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1 es sort # 1 sort GET jeff/doc/_search {"query": {"match": {"from": "gu"}} "sort": [{"age": {"order": "desc"}]} # Ascending GET jeff/doc/_search {"query": {"match": {"from": "gu"}} "sort": [{"age": {"order": "asc"}]} # not all types support sorting (only numeric types are allowed to sort) GET jeff/doc/_search {"query": {"match": {"from": "gu"}} "sort": [{"name": {"order": "asc"}}]} 2 the difference between match and match # the difference between match and match_all? Mach means to query. Look it up according to the field. Match_all checks all GET jeff/doc/_search {"query": {"match_all": {} 3 pagination query GET jeff/doc/_search {"query": {"match_all": {}}, "sort": [{"age": {"order": "desc"}], "from": 2 "size": 1} # "from": 2 The representative starts with the second item and takes a "size": "with this query, how do you page it? There are 10 pieces of data on one page: "from": 0, "size": 10. The second page: "from": 10, "size": 10. The third page: "from": 20, "size": 104 es combination query # more than conditions, and, or, not# pairs to es is Boolean query, must,should,must_not Filtermust-and should-ormust_not-notfilter-- filter # 1 combined query must# query form gu and age=30 data GET gyy/doc/_search {"query": {"bool": {"must": [{"match": {"from": "gu"}} {"match": {"age": "30"} # query form gu data () GET gyy/doc/_search {"query": {"bool": {"must": [{"match": {"from": "gu"} # Ibid. GET gyy/doc/_search {"query": {"match": {"from": "gu"} # 2 combined query should Or the condition GET gyy/doc/_search {"query": {"bool": {"should": [{"match": {"from": "gu"}} {"match": {"tags": "closed month"} # 3 the must_not of the combined query is anti-GET gyy/doc/_search {"query": {"bool": {"must_not": [{"match": { "from": "gu"}} {"match": {"tags": "cute"}}, {"match": {"age": 18} # `filter` conditional filter query The range of filter conditions is indicated by `range`, and `gt` is greater than # gt: greater than lt: less than get: greater than or equal to let: less than or equal to GET gyy/doc/_search {"query": {"bool": {"must": [{"match": {"from": "gu"}}] "filter": {"range": {"age": {"gt": 25} # query all data under the age of 18 GET gyy/doc/_search {"query": {"bool": {"filter": {"range": { "age": {"lte": 18} 5 result filter display suffix # filter the result Similar to the following select * from user Select name,age from user # query GET gyy/doc/_search {"query": {"match": {"name": "Gu Laoer"}}, "_ source": ["name", "age"]} 6 results highlight # 3 results highlight (default) GET gyy/doc/_search {"query": {"match": {"name": "Stone"}} "highlight": {"fields": {"name": {} # Custom highlighted style GET gyy/chengyuan/_search {"query": {"match": {"from": "gu"}}, "highlight": {"pre_tags": "," post_tags ":" "fields": {"from": {}

Summary:

Mixed development, you know how to handle it.

How do you deal with the separation of the front and rear ends?

The string is returned directly in josn format, and the front end renders itself.

The most commonly used is match+ Boolean + highlight + paging.

7 aggregate query avg, max, min, sum, grouping # aggregate query # 1 aggregate query avgselect max (age) as my_avg from user GET gyy/doc/_search {"query": {"match": {"from": "gu"}}, "aggs": {"my_avg": {"avg": {"field": "age"}, "_ source": ["name", "age"]} # 2 max,size=0 of aggregate query does not fetch data As long as the result of max GET gyy/doc/_search {"query": {"match": {"from": "gu"}}, "aggs": {"my_max": {"max": {"field": "age"} "size": 0} # 3 aggregated minGET gyy/doc/_search {"query": {"match": {"from": "gu"}, "aggs": {"my_min": {"min": {"field": "age"} "size": 0} # 4 aggregate query sumGET gyy/doc/_search {"query": {"match": {"from": "gu"}, "aggs": {"my_sum": {"sum": {"field": "age"}}, "size": 0} # 5 aggregate GET gyy/doc/_search {"size": 0 "query": {"match_all": {}}, "aggs": {"age_group": {"range": {"field": "age", "ranges": [{"from": 15, "to": 20}, {"from": 20} "to": 25}, {"from": 25 "to": 30}]}} 8 mapping and _ template template GET _ template/user_instagram # View template PUT _ template/user_instagram # modify template {and field information data} GET user_instagram/_mapping # view index information PUT user_instagram/_mapping # modify index information {and field information data}

If there is a name field in the template, it will automatically match in the index when it is saved.

