Intro talk for UNC School of Information and Library Science. Covers basics of Lucene and Solr as well as info on Lucene/Solr jobs, opportunities, etc.
This document provides an overview of Lucene and how it can be used with MySQL. It discusses:
- What Lucene is and its origins as an open source information retrieval library.
- How Lucene works as a toolkit for building search applications rather than a turnkey search engine.
- Core Lucene classes like IndexWriter, Directory, Analyzer, and Document that are used for indexing data.
- Classes like IndexSearcher and Query that support basic search operations through queries and hits.
- Examples of loading data from a MySQL database into a Lucene index and performing searches on that indexed data.
Apache Solr/Lucene Internals by Anatoliy SokolenkoProvectus
This document provides an overview of Apache Lucene and Solr. It discusses Lucene's data model, index structure, basic indexing and search flows. It also summarizes how Solr builds on Lucene to provide enterprise-level search capabilities with features like sharding, replication, and faceting. The document also covers text analysis in Lucene, spell checking, and references for further reading.
This document provides an introduction to Apache Lucene and Solr. It begins with an overview of information retrieval and some basic concepts like term frequency-inverse document frequency. It then describes Lucene as a fast, scalable search library and discusses its inverted index and indexing pipeline. Solr is introduced as an enterprise search platform built on Lucene that provides features like faceting, scalability and real-time indexing. The document concludes with examples of how Lucene and Solr are used in applications and websites for search, analytics, auto-suggestion and more.
This document provides an overview of using Apache Lucene and Solr for building a search engine. It outlines the basic search engine pipeline of crawling, parsing, indexing, ranking and searching data. It then introduces Lucene as a free and open source indexing and search library, describing its strengths like speed and flexibility. It provides examples of using the Lucene API for indexing, searching and deleting documents. Finally, it describes Apache Solr as a wrapper for Lucene that provides a REST API and administration interface for building search applications.
Lucene is a free and open source information retrieval (IR) library written in Java. It is widely used to add search functionality to applications. Lucene features fast and scalable indexing and search, and supports various query types including phrase, wildcard, fuzzy and range queries. The Lucene project includes related sub-projects like Solr (search server), Nutch (web crawler), and Mahout (machine learning).
Apache Lucene: Searching the Web and Everything Else (Jazoon07)dnaber
Apache Lucene is a free and open-source search library that provides indexing and searching capabilities. It includes Lucene Java, a core Java library, Solr, a search server with web administration, and Nutch, an open-source web crawler and search engine. Lucene Java provides indexing and searching capabilities, Solr adds web-based administration and HTTP access, and Nutch crawls websites and indexes content.
Apache Lucene is an open source Java-based search engine library. It allows adding full-text search capabilities to applications. Lucene indexes and searches documents, and is independent of file format. It analyzes text through tokenization and converts it into indexes. Common analyzers include Whitespace, Simple, Stop, and Standard analyzers.
This document provides an overview of searching and Apache Lucene. It discusses what a search engine is and how it builds an index and answers queries. It then describes Apache Lucene as a high-performance Java-based search engine library. Key features of Lucene like its powerful query syntax, relevance ranking, and flexibility are outlined. Examples of indexing and searching code in Lucene are also provided. The document concludes with a discussion of Lucene's scalability and how it can handle increasing query rates, index sizes, and update rates.
Apache Lucene is a high-performance, full-featured text search engine library written in Java. It provides indexing and searching capabilities over various document formats. The Lucene architecture involves indexing documents, building queries, searching the index, and returning results. Core classes for indexing include IndexWriter, Directory, Analyzer, Document, and Field. Core searching classes are IndexSearcher, Query, QueryParser, TopDocs, and ScoreDoc. A demo was presented to index and search documents using Lucene's core classes.
This document discusses using Lucene to index both static and dynamic web pages. It describes parsing Apache web server logs to extract parameters for dynamic pages and generate URLs. These URLs are then used to fetch results pages, which are analyzed and indexed. The indexing process is implemented in a Java program that reads logs, generates URLs, and uses Lucene to extract text and build an index from the dynamic content. A demo shows searching the index from both a command prompt and web interface. Lucene provides powerful yet easy to use search capabilities and can index dynamic pages not normally accessible to search engines.
Solr 101 was a presentation about the Solr search platform. It introduced Solr, explaining that it is an open-source enterprise search platform built on Lucene. It covered key Solr concepts like indexing, documents, fields, queries and facets. The presentation also discussed Solr features, how it works, and how to scale Solr through techniques like multicore, replication and sharding. Finally, it provided two case studies on how Sparebank1 and Komplett implemented Solr to improve their search capabilities.
This document provides an overview of Lucene scoring and sorting algorithms. It describes how Lucene constructs a Hits object to handle scoring and caching of search results. It explains that Lucene scores documents by calling the getScore() method on a Scorer object, which depends on the type of query. For boolean queries, it typically uses a BooleanScorer2. The scoring process advances through documents matching the query terms. Sorting requires additional memory to cache fields used for sorting.
Search is everywhere, and therefore so is Apache Lucene. While providing amazing out-of-the-box defaults, there’s enough projects weird enough to require custom search scoring and ranking. In this talk, I’ll walk through how to use Lucene to implement your custom scoring and search ranking. We’ll see how you can achieve both amazing power (and responsibility) over your search results. We’ll see the flexibility of Lucene’s data structures and explore the pros/cons of custom Lucene scoring vs other methods of improving search relevancy.
Got data? Let's make it searchable! This presentation will demonstrate getting documents into Solr quickly, will provide some tips in adjusting Solr's schema to match your needs better, and finally will discuss how to showcase your data in a flexible search user interface. We'll see how to rapidly leverage faceting, highlighting, spell checking, and debugging. Even after all that, there will be enough time left to outline the next steps in developing your search application and taking it to production.
The document discusses Thomas Rabaix's involvement with Symfony including developing plugins, writing a book, and now working for Ekino. It also provides an overview of a talk on Solr including indexing, searching, administration and deployment of Solr. The talk covers what Solr is, indexing documents, filtering queries, and how Solr integrates with Apache projects like Nutch and Tika.
This document provides an overview of Azure Search including supported data sources, pricing plans, indexing and querying capabilities. Key points include:
- Azure Search supports Cosmos DB, SQL databases, Blob Storage and Table Storage as data sources.
