django+django-haystack+Whoosh(后期切换引擎为Elasticsearch+ik)+Jieba+mysql

2023-09-14 11:37

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1.前提准备

环境介绍

haystack是django的开源搜索框架，该框架支持Solr, Elasticsearch, Whoosh, *Xapian*搜索引擎，不用更改代码，直接切换引擎，减少代码量。
搜索引擎使用Whoosh，这是一个由纯Python实现的全文搜索引擎，没有二进制文件等，比较小巧，配置比较简单，当然性能自然略低。whoosh和xapian的性能差距还是比较明显。索引和搜索的速度有近4倍的差距，在full cache情况下的性能差距更是达到了60倍。
中文分词+，由于Whoosh自带的是英文分词，对中文的分词支持不是太好，故用jieba替换whoosh的分词组件。
Elasticsearch：开源的搜索引擎，本文版本为7.6.0
其他：Python3.6.5, Django2.2

安装环境

pip3 install django==2.2 -i https://pypi.douban.com/simplepip3 install whoosh  -i https://pypi.douban.com/simplepip3 install django-haystack -i https://pypi.douban.com/simplepip3 install jieba -i https://pypi.douban.com/simplepip3 install pymysql -i https://pypi.douban.com/simplepip3 install elasticsearch==7.6.0 -i https://pypi.douban.com/simple/

项目结构

 - Project   - Project     - settings.py   - blog     - models.py

表结构

models.py

from django.db import modelsclass UserInfo(models.Model):    username = models.CharField(verbose_name='用户名', max_length=225)    def __str__(self):        return self.usernameclass Tag(models.Model):    name = models.CharField(verbose_name='标签名称', max_length=225)    def __str__(self):        return self.nameclass Article(models.Model):    title = models.CharField(verbose_name='标题', max_length=225)    content = models.CharField(verbose_name='内容', max_length=225)    # 外键    username = models.ForeignKey(verbose_name='用户', to='UserInfo', on_delete=models.DO_NOTHING)    tag = models.ForeignKey(verbose_name='标签', to='Tag', on_delete=models.DO_NOTHING)    def __str__(self):        return self.title

图解

本文优势

集全网的django+django-haystack+Whoosh的总结，取其精华，去其糟粕，加入了新的注解。

如果你想你的es或者Whoosh集成到django上，那你来对地方了

django+django-haystack+Whoosh+Jieba+mysql

1. setting.py配置

# 数据库配置DATABASES = {    'default': {        'ENGINE': 'django.db.backends.mysql',        'NAME': 'dj_ha',        'USER': 'root',        'PASSWORD': 'foobared',        'HOST': '106.14.42.253',        'PORT': '11111',    }}# appINSTALLED_APPS = [           'haystack', ]# 本教程使用的是Whoosh，故配置如下HAYSTACK_CONNECTIONS = {    'default': {        'ENGINE': 'haystack.backends.whoosh_backend.WhooshEngine',        'PATH': os.path.join(os.path.dirname(__file__), 'whoosh_index'),    },}# 自动更新索引HAYSTACK_SIGNAL_PROCESSOR = 'haystack.signals.RealtimeSignalProcessor'# 设置每页显示的数目，默认为20，可以自己修改HAYSTACK_SEARCH_RESULTS_PER_PAGE = 8

2. 为表模型创建索引，search_indexes.py

如果你想针对某个app，例如blog做全文检索，则必须在blog的目录下面，建立search_indexes.py文件，文件名不能修改，必须叫search_indexes.py

from haystack import indexesfrom .models import Article# ArticleIndex：固定写法 表名Indexclass ArticleIndex(indexes.SearchIndex, indexes.Indexable):    # 固定写法  document=True：haystack和搜索引擎，将给text字段分词,建立索引,使用此字段的内容作为索引进行检索    # use_template=True,使用自己的模板,与document=True进行搭配，自定义检索字段模板(允许谁可以被全文检索,就是谁被建立索引)    text = indexes.CharField(document=True, use_template=True)    # 以下字段作为辅助数据,便于调用,最后也不知道怎么辅助,我注释了,也不影响搜索    # title：写入引擎的字段名,model_attr='title'：相对应的表模型字段名，    title = indexes.CharField(model_attr='title')    content = indexes.CharField(model_attr='content')    username = indexes.CharField(model_attr='username')    tag = indexes.CharField(model_attr='tag')    def get_model(self):        # 需要建立索引的模型        return Article    def index_queryset(self, using=None):        """Used when the entire index for model is updated."""        # 写入引擎的数据,必须返回queryset类型        return self.get_model().objects.all()

