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.
An accompanying blog post about this subject can be found at http://www.jurriaanpersyn.com/archives/2013/11/18/introduction-to-elasticsearch/
Learning To Rank has been the first integration of machine learning techniques with Apache Solr allowing you to improve the ranking of your search results using training data.
One limitation is that documents have to contain the keywords that the user typed in the search box in order to be retrieved(and then reranked). For example, the query “jaguar” won’t retrieve documents containing only the terms “panthera onca”. This is called the vocabulary mismatch problem.
Neural search is an Artificial Intelligence technique that allows a search engine to reach those documents that are semantically similar to the user’s information need without necessarily containing those query terms; it learns the similarity of terms and sentences in your collection through deep neural networks and numerical vector representation(so no manual synonyms are needed!).
This talk explores the first Apache Solr official contribution about this topic, available from Apache Solr 9.0.
We start with an overview of neural search (Don’t worry - we keep it simple!): we describe vector representations for queries and documents, and how Approximate K-Nearest Neighbor (KNN) vector search works. We show how neural search can be used along with deep learning techniques (e.g, BERT) or directly on vector data, and how we implemented this feature in Apache Solr, giving usage examples!
Join us as we explore this new exciting Apache Solr feature and learn how you can leverage it to improve your search experience!
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
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.
An accompanying blog post about this subject can be found at http://www.jurriaanpersyn.com/archives/2013/11/18/introduction-to-elasticsearch/
Learning To Rank has been the first integration of machine learning techniques with Apache Solr allowing you to improve the ranking of your search results using training data.
One limitation is that documents have to contain the keywords that the user typed in the search box in order to be retrieved(and then reranked). For example, the query “jaguar” won’t retrieve documents containing only the terms “panthera onca”. This is called the vocabulary mismatch problem.
Neural search is an Artificial Intelligence technique that allows a search engine to reach those documents that are semantically similar to the user’s information need without necessarily containing those query terms; it learns the similarity of terms and sentences in your collection through deep neural networks and numerical vector representation(so no manual synonyms are needed!).
This talk explores the first Apache Solr official contribution about this topic, available from Apache Solr 9.0.
We start with an overview of neural search (Don’t worry - we keep it simple!): we describe vector representations for queries and documents, and how Approximate K-Nearest Neighbor (KNN) vector search works. We show how neural search can be used along with deep learning techniques (e.g, BERT) or directly on vector data, and how we implemented this feature in Apache Solr, giving usage examples!
Join us as we explore this new exciting Apache Solr feature and learn how you can leverage it to improve your search experience!
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
In this presentation, we are going to discuss how elasticsearch handles the various operations like insert, update, delete. We would also cover what is an inverted index and how segment merging works.
Presented by Adrien Grand, Software Engineer, Elasticsearch
Although people usually come to Lucene and related solutions in order to make data searchable, they often realize that it can do much more for them. Indeed, its ability to handle high loads of complex queries make Lucene a perfect fit for analytics applications and, for some use-cases, even a credible replacement for a primary data-store. It is important to understand the design decisions behind Lucene in order to better understand the problems it can solve and the problems it cannot solve. This talk will explain the design decisions behind Lucene, give insights into how Lucene stores data on disk and how it differs from traditional databases. Finally, there will be highlights of recent and future changes in Lucene index file formats.
오픈소스 검색엔진인 Elasticsearch 어떻게 저장하고 조회하는지 검색엔진의 개념에 대해서 간단히 살펴보고, Node.js 로 구현된 아주 간단한 예제를 소개합니다.
- 검색엔진과 Elasticsearch 소개
- Elasticsearch에서의 색인
- Elasticsearch에서의 조회
- Node.js 로 구현된 예제 소개
* 자바카페
자바카페 페이스북 : https://www.facebook.com/groups/javacafe/
자바카페 기술 블로그 : http://tech.javacafe.io/
ElasticSearch introduction talk. Overview of the API, functionality, use cases. What can be achieved, how to scale? What is Kibana, how it can benefit your business.
An introduction to elasticsearch with a short demonstration on Kibana to present the search API. The slide covers:
- Quick overview of the Elastic stack
- indexation
- Analysers
- Relevance score
- One use case of elasticsearch
The query used for the Kibana demonstration can be found here:
https://github.com/melvynator/elasticsearch_presentation
Beyond SQL: Speeding up Spark with DataFramesDatabricks
In this talk I describe how you can use Spark SQL DataFrames to speed up Spark programs, even without writing any SQL. By writing programs using the new DataFrame API you can write less code, read less data and let the optimizer do the hard work.
