49 Python distributed crawler build search engine Scrapy explaining-elasticsearch (search engine) implement search results pagination with Django

Logical processing functions

Calculate Search Time-consuming
Before starting the search: Start_time = DateTime.Now () Gets the current time
At the end of the search: End_time = DateTime.Now () Gets the current time
Last_time = (end_time-start_time). Total_seconds () end time minus start time equals times, converted to seconds

From django.shortcuts import render# Create your views here.from django.shortcuts import Render,httpresponsefrom django.v Iews.generic.base Import viewfrom app1.models import lagoutype # Importing Operation Elasticsearch (search engine) class import Jsonfrom ELA Sticsearch Import Elasticsearch # Importing native Elasticsearch (search engine) interface client = Elasticsearch (hosts=["127.0.0.1"]) # Connection native Elasticsearchfrom datetime import Datetimedef Indexluoji (Request): Print (Request.method) # Gets the path of the user request return render (Request, ' index.html ') def Suggestluoji (Request): # Search Auto-complete logic processing key_words = Request .                                  Get.get (' s ', ') # gets to the request word Re_datas = [] If key_words:s = Lagoutype.search ()            # Instantiation of search query for Elasticsearch (search engine) class S = s.suggest (' my_suggest ', Key_words, completion={ "Field": "Suggest", "fuzzy": {"fuzziness": 1}, "Size": 5}) su Ggestions = S.execute_suGgest () for match in Suggestions.my_suggest[0].options:source = Match._source Re_datas.appen D (source["title"]) return HttpResponse (Json.dumps (Re_datas), content_type= "Application/json") def Searchluoji ( Request): # search Logic processing key_words = Request. Get.get (' Q ', ') # Gets the requested Word page = Request. Get.get (' P ', ' 1 ') # gets access page number try:page = Int (page) Except:page = 1 Start_                                   Time = DateTime.Now () # Gets the current times response = Client.search (                                          # Native Elasticsearch Interface Search () method, that is, can support the native Elasticsearch statement query index= "Lagou",                                                  # Set index name doc_type= "Biao", # Set table name body={                               # write Elasticsearch statement "query": {"Multi_match": { # multi_match Query "query": key_words, # query keyword "fields": ["Tit                                          Le "," description "] # query Field}}," from ": (page-1) *10, # get "Size" from the first few: 10, # Get how many data "Highligh         T ": {# query keyword highlighting processing" pre_tags ": [' <span class=" KeyWord ">"],                                     # Highlight start Tag "post_tags": [' </span> '], # Highlight end tag ' fields ': {                    # Highlight Set "title": {}, # Highlight field "description": {} # Highlight field}}) End_time = DateTime. Now () # Gets the current time Last_time = (end_time-start_time). Total_seconds () # end time minus start Time equals times, converted into seconds total_nums = response["hits" ["Total"] # Gets the overall number of results of the query if (page%) > 0:                                               # count Pages paga_nums = Int (TOTAL_NUMS/10) +1 else:paga_nums = Int (TOTAL_NUMS/10) hit_list = []                        # set a list to store the information you have searched for, and return it to the HTML page for hits in response["hits" ["Hits"]:  # Loop Query to result hit_dict = {} # Set a dictionary to store the loop result if "title" in hit["Highlight"]: # determines the title field if the highlighted field has a class tolerance hit_dict["title" = "". Join (hit["highlight"] [" Title "]) # Gets the highlight of the title else:hit_dict[" title "] = hit[" _source "[" title "] # otherwise gets not highlighted The title if "description" in hit["highlight"]: # Determines the Description field if the highlighted field has a class tolerance Hit_ dict["description"] = "". Join (hit["Highlight" ["description"]) [: 500] # Get the highlighted description Else:hit_di ct["description"] = hit["_source" ["description"] # Otherwise get not highlighted in the description hit_dict["url"] = hit["_source" ["url"] # Gets the return URL hit_list.append (hit_dict) # will get to the contents of the dictionary, added to the list return render (req Uest, ' result.html ', {"page": page, # current Page "Total_nums": Total_nu                                           MS, # Data total number of "all_hits": hit_list, # Data list "Key_words": key_words, # Search word "paga_nums": paga_nums, # page                                           Number "Last_time": Last_time # Search Time }) # Displays the page and returns the list and search terms to the HTML

Html

<! DOCTYPE html >
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49 Python distributed crawler build search engine Scrapy explaining-elasticsearch (search engine) implement search results pagination with Django