- Pricing plans range from free to $2,850 per month based on storage, indexes, search units and replicas.
- Features include scoring profiles, boosting, suggesters, autocomplete, simple and Lucene query syntax, analyzers and escaping special characters.
- Data change detection policies support change tracking in SQL and high watermarks in Cosmos DB. Deletion detection also supported.
- Cognitive Search enables natural language processing and image processing skills
This document provides a summary of the Solr search platform. It begins with introductions from the presenter and about Lucid Imagination. It then discusses what Solr is, how it works, who uses it, and its main features. The rest of the document dives deeper into topics like how Solr is configured, how to index and search data, and how to debug and customize Solr implementations. It promotes downloading and experimenting with Solr to learn more.
How Solr Search Works - A tech Talk at Atlogys Delhi Office by our Senior Technologist Rajat Jain. The lecture takes a deep dive into Solr - what it is, how it works, what it does and its inbuilt architecture. A wonderful technical session with many live examples, a sneak peak into solr code and config files and a live demo. Part of Atlogys Academy Series.
Building Intelligent Search Applications with Apache Solr and PHP5israelekpo
ZendCon 2010 - Building Intelligent Search Applications with Apache Solr and PHP5. This is a presentation on how to create intelligent web-based search applications using PHP 5 and the out-of-the-box features available in Solr 1.4.1 After we finish we finish the illustration of adding, updating and removing data from the Solr index, we will discuss how to add features such as auto-completion, hit highlighting, faceted navigation, spelling suggestions etc
The document provides an overview and agenda for an Apache Solr crash course. It discusses topics such as information retrieval, inverted indexes, metrics for evaluating IR systems, Apache Lucene, the Lucene and Solr APIs, indexing, searching, querying, filtering, faceting, highlighting, spellchecking, geospatial search, and Solr architectures including single core, multi-core, replication, and sharding. It also provides tips on performance tuning, using plugins, and developing a Solr-based search engine.
Introduction to Solr, presented at Bangkok meetup in April 2014:
http://www.meetup.com/bkk-web/events/172090992/
Covers high-level use-cases for Solr. Demos include support for Thai language (with GitHub link for source).
Has slides showcasing Solr-ecosystem as well as couple of ideas for possible Solr-specific learning projects.
This talk moves beyond the standard introduction into Elasticsearch and focuses on how Elasticsearch tries to fulfill its near-realtime contract. Specifically, I’ll show how Elasticsearch manages to be incredibly fast while handling huge amounts of data. After a quick introduction, we will walk through several search features and how the user can get the most out of the Elasticsearch. This talk will go under the hood exploring features like search, aggregations, highlighting, (non-)use of probabilistic data structures and more.
Introduction to Lucene & Solr and UsecasesRahul Jain
Rahul Jain gave a presentation on Lucene and Solr. He began with an overview of information retrieval and the inverted index. He then discussed Lucene, describing it as an open source information retrieval library for indexing and searching. He discussed Solr, describing it as an enterprise search platform built on Lucene that provides distributed indexing, replication, and load balancing. He provided examples of how Solr is used for search, analytics, auto-suggest, and more by companies like eBay, Netflix, and Twitter.
Solr Recipes provides quick and easy steps for common use cases with Apache Solr. Bite-sized recipes will be presented for data ingestion, textual analysis, client integration, and each of Solr’s features including faceting, more-like-this, spell checking/suggest, and others.
Apache solr is an enterprise search engine. It facilitates indexing of large number of documents of any size and provides very robust search techniques. This ppt provides brief introduction of it.
The DataImportHandler allows importing data from relational databases and XML into Solr. It supports both full and incremental ("delta") imports and allows denormalizing and transforming data through configuration. The handler is configured through data-config.xml and can be extended through custom transformers, entity processors, data sources, and event listeners.
Assamese search engine using SOLR by Moinuddin Ahmed ( moin )'Moinuddin Ahmed
Its a search engine i developed for my mother tongue, Assamese. I used Nutch-Lucene-Solr to make this possible. I'm open for comments and suggestions.
Email: [email protected]
This document provides an overview of searching and Apache Lucene. It discusses what a search engine is and how it builds an index and answers queries. It then describes Apache Lucene as a high-performance Java-based search engine library. Key features of Lucene like its powerful query syntax, relevance ranking, and flexibility are outlined. Examples of indexing and searching code in Lucene are also provided. The document concludes with a discussion of Lucene's scalability and how it can handle increasing query rates, index sizes, and update rates.
Apache Lucene is a high-performance, full-featured text search engine library written in Java. It provides indexing and searching capabilities over various document formats. The Lucene architecture involves indexing documents, building queries, searching the index, and returning results. Core classes for indexing include IndexWriter, Directory, Analyzer, Document, and Field. Core searching classes are IndexSearcher, Query, QueryParser, TopDocs, and ScoreDoc. A demo was presented to index and search documents using Lucene's core classes.
This document discusses using Lucene to index both static and dynamic web pages. It describes parsing Apache web server logs to extract parameters for dynamic pages and generate URLs. These URLs are then used to fetch results pages, which are analyzed and indexed. The indexing process is implemented in a Java program that reads logs, generates URLs, and uses Lucene to extract text and build an index from the dynamic content. A demo shows searching the index from both a command prompt and web interface. Lucene provides powerful yet easy to use search capabilities and can index dynamic pages not normally accessible to search engines.
Solr 101 was a presentation about the Solr search platform. It introduced Solr, explaining that it is an open-source enterprise search platform built on Lucene. It covered key Solr concepts like indexing, documents, fields, queries and facets. The presentation also discussed Solr features, how it works, and how to scale Solr through techniques like multicore, replication and sharding. Finally, it provided two case studies on how Sparebank1 and Komplett implemented Solr to improve their search capabilities.
This document provides an overview of Lucene scoring and sorting algorithms. It describes how Lucene constructs a Hits object to handle scoring and caching of search results. It explains that Lucene scores documents by calling the getScore() method on a Scorer object, which depends on the type of query. For boolean queries, it typically uses a BooleanScorer2. The scoring process advances through documents matching the query terms. Sorting requires additional memory to cache fields used for sorting.
Search is everywhere, and therefore so is Apache Lucene. While providing amazing out-of-the-box defaults, there’s enough projects weird enough to require custom search scoring and ranking. In this talk, I’ll walk through how to use Lucene to implement your custom scoring and search ranking. We’ll see how you can achieve both amazing power (and responsibility) over your search results. We’ll see the flexibility of Lucene’s data structures and explore the pros/cons of custom Lucene scoring vs other methods of improving search relevancy.
