評分、過濾模式來進行。這些語句對評分沒有貢獻，隻是根據過濾标準來排除或包含文檔。 5.5 後不支援filtered ，使用 bool/must/filter 代替，官方文檔：https://www.elastic.co/guide/cn/elasticsearch/guide/current/combining-queries-together.html
主意官方沒有用query 開始，直接用bool ，我用官方是報錯的
{
"bool": {
"must": { "match": { "title": "how to make millions" }},
"must_not": { "match": { "tag": "spam" }},
"should": [
{ "match": { "tag": "starred" }}
],
"filter": {
"range": { "date": { "gte": "2014-01-01" }}
}
}
}
3.2 全文搜尋
搜尋所有喜歡“rock climbing”的員工
GET /megacorp/employee/_search 結果出來的根據相關性排序，記錄二其實跟我們搜尋的相關性不大
{
"query" : {
"match" : {
"about" : "rock climbing"
}
}
}
------------------------------------------------
{
"took": 1385,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"failed": 0
},
"hits": {
"total": 2,
"max_score": 0.53484553,
"hits": [
{
"_index": "megacorp",
"_type": "employee",
"_id": "1",
"_score": 0.53484553,
"_source": {
"first_name": "John",
"last_name": "Smith",
"age": 25,
"about": "I love to go rock climbing",
"interests": [
"sports",
"music"
]
}
},
{
"_index": "megacorp",
"_type": "employee",
"_id": "2",
"_score": 0.26742277,
"_source": {
"first_name": "Jane",
"last_name": "Smith",
"age": 32,
"about": "I like to collect rock albums",
"interests": [
"music"
]
}
}
]
}
}
3.3 短語搜尋
全文搜尋搜尋的結果不一定是你想要的，比如有的不是攀岩的員工也展示出來，下面查詢match_phrase能查出所有喜歡攀岩的使用者展示出來。
GET /megacorp/employee/_search
{
"query" : {
"match_phrase" : {
"about" : "rock climbing"
}
},
"highlight": { 高亮顯示字段
"fields" : {
"about" : {}
}
}
}
-------------------------------------
{
"took": 429,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"failed": 0
},
"hits": {
"total": 1,
"max_score": 0.53484553,
"hits": [
{
"_index": "megacorp",
"_type": "employee",
"_id": "1",
"_score": 0.53484553,
"_source": {
"first_name": "John",
"last_name": "Smith",
"age": 25,
"about": "I love to go rock climbing",
"interests": [
"sports",
"music"
]
},
"highlight": {
"about": [
"I love to go <em>rock</em> <em>climbing</em>" 高亮顯示
]
}
}
]
}
}
4. 聚合查詢
4.1 簡單聚合
最後，我們還有一個需求需要完成：允許管理者在職員目錄中進行一些分析。 Elasticsearch有一個功能叫做聚合(aggregations)，它允許你在資料上生成複雜的分析統計。它很像SQL中的GROUP BY但是功能更強大。
GET /megacorp/employee/_search
{
"aggs": {
"all_interests": {
"terms": { "field": "interests" }
}
}
}
----------------------------------------------------------
{
"took": 6,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"failed": 0
},
"hits": {
"total": 3,
"max_score": 1,
"hits": [
{
"_index": "megacorp",
"_type": "employee",
"_id": "2",
"_score": 1,
"_source": {
"first_name": "Jane",
"last_name": "Smith",
"age": 32,
"about": "I like to collect rock albums",
"interests": [
"music"
]
}
},
{
"_index": "megacorp",
"_type": "employee",
"_id": "1",
"_score": 1,
"_source": {
"first_name": "John",
"last_name": "Smith",
"age": 25,
"about": "I love to go rock climbing",
"interests": [
"sports",
"music"
]
}
},
{
"_index": "megacorp",
"_type": "employee",
"_id": "3",
"_score": 1,
"_source": {
"first_name": "Douglas",
"last_name": "Fir",
"age": 35,
"about": "I like to build cabinets",
"interests": [
"forestry"
]
}
}
]
},
"aggregations": {
"all_interests": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "music",
"doc_count": 2
},
{
"key": "forestry",
"doc_count": 1
},
{
"key": "sports",
"doc_count": 1
}
]
}
}
}
報錯：Fielddata is disabled on text fields by default. Set fielddata=true on [your_field_name] in order to load fielddata in memory by uninverting the inverted index. Note that this can however use significant memory.
