Through the simple example above, we have learned about conditional search for structured data. Now, let's look at full-text search-how to find the most relevant article by matching the texts in all fields. Full-text search has two most important aspects:
Similarity Calculation
You can use TF/IDF (see [Relevance-Intro]), geographical proximity, fuzzy
/IDF will produce some surprising results.
Consider using the first_name and last_name fields to search for "Peter Smith. Peter is a common name, and Smith is a common surname-they have low IDF. But what if there is another person named Smith Williams in the index? Smith is very rare as a name, so it has a very high IDF value!
A simple query like the following will put Smith Williams before Peter Smith ), although Peter Smith is clearly a better match
Requirement: The Boostapp column in the index is used as the base score for the rating, and is attenuated by time based on the Carpublishtime (Data Refresh Time field).Implemented based on groovy scripting.1. Query Script mode:{"Fields ": [ "Boost", "Ucarid", "Boostapp", "Carpublishtime" ], "query": { " Function_score ": {" query ": { " Match_all ": {} }, " Functions ": [ { " Script_score ": { "Script": "Import java.util.date;imp
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
Elasticsearch October 2014 briefing, elasticsearch1. Elasticsearch Updates
1.1 released Kibana 4 Beta 1 and Beta 1.1
Kibana 4 is different from Kibana in layout, configuration, and bottom-layer Chart Drawing. After learning the functional requirements of many communities based on Kibana 3, Kibana's self-Kibana 2 major change resulted in the second major change made by Kibana 3. Kibana has always been commit
Install Logstash 2.2.0 and Elasticsearch 2.2.0 on CentOS
This article describes how to install logstash 2.2.0 and elasticsearch 2.2.0. The operating system environment version is CentOS/Linux 2.6.32-504.23.4.el6.x86 _ 64.
JDK installation is required. It is generally available in the operating system. It is only a version issue and will be mentioned later.
Kibana is only a front-end UI written in pure JavaS
Why do I need a search engineThe purpose of the search is to quickly look for what is needed without browsing the entire site. The results should be sequential, the higher the correlation, the better the result should be. Filter to optimize the overall relevance of the search results
The search cannot be too slow
Becau
This is the first article in the Elasticsearch 2.4 release series:
Elasticsearch First article: Installing Elasticsearch under Windows
Elasticsearch Introduction Second article: Cluster configuration
Elasticsearch Introduction Third: Index
Installation Preparation:The only requirement to install Elasticsearch is to install the official version of Java, including the corresponding JDK.Installing ElasticsearchFirst download the latest version of the Elasticsearch compression package to the official website.You can use the command to fill in the latest available download links:curl -L -O https://artifacts.elastic.co/downloads/
Tags: Front remove network general multi-tenant node work HTTPS problemOriginal address: http://blog.csdn.net/w12345_ww/article/details/52182264. Copyright belongs to the original authorThese two days in the project to involve the use of elasticsearch, on the internet to search for some of this information, found that Elasticsearch installation is divided into si
Abstract: intends to write several elasticsearch use experience. First, start with the horizontal comparison of Elasticsearch and Sphinx. Cross-correlation is a good way to react to the pros and exposures of the problem. I am the Sphinx camp to the Elasticsearch camp, both are mature open source search engine, each has
centralize logging on CentOS 7 using Logstash and Kibana
Centralized logging is useful when trying to identify a problem with a server or application because it allows you to search all logs in a single location. It is also useful because it allows you to identify issues across multiple servers by associating their logs within a specific time frame. This series of tutorials will teach you how to install Logstash and Kibana on CentOS, and then how to
ElasticSearch configuration example and elasticsearch example
##################### ElasticSearch configuration example ################ #####
# This file contains an overview of various configuration settings,# Targeted at operations staff. Application developers shoshould# Consult the guide.# This file contains an overview of various configurations. It is desig
Tutorial on using Python to operate Elasticsearch data indexes, elasticsearch tutorial
Elasticsearch is a distributed and Restful search and analysis server. Like Apache Solr, it is also an Indexing Server Based on ce. However, I think Elasticsearch has the following advanta
Elasticsearch top Course Series video tutorial-core knowledge, elasticsearch Course
Http://pan.baidu.com/s/1skUv0BV
Elasticsearch top master series course video tutorial-core knowledge (courseware + Source Code)
--------------------------------------------------------------------
01: course introduction. avi
02: Tell you what
Getting started with Elasticsearch, elasticsearch
Elasticsearch is a highly scalable open-source full-text search and analysis engine. It can store, search, and analyze large-scale data quickly and in near real time. It is generally used as the underlying engine/technology t
1.ElasticSearch Simple DescriptionA.elasticsearch is a Lucene-based search server with distributed multiuser capabilities, Elasticsearch is an open source project (Apache License terms) developed in Java, based on a restful web interface that enables real-time search, Stable, reliable, fast, high performance, easy to i
Elasticsearch is an open source, distributed, restful search engine built on Lucene. Designed for cloud computing, to achieve real-time search, stable, reliable, fast, easy to install and use. Supports the use of JSON for data indexing over HTTP.
stand-alone Environment
Stand-alone version of the Elasticsearch operati
The content source of this page is from Internet, which doesn't represent Alibaba Cloud's opinion;
products and services mentioned on that page don't have any relationship with Alibaba Cloud. If the
content of the page makes you feel confusing, please write us an email, we will handle the problem
within 5 days after receiving your email.
If you find any instances of plagiarism from the community, please send an email to:
info-contact@alibabacloud.com
and provide relevant evidence. A staff member will contact you within 5 working days.