Got data? Let's make it searchable! This presentation will demonstrate getting documents into Solr quickly, will provide some tips in adjusting Solr's schema to match your needs better, and finally will discuss how to showcase your data in a flexible search user interface. We'll see how to rapidly leverage faceting, highlighting, spell checking, and debugging. Even after all that, there will be enough time left to outline the next steps in developing your search application and taking it to production.
The document discusses Thomas Rabaix's involvement with Symfony including developing plugins, writing a book, and now working for Ekino. It also provides an overview of a talk on Solr including indexing, searching, administration and deployment of Solr. The talk covers what Solr is, indexing documents, filtering queries, and how Solr integrates with Apache projects like Nutch and Tika.
This document provides an overview of Azure Search including supported data sources, pricing plans, indexing and querying capabilities. Key points include:
- Azure Search supports Cosmos DB, SQL databases, Blob Storage and Table Storage as data sources.
- Pricing plans range from free to $2,850 per month based on storage, indexes, search units and replicas.
- Features include scoring profiles, boosting, suggesters, autocomplete, simple and Lucene query syntax, analyzers and escaping special characters.
- Data change detection policies support change tracking in SQL and high watermarks in Cosmos DB. Deletion detection also supported.
- Cognitive Search enables natural language processing and image processing skills
This document provides a summary of the Solr search platform. It begins with introductions from the presenter and about Lucid Imagination. It then discusses what Solr is, how it works, who uses it, and its main features. The rest of the document dives deeper into topics like how Solr is configured, how to index and search data, and how to debug and customize Solr implementations. It promotes downloading and experimenting with Solr to learn more.
How Solr Search Works - A tech Talk at Atlogys Delhi Office by our Senior Technologist Rajat Jain. The lecture takes a deep dive into Solr - what it is, how it works, what it does and its inbuilt architecture. A wonderful technical session with many live examples, a sneak peak into solr code and config files and a live demo. Part of Atlogys Academy Series.
Building Intelligent Search Applications with Apache Solr and PHP5israelekpo
ZendCon 2010 - Building Intelligent Search Applications with Apache Solr and PHP5. This is a presentation on how to create intelligent web-based search applications using PHP 5 and the out-of-the-box features available in Solr 1.4.1 After we finish we finish the illustration of adding, updating and removing data from the Solr index, we will discuss how to add features such as auto-completion, hit highlighting, faceted navigation, spelling suggestions etc
The document provides an overview and agenda for an Apache Solr crash course. It discusses topics such as information retrieval, inverted indexes, metrics for evaluating IR systems, Apache Lucene, the Lucene and Solr APIs, indexing, searching, querying, filtering, faceting, highlighting, spellchecking, geospatial search, and Solr architectures including single core, multi-core, replication, and sharding. It also provides tips on performance tuning, using plugins, and developing a Solr-based search engine.
Introduction to Solr, presented at Bangkok meetup in April 2014:
http://www.meetup.com/bkk-web/events/172090992/
Covers high-level use-cases for Solr. Demos include support for Thai language (with GitHub link for source).
Has slides showcasing Solr-ecosystem as well as couple of ideas for possible Solr-specific learning projects.
This talk moves beyond the standard introduction into Elasticsearch and focuses on how Elasticsearch tries to fulfill its near-realtime contract. Specifically, I’ll show how Elasticsearch manages to be incredibly fast while handling huge amounts of data. After a quick introduction, we will walk through several search features and how the user can get the most out of the Elasticsearch. This talk will go under the hood exploring features like search, aggregations, highlighting, (non-)use of probabilistic data structures and more.
Introduction to Lucene & Solr and UsecasesRahul Jain
Rahul Jain gave a presentation on Lucene and Solr. He began with an overview of information retrieval and the inverted index. He then discussed Lucene, describing it as an open source information retrieval library for indexing and searching. He discussed Solr, describing it as an enterprise search platform built on Lucene that provides distributed indexing, replication, and load balancing. He provided examples of how Solr is used for search, analytics, auto-suggest, and more by companies like eBay, Netflix, and Twitter.
Solr Recipes provides quick and easy steps for common use cases with Apache Solr. Bite-sized recipes will be presented for data ingestion, textual analysis, client integration, and each of Solr’s features including faceting, more-like-this, spell checking/suggest, and others.
Apache solr is an enterprise search engine. It facilitates indexing of large number of documents of any size and provides very robust search techniques. This ppt provides brief introduction of it.
The DataImportHandler allows importing data from relational databases and XML into Solr. It supports both full and incremental ("delta") imports and allows denormalizing and transforming data through configuration. The handler is configured through data-config.xml and can be extended through custom transformers, entity processors, data sources, and event listeners.
Assamese search engine using SOLR by Moinuddin Ahmed ( moin )'Moinuddin Ahmed
Its a search engine i developed for my mother tongue, Assamese. I used Nutch-Lucene-Solr to make this possible. I'm open for comments and suggestions.
Email: [email protected]
Do you need an external search platform for Adobe Experience Manager?therealgaston
Experience Manager provides some basic search capabilities out of the box. In this talk, we'll explore an external search platform for implementing an Experience Manager powered, search-driven site. As an example, we will use Apache Solr as a reference implementation and describe best practices for indexing content, exposing non-Experience Manager content via search, delivering search-driven experiences, and deploying the solution in a production setting.
Search Engine-Building with Lucene and SolrKai Chan
These are the slides for the session I presented at SoCal Code Camp San Diego on July 27, 2013.
http://www.socalcodecamp.com/socalcodecamp/session.aspx?sid=6b28337d-6eae-4003-a664-5ed719f43533
The document introduces Lucene, Solr, and Nutch. It describes Lucene as a Java library for indexing and searching that is powerful and fast. It describes Solr as an HTTP-based index and search server with a web-based administration panel. It describes Nutch as Internet search engine software that includes a web crawler and is powerful for vertical search engines. It then provides instructions on installing Solr and includes an example of starting Solr, adding data to indexes, and demoing searching.
