Python教程

Python Elasticsearch DSL 搜索

本文主要是介绍Python Elasticsearch DSL 搜索,对大家解决编程问题具有一定的参考价值,需要的程序猿们随着小编来一起学习吧!

  使用ElasticSearch DSL进行搜索

  Search主要包括:

  查询(queries)过滤器(filters)聚合(aggreations)排序(sort)分页(pagination)额外的参数(additional parameters)相关性(associated)

  创建一个查询对象

  from elasticsearch import Elasticsearch

  from elasticsearch_dsl import Search

  client=Elasticsearch()

  s=Search(using=client)

  初始化测试数据

  # 创建一个查询语句s=Search().using(client).query("match", title="python")# 查看查询语句对应的字典结构print(s.to_dict())# {'query': {'match': {'title': 'python'}}}# 发送查询请求到Elasticsearchresponse=s.execute()# 打印查询结果for hit in s: print(hit.title)# Out:Python is good!Python very quickly# 删除查询s.delete()

  第一个查询语句

  # 创建一个查询语句

  s=Search().using(client).query("match", title="python")

  # 查看查询语句对应的字典结构

  print(s.to_dict())

  # {'query': {'match': {'title': 'python'}}}

  # 发送查询请求到Elasticsearch

  response=s.execute()

  # 打印查询结果

  for hit in s:

  print(hit.title)

  # Out:

  Python is good!

  Python very quickly

  # 删除查询

  s.delete()

  1、Queries

  # 创建一个多字段查询

  multi_match=MultiMatch(query='python', fields=['title', 'body'])

  s=Search().query(multi_match)

  print(s.to_dict())

  # {'query': {'multi_match': {'fields': ['title', 'body'], 'query': 'python'}}}

  # 使用Q语句

  q=Q("multi_match", query='python', fields=['title', 'body'])

  # 或者

  q=Q({"multi_match": {"query": "python", "fields": ["title", "body"]}})

  s=Search().query(q)

  print(s.to_dict())

  # If you already have a query object, or a dict

  # representing one, you can just override the query used

  # in the Search object:

  s.query=Q('bool', must=[Q('match', title='python'), Q('match', body='best')])

  print(s.to_dict())

  # 查询组合

  q=Q("match", title='python') | Q("match", title='django')

  s=Search().query(q)

  print(s.to_dict())

  # {"bool": {"should": [...]}}

  q=Q("match", title='python') & Q("match", title='django')

  s=Search().query(q)

  print(s.to_dict())

  # {"bool": {"must": [...]}}

  q=~Q("match", title="python")

  s=Search().query(q)

  print(s.to_dict())

  # {"bool": {"must_not": [...]}}2、Filters

  s=Search()

  s=s.filter('terms', tags=['search', 'python'])

  print(s.to_dict())

  # {'query': {'bool': {'filter': [{'terms': {'tags': ['search', 'python']}}]}}}

  s=s.query('bool', filter=[Q('terms', tags=['search', 'python'])])

  print(s.to_dict())

  # {'query': {'bool': {'filter': [{'terms': {'tags': ['search', 'python']}}]}}}

  s=s.exclude('terms', tags=['search', 'python'])

  # 或者

  s=s.query('bool', filter=[~Q('terms', tags=['search', 'python'])])

  print(s.to_dict())

  # {'query': {'bool': {'filter': [{'bool': {'must_not': [{'terms': {'tags': ['search', 'python']}}]}}]}}}3、Aggregations

  s=Search()

  a=A('terms', filed='title')

  s.aggs.bucket('title_terms', a)

  print(s.to_dict())

  # {

  # 'query': {

  # 'match_all': {}

  # },

  # 'aggs': {

  # 'title_terms': {

  # 'terms': {'filed': 'title'}

  # }

  # }

  # }

  # 或者

  s=Search()

  s.aggs.bucket('articles_per_day', 'date_histogram', field='publish_date', interval='day') \

  .metric('clicks_per_day', 'sum', field='clicks') \

  .pipeline('moving_click_average', 'moving_avg', buckets_path='clicks_per_day') \

  .bucket('tags_per_day', 'terms', field='tags')

  s.to_dict()

  # {

  # "aggs": {

  # "articles_per_day": {

  # "date_histogram": { "interval": "day", "field": "publish_date" },

  # "aggs": {

  # "clicks_per_day": { "sum": { "field": "clicks" } },

  # "moving_click_average": { "moving_avg": { "buckets_path": "clicks_per_day" } },

  # "tags_per_day": { "terms": { "field": "tags" } }

  # }

  # }

  # }

  # }4、Sorting

  s=Search().sort(

  'category',

  '-title',

  {"lines" : {"order" : "asc", "mode" : "avg"}}

  )5、Pagination

  s=s[10:20]

  # {"from": 10, "size": 10}6、Extra Properties and parameters

  s=Search()

  # 设置扩展属性使用`.extra()`方法

  s=s.extra(explain=True)

  # 设置参数使用`.params()`

  s=s.params(search_type="count")

  # 如要要限制返回字段,可以使用`source()`方法

  # only return the selected fields

  s=s.source(['title', 'body'])

  # don't return any fields, just the metadata

  s=s.source(False)

  # explicitly include/exclude fields

  s=s.source(include=["title"], exclude=["user.*"])

  # reset the field selection

  s=s.source(None)

  # 使用dict序列化一个查询

  s=Search.from_dict({"query": {"match": {"title": "python"}}})

  # 修改已经存在的查询

  s.update_from_dict({"query": {"match": {"title": "python"}}, "size": 42})

这篇关于Python Elasticsearch DSL 搜索的文章就介绍到这儿,希望我们推荐的文章对大家有所帮助,也希望大家多多支持为之网!