3. 创建被检索的模板(允许谁可以全文检索)

这个数据模板的作用是对Article.title, Article.content,Article.username.username

这三个字段建立索引，当检索的时候会对这三个字段的内容，做全文检索匹配。

数据模板的路径为yourapp/templates/search/indexes/yourapp/note_text.txt，

例如本例子为blog/templates/search/indexes/blog/article_text.txt 文件名必须为要索引的小写模型类名_text.txt

https://blog.csdn.net/qq_52385631/article/details/{{ object.title }}https://blog.csdn.net/qq_52385631/article/details/{{ object.content }}https://blog.csdn.net/qq_52385631/article/details/{{ object.username.username }}

4. 路由

urls.py配置(用内置的视图，后期可以自定义，本文也有介绍)

# urls.pyfrom django.contrib import adminfrom django.urls import path, include, re_pathurlpatterns = [    path('admin/', admin.site.urls),    # 配置的搜索路由，路由可以自定义，include('haystack.urls')固定    re_path(r'^search/', include('haystack.urls')),]

haystack.urls的内容(内置的，只是我拉出来，让你看一下，不需要进行修改)

from django.urls import pathfrom haystack.views import SearchViewurlpatterns = [path("", SearchView(), name="haystack_search")]

5. search.html

SearchView()视图函数默认使用的html模板为当前app目录下，

路径为app名称，/templates/search/search.html

所以需要在blog/templates/search/下添加search.html文件，内容为

search.html(原生)

Search
{% load highlight %}            https://blog.csdn.net/qq_52385631/article/details/{{ form.as_table }}        {#        https://blog.csdn.net/qq_52385631/article/details/{{ form.title.label }}#}                                                                            
    
    {% if query %}        返回结果
        {% for result in page.object_list %}                                                    {#                https://blog.csdn.net/qq_52385631/article/details/{{ result.object.title }}#}                {% highlight result.object.title with query %}            
                            {% highlight result.object.content with query %}                {#                https://blog.csdn.net/qq_52385631/article/details/{{ result.object.content }}#}                    {% empty %}            没有查询到结果！！！
        {% endfor %}                {% if page.has_previous or page.has_next %}                            {% if page.has_previous %}{% endif %}«                Previous{% if page.has_previous %}{% endif %}                |                {% if page.has_next %}{% endif %}Next »                {% if page.has_next %}{% endif %}            
        {% endif %}    {% else %}        {# Show some example queries to run, maybe query syntax, something else? #}    {% endif %}

后端返回数据介绍

# print(context)        """        {        'query': '刘',        'form': ,        'page': ,        'paginator': ,        'suggestion': None}        """# print(context.get('page').__dict__)        """        {        'object_list':             [            ,             ,                     ],         'number': 1,         'paginator':         }        """

前端返回数据介绍

{% load highlight %}：高亮加载 内置的会省略搜到的内容，之前的内容{% load my_filters_and_tags %}：自定义高亮form.as_table：生成表格,里边会自动成成input标签query：查询的参数page.object_list：返回的查询一页数据result：数据对象集result.object：当前查询的数据对象page.has_previous or page.has_next：分页

6. 高亮配置

# 7.高亮加载            # 1.使用默认值{% highlight result.summary with query %}# 案例{% highlight result.object.title with query %}            # 2.这里我们为 https://blog.csdn.net/qq_52385631/article/details/{{ result.summary }}里所有的 https://blog.csdn.net/qq_52385631/article/details/{{ query }} 指定了一个
标签，并且将class设置为highlight_me_please，这样就可以自己通过CSS为https://blog.csdn.net/qq_52385631/article/details/{{ query }}添加高亮效果了，怎么样，是不是很科学呢{% highlight result.summary with query html_tag "div" css_class "highlight_me_please" %} # 3.这里可以限制最终https://blog.csdn.net/qq_52385631/article/details/{{ result.summary }}被高亮处理后的长度{% highlight result.summary with query max_length 40 %}           # 5.自定义使用(后面会介绍)# 5.4格式{% myhighlight  with  [css_class "class_name"] [html_tag "span"] [max_length 200] [start_head True] %}# 5.2使用一{% myhighlight result.object.content with query css_class "highlighted" html_tag "span" max_length 200 start_head True %}# 5.3自定义二{% myhighlight result.object.content with query css_class "highlighted" start_head True %}