Alfresco is the largest open source content management company. Their new Alfresco 4 platform provides improved interfaces, collaboration features, and cloud connectivity. Key features include enhanced interfaces for viewing, editing and uploading content; social features like liking, following and notifications; mobile and tablet apps; and improved performance, scalability and administration through their new Solr-based index server. Alfresco aims to provide a flexible platform for managing content across desktop, web, mobile, social media and cloud-based environments.
Webinar: MongoDB and Polyglot Persistence ArchitectureMongoDB
Polyglot persistence is about using multiple databases in concert with one another as part of a larger datastore ecosystem. The advantage is that your database layer uses a set of specialized tools to deliver overall value and functionality while simplifying data modeling by separating command and query responsibilities. The arrival of MongoDB and it’s flexible schemas further increases the possibilities of polyglot architectures.
This document compares the performance and scalability of Elasticsearch and Solr for two use cases: product search and log analytics. For product search, both products performed well at high query volumes, but Elasticsearch handled the larger video dataset faster. For logs, Elasticsearch performed better by using time-based indices across hot and cold nodes to isolate newer and older data. In general, configuration was found to impact performance more than differences between the products. Proper testing with one's own data is recommended before making conclusions.
This document provides an overview of Apache Lucene, Apache Nutch, and Apache Solr for search and indexing large amounts of structured and unstructured data. It discusses how Hadoop fits into the search ecosystem for distributed indexing and querying capabilities. Key components discussed include Lucene for indexing, Nutch for web crawling and indexing, Solr for search infrastructure, and ZooKeeper for coordination across distributed search nodes.
Apache Lucene is a free and open-source information retrieval software library. It allows full-text searches and indexing across various data sources including documents, emails and databases. Lucene includes modules like Solr for search servers and Nutch for web crawling. Lucene is lightweight, fast and scalable with a large community. It provides powerful and customizable search capabilities but requires developers to handle tasks like document conversion.
Presented at Indian Institute of Information Technology (IIIT) Allahabad on 21 Oct 2009 to students about the Apache Software Foundation, Lucene, Solr, Hadoop and on the benefits of contributing to open source projects. The target audience was sophomore, junior and senior B.Tech students.
The document provides an overview of Lucene, an open source search library. It discusses Lucene concepts like indexing, searching, analysis and contributions. The tutorial covers the basics of indexing and searching documents, analyzing text, and popular contributed modules like highlighting, spellchecking and finding similar documents. Attendees will gain hands-on experience with Lucene through code examples and exercises.
ElasticSearch in Production: lessons learnedBeyondTrees
ElasticSearch is an open source search and analytics engine that allows for scalable full-text search, structured search, and analytics on textual data. The author discusses her experience using ElasticSearch at Udini to power search capabilities across millions of articles. She shares several lessons learned around indexing, querying, testing, and architecture considerations when using ElasticSearch at scale in production environments.
This document provides an overview of the search engine capabilities of Apache Solr/Lucene. It begins with an introduction to search engines and their capabilities. It then discusses Apache Lucene as a full-text search library and Apache Solr as an enterprise search platform built on Lucene. Key features of Lucene like indexing, querying, and its architecture are described. The document also explores Solr's features such as caching, SolrCloud, and its architecture. It provides examples of queries in Solr and references for further information.
Elasticsearch is a search and analytics engine that allows real-time processing of data as it flows into systems. It enables exploring and gaining insights from data through real-time search and analytics capabilities. Elasticsearch is distributed, high available, and multi-tenant, allowing it to scale horizontally as needs grow. It uses Lucene for powerful full text search and is document-oriented, schema-free, and has a RESTful API.
HOW TO USE APACHE SOLR TO THE FULLEST EFFORTS: A TECHNICAL EXPLORATION OF SEARCH INDEXING
A search tool improves a website's user experience by making it easier and faster for a user to find what they're looking for. Greater emphasis should be placed on huge, e-commerce, and dynamically updated websites (news sites, blogs).
One of the most well-liked search engines utilized by websites of all sizes is Apache Solr. It is a Java-based open-source search engine that enables you to look up information such as articles, goods, customer reviews, and more. In this article, we will examine Apache Solr in more detail.
What makes Apache Solr so well-liked?
Full-text search, hit highlighting, faceted search, real-time indexing, dynamic clustering, database integration, NoSQL features (non-relational database), and rich document handling are all features of Apace Solr Web Development that make it quick and versatile. These features include the ability to index a variety of document formats, including PDF, MS Office, and Open Office, as well as the ability to index new content instantly.
Some useful information regarding Apache Solr
As a search engine for their websites and publications, CNET Networks, Inc. initially created it. Later, it became an Apache top-level project after being open-sourced. Supports a variety of programming languages, including Ruby, PHP, Java, and Python. Additionally, it offers these languages' APIs.
Has integrated capability for geographic search, enabling location-based content searches. Particularly beneficial for websites like tourism and real estate portals. Use APIs and plugins to support sophisticated search capabilities like spell checking, autocomplete, and custom search. Use Lucene for searching and indexing. What is Apache Lucene An open-source Java search library called Lucene makes it simple to incorporate search or information retrieval into an application. It utilizes a robust search algorithm and is adaptable, strong, and accurate.
Although Lucene is best recognized for its full-text search capabilities, it may also be used to classify documents, analyze data, and retrieve information. Along with English, it also supports a wide variety of additional languages, including German, French, Spanish, Chinese, and Japanese.
Describe indexing
Indexing is the first step for all search engines. The conversion of original data into a highly effective cross-reference lookup to speed up search is known as indexing. Data is not directly indexed by search engines. Tokens (atomic components) are first separated out from the texts. Consulting the search index and obtaining the document that matches the query constitute searching.
Benefits of indexing
• Information retrieval that is quick and accurate (collects, parses, and saves)
• The search engine needs extra time to scan each document without indexing.
• indices of flow
• indices of flow
The document will first be examined and divided into tokens.
May 2012 JaxDUG presentation by Zachary Gramana on using the Lucene.NET library to add search functionality to .NET applications. Contains an overview of search/information retrieval concepts and highlights some common use-cases.
JavaEdge09 : Java Indexing and SearchingShay Sofer
From AlphaCSP's Java conference - JavaEdge09. The presentation of myself and Evgeny Borisov about 'Java Indexing and Searching'
In this session we discussed the need of Full Test Search (as opposed to regular textual/SQL search) , Lucene and it's OO mismatches, the solution that Hibernate Search provides to those mismatches and then a bit about Lucene's scoring algorithm.
