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})