7.自定义

自定义返回内容

在app下新建一个文件名称search_views

# 重写SearchView，实现自定义内容# blog/search_views.pyfrom haystack.views import SearchView# 导入模块from .models import *class MySeachView(SearchView):    def extra_context(self):  # 重载extra_context来添加额外的context内容        context = super(MySeachView, self).extra_context()        my_str = '111'        context['my_str'] = my_str        # print(context)        return context

修改路由

from django.contrib import adminfrom django.urls import path, include, re_pathfrom blog import search_viewsurlpatterns = [    path('admin/', admin.site.urls),    # 原生的    # re_path(r'^search/', include('haystack.urls')),    # 自己的    re_path(r'^search/', search_views.MySeachView(), name='haystack_search'),]

前端使用

圆明园:https://blog.csdn.net/qq_52385631/article/details/{{ my_str }}

自定义search.html模板

保证有一个from，get请求，input标签的name=q，value=Search，

    Search:

Search:

自定义高亮显示(原生的会省略)

新建文件夹templatetags

添加blog/templatetags/my_filters_and_tags.py 文件和 blog/templatetags/highlighting.py 文件，

内容如下（源码分别位于haystack/templatetags/lighlight.py 和 haystack/utils/lighlighting.py 中）：
my_filters_and_tags.py

# encoding: utf-8from __future__ import absolute_import, division, print_function, unicode_literals from django import templatefrom django.conf import settingsfrom django.core.exceptions import ImproperlyConfiguredfrom django.utils import six from haystack.utils import importlib register = template.Library() class HighlightNode(template.Node):    def __init__(self, text_block, query, html_tag=None, css_class=None, max_length=None, start_head=None):        self.text_block = template.Variable(text_block)        self.query = template.Variable(query)        self.html_tag = html_tag        self.css_class = css_class        self.max_length = max_length        self.start_head = start_head         if html_tag is not None:            self.html_tag = template.Variable(html_tag)         if css_class is not None:            self.css_class = template.Variable(css_class)         if max_length is not None:            self.max_length = template.Variable(max_length)         if start_head is not None:            self.start_head = template.Variable(start_head)     def render(self, context):        text_block = self.text_block.resolve(context)        query = self.query.resolve(context)        kwargs = {}         if self.html_tag is not None:            kwargs['html_tag'] = self.html_tag.resolve(context)         if self.css_class is not None:            kwargs['css_class'] = self.css_class.resolve(context)         if self.max_length is not None:            kwargs['max_length'] = self.max_length.resolve(context)         if self.start_head is not None:            kwargs['start_head'] = self.start_head.resolve(context)         # Handle a user-defined highlighting function.        if hasattr(settings, 'HAYSTACK_CUSTOM_HIGHLIGHTER') and settings.HAYSTACK_CUSTOM_HIGHLIGHTER:            # Do the import dance.            try:                path_bits = settings.HAYSTACK_CUSTOM_HIGHLIGHTER.split('.')                highlighter_path, highlighter_classname = '.'.join(path_bits[:-1]), path_bits[-1]                highlighter_module = importlib.import_module(highlighter_path)                highlighter_class = getattr(highlighter_module, highlighter_classname)            except (ImportError, AttributeError) as e:                raise ImproperlyConfigured("The highlighter '%s' could not be imported: %s" % (settings.HAYSTACK_CUSTOM_HIGHLIGHTER, e))        else:            from .highlighting import Highlighter            highlighter_class = Highlighter         highlighter = highlighter_class(query, **kwargs)        highlighted_text = highlighter.highlight(text_block)        return highlighted_text  @register.tagdef myhighlight(parser, token):    """    Takes a block of text and highlights words from a provided query within that    block of text. Optionally accepts arguments to provide the HTML tag to wrap    highlighted word in, a CSS class to use with the tag and a maximum length of    the blurb in characters.    Syntax::        {% highlight  with  [css_class "class_name"] [html_tag "span"] [max_length 200] %}    Example::        # Highlight summary with default behavior.        {% highlight result.summary with request.query %}        # Highlight summary but wrap highlighted words with a div and the        # following CSS class.        {% highlight result.summary with request.query html_tag "div" css_class "highlight_me_please" %}        # Highlight summary but only show 40 characters.        {% highlight result.summary with request.query max_length 40 %}    """    bits = token.split_contents()    tag_name = bits[0]     if not len(bits) % 2 == 0:        raise template.TemplateSyntaxError(u"'%s' tag requires valid pairings arguments." % tag_name)     text_block = bits[1]     if len(bits) < 4:        raise template.TemplateSyntaxError(u"'%s' tag requires an object and a query provided by 'with'." % tag_name)     if bits[2] != 'with':        raise template.TemplateSyntaxError(u"'%s' tag's second argument should be 'with'." % tag_name)     query = bits[3]     arg_bits = iter(bits[4:])    kwargs = {}     for bit in arg_bits:        if bit == 'css_class':            kwargs['css_class'] = six.next(arg_bits)         if bit == 'html_tag':            kwargs['html_tag'] = six.next(arg_bits)         if bit == 'max_length':            kwargs['max_length'] = six.next(arg_bits)         if bit == 'start_head':            kwargs['start_head'] = six.next(arg_bits)     return HighlightNode(text_block, query, **kwargs)