Elasticsearch is a distributed, RESTful, free and open source search engine based on Apache Lucene. It allows for fast full text searches across large volumes of data. Documents are indexed in Elasticsearch to build an inverted index that allows for fast keyword searches. The index maps words or numbers to their locations in documents for fast retrieval. Elasticsearch uses Apache Lucene to create and manage the inverted index.
The document compares and summarizes two open source search engines: Sphinx and Apache Solr. Sphinx is a full-text search server written in C++ that allows indexing and searching data stored in SQL databases, NoSQL storage, or files. It offers text processing features, simple APIs, and can scale to billions of documents. Apache Solr is a popular, high performance open source enterprise search platform built on Apache Lucene. It provides powerful full-text search capabilities along with features like hit highlighting, faceted search, and database integration. Both Sphinx and Solr are open source tools for building search functionality into applications.
Grant Ingersoll discussed using open source projects like Lucene for building an open search lab (OSL). Lucene is part of a large ecosystem of open source projects including Solr, Hadoop, Mahout, and others. It provides functionality for indexing, searching, and analyzing large amounts of data. The OSL could use a service-oriented architecture with Lucene and related projects to build a distributed, scalable system for content acquisition, storage, search and machine learning. Lucene is well-suited for information retrieval and data structure research.
A customized web search engine is graduation project .This presentation displays what search engine is and open source software which used in this project
Deep Dive on ElasticSearch Meetup event on 23rd May '15 at www.meetup.com/abctalks
Agenda:
1) Introduction to NOSQL
2) What is ElasticSearch and why is it required
3) ElasticSearch architecture
4) Installation of ElasticSearch
5) Hands on session on ElasticSearch
Welcome to
How can I develop for Apache Solr in 2023?
The capacity to search is a core element of most modern systems. They must incorporate enormous amounts of data while yet allowing the end user to get what they're looking for quickly. DevOps must go beyond conventional databases with difficult and unintuitive (even if brilliant and imaginative) SQL query-based solutions in order to integrate search functions.
A free, open-source search engine built on the Apache Lucene architecture is Searching On Lucene with Replication (Apache Solr). One of the most widely used search engines nowadays, it has been available since 2004. It is a part of the Apache Lucene project. Contrarily, Solr is more than just a search engine; it's also frequently utilized as a key-value store and a document-based NoSQL database with transactional capabilities.
What is the development scope of Apache Solr?
Open-source search platform Solr can be used to make search applications. It was built on top of the full-text search engine Lucene. A quick, scalable, and enterprise-ready search engine is Solr. Applications built with Solr are intelligent and perform very well.In order to enhance the search functionality of CNET Networks' corporate website, Yonik Seely created Solr in 2004. In January 2006, it was accepted as an open-source undertaking by the Apache Software Foundation. The most recent version, Solr 6.0, includes capability for parallel SQL query execution and was released in 2016. Solr and Hadoop might collaborate. Since Hadoop manages a large number of data, Solr helps us find the crucial information from such a vast source.
What functions and duties do developers for Apache Solr perform?
Apache Solr developers collaborate with a group of talented engineers to design and build the next iteration of a company's mobile apps. Other technical and app development teams work closely with the developers to generate the product.
A developer's main responsibilities after securing remote Apache Solr developer employment are as follows:
Develop, keep up with, and enhance new search functionality for the program.
Open-source search APIs and SDKs should be created, improved, and maintained.
Develop and keep up strong query rewriting capabilities.
Make unit test cases for the Solr search engine that are automated.
Design, develop, assess, and test the Solr search engine in collaboration with cross-functional teams.
How to become an Apache Solr developer?
Let's examine the procedures for training to become an Apache Solr developer. To start, keep in mind that no academic degree is necessary to work as an Apache Solr developer. You can learn Apache Solr programming and use it as a vocation, whether you have a degree or not, are smart or inexperienced. All that is needed is real-world experience and a grasp of the necessary non-technical and technical skills.
However, you may have heard that roles for remote Apache Solr developers call for a bachelor's or master's degree in
This document provides an overview of real-time big data processing using Apache Kafka, Spark Streaming, Scala, and Elastic Search. It defines key concepts like big data, real-time big data, and describes technologies like Hadoop, Apache Kafka, Spark Streaming, Scala, and Elastic Search and how they can be used together for real-time big data processing. The document also provides details about each technology and how they fit into an overall real-time big data architecture.
PyCon India 2012: Rapid development of website search in pythonChetan Giridhar
The document discusses developing website search capabilities in Python. It provides an overview of typical search engine components like indexing, analyzing, and searching. It then compares two Python search libraries - Pylucene and Whoosh. Benchmark tests on indexing, committing, and searching a 1GB dataset showed Whoosh to outperform Pylucene in speed. The document recommends designing search as an independent, pluggable component and considers Whoosh and Pylucene as good options for rapid development and integration into Python web projects.
Solr is a great tool to have in the data scientist toolbox. In this talk, I walk through several demos of using Solr to data science activities as well as explore various use cases for Solr and data science
This document discusses how search has evolved beyond traditional text search to support additional use cases like recommendations and analytics. It introduces LucidWorks' products like Solr and SiLK that leverage Hadoop to power search and discovery across large datasets. New features in Solr 4 like reduced memory usage and joins are highlighted. Demos are presented on applications in ecommerce, healthcare, and finance.
Data IO: Next Generation Search with Lucene and Solr 4Grant Ingersoll
The document summarizes new features and capabilities in Lucene and Solr 4 for search. Key highlights include Lucene being faster and more memory efficient through improvements like native near real-time support and string handling. Solr 4 adds new features for search, faceting, relevance, indexing and geospatial search. It also improves capabilities for scaling Solr through distribution and dynamic scaling in SolrCloud. The document provides examples of how Lucene and Solr can be applied to problems beyond traditional search like recommendations, backups and indexing of documents.
A 1 hour intro to search, Apache Lucene and Solr, and LucidWorks Search. Contains a quick start with LucidWorks Search and a demo using financial data (See Github prj: http://bit.ly/lws-financial) as well as some basic vocab and search explanations
http://sigir2013.ie/industry_track.html#GrantIngersoll
Abstract: Apache Lucene and Solr are the most widely deployed search technology on the planet, powering sites like Twitter, Wikipedia, Zappos and countless applications across a large array of domains. They are also free, open source, extensible and extremely scalable. Lucene and Solr also contain a large number of features for solving common information retrieval problems ranging from pluggable posting list compression and scoring algorithms to faceting and spell checking. Increasingly, Lucene and Solr also are being (ab)used to power applications going way beyond the search box. In this talk, we'll explore the features and capabilities of Lucene and Solr 4.x, as well as look at how to (ab)use your search engine technology for fun and profit.
