一、Elasticsearch的基本概念
- Index:Elasticsearch用来存储数据的逻辑区域,它类似于关系型数据库中的database 概念。一个index可以在一个或者
版权声明:本文遵循 CC 4.0 BY-SA 版权协议,若要转载请务必附上原文出处链接及本声明,谢谢合作! 多个shard上面,同时一个shard也可能会有多个replicas。 - Document:Elastic
版权声明:本文遵循 CC 4.0 BY-SA 版权协议,若要转载请务必附上原文出处链接及本声明,谢谢合作! search里面存储的实体数据,类似于关系数据版权声明:本文遵循 CC 4.0 BY-SA 版权协议,若要转载请务必附上原文出处链接及本声明,谢谢合作! 中一个table里面的一行数据。 document由多个field组成,不同的document里面同名的field一定具有相同的类型。document里面field可以重复出现,也就是一个field会有多个值,即multivalued。 - Document type:为了查询需要,一个index可能会有多种document,也就是document type. 它类似于关系型数据库中的 table 概念。但需要注意,不同document里面同名的field一定要是相同类型的。
- Mapping:它类似于关系型数据库中的 schema 定义概念。存储field的相关映射信息,不同document type会有不同的mapping。
下图是ElasticSearch和关系型数据库的一些术语比较:
Relationnal database | Elasticsearch |
---|---|
Database | Index |
Table | Type |
Row | Document |
Column | Field |
Schema | Mapping |
Index | Everything is indexed |
SQL | Query DSL |
SELECT * FROM table… | GET http://… |
UPDATE table SET | PUT http://… |
二、Elasticsearch DSL 简介
1、Install
$ pip install elasticsearch-dsl
2、Create Index and Document
from datetime import datetime
from elasticsearch_dsl import DocType, Date, Integer, Keyword, Text
from elasticsearch_dsl.connections import connections
# Define a default Elasticsearch client
connections.create_connection(hosts=['localhost'])
class Article(DocType):
title = Text(analyzer='snowball', fields={'raw': Keyword()})
body = Text(analyzer='snowball')
tags = Keyword()
published_from = Date()
lines = Integer()
class Meta:
index = 'blog'
def save(self, ** kwargs):
self.lines = len(self.body.split())
return super(Article, self).save(** kwargs)
def is_published(self):
return datetime.now() >= self.published_from
# create the mappings in elasticsearch
Article.init()
创建了一个索引为blog,文档为article的Elasticsearch数据库和表。
必须执行Article.init()
方法。 这样Elasticsearch才会根据你的DocType产生对应的Mapping。否则Elasticsearch就会在你第一次创建Index
现在我们可以通过Elasticsearch Restful API来检查
http GET http://127.0.0.1:9200/blog/_mapping/
{"blog":
{"mappings":
{"article":
{"properties":{
"body":{"type":"text","analyzer":"snowball"},
"lines":{"type":"integer"},
"published_from":{"type":"date"},
"tags":{"type":"keyword"},
"title":{"type":"text","fields":{"raw":{"type":"keyword"}},"analyzer":"snowball"}
}
}}
}
}
三、Elasticsearch CRUD 操作
1、Create an article
# create and save and article
article = Article(meta={'id': 1}, title='Hello elasticsearch!', tags=['elasticsearch'])
article.body = ''' looong text '''
article.published_from = datetime.now()
article.save()
=>Restful API
http POST http://127.0.0.1:9200/blog/article/1 title="hello elasticsearch" tags:='["elasticsearch"]'
HTTP/1.1 201 Created
Content-Length: 73
Content-Type: application/json; charset=UTF-8
{
"_id": "1",
"_index": "blog",
"_type": "article",
"_version": 1,
"created": true
}
2、Get a article
article = Article.get(id=1)
# 如果获取一个不存在的文章则返回None
a = Article.get(id='no-in-es')
a is None
# 还可以获取多个文章
articles = Article.mget([1, 2, 3])
=>Restful API
http GET http://127.0.0.1:9200/blog/article/1
HTTP/1.1 200 OK
Content-Length: 141
Content-Type: application/json; charset=UTF-8
{
"_id": "1",
"_index": "blog",
"_source": {
"tags": [
"elasticsearch"
],
"title": "hello elasticsearch"
},
"_type": "article",
"_version": 1,
"found": true
}
3、Update a article
article = Article.get(id=1)
article.tags = ['elasticsearch', 'hello']
article.save()
# 或者
article.update(body='Today is good day!', published_by='me')
=>Restful API
http PUT http://127.0.0.1:9200/blog/article/1 title="hello elasticsearch" tags:='["elasticsearch", "hello"]'
HTTP/1.1 200 OK
Content-Length: 74
Content-Type: application/json; charset=UTF-8
{
"_id": "1",
"_index": "blog",
"_type": "article",
"_version": 2,
"created": false
}
4、Delete a article
article = Article.get(id=1)
article.delete()
=> Restful API
http DELETE http://127.0.0.1:9200/blog/article/1
HTTP/1.1 200 OK
Content-Length: 71
Content-Type: application/json; charset=UTF-8
{
"_id": "1",
"_index": "blog",
"_type": "article",
"_version": 4,
"found": true
}
http HEAD http://127.0.0.1:9200/blog/article/1
HTTP/1.1 404 Not Found
Content-Length: 0
Content-Type: text/plain; charset=UTF-8
四、ElasticSearch DSL 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
def add_article(id_, title, body, tags):
article = Article(meta={'id': id_}, title=title, tags=tags)
article.body = body
article.published_from = datetime.now()
article.save()
def init_test_data():
add_article(2, 'Python is good!', 'Python is good!', ['python'])
add_article(3, 'Elasticsearch', 'Distributed, open source search and analytics engine', ['elasticsearch'])
add_article(4, 'Python very quickly', 'Python very quickly', ['python'])
add_article(5, 'Django', 'Python Web framework', ['python', 'django'])
# 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})