elasticsearch crawler

the index name Doc_type= "Biao", # Sets the table name body={ # write Elasticsearch statement "query": {"Multi_match": {# mu Lti_match query "Query": key_words, # query keyword "fields": ["title", "description"] # query Field}}, "from": 0, # get "Size" from the first few: 10, # Get how many data "Highli Ght ": {# query keyword highlighting processing" pre

than #boost is a weight that can be set to a given field with a weight get jobbole/job/_ search{ "Query": {"range": {"comments": {" GTE": Ten, "LTE": +, "Boost": 2.0 } } }}Range field value is a time range query#range字段值为时间范围查询 # Query the time value range of a field # Note: The field value must be time #gte greater than or equal to #ge greater than #lte less than or equal to #lt less than #now for the current time get jobbole/job/_search{ " Query ": {" range ":

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

The web crawler architecture, on top of Nutch+hadoop, is a typical distributed Offline batch processing architecture with excellent throughput and crawl performance and a large number of configuration customization options. Because the crawler is only responsible for the crawling of network resources, a distributed search engine is needed for real-time indexing and searching of the network resources crawled

The Web Crawler architecture is a typical distributed offline batch processing architecture on top of nutch + hadoop. It has excellent throughput and capture performance and provides a large number of configuration customization options. Because web crawlers only capture network resources, a distributed search engine is required to index and search network resources captured by web crawlers in real time. The search engine architecture is built on

No. 365, Python distributed crawler build search engine Scrapy explaining-elasticsearch (search engine) queryElasticsearch (search engine) queryElasticsearch is a very powerful search engine that uses it to quickly query to the required data.Enquiry Category:　　Basic Query : Query with Elasticsearch built-in query criteria　　Combine queries: Combine multiple query

No. 371, Python distributed crawler build search engine Scrapy explaining-elasticsearch (search engine) with Django implementation of my search and popularThe simple implementation principle of my search elementsWe can use JS to achieve, first use JS to get the input of the search termSet an array to store search terms,Determine if the search term exists in the array if the original word is deleted, re-plac

by BOOL # with BOOL including must should must_not filter to complete the # format as follows: #bool: {# "filter": [], the filter of the field, Do not participate in the scoring # "must": [], if there are multiple queries, must meet "and" # " should": [], if there are multiple queries, satisfy one or more of the matching "or" # "Must_not": [], on the contrary, the query word is not satisfied with the match "inverse, non-" #} #获取tags字段值为空或者为null的数据, if the dat

No. 362, Python distributed crawler build search engine Scrapy explaining-elasticsearch (search engine) basic index and document CRUD operationsElasticsearch (search engine) basic index and document CRUD operationsthat is, basic indexing and documentation, adding, deleting, changing, checking , manipulatingNote: The following operations are all operating in the KibanaNo. 362, Python distributed

No. 364, Python distributed crawler build search engine Scrapy explaining-elasticsearch (search engine) mapping mapping management1, mapping (mapping) Introductionmapping : When creating an index, you can pre-define the type of field and related propertiesElasticsearch guesses the field mappings you want based on the underlying type of the JSON source data, converts the input data into searchable index entr

change, unchanged original data) "recommended"POST Index name/table/id/_update{ "Doc": { "field": Value, "field": Value }}#修改文档 (incremental modification, unmodified original data unchanged) POST jobbole/job/1/_update{ "Doc": { "comments": "City ": "Tianjin" }}8. Delete the index, delete the documentDelete index name/table/ID delete a specified document in the indexDelete index name deletes a specified index#删除索引里的一个指定文档DELETE jobbole/job/1# Delete a specified index delete jobbo

No. 371, Python distributed crawler build search engine Scrapy explaining-elasticsearch (search engine) with Django implementation of my search and popularThe simple implementation principle of my search elementsWe can use JS to achieve, first use JS to get the input of the search termSet an array to store search terms,Determine if the search term exists in the array if the original word is deleted, re-plac

Inverted indexThe inverted index stems from the fact that a record needs to be found based on the value of the property. Each entry in this index table includes an attribute value and the address of each record that has that property value. Because the property value is not determined by the record, it is determined by the property value to determine the position of the record, and is therefore called an inverted index (inverted). A file with an inverted index is called an inverted index file (i

": "Jobbole", "_type": "Job", "_id": "6"}}{"title": "Development", "Salary_min": "City": "Beijing", " Company ": {" name ":" Baidu "," company_addr ":" Beijing Software Park "}," Publish_date ":" 2017-4-16 "," Comments ": 15}Bulk Bulk Operations Bulk Delete dataPOST _bulk{"Delete": {"_index": "Jobbole", "_type": "Job", "_id": "5"}}{"delete": {"_index": "Jobbole", "_type": "Job", "_ ID ":" 6 "}}Bulk Bulk Operations Batch modification dataPOST _bulk{"Update": {"_index": "Jobbole", "_type": "Job",

First, window installation Elasticsearch installationThe client version of Elasticsearch must be consistent with the main version of the server version.1, Java Installation "slightly" 2, Elasticsearch downloadAddress: https://www.elastic.co/downloads/past-releasesSelect the appropriate version, use elasticsearch5.4.3 download zip here3, decompression

In order to make it easier for you to find the part that you need to reference more quickly, the part that has been translated is done according to the catalogue of the authoritative guide, and I hope to be helpful. Start (Getting Started) 1. You know, to search English original link: you Know, for Search 2. Life in the cluster Translation Links:How the [Elasticsearch] cluster works-part I.How the [Elasticsearch