using  
    指明用那个已经连接的对象
query  
    接收的是查询体语句
exclude
    接收的是不匹配的字段 就像 must_not
    
filter
    接收的是过滤语句 ，过滤的条件意思是在返回结果中有这些条件的信息       

KeyError: "There is no connection with alias ‘qa‘."

from elasticsearch import Elasticsearch
from elasticsearch_dsl import Search

client = Elasticsearch()

s = Search(using=client)

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

s.execute()

for hit in s:

    print(log_time = "%s|%s" % (hit.Ds, hit.Us‘))
    print(hit.beat[‘hostname‘],hit.FileName, hit.FileNum,hit.Messager)

s.to_dict()

from elasticsearch_dsl.query import MultiMatch, Match

# {"multi_match": {"query": "python django", "fields": ["title", "body"]}}
MultiMatch(query=‘python django‘, fields=[‘title‘, ‘body‘])

# {"match": {"title": {"query": "web framework", "type": "phrase"}}}
Match(title={"query": "web framework", "type": "phrase"})

from elasticsearch_dsl import Q
Q("multi_match", query=‘python django‘, fields=[‘title‘, ‘body‘])
Q({"multi_match": {"query": "python django", "fields": ["title", "body"]}})
# 这两种方式最后转换的结果是一致的
MultiMatch(fields=[‘title‘, ‘body‘], query=‘python django‘)

q = Q("multi_match", query=‘python django‘, fields=[‘title‘, ‘body‘])
s = s.query(q)

s = s.query("multi_match", query=‘python django‘, fields=[‘title‘, ‘body‘])

Q("match", title=‘python‘) | Q("match", title=‘django‘)
# {"bool": {"should": [...]}}
# 匹配到任意条件即可

Q("match", title=‘python‘) & Q("match", title=‘django‘)
# {"bool": {"must": [...]}}
# 列表里的条件必须同时匹配

~Q("match", title="python")
# {"bool": {"must_not": [...]}}
# 非

In [193]: sa = Search().query().query(‘match‘,title=‘python‘).query(‘match‘,body=‘django‘)

In [194]: sa.to_dict()
Out[194]:
{
    "query": {
        "bool": {
            "must": [
                {"match": {
                    "title": "python"}
                },
                {"match": {
                    "body": "django"}
                }
            ]
        }
    }
}

q = Q(‘bool‘,
    must=[Q(‘match‘, title=‘python‘)],
    should=[Q(...), Q(...)],
    minimum_should_match=1
)
s = Search().query(q)

s = Search()
s = s.filter(‘terms‘, tags=[‘search‘, ‘python‘])
# {‘query‘: {‘bool‘: {‘filter‘: [{‘terms‘: {‘tags‘: [‘search‘, ‘python‘]}}]}}}

s = Search()
s = s.query(‘bool‘, filter=[Q(‘terms‘, tags=[‘search‘, ‘python‘])])
# {‘query‘: {‘bool‘: {‘filter‘: [{‘terms‘: {‘tags‘: [‘search‘, ‘python‘]}}]}}}

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

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

A(‘terms‘, field=‘tags‘)
# {"terms": {"field": "tags"}}

a = A(‘terms‘, field=‘category‘)
# {‘terms‘: {‘field‘: ‘category‘}}

a.metric(‘clicks_per_category‘, ‘sum‘, field=‘clicks‘)    .bucket(‘tags_per_category‘, ‘terms‘, field=‘tags‘)
# {
#   ‘terms‘: {‘field‘: ‘category‘},
#   ‘aggs‘: {
#     ‘clicks_per_category‘: {‘sum‘: {‘field‘: ‘clicks‘}},
#     ‘tags_per_category‘: {‘terms‘: {‘field‘: ‘tags‘}}
#   }
# }

s = Search()
a = A(‘terms‘, field=‘category‘)
s.aggs.bucket(‘category_terms‘, a)
# {
#   ‘aggs‘: {
#     ‘category_terms‘: {
#       ‘terms‘: {
#         ‘field‘: ‘category‘
#       }
#     }
#   }
# }

