Elastic search
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This document provides an overview and introduction to Elastic Search. It discusses what Elastic Search is, why it is useful, common applications, key concepts and how to use it with Docker. Elastic Search is described as a distributed, open source, NoSQL database specialized for full-text search and analysis of structured and unstructured data. It indexes and stores data and allows for fast searching across large volumes of data.
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quick intro to elastic search
ElasticSearch is an open source, distributed, RESTful search and analytics engine. It allows storage and search of documents in near real-time. Documents are indexed and stored across multiple nodes in a cluster. The documents can be queried using a RESTful API or client libraries. ElasticSearch is built on top of Lucene and provides scalability, reliability and availability.
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- Elasticsearch uses Lucene under the hood for indexing and search. It is part of the ELK (Elasticsearch, Logstash, Kibana) stack and is open source.
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Presentation held on 5.10.2014 on http://2014.webcampzg.org/talks/.
Although ElasticSearch (ES) primary purpose is to be used as index/search server, in its featureset ES overlaps with common NoSql database; better to say, document database.
Why this could be interesting and how this could be used effectively?
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- it is not unusual to use ES additionally as an document database (store and retrieve)
- an use-case will be presented where ES can be used as a single database in the system (benefits and drawbacks)
- what if a relational database is introduced in previosly demonstrated system (benefits and drawbacks)
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- Documents are stored in indexes which can be queried using a RESTful API or with query languages like the Query DSL.
- CRUD operations allow indexing, retrieving, updating, and deleting documents. More operations can be performed efficiently using the bulk API.
- Documents are analyzed and indexed to support full-text search queries and structured queries against specific fields. Mappings and analyzers define how text is processed for searching.
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Talk given for the #phpbenelux user group, March 27th in Gent (BE), with the goal of convincing developers that are used to build php/mysql apps to broaden their horizon when adding search to their site. Be sure to also have a look at the notes for the slides; they explain some of the screenshots, etc.
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- It allows for distributed logging, document indexing, inexact searches, and custom relevance scoring.
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Episerver Find is an event-driven search engine built on top of Elasticsearch that is well-suited for Episerver projects. It separates commands and queries using CQRS, with Episerver handling simple queries and Elasticsearch handling more complex queries for improved performance. Choosing the right tools like Episerver for content management, Elasticsearch for search, and a customizable cloud platform allows building a scalable solution for projects of any size.
06 integrate elasticsearch
在這個大數據的時代, 使用者最常使用的功能之一就是檢索。而在一般的網頁應用程式的設計中最容易拖慢速度的不是檢索(Search)就是產生report(尤其是Summary或是Aggregation)。
如何在幾TB的數據量下面而又能快速反應使用者對檢索(Search)或summary/aggregation reporting的需求, 我們需要能夠快速檢索的引擎。想知道如何輕輕鬆鬆地整合一個檢索的引擎, 而且是能夠很容易擴展的幾十個Ｎodes的檢索clusters嗎？想知道Stackoverflow或GitHub這些擁有幾十上百TB資料的網路大神如何提供檢索的功能嗎？在這個最終篇的課程中,讓我們一起登頂吧 (這是一本跑歩熱血小說中, 男主角對其它組員的勉勵slogan~~嗯…有點冷!!)
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The document provides an overview of Elasticsearch and how it can be used to make software smarter. It discusses how Elasticsearch works and its advantages over other search technologies like SQL and Sphinx. The document also includes case studies of four projects that used Elasticsearch for tasks like search, recommendations, and parsing classified ads. It covers how to install and configure Elasticsearch, as well as how to query an Elasticsearch index through its RESTful API.
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Elasticsearch is a highly scalable and distributed search engine that allows for storing and searching of documents in JSON format. It uses Apache Lucene for indexing and searching but adds features for clustering, auto-sharding, replication, and more. Elasticsearch can scale horizontally by adding more nodes as needed and uses RESTful APIs to allow configuration and querying of the cluster. It aims to be easy to use, schema-free, and highly available.
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- 2. AGENDA 1. What is Elastic Search ? 2. Why I do need Elastic Search ? 3. Which Application for Using ? 4. Elastic Directory Information 5. Elastic Concepts 6. Elastic With Docker 7. Demo
- 3. WHAT IS ELASTIC SEARCH ? "You Know, for Search"
- 4. WHAT IS ELASTIC SEARCH ? • Elasticsearch NoSql distributed full-text database • It is a very highly-scalable document storage engine that specializes in text base search
- 5. WHAT IS ELASTIC SEARCH ? • ES is developed in Java • ES is released as open source under the terms of the Apache License
- 6. What is Elastic Search ? Supported by .Net C#, Python , PHP etc. It has JSON based nature along with simple REST API
- 7. What is Elastic Search ? Created by Shey Bannon
- 8. Why do I need Elastic Search ? SPEED RELEVANCY STATISTICS
- 9. Which Application for using ? ALL DATA TYPES SEARCHING
- 10. GEO LOCATION SEARCHING & DISTANCE CALCULATION Which Application for using ?
- 11. Which Application for using ? DATAANALYSIS
- 12. Elastic Directory Information DATA CONFIG
- 14. ELASTIC CONCEPTS
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- 20. Queries JUST DO IT