If there is no age field in the template, the index cannot find the field and cannot save it.

You need to add fields to the template now, and then add fields to the index. After the index is generated, you need to add fields manually, which will not be generated automatically.

# View index information-"mapping dictionary -" mapping (type, table type, table structure) GET user_instagram/_mapping# 6.x an index can only have one mapping type (only one table) # create a mapping # create an index And set the mapping PUT _ template/user_instagram {"order": 1, "index_patterns": ["user_instagram-v1_0"], "settings": {"index": {"default_pipeline": "auto_timestamp_pipeline" "mapping": {"total_fields": {"limit": "10000"}, "refresh_interval": "600s", "number_of_shards": "8", "number_of_replicas": "0", "max_inner_result_window": "50000"}} "mappings": {"_ meta": {"software_version_mapping": "1.0"}," dynamic ":" strict "," properties ": {" is_private ": {" type ":" boolean "} "full_name": {"type": "text"}, "create_time": {"type": "date"}, "avatar_url": {"type": "text"}, "user_id": {"eager_global_ordinals": true "type": "keyword"}, "follower_num": {"type": "integer"}, "following_num": {"type": "integer"}, "post_count": {"type": "integer"} "nickname": {"type": "text", "fields": {"keyword": {"ignore_above": 256, "type": "keyword"}}, "doc_values": false} "requested_by_viewer": {"type": "boolean"}, "is_verified": {"type": "boolean"}, "followed_by_viewer": {"type": "boolean"} "aliases": {"user_instagram": {} # insert test data PUT books/_doc/1 {"title": "Big head son thief father", "price": 100," addr ":" Beijing Tiananmen Square "," company ": {" name ":" I love Beijing Tiananmen Square "," company_addr ":" my home is in the Northeast Songhua River silly girl " "employee_count": 10}, "publish_date": "2019-08-19"} PUT books/_doc/2 {"title": "Snow White and the Ten Dwarfs", "price": "99", "addr": "Dark Senli", "company": {"name": "my hometown is in Shanghai", "company_addr": "Friends walk together for Life", "employee_count": 10} "publish_date": "2018-05-19"} PUT books/_doc/3 {"title": "Snow White and the Ten Dwarfs", "price": "99", "addr": "Dark Senli", "age": 18} # View Mapping GET booksGET books/_mapping

What is the mapping? What's the use of mapping? Specifies the table structure (not mandatory), which field can be used for full-text retrieval, whether it is a numeric type, or a Boolean type

Once the mapping type is determined, it cannot be modified later, but fields can be inserted

9 ik participle # full-text search, with mapping, determines that I can do full-text search on a field # es default participle is friendly to English, using Chinese participle (plug-in for es), ik (author, Chinese) Elasticsearch Open Source Community Manager) # is a plug-in for es (how es installs plug-ins) # first: command line (built-in plug-in) bin/elasticsearch-plugin install analysis-smartcn installs Chinese word splitter # second: url installation (third-party plug-in) bin/elasticsearch-plugin install https://github.com/medcl/elasticsearch-analysis-ik/releases/download/v7.5.0/elasticsearch-analysis-ik-7.5 .0.zip # third: manual installation (recommended) # download Unzip it to the plugins path of es and restart es # Note: ik Segmentation and es version must correspond to # two word segmentation methods # ik_smart: participle # ik_max_word: participle more # ik_ smartphones, large granularity GET _ analyze {"analyzer": "ik_smart", "text": "Shanghai Tap Water comes from the Sea"} # ik_ smartphones have many words Small granularity GET _ analyze {"analyzer": "ik_max_word" "text": "Shanghai Tap Water comes from the Sea"} # configure your actions after # when creating the mapping: # article title: ik_max_word# article content: ik_smart# Abstract # author # difference between creation time 10 term and match # match: let's go out today to play-"participle--" search by participle # term: let's go out today to play-- " Directly take [let's go out to play today]-> query in the index # can't find the content Directly take the Python crawler to check, because there is no index, so can not find GET books/_search {"query": {"term": {"title": "Python crawler"}} # can be found, and all with python found out # Python crawler divided words, respectively take these two words to look up, with the python keyword With crawler keywords can find GET books/_search {"query": {"match": {"title": "Python crawler"}} above is about the "Elasticsearch aggregation query and sorting methods" of this article, I believe we all have a certain understanding, I hope the editor to share the content to help you, if you want to learn more about the relevant knowledge, please follow the industry information channel.

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