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" } }
#       }
#     }
#   }
# }

s = Search()

s.aggs.bucket(‘per_category‘, ‘terms‘, field=‘category‘)
s.aggs[‘per_category‘].metric(‘clicks_per_category‘, ‘sum‘, field=‘clicks‘)
s.aggs[‘per_category‘].bucket(‘tags_per_category‘, ‘terms‘, field=‘tags‘)

s = Search().sort(
    ‘category‘,
    ‘-title‘,
    {"lines" : {"order" : "asc", "mode" : "avg"}}
)

s = s.sort()

s = s[10:20]
# {"from": 10, "size": 10}

for hit in s.scan():
    print(hit.title)

s = s.highlight_options(order=‘score‘)

s = s.highlight(‘title‘)
# or, including parameters:
s = s.highlight(‘title‘, fragment_size=50)

response = s.execute()
for hit in response:
    for fragment in hit.meta.highlight.title:
        print(fragment)

s = s.suggest(‘my_suggestion‘, ‘pyhton‘, term={‘field‘: ‘title‘})

s = s.suggest(‘my_suggestion‘, ‘pyhton‘, term={‘field‘: ‘title‘})
suggestions = s.execute_suggest()

print(suggestions.my_suggestion)

s = s.extra(explain=True)

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

＃只返回选定的字段
s = s.source([‘title‘, ‘body‘])

＃不返回任何字段，只是元数据
s = s.source(False)

＃明确包含/排除字段
s = s.source(include=["title"], exclude=["user.*"])

＃重置字段选择
s = s.source(None)

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

In [2]: s = Search()

In [4]: s.to_dict()
Out[4]: {‘query‘: {‘match_all‘: {}}}

In [5]: s.update_from_dict({"query": {"match": {"title": "python"}}, "size": 42}
   ...: )
Out[5]: <elasticsearch_dsl.search.Search at 0x10a7c8550>

In [6]: s.to_dict()
Out[6]: {‘query‘: {‘match‘: {‘title‘: ‘python‘}}, ‘size‘: 42}

response = s.execute()

print(response.success())
# 是否成功
# True

print(response.took)
# 命中数
# 12

print(response.hits.total)

print(response.suggest.my_suggestions)

response = s.execute()
print(‘Total %d hits found.‘ % response.hits.total)
for h in response:
    print(h.title, h.body)

response = s.execute()
h = response.hits[0]
print(‘/%s/%s/%s returned with score %f‘ % (
    h.meta.index, h.meta.doc_type, h.meta.id, h.meta.score))

for tag in response.aggregations.per_tag.buckets:
    print(tag.key, tag.max_lines.value)

from elasticsearch_dsl import MultiSearch, Search

ms = MultiSearch(index=‘blogs‘)

ms = ms.add(Search().filter(‘term‘, tags=‘python‘))
ms = ms.add(Search().filter(‘term‘, tags=‘elasticsearch‘))

responses = ms.execute()

for response in responses:
    print("Results for query %r." % response.search.query)
    for hit in response:
        print(hit.title)

from elasticsearch_dsl import Keyword, Mapping, Nested, Text

# name your type
m = Mapping(‘my-type‘)

# add fields
m.field(‘title‘, ‘text‘)

# you can use multi-fields easily
m.field(‘category‘, ‘text‘, fields={‘raw‘: Keyword()})

# you can also create a field manually
comment = Nested()
comment.field(‘author‘, Text())
comment.field(‘created_at‘, Date())

# and attach it to the mapping
m.field(‘comments‘, comment)

# you can also define mappings for the meta fields
m.meta(‘_all‘, enabled=False)

# save the mapping into index ‘my-index‘
m.save(‘my-index‘)

# get the mapping from our production cluster
m = Mapping.from_es(‘my-index‘, ‘my-type‘, using=‘prod‘)

# update based on data in QA cluster
m.update_from_es(‘my-index‘, using=‘qa‘)

# update the mapping on production
# 在生产上更新映射
m.save(‘my-index‘, using=‘prod‘)

from elasticsearch_dsl import analyzer, tokenizer

my_analyzer = analyzer(‘my_analyzer‘,
    tokenizer=tokenizer(‘trigram‘, ‘nGram‘, min_gram=3, max_gram=3),
    filter=[‘lowercase‘]
)

from datetime import datetime
from elasticsearch_dsl import DocType, Date, Nested, Boolean,     analyzer, InnerObjectWrapper, Completion, Keyword, Text

html_strip = analyzer(‘html_strip‘,
    tokenizer="standard",
    filter=["standard", "lowercase", "stop", "snowball"],
    char_filter=["html_strip"]
)

class Comment(InnerObjectWrapper):
    def age(self):
        return datetime.now() - self.created_at

class Post(DocType):
    title = Text()
    title_suggest = Completion()
    created_at = Date()
    published = Boolean()
    category = Text(
        analyzer=html_strip,
        fields={‘raw‘: Keyword()}
    )

    comments = Nested(
        doc_class=Comment,
        properties={
            ‘author‘: Text(fields={‘raw‘: Keyword()}),
            ‘content‘: Text(analyzer=‘snowball‘),
            ‘created_at‘: Date()
        }
    )

    class Meta:
        index = ‘blog‘

    def add_comment(self, author, content):
        self.comments.append(
          {‘author‘: author, ‘content‘: content})

    def save(self, ** kwargs):
        self.created_at = datetime.now()
        return super().save(** kwargs)