Grant Ingersoll, CTO of LucidWorks, presented on new features and capabilities in Lucene 4 and Solr 4. Key highlights include major performance improvements in Lucene through optimizations like DocValues and native Near Real Time support. Solr 4 features faster indexing and querying, improved geospatial support, and enhancements to SolrCloud including transaction logging for reliability. LucidWorks is continuing to advance Lucene and Solr to provide more flexible, scalable, and robust open source search capabilities.
Presentation from March 18th, 2013 Triangle Java User Group on Taming Text. Presentation covers search, question answering, clustering, classification, named entity recognition, etc. See http://www.manning.com/ingersoll for more.
This document discusses scalable machine learning using Apache Hadoop and Apache Mahout. It describes what scalable machine learning means in the context of large datasets, provides examples of common machine learning use cases like search and recommendations, and outlines approaches for scaling machine learning algorithms using Hadoop. It also describes the capabilities of the Apache Mahout machine learning library for collaborative filtering, clustering, classification and other tasks on Hadoop clusters.
Large Scale Search, Discovery and Analytics in ActionGrant Ingersoll
The document discusses large scale search, discovery, and analysis. It describes how search has evolved beyond basic keyword search to require a holistic view of both user data and user interactions. It provides examples of use cases where advanced search, discovery, and analytics can provide insights from large amounts of data. Key challenges discussed include balancing performance, relevance, and operations across computation and storage systems.
Lucene 4 was recently released with key features including improved language analysis support for over 30 languages, faster indexing and storage capabilities, and pluggable similarity models. The large and diverse Lucene community is always testing to improve performance and relevance. Lucene remains an open source option for text search in applications beyond traditional search engines.
Large Scale Search, Discovery and Analytics with Hadoop, Mahout and SolrGrant Ingersoll
This document discusses large scale search, discovery, and analytics using Apache Solr, Apache Mahout, and Apache Hadoop. It provides an overview of using these tools together for an integrated system that allows for search, discovery of related content, and analytics over large datasets. It describes challenges in building such a system and achieving relevance, performance, and scalability across different components for search, discovery, and analytics functions.
Large Scale Search, Discovery and Analytics with Hadoop, Mahout and SolrGrant Ingersoll
This document discusses large scale search, discovery, and analytics using Apache Solr, Apache Mahout, and Apache Hadoop. It provides an overview of using these tools together for an integrated system that allows for search, discovery of related content, and analytics over large datasets. It describes challenges in building such a system and achieving relevance, performance, and scalability across different components for search, discovery, and analytics use cases.
The document summarizes some unexpected uses of the Apache Lucene library beyond traditional text search. In 3 sentences: Lucene can be used as a fast key-value store, to index and store content in various file formats, and for machine learning tasks like classifying unlabeled documents into predefined categories using vector space models and analyzing document similarity. It also discusses using Lucene for record linkage, question answering systems, randomized testing to improve code quality, and performance improvements in newer Lucene versions.
Starfish: A Self-tuning System for Big Data AnalyticsGrant Ingersoll
Slides from Shivnath Babu's talk at the Triangle Hadoop User Group's April 2011 meeting on Starfish. See also http://www.trihug.org
Machine learning is used widely on the web today. Apache Mahout provides scalable machine learning libraries for common tasks like recommendation, clustering, classification and pattern mining. It implements many algorithms like k-means clustering in a MapReduce framework allowing them to scale to large datasets. Mahout functionality includes collaborative filtering, document clustering, categorization and frequent pattern mining.
Machine learning is used widely on the internet for applications like search, recommendations, and social networking. Apache Mahout is an open source machine learning library that provides scalable machine learning algorithms to analyze large datasets. Mahout includes algorithms for recommendations, clustering, classification, and pattern mining. Many Mahout algorithms are implemented using MapReduce to allow them to scale to large datasets on Hadoop. One example is K-means clustering, which is parallelized across MapReduce jobs to iteratively calculate cluster centroids.
Intelligent Apps with Apache Lucene, Mahout and FriendsGrant Ingersoll
This document discusses building intelligent applications using various open source tools such as Apache Lucene, Mahout, OpenNLP, and others. It defines intelligent applications as those that learn from past behavior and data to adapt and provide personalized insights. Examples of intelligent applications mentioned include Netflix and Amazon. The document then provides an overview of various tools that can be used as building blocks for different components of intelligent applications, such as acquisition, language analysis, search, organization, and user modeling. It also gives examples of how to tie these tools together in an intelligent application and provides resources for further information.
This document provides an overview of machine learning and the Apache Mahout project. It defines machine learning and common use cases such as recommendations, classification, and pattern mining. It then describes what Mahout is, how to get started with Mahout including preparing data, and examples of algorithms like recommendations, clustering, topic modeling, and frequent pattern mining. Future plans for Mahout are also mentioned.
Mobile App Development Company in Saudi ArabiaSteve Jonas
EmizenTech is a globally recognized software development company, proudly serving businesses since 2013. With over 11+ years of industry experience and a team of 200+ skilled professionals, we have successfully delivered 1200+ projects across various sectors. As a leading Mobile App Development Company In Saudi Arabia we offer end-to-end solutions for iOS, Android, and cross-platform applications. Our apps are known for their user-friendly interfaces, scalability, high performance, and strong security features. We tailor each mobile application to meet the unique needs of different industries, ensuring a seamless user experience. EmizenTech is committed to turning your vision into a powerful digital product that drives growth, innovation, and long-term success in the competitive mobile landscape of Saudi Arabia.
AI Changes Everything – Talk at Cardiff Metropolitan University, 29th April 2...Alan Dix
Talk at the final event of Data Fusion Dynamics: A Collaborative UK-Saudi Initiative in Cybersecurity and Artificial Intelligence funded by the British Council UK-Saudi Challenge Fund 2024, Cardiff Metropolitan University, 29th April 2025
https://alandix.com/academic/talks/CMet2025-AI-Changes-Everything/
Is AI just another technology, or does it fundamentally change the way we live and think?