解決：參考官方：https://www.elastic.co/guide/en/elasticsearch/reference/current/fielddata.html
在建立索引時，這個字段預設采用的是text 的類型，而該類型并不支援聚合（aggregations），因為這個如果load 到記憶體需要消耗很多空間。官方在設定打開前，特别提醒請考慮是否真的有必要打開。是以請自己考慮！！
是以我們打開一個fielddata 的參數：
PUT /megacorp/_mapping/employee
{
"properties": {
"interests": {
"type": "text",
"fielddata": true
}
}
}
4.2 條件聚合
加上where條件的聚合，查找所有姓"Smith"的人最大的共同點（興趣愛好）：
GET /megacorp/employee/_search
{
"query": {
"match": {
"last_name": "smith"
}
},
"aggs": {
"all_interests": {
"terms": {
"field": "interests"
}
}
}
}
------------------------------------------------------
{
"took": 9,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"failed": 0
},
"hits": {
"total": 2,
"max_score": 0.2876821,
"hits": [
{
"_index": "megacorp",
"_type": "employee",
"_id": "2",
"_score": 0.2876821,
"_source": {
"first_name": "Jane",
"last_name": "Smith",
"age": 32,
"about": "I like to collect rock albums",
"interests": [
"music"
]
}
},
{
"_index": "megacorp",
"_type": "employee",
"_id": "1",
"_score": 0.2876821,
"_source": {
"first_name": "John",
"last_name": "Smith",
"age": 25,
"about": "I love to go rock climbing",
"interests": [
"sports",
"music"
]
}
}
]
},
"aggregations": {
"all_interests": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "music",
"doc_count": 2
},
{
"key": "sports",
"doc_count": 1
}
]
}
}
}
4.3 分級聚合
統計每種興趣下職員的平均年齡：
GET /megacorp/employee/_search
{
"aggs" : {
"all_interests" : {
"terms" : { "field" : "interests" },
"aggs" : {
"avg_age" : {
"avg" : { "field" : "age" }
}
}
}
}
}
------------------------------------------------------
{
"took": 182,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"failed": 0
},
"hits": {
"total": 3,
"max_score": 1,
"hits": [
{
"_index": "megacorp",
"_type": "employee",
"_id": "2",
"_score": 1,
"_source": {
"first_name": "Jane",
"last_name": "Smith",
"age": 32,
"about": "I like to collect rock albums",
"interests": [
"music"
]
}
},
{
"_index": "megacorp",
"_type": "employee",
"_id": "1",
"_score": 1,
"_source": {
"first_name": "John",
"last_name": "Smith",
"age": 25,
"about": "I love to go rock climbing",
"interests": [
"sports",
"music"
]
}
},
{
"_index": "megacorp",
"_type": "employee",
"_id": "3",
"_score": 1,
"_source": {
"first_name": "Douglas",
"last_name": "Fir",
"age": 35,
"about": "I like to build cabinets",
"interests": [
"forestry"
]
}
}
]
},
"aggregations": {
"all_interests": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "music",
"doc_count": 2,
"avg_age": {
"value": 28.5
}
},
{
"key": "forestry",
"doc_count": 1,
"avg_age": {
"value": 35
}
},
{
"key": "sports",
"doc_count": 1,
"avg_age": {
"value": 25
}
}
]
}
}
}
官方文檔：https://www.elastic.co/guide/cn/elasticsearch/guide/current/index.html
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