# create the mappings in Elasticsearch
Post.init()

# instantiate the document
first = Post(title=‘My First Blog Post, yay!‘, published=True)
# assign some field values, can be values or lists of values
first.category = [‘everything‘, ‘nothing‘]
# every document has an id in meta
first.meta.id = 47


# save the document into the cluster
first.save()

post = Post(meta={‘id‘: 42})

# prints 42, same as post._id
print(post.meta.id)

# override default index, same as post._index
post.meta.index = ‘my-blog‘

# retrieve the document
first = Post.get(id=42)
# now we can call methods, change fields, ...
first.add_comment(‘me‘, ‘This is nice!‘)
# and save the changes into the cluster again
first.save()

# you can also(也) update just individual fields which will call the update API
# and also(并且) update the document in place（首先）
first.update(published=True, published_by=‘me‘)

p = Post.get(id=‘not-in-es‘, ignore=404)
p is None

posts = Post.mget([42, 47, 256])

raise_on_error
    如果True（默认），那么任何错误都会引发异常。否则，包含错误的所有文档将被视为丢失。
missing
    可以有三个可能的值：（‘none‘默认）‘raise‘和 ‘skip‘。如果文档丢失或出错，将被替换为None，将引发异常或文档将完全跳过。

# name of the type and index in elasticsearch
Post._doc_type.name
Post._doc_type.index

# the raw Mapping object
# 原始映射对象
Post._doc_type.mapping

# the optional name of the parent type (if defined)
# 父类型的可选名称（如果已定义）
Post._doc_type.parent

Post._doc_type.refresh()

first = Post.get(id=42)
first.delete()

# by calling .search we get back a standard Search object
# 通过调用 .search()， 我们得到一个标准的搜索对象
s = Post.search()
# the search is already limited to the index and doc_type of our document
s = s.filter(‘term‘, published=True).query(‘match‘, title=‘first‘)


results = s.execute()

# when you execute the search the results are wrapped in your document class (Post)
for post in results:
    print(post.meta.score, post.title)

s = Search()
s = s.doc_type(Post)

s = Post.search()
s = s.suggest(‘title_suggestions‘, ‘pyth‘, completion={‘field‘: ‘title_suggest‘})

# you can even execute just the suggestions via the _suggest API
suggestions = s.execute_suggest()

for result in suggestions.title_suggestions:
    print(‘Suggestions for %s:‘ % result.text)
    for option in result.options:
        print(‘  %s (%r)‘ % (option.text, option.payload))

class Post(DocType):
    title = Text()

    class Meta:
        all = MetaField(enabled=False)
        parent = MetaField(type=‘blog‘)
        dynamic = MetaField(‘strict‘)

from elasticsearch_dsl import Index, DocType, Text, analyzer

blogs = Index(‘blogs‘)

# define custom settings
blogs.settings(
    number_of_shards=1,
    number_of_replicas=0
)

# define aliases
blogs.aliases(
    old_blogs={}
)

# register a doc_type with the index
blogs.doc_type(Post)

# can also be used as class decorator when defining the DocType
@blogs.doc_type
class Post(DocType):
    title = Text()

# You can attach custom analyzers to the index

html_strip = analyzer(‘html_strip‘,
    tokenizer="standard",
    filter=["standard", "lowercase", "stop", "snowball"],
    char_filter=["html_strip"]
)

blogs.analyzer(html_strip)

# delete the index, ignore if it doesn‘t exist
blogs.delete(ignore=404)

# create the index in elasticsearch
blogs.create()

blogs = Index(‘blogs‘, using=‘production‘)
blogs.settings(number_of_shards=2)
blogs.doc_type(Post)

# create a copy of the index with different name
company_blogs = blogs.clone(‘company-blogs‘)

# create a different copy on different cluster
dev_blogs = blogs.clone(‘blogs‘, using=‘dev‘)
# and change its settings
dev_blogs.setting(number_of_shards=1)