highlighting.py

# encoding: utf-8 from __future__ import absolute_import, division, print_function, unicode_literals from django.utils.html import strip_tags  class Highlighter(object):    #默认值    css_class = 'highlighted'    html_tag = 'span'    max_length = 200    start_head = False    text_block = ''     def __init__(self, query, **kwargs):        self.query = query         if 'max_length' in kwargs:            self.max_length = int(kwargs['max_length'])         if 'html_tag' in kwargs:            self.html_tag = kwargs['html_tag']         if 'css_class' in kwargs:            self.css_class = kwargs['css_class']         if 'start_head' in kwargs:            self.start_head = kwargs['start_head']         self.query_words = set([word.lower() for word in self.query.split() if not word.startswith('-')])     def highlight(self, text_block):        self.text_block = strip_tags(text_block)        highlight_locations = self.find_highlightable_words()        start_offset, end_offset = self.find_window(highlight_locations)        return self.render_html(highlight_locations, start_offset, end_offset)     def find_highlightable_words(self):        # Use a set so we only do this once per unique word.        word_positions = {}         # Pre-compute the length.        end_offset = len(self.text_block)        lower_text_block = self.text_block.lower()         for word in self.query_words:            if not word in word_positions:                word_positions[word] = []             start_offset = 0             while start_offset < end_offset:                next_offset = lower_text_block.find(word, start_offset, end_offset)                 # If we get a -1 out of find, it wasn't found. Bomb out and                # start the next word.                if next_offset == -1:                    break                 word_positions[word].append(next_offset)                start_offset = next_offset + len(word)         return word_positions     def find_window(self, highlight_locations):        best_start = 0        best_end = self.max_length         # First, make sure we have words.        if not len(highlight_locations):            return (best_start, best_end)         words_found = []         # Next, make sure we found any words at all.        for word, offset_list in highlight_locations.items():            if len(offset_list):                # Add all of the locations to the list.                words_found.extend(offset_list)         if not len(words_found):            return (best_start, best_end)         if len(words_found) == 1:            return (words_found[0], words_found[0] + self.max_length)         # Sort the list so it's in ascending order.        words_found = sorted(words_found)         # We now have a denormalized list of all positions were a word was        # found. We'll iterate through and find the densest window we can by        # counting the number of found offsets (-1 to fit in the window).        highest_density = 0         if words_found[:-1][0] > self.max_length:            best_start = words_found[:-1][0]            best_end = best_start + self.max_length         for count, start in enumerate(words_found[:-1]):            current_density = 1             for end in words_found[count + 1:]:                if end - start < self.max_length:                    current_density += 1                else:                    current_density = 0                 # Only replace if we have a bigger (not equal density) so we                # give deference to windows earlier in the document.                if current_density > highest_density:                    best_start = start                    best_end = start + self.max_length                    highest_density = current_density         return (best_start, best_end)     def render_html(self, highlight_locations=None, start_offset=None, end_offset=None):        # Start by chopping the block down to the proper window.        #text_block为内容，start_offset,end_offset分别为第一个匹配query开始和按长度截断位置        text = self.text_block[start_offset:end_offset]         # Invert highlight_locations to a location -> term list        term_list = []         for term, locations in highlight_locations.items():            term_list += [(loc - start_offset, term) for loc in locations]         loc_to_term = sorted(term_list)         # Prepare the highlight template        if self.css_class:            hl_start = '<%s class="%s">' % (self.html_tag, self.css_class)        else:            hl_start = '<%s>' % (self.html_tag)         hl_end = '' % self.html_tag         # Copy the part from the start of the string to the first match,        # and there replace the match with a highlighted version.        #matched_so_far最终求得为text中最后一个匹配query的结尾        highlighted_chunk = ""        matched_so_far = 0        prev = 0        prev_str = ""         for cur, cur_str in loc_to_term:            # This can be in a different case than cur_str            actual_term = text[cur:cur + len(cur_str)]             # Handle incorrect highlight_locations by first checking for the term            if actual_term.lower() == cur_str:                if cur < prev + len(prev_str):                    continue                 #分别添上每个query+其后面的一部分（下一个query的前一个位置）                highlighted_chunk += text[prev + len(prev_str):cur] + hl_start + actual_term + hl_end                prev = cur                prev_str = cur_str                 # Keep track of how far we've copied so far, for the last step                matched_so_far = cur + len(actual_term)         # Don't forget the chunk after the last term        #加上最后一个匹配的query后面的部分        highlighted_chunk += text[matched_so_far:]         #如果不要开头not start_head才加点        if start_offset > 0 and not self.start_head:            highlighted_chunk = '...%s' % highlighted_chunk         if end_offset < len(self.text_block):            highlighted_chunk = '%s...' % highlighted_chunk         #可见到目前为止还不包含start_offset前面的，即第一个匹配的前面的部分（text_block[:start_offset]），如需展示(当start_head为True时)便加上        if self.start_head:            highlighted_chunk = self.text_block[:start_offset] + highlighted_chunk        return highlighted_chunk

