Elasticsearch

•

1 j'aime•323 vues

shengyan cheng

Early DRAFT, still work in progress Update 2 I'm a busy and lazy guy, so it might never finish.

Logiciels

About Me
• ASP.NET Mvc , C#
• MSSQL, Elasticsearch
• CI ( TFS, Jenkins )
• JS , Jquery, VueJS
• RabbitMQ, Masstransit , AKKA
• DDD, Micoservice
• Azure, DFS
• Microsoft Solution
• No Any License
• Speaker of gamania
• github.com/platstar

Solution
Lucene ( http://lucene.apache.org/core/ )
Solr ( http://lucene.apache.org/solr/ )
Elasticsearch ( https://www.elastic.co/ )

Lucene
• Scalable, High-Performance Indexing
• Powerful, Accurate and Efficient Search Algorithms
• Cross-Platform Solution
• The Apache Software Foundation
http://lucene.apache.org/core

Solr
Solr is the popular, blazing-fast, open source enterprise search
platform built on Apache Lucene.
http://lucene.apache.org/solr/

Elasticsearch
Elasticsearch is a distributed, JSON-based search and analytics engine
designed for horizontal scalability, maximum reliability, and easy
management.
https://www.elastic.co

Elasticsearch-vs-Solr
http://solr-vs-elasticsearch.com/

Elasticsearch-vs-Solr
• Real-time Performance
• Platform Support
• Cluster
• Follow the Trend
http://db-engines.com/en/ranking/search+engine

Elasticsearch-vs-Solr
http://i.zhcy.tk/blog/elasticsearchyu-solr

Elasticsearch-vs-MongoDB
Everything depend on ..
http://blog.quarkslab.com/mongodb-vs-elasticsearch-the-quest-of-
the-holy-performances.html

Elasticsearch
ELK, ElasticStack
Use Cases
Key Point
Resource

Key Point
• Document Mapping 決定了效能和功能, 50%
• DSL 查詢語法與評分機制 , 20%
• Plugin , 20%
• Cluster , 10%

Key Point
• 提供現有可靠高效能的搜尋方案,唯細節和經驗仍需掌握
• 特定應用仍需客製所需
• 中文處理是關鍵問題

Resource
• 官方網站論壇 ( discuss.elastic.co)
• 中文書籍
• 實體課程
• GitHub
• 其他

Quick Start
Download
Installation
Plugin
Resource

Download
https://www.elastic.co
https://github.com/elastic/elasticsearch

Plugin
• Concept
• Installation
• Issues

Contenu connexe

Tendances

Elasticsearch Intro

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Neil Alwin Hermosilla

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Sandeep Adwankar

Have you wondered how you can use your corporate directory for accessing AWS? Or how you can build an AWS-powered application accessible to the millions of users from social identity providers like Amazon, Google, or Facebook? If so, this session will give you the tools you need to get started. It will provide a variety of examples to make it easier for you to use other identity pools with AWS, as well as cover open standards like Security Assertion Markup Language (SAML). Anyone who deals with external identities won't want to miss this session.

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AWS offers a number of services that help you easily deploy and run applications in the cloud. Come to this session to learn how to choose among these options. Through interactive demonstrations, this session will show you how to get an application running using AWS OpsWorks and AWS Elastic Beanstalk application management services. You will also learn how to use AWS CloudFormation templates to document, version control, and share your application configuration. This session will cover topics like application updates, customization, and working with resources such as load balancers and databases. This session is recommended for people who understand AWS and want to know more about deployment options for their applications.

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Tendances (18)

Elasticsearch Intro

DevCon Summit 2016

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NoSQL (Not Only SQL) is believed to be a superset of, or sometimes an intersecting set with, relational SQL databases. The concept itself is still shaping, but already now we can say for sure: NoSQL addresses the task of storing and retrieving the data of large volumes in the systems with high load. There is another very important angle in perceiving the concept: NoSQL systems can allow storing and efficient searching of the unstructured or semi-unstructured data, like completely raw or preprocessed documents. Using the example of one world-class document retrieval system Apache SOLR (performant HTTP wrapper around Apache Lucene) as a reference we will check upon its use cases, horizontal and vertical scalability, faceted search, distribution and load balancing, crawling, extendability, linguistic support, integration with relational databases and much more. Dmitry Kan will shortly touch upon *hot* topic of cloud computing using the famous project Apache Hadoop and will help the audience to see whether SOLR shines through the cloud.