Every technology has a direct impact with micro-ethical consequences, some good, some bad. However more profound are the ways in which some technologies reshape the very fabric of society with macro-ethical impacts. The invention of the stirrup revolutionised mounted combat, but as a side effect gave rise to the feudal system, which still shapes politics today. The internal combustion engine offers personal freedom and creates pollution, but has also transformed the nature of urban planning and international trade. When we look at AI the micro-ethical issues, such as bias, are most obvious, but the macro-ethical challenges may be greater.
At a micro-ethical level AI has the potential to deepen social, ethnic and gender bias, issues I have warned about since the early 1990s! It is also being used increasingly on the battlefield. However, it also offers amazing opportunities in health and educations, as the recent Nobel prizes for the developers of AlphaFold illustrate. More radically, the need to encode ethics acts as a mirror to surface essential ethical problems and conflicts.
At the macro-ethical level, by the early 2000s digital technology had already begun to undermine sovereignty (e.g. gambling), market economics (through network effects and emergent monopolies), and the very meaning of money. Modern AI is the child of big data, big computation and ultimately big business, intensifying the inherent tendency of digital technology to concentrate power. AI is already unravelling the fundamentals of the social, political and economic world around us, but this is a world that needs radical reimagining to overcome the global environmental and human challenges that confront us. Our challenge is whether to let the threads fall as they may, or to use them to weave a better future.
DevOpsDays Atlanta 2025 - Building 10x Development Organizations.pptxJustin Reock
Building 10x Organizations with Modern Productivity Metrics
10x developers may be a myth, but 10x organizations are very real, as proven by the influential study performed in the 1980s, ‘The Coding War Games.’
Right now, here in early 2025, we seem to be experiencing YAPP (Yet Another Productivity Philosophy), and that philosophy is converging on developer experience. It seems that with every new method we invent for the delivery of products, whether physical or virtual, we reinvent productivity philosophies to go alongside them.
But which of these approaches actually work? DORA? SPACE? DevEx? What should we invest in and create urgency behind today, so that we don’t find ourselves having the same discussion again in a decade?
Enhancing ICU Intelligence: How Our Functional Testing Enabled a Healthcare I...Impelsys Inc.
Impelsys provided a robust testing solution, leveraging a risk-based and requirement-mapped approach to validate ICU Connect and CritiXpert. A well-defined test suite was developed to assess data communication, clinical data collection, transformation, and visualization across integrated devices.
Spark is a powerhouse for large datasets, but when it comes to smaller data workloads, its overhead can sometimes slow things down. What if you could achieve high performance and efficiency without the need for Spark?
At S&P Global Commodity Insights, having a complete view of global energy and commodities markets enables customers to make data-driven decisions with confidence and create long-term, sustainable value. 🌍
Explore delta-rs + CDC and how these open-source innovations power lightweight, high-performance data applications beyond Spark! 🚀
Semantic Cultivators : The Critical Future Role to Enable AIartmondano
By 2026, AI agents will consume 10x more enterprise data than humans, but with none of the contextual understanding that prevents catastrophic misinterpretations.
AI EngineHost Review: Revolutionary USA Datacenter-Based Hosting with NVIDIA ...SOFTTECHHUB
I started my online journey with several hosting services before stumbling upon Ai EngineHost. At first, the idea of paying one fee and getting lifetime access seemed too good to pass up. The platform is built on reliable US-based servers, ensuring your projects run at high speeds and remain safe. Let me take you step by step through its benefits and features as I explain why this hosting solution is a perfect fit for digital entrepreneurs.
Big Data Analytics Quick Research Guide by Arthur MorganArthur Morgan
This is a Quick Research Guide (QRG).
QRGs include the following:
- A brief, high-level overview of the QRG topic.
- A milestone timeline for the QRG topic.
- Links to various free online resource materials to provide a deeper dive into the QRG topic.
- Conclusion and a recommendation for at least two books available in the SJPL system on the QRG topic.
QRGs planned for the series:
- Artificial Intelligence QRG
- Quantum Computing QRG
- Big Data Analytics QRG
- Spacecraft Guidance, Navigation & Control QRG (coming 2026)
- UK Home Computing & The Birth of ARM QRG (coming 2027)
Any questions or comments?
- Please contact Arthur Morgan at [email protected].
100% human made.
How Can I use the AI Hype in my Business Context?Daniel Lehner
𝙄𝙨 𝘼𝙄 𝙟𝙪𝙨𝙩 𝙝𝙮𝙥𝙚? 𝙊𝙧 𝙞𝙨 𝙞𝙩 𝙩𝙝𝙚 𝙜𝙖𝙢𝙚 𝙘𝙝𝙖𝙣𝙜𝙚𝙧 𝙮𝙤𝙪𝙧 𝙗𝙪𝙨𝙞𝙣𝙚𝙨𝙨 𝙣𝙚𝙚𝙙𝙨?
Everyone’s talking about AI but is anyone really using it to create real value?
Most companies want to leverage AI. Few know 𝗵𝗼𝘄.
✅ What exactly should you ask to find real AI opportunities?
✅ Which AI techniques actually fit your business?
✅ Is your data even ready for AI?
If you’re not sure, you’re not alone. This is a condensed version of the slides I presented at a Linkedin webinar for Tecnovy on 28.04.2025.
Artificial Intelligence is providing benefits in many areas of work within the heritage sector, from image analysis, to ideas generation, and new research tools. However, it is more critical than ever for people, with analogue intelligence, to ensure the integrity and ethical use of AI. Including real people can improve the use of AI by identifying potential biases, cross-checking results, refining workflows, and providing contextual relevance to AI-driven results.
News about the impact of AI often paints a rosy picture. In practice, there are many potential pitfalls. This presentation discusses these issues and looks at the role of analogue intelligence and analogue interfaces in providing the best results to our audiences. How do we deal with factually incorrect results? How do we get content generated that better reflects the diversity of our communities? What roles are there for physical, in-person experiences in the digital world?
Linux Support for SMARC: How Toradex Empowers Embedded DevelopersToradex
Toradex brings robust Linux support to SMARC (Smart Mobility Architecture), ensuring high performance and long-term reliability for embedded applications. Here’s how:
• Optimized Torizon OS & Yocto Support – Toradex provides Torizon OS, a Debian-based easy-to-use platform, and Yocto BSPs for customized Linux images on SMARC modules.