前端使用

{% load my_filters_and_tags %}{% myhighlight result.object.content with query css_class "highlighted" html_tag "span" max_length 200 start_head True %}

8. 目前位置搜索已经完成，可以重建索引，同步数据，测试一下

python manage.py rebuild_index

9.jieba分词器配置

9.1 先从python包中复制whoosh_backend.py到app中，并改名为whoosh_cn_backend.py

文件路径：\site-packages\haystack\backends\whoosh_backend.py

在这里插入图片描述

复制到的路径：

9.2 对whoosh_cn_backend.py做以下修改：

1、导入 ChineseAnalyzefrom jieba.analyse import ChineseAnalyzer2、替换schema_fields[field_class.index_fieldname] = TEXT(下的analyzeranalyzer=ChineseAnalyzer(),

9.3 在django的配置文件中，修改搜索引擎

HAYSTACK_CONNECTIONS = {    'default': {        # 设置haystack的搜索引擎        'ENGINE': 'blog.whoosh_cn_backend.WhooshEngine',        # 'ENGINE': 'haystack.backends.whoosh_backend.WhooshEngine',        # 设置索引文件的位置        'PATH': os.path.join(BASE_DIR, 'whoosh_index'),    }}

10 django+django-haystack+Elasticsearch7.5+ik+mysql

10.0 切换成es引擎，除了settings.py和把jieba换成ik，其他步骤跟上面的都一样

如果一开始，就是奔着es+ik来的，那步骤9 jieba分词器配置 不用看，直接从步骤8跳到这里来

10.1 安装es，ik

基于docker安装Elasticsearch+ElasticSearch-Head+IK分词器_骑台风走的博客-CSDN博客基于docker安装Elasticsearch+ElasticSearch-Head+IK分词器https://blog.csdn.net/qq_52385631/article/details/126567059?spm=1001.2014.3001.5501

10.2 使用ik重写es7.5引擎

10.2.1 新建elasticsearch_ik_backend.py(在自己的app下)

在 blog应用下新建名为 elasticsearch7_ik_backend.py 的文件，继承 Elasticsearch7SearchBackend（后端）和 Elasticsearch7SearchEngine（搜索引擎）并重写建立索引时的分词器设置