Word2Vec model to generate synonyms on the fly in Apache Lucene.pdfSease
f you want to expand your query/documents with synonyms in Apache Lucene, you need to have a predefined file containing the list of terms that share the same semantic. It’s not always easy to find a list of basic synonyms for a language and, even if you find it, this doesn’t necessarily match with your contextual domain.
The term “daemon” in the domain of operating system articles is not a synonym of “devil” but it’s closer to the term “process”.
Word2Vec is a two-layer neural network that takes as input a text and outputs a vector representation for each word in the dictionary. Two words with similar meanings are identified with two vectors close to each other.
Introduction to Elastic Search
Elastic Search Terminology
Index, Type, Document, Field
Comparison with Relational Database
Understanding of Elastic architecture
Clusters, Nodes, Shards & Replicas
Search
How it works?
Inverted Index
Installation & Configuration
Setup & Run Elastic Server
Elastic in Action
Indexing, Querying & Deleting
Building a semantic search system - one that can correctly parse and interpret end-user intent and return the ideal results for users’ queries - is not an easy task. It requires semantically parsing the terms, phrases, and structure within queries, disambiguating polysemous terms, correcting misspellings, expanding to conceptually synonymous or related concepts, and rewriting queries in a way that maps the correct interpretation of each end user’s query into the ideal representation of features and weights that will return the best results for that user. Not only that, but the above must often be done within the confines of a very specific domain - ripe with its own jargon and linguistic and conceptual nuances.
This talk will walk through the anatomy of a semantic search system and how each of the pieces described above fit together to deliver a final solution. We'll leverage several recently-released capabilities in Apache Solr (the Semantic Knowledge Graph, Solr Text Tagger, Statistical Phrase Identifier) and Lucidworks Fusion (query log mining, misspelling job, word2vec job, query pipelines, relevancy experiment backtesting) to show you an end-to-end working Semantic Search system that can automatically learn the nuances of any domain and deliver a substantially more relevant search experience.
With the advent of deep learning and algorithms like word2vec and doc2vec, vectors-based representations are increasingly being used in search to represent anything from documents to images and products. However, search engines work with documents made of tokens, and not vectors, and are typically not designed for fast vector matching out of the box. In this talk, I will give an overview of how vectors can be derived from documents to produce a semantic representation of a document that can be used to implement semantic / conceptual search without hurting performance. I will then describe a few different techniques for efficiently searching vector-based representations in an inverted index, including LSH, vector quantization and k-means tree, and compare their performance in terms of speed and relevancy. Finally, I will describe how each technique can be implemented efficiently in a lucene-based search engine such as Solr or Elastic Search.
In this presentation, we are going to discuss how elasticsearch handles the various operations like insert, update, delete. We would also cover what is an inverted index and how segment merging works.
Presented by Adrien Grand, Software Engineer, Elasticsearch
Although people usually come to Lucene and related solutions in order to make data searchable, they often realize that it can do much more for them. Indeed, its ability to handle high loads of complex queries make Lucene a perfect fit for analytics applications and, for some use-cases, even a credible replacement for a primary data-store. It is important to understand the design decisions behind Lucene in order to better understand the problems it can solve and the problems it cannot solve. This talk will explain the design decisions behind Lucene, give insights into how Lucene stores data on disk and how it differs from traditional databases. Finally, there will be highlights of recent and future changes in Lucene index file formats.
오픈소스 검색엔진인 Elasticsearch 어떻게 저장하고 조회하는지 검색엔진의 개념에 대해서 간단히 살펴보고, Node.js 로 구현된 아주 간단한 예제를 소개합니다.
- 검색엔진과 Elasticsearch 소개
- Elasticsearch에서의 색인
- Elasticsearch에서의 조회
- Node.js 로 구현된 예제 소개
* 자바카페
자바카페 페이스북 : https://www.facebook.com/groups/javacafe/
자바카페 기술 블로그 : http://tech.javacafe.io/
ElasticSearch introduction talk. Overview of the API, functionality, use cases. What can be achieved, how to scale? What is Kibana, how it can benefit your business.
An introduction to elasticsearch with a short demonstration on Kibana to present the search API. The slide covers:
- Quick overview of the Elastic stack
- indexation
- Analysers
- Relevance score
- One use case of elasticsearch
The query used for the Kibana demonstration can be found here:
https://github.com/melvynator/elasticsearch_presentation
Beyond SQL: Speeding up Spark with DataFramesDatabricks
In this talk I describe how you can use Spark SQL DataFrames to speed up Spark programs, even without writing any SQL. By writing programs using the new DataFrame API you can write less code, read less data and let the optimizer do the hard work.
Word2Vec model to generate synonyms on the fly in Apache Lucene.pdfSease
f you want to expand your query/documents with synonyms in Apache Lucene, you need to have a predefined file containing the list of terms that share the same semantic. It’s not always easy to find a list of basic synonyms for a language and, even if you find it, this doesn’t necessarily match with your contextual domain.