• Seamless Integration with i.MX 8M Plus and i.MX 95 – Toradex SMARC solutions leverage NXP’s i.MX 8 M Plus and i.MX 95 SoCs, delivering power efficiency and AI-ready performance.
• Secure and Reliable – With Secure Boot, over-the-air (OTA) updates, and LTS kernel support, Toradex ensures industrial-grade security and longevity.
• Containerized Workflows for AI & IoT – Support for Docker, ROS, and real-time Linux enables scalable AI, ML, and IoT applications.
• Strong Ecosystem & Developer Support – Toradex offers comprehensive documentation, developer tools, and dedicated support, accelerating time-to-market.
With Toradex’s Linux support for SMARC, developers get a scalable, secure, and high-performance solution for industrial, medical, and AI-driven applications.
Do you have a specific project or application in mind where you're considering SMARC? We can help with Free Compatibility Check and help you with quick time-to-market
For more information: https://www.toradex.com/computer-on-modules/smarc-arm-family
This is the keynote of the Into the Box conference, highlighting the release of the BoxLang JVM language, its key enhancements, and its vision for the future.
Technology Trends in 2025: AI and Big Data AnalyticsInData Labs
At InData Labs, we have been keeping an ear to the ground, looking out for AI-enabled digital transformation trends coming our way in 2025. Our report will provide a look into the technology landscape of the future, including:
-Artificial Intelligence Market Overview
-Strategies for AI Adoption in 2025
-Anticipated drivers of AI adoption and transformative technologies
-Benefits of AI and Big data for your business
-Tips on how to prepare your business for innovation
-AI and data privacy: Strategies for securing data privacy in AI models, etc.
Download your free copy nowand implement the key findings to improve your business.
2. The How Many GameHow many of you:Have taken a class in Information Retrieval (IR)?Are doing work/research in IR?Have heard of or are using Lucene?Have heard of or are using Solr?Are doing work on core IR algorithms such as compression techniques or scoring?Are doing UI/Application work/research as they relate to search?
3. TopicsBrief BioSearch 101 (skip?)What is:Apache LuceneApache SolrWhat can they do?Features and functionalityIntangiblesWhat’s new in Lucene and Solr?How can they help my research/work/____?
4. Brief BioApache Lucene/Solr CommitterApache Mahout co-founderScalable Machine LearningCo-founder of Lucid Imaginationhttp://www.lucidimagination.comPreviously worked at Center for Natural Lang. Processing at Syracuse Univ. with Dr. LiddyCo-Author of upcoming “Taming Text” (Manning Publications)http://www.manning.com/ingersoll
5. Search 101Search tools are designed for dealing with fuzzy data/questionsWorks well with structured and unstructured dataPerforms well when dealing with large volumes of dataMany apps don’t need the limits that databases place on contentSearch fits well alongside a DB tooGiven a user’s information need, (query) find and, optionally, score content relevant to that needMany different ways to solve this problem, each with tradeoffsWhat’s “relevant” mean?
6. Vector Space Model (VSM) for relevanceCommon across many search enginesApache Lucene is a highly optimized implementation of the VSMSearch 101RelevanceIndexingFinds and maps terms and documents Conceptually similar to a book indexAt the heart of fast search/retrieve
7. Apache Lucene in a Nutshellhttp://lucene.apache.org/javaJava based Application Programming Interface (API) for adding search and indexing functionality to applicationsFast and efficient scoring and indexing algorithmsLots of contributions to make common tasks easier:Highlighting, spatial, Query Parsers, Benchmarking tools, etc.Most widely deployed search library on the planet
8. Lucene BasicsContent is modeled via Documents and FieldsContent can be text, integers, floats, dates, customAnalysis can be employed to alter content before indexingSearches are supported through a wide range of Query optionsKeywordTermsPhrasesWildcardsMany, many more
9. Apache Solr in a Nutshellhttp://lucene.apache.org/solrLucene-based Search Server + other features and functionalityAccess Lucene over HTTP:Java, XML, Ruby, Python, .NET, JSON, PHP, etc.Most programming tasks in Lucene are configuration tasks in SolrFaceting (guided navigation, filters, etc.)Replication and distributed search supportLucene Best Practices
12. Quick Solr/Lucene DemoPre-reqs:Apache Ant 1.7.x, Subversion (SVN)Command Line 1:svn co https://svn.apache.org/repos/asf/lucene/dev/trunksolr-trunkcdsolr-trunk/solr/ant examplecd examplejava –Dsolr.clustering.enabled=true –jar start.jarCommand Line 2cd exampledocs; java –jar post.jar *.xmlhttp://localhost:8983/solr/browse?q=&debugQuery=true&annotateBrowse=true
13. Other FeaturesData Import HandlerDatabase, Mail, RSS, etc.Rich document support via Apache TikaPDF, MS Office, Images, etc.Replication for high query volumeDistributed search for large indexesProduction systems with 1B+ documentsConfigurable Analysis chain and other extension pointsTotal control over tokenization, stemming, etc.
14. IntangiblesOpen SourceFlexible, non-restrictive licenseApache License v2 – non-viral“Do what you want with the software, just don’t claim you wrote it”Large community willing to helpGreat place to learn about real world IR systemsMany books and other documentationLucene in Action by Hatcher, McCandless and Gospodnetic
16. Other New ItemsMany new Analyzers (tokenizers, etc.)Richer Language support (Hindi, Indonesian, Arabic, …)Richer Geospatial (Local) Search capabilitiesScore, filter, sort by distancehttp://wiki.apache.org/solr/SpatialSearchResults GroupingGroup Related Resultshttp://wiki.apache.org/solr/FieldCollapsingMore Faceting CapabilitiesPivotNew underlying algorithms
19. Other Things that Can HelpNutchCrawlinghttp://nutch.apache.orgMahoutMachine learning (clustering, classification, others)http://mahout.apache.orgOpenNLPPart of Speech, Parsers, Named Entity Recognitionhttp://incubator.apache.org/opennlpOpen Relevance ProjectRelevance Judgmentshttp://lucene.apache.org/openrelevance
#12: Rather than talk you through a lot of the features and functionality, let me show you
#13: Do thisExample Queries:ipod184-pin DDRCover: Querying, scoring, faceting, clustering, function queries, spatial, grouping, more like this, indexing