from haystack.backends.elasticsearch7_backend import Elasticsearch7SearchBackend, Elasticsearch7SearchEngine"""分析器主要有两种情况会被使用：第一种是插入文档时，将text类型的字段做分词然后插入倒排索引，第二种就是在查询时，先对要查询的text类型的输入做分词，再去倒排索引搜索如果想要让 索引 和 查询 时使用不同的分词器，ElasticSearch也是能支持的，只需要在字段上加上search_analyzer参数在索引时，只会去看字段有没有定义analyzer，有定义的话就用定义的，没定义就用ES预设的在查询时，会先去看字段有没有定义search_analyzer，如果没有定义，就去看有没有analyzer，再没有定义，才会去使用ES预设的"""DEFAULT_FIELD_MAPPING = {    "type": "text",    "analyzer": "ik_max_word",    # "analyzer": "ik_smart",    "search_analyzer": "ik_smart"}class Elasticsearc7IkSearchBackend(Elasticsearch7SearchBackend):    def __init__(self, *args, **kwargs):        self.DEFAULT_SETTINGS['settings']['analysis']['analyzer']['ik_analyzer'] = {            "type": "custom",            "tokenizer": "ik_max_word",            # "tokenizer": "ik_smart",        }        super(Elasticsearc7IkSearchBackend, self).__init__(*args, **kwargs)class Elasticsearch7IkSearchEngine(Elasticsearch7SearchEngine):    backend = Elasticsearc7IkSearchBackend

10.3 修改settings.py(切换成功)

# es 7.x配置HAYSTACK_CONNECTIONS = {    'default': {        # 'ENGINE': 'haystack.backends.elasticsearch7_backend.Elasticsearch7SearchEngine',        'ENGINE': 'blog.elasticsearch_ik_backend.Elasticsearch7IkSearchEngine',        # 'URL': 'http://106.14.42.253:9200/',        'URL': 'http://106.14.42.253:9200/',        # elasticsearch建立的索引库的名称，一般使用项目名作为索引库        'INDEX_NAME': 'elastic_new',    },}

10.4 重建索引，同步数据

python manage.py rebuild_index

10.5 补充

10.5.1 未成功切换成ik

haystack 原先加载的是 ...\venv\Lib\site-packages\haystack\backends 文件夹下的 elasticsearch7_backend.py 文件，打开即可看到 elasticsearch7 引擎的默认配置

若用上述方法建立出来的索引字段仍使用 snowball 分词器，则将原先elasticsearch7_backend.py 文件中的 DEFAULT_FIELD_MAPPING 也修改为 ik 分词器（或许是因为版本问题）

位置：D:\py_virtualenv\dj_ha\Lib\site-packages\haystack\backends\elasticsearch7_backend.py

修改内容：

DEFAULT_FIELD_MAPPING = {    "type": "text",    "analyzer": "ik_max_word",    "search_analyzer": "ik_smart",}

10.5.2 es6版本加入ik，重写引擎

from haystack.backends.elasticsearch_backend import ElasticsearchSearchBackendfrom haystack.backends.elasticsearch_backend import ElasticsearchSearchEngineclass IKSearchBackend(ElasticsearchSearchBackend):    DEFAULT_ANALYZER = "ik_max_word" # 这里将 es 的 默认 analyzer 设置为 ik_max_word    def __init__(self, connection_alias, **connection_options):        super().__init__(connection_alias, **connection_options)    def build_schema(self, fields):        content_field_name, mapping = super(IKSearchBackend, self).build_schema(fields)        for field_name, field_class in fields.items():            field_mapping = mapping[field_class.index_fieldname]            if field_mapping["type"] == "string" and field_class.indexed:                if not hasattr(                    field_class, "facet_for"                ) and not field_class.field_type in ("ngram", "edge_ngram"):                    field_mapping["analyzer"] = getattr(                        field_class, "analyzer", self.DEFAULT_ANALYZER                    )            mapping.update({field_class.index_fieldname: field_mapping})        return content_field_name, mappingclass IKSearchEngine(ElasticsearchSearchEngine):    backend = IKSearchBackend

11.实时更新索原理：采用信号

配置

# 在django配置文件中，添加索引值，文章更新的时候，就会自动更新索引值HAYSTACK_SIGNAL_PROCESSOR = 'haystack.signals.RealtimeSignalProcessor'

RealtimeSignalProcessor源码如下：

class RealtimeSignalProcessor(BaseSignalProcessor):    """    Allows for observing when saves/deletes fire & automatically updates the    search engine appropriately.    当 检索对象出现保存或者删除的时候更新索引值。    """    def setup(self):        # Naive (listen to all model saves).        models.signals.post_save.connect(self.handle_save)        models.signals.post_delete.connect(self.handle_delete)             # Efficient would be going through all backends & collecting all models        # being used, then hooking up signals only for those.    def teardown(self):        # Naive (listen to all model saves).        models.signals.post_save.disconnect(self.handle_save)        models.signals.post_delete.disconnect(self.handle_delete)        # Efficient would be going through all backends & collecting all models        # being used, then disconnecting signals only for those.