The term “daemon” in the domain of operating system articles is not a synonym of “devil” but it’s closer to the term “process”.
Word2Vec is a two-layer neural network that takes as input a text and outputs a vector representation for each word in the dictionary. Two words with similar meanings are identified with two vectors close to each other.
Introduction to Elastic Search
Elastic Search Terminology
Index, Type, Document, Field
Comparison with Relational Database
Understanding of Elastic architecture
Clusters, Nodes, Shards & Replicas
Search
How it works?
Inverted Index
Installation & Configuration
Setup & Run Elastic Server
Elastic in Action
Indexing, Querying & Deleting
Building a semantic search system - one that can correctly parse and interpret end-user intent and return the ideal results for users’ queries - is not an easy task. It requires semantically parsing the terms, phrases, and structure within queries, disambiguating polysemous terms, correcting misspellings, expanding to conceptually synonymous or related concepts, and rewriting queries in a way that maps the correct interpretation of each end user’s query into the ideal representation of features and weights that will return the best results for that user. Not only that, but the above must often be done within the confines of a very specific domain - ripe with its own jargon and linguistic and conceptual nuances.
This talk will walk through the anatomy of a semantic search system and how each of the pieces described above fit together to deliver a final solution. We'll leverage several recently-released capabilities in Apache Solr (the Semantic Knowledge Graph, Solr Text Tagger, Statistical Phrase Identifier) and Lucidworks Fusion (query log mining, misspelling job, word2vec job, query pipelines, relevancy experiment backtesting) to show you an end-to-end working Semantic Search system that can automatically learn the nuances of any domain and deliver a substantially more relevant search experience.
With the advent of deep learning and algorithms like word2vec and doc2vec, vectors-based representations are increasingly being used in search to represent anything from documents to images and products. However, search engines work with documents made of tokens, and not vectors, and are typically not designed for fast vector matching out of the box. In this talk, I will give an overview of how vectors can be derived from documents to produce a semantic representation of a document that can be used to implement semantic / conceptual search without hurting performance. I will then describe a few different techniques for efficiently searching vector-based representations in an inverted index, including LSH, vector quantization and k-means tree, and compare their performance in terms of speed and relevancy. Finally, I will describe how each technique can be implemented efficiently in a lucene-based search engine such as Solr or Elastic Search.
Is Your Index Reader Really Atomic or Maybe Slow?lucenerevolution
Presented by Uwe Schindler | SD DataSolutions GmbH - See conference video - http://www.lucidimagination.com/devzone/events/conferences/lucene-revolution-2012
Since the first day, Apache Lucene exposed the two fundamental concepts of reading and writing an index directly through IndexReader & IndexWriter. However, the API did not reflect reality; from the IndexWriter perspective this was desirable but when reading the index this caused several problems in the past. In reality a Lucene index is not a single index while logically treated as a such. This talk will introduce the new API classes AtomicReader and CompositeReader added in Lucene 4.0 as very general interfaces, and DirectoryReader, which most people know as the segment-based “Lucene index on disk”. The talk will also cover more changes and improvements to the search API like reader contexts that allow to convert local document ids to global ones from IndexSearcher. Lucene changed all IndexReaders to be read-only, so it’s no longer possible to modify indexes using those classes. Finally, Uwe Schindler will show migration paths from custom norm values to the various new ranking models that were added to Lucene; this includes using Similarity with Lucene 4.0’s DocValues as replacement for norms.
Must be similar to screenshotsI must be able to run the projects.docxherthaweston
Must be similar to screenshots
I must be able to run the projects on Eclipse so that I can upload the codes to my Github account
The projects must say that they were created by
Juliet Mercado
Zachary Willis
Ihor Panchenko
Craig Anderson
Building a Search Engine, Part I: Governance, Workflow, and UI
(This is the first project in this series)
You are going to design, build, and test a scaled-down version of “Google Search”. Rather than searching the Internet's files, you will only search local files added to your search engine's index. Your search engine will allow an administrator to add, update, and remove files from the index. Users will be able to enter search terms, and select between Boolean AND, OR, or PHRASE search. The matching file names (if any) are then displayed in a list.
You also need to design the system architecture (the high-level design), so you can plan each part.
Search Engine Project Proposal:
Build a search engine with simple GUI, that can do AND, OR, and PHRASE Boolean searches on a small set of text files. The user should be able to say the type of search to do, and enter some search terms. The results should be a list of file pathnames that match the search. This should be a stand-alone application
User Interfaces
In addition to the main user interface (for doing searching), you will need a separate administrator or maintenance interface to manage your application. It should be easy to add and remove files (from the set of indexed files), and to regenerate the