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Roopendra Vishwakarma

The Apereo OAE Bootcamp offers an introduction into back-end and front-end development for the Apereo OAE project. The back-end development part focuses on learning the different extension points behind the scenes in the service layer of OAE. A back-end component for OAE that exposes a REST API is built from scratch. Back-end development topics include: - Node.js NPM module system - OAE back-end application life-cycle - Data-modelling with Apache Cassandra and writing CQL queries from Node.js - Using the OAE APIs to expose back-end functionality for the web VIA RESTful APIs - Writing back-end unit tests using Grunt and Mocha. If time permits, the following will also be covered: - Integrating with OAE's ElasticSearch query and index functionality - Integrating with OAE's Activity and Notifications functionality - Integration with OAE's Admin Configuration functionality The front-end development part focuses on writing a UI widget using the REST APIs developed in the back-end development part. Front-end development topics include: - Integrating with the OAE Widget loading system - Writing internationalizable templates with TrimPath and the widget i18n and l10n functionality - Interacting with the core OAE UI APIs - Using bootstrap 3 to design responsive UI layouts for your widgets - Writing front-end unit tests using Grunt and CasperJS

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Running Elasticsearch often requires specialized expertise and significant resources to operate and manage infrastructure and Elasticsearch software. Amazon Elasticsearch Service makes it easy to deploy, operate, and scale Elasticsearch in AWS. In this webinar, we will walk through how to launch a fully functional Amazon Elasticsearch domain, load your data, and analyze it using the built-in Kibana integration. We will also cover the CloudWatch Logs integration, which enables you to have your log data, such as VPC logs, automatically loaded into your Amazon Elasticsearch domain for analysis and exploration.

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In the age of information and big data, ability to quickly and easily find a needle in a haystack is extremely important. Elasticsearch is a distributed and scalable search engine which provides rich and flexible search capabilities. Social networks (Facebook, LinkedIn), media services (Netflix, SoundCloud), Q&A sites (StackOverflow, Quora, StackExchange) and even GitHub - they all find data for you using Elasticsearch. In conjunction with Logstash and Kibana, Elasticsearch becomes a powerful log engine which allows to process, store, analyze, search through and visualize your logs. Video: https://www.youtube.com/watch?v=GL7xC5kpb-c Scripts for the Demo: https://github.com/opanchenko/morning-at-lohika-ELK

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The importance of search for modern applications is evident and nowadays it is higher than ever. A lot of projects use search forms as a primary interface for communication with a user. Though implementation of an intelligent search functionality is still a challenge and we need a good set of tools. In this presentation, I will talk through the high-level architecture and benefits of Elasticsearch with some examples. Aside from that, we will also take a look at its existing competitors, their similarities, and differences.

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Organizations are collecting an ever-increasing amount of data from numerous sources such as log systems, click streams, and connected devices. Launched in 2009, Elasticsearch —an open-source analytics and search engine— has emerged as a popular tool for real-time analytics and visualization of data. Some of the most common use cases include risk assessment, error detection, and sentiment analysis. However, as data volumes and applications grow, managing Elasticsearch clusters can consume significant IT resources while adding little or no differentiated value to the organization. Amazon Elasticsearch Service (Amazon ES) is a managed service that makes it easy to deploy, operate, and scale Elasticsearch clusters in the AWS Cloud. Amazon ES offers the benefits of a managed service, including cluster provisioning, easy configuration, replication for high availability, scaling options, data durability, security, and node monitoring. This session presents a technical deep dive on Amazon ES. Attendees learn: Common challenges with real-time data analytics and visualization and how to address them; the benefits, reference architecture, and best practices for using Amazon ES; and data ingestion options with Amazon DynamoDB, AWS Lambda, and Amazon Kinesis.

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Elasticsearch

1. Elasticsearch Shengyan Cheng

2. About Me • ASP.NET Mvc , C# • MSSQL, Elasticsearch • CI ( TFS, Jenkins ) • JS , Jquery, VueJS • RabbitMQ, Masstransit , AKKA • DDD, Micoservice • Azure, DFS • Microsoft Solution • No Any License • Speaker of gamania • github.com/platstar

3. Solution Lucene ( http://lucene.apache.org/core/ ) Solr ( http://lucene.apache.org/solr/ ) Elasticsearch ( https://www.elastic.co/ )

4. Lucene • Scalable, High-Performance Indexing • Powerful, Accurate and Efficient Search Algorithms • Cross-Platform Solution • The Apache Software Foundation http://lucene.apache.org/core

5. Solr Solr is the popular, blazing-fast, open source enterprise search platform built on Apache Lucene. http://lucene.apache.org/solr/

6. Elasticsearch Elasticsearch is a distributed, JSON-based search and analytics engine designed for horizontal scalability, maximum reliability, and easy management. https://www.elastic.co

7. Why Elasticsearch? V.s.Solr V.s.MongoDB

8. Elasticsearch-vs-Solr http://solr-vs-elasticsearch.com/

9. Elasticsearch-vs-Solr • Real-time Performance • Platform Support • Cluster • Follow the Trend http://db-engines.com/en/ranking/search+engine

10. Elasticsearch-vs-Solr http://i.zhcy.tk/blog/elasticsearchyu-solr

11. Elasticsearch-vs-MongoDB Everything depend on .. http://blog.quarkslab.com/mongodb-vs-elasticsearch-the-quest-of- the-holy-performances.html

12. Elasticsearch ELK, ElasticStack Use Cases Key Point Resource

13. ELK, Elastic Stack

14. ELK, Elastic Stack

15. Use Case

16. Use Case

17. Key Point • Document Mapping 決定了效能和功能, 50% • DSL 查詢語法與評分機制 , 20% • Plugin , 20% • Cluster , 10%

18. Key Point • 提供現有可靠高效能的搜尋方案,唯細節和經驗仍需掌握 • 特定應用仍需客製所需 • 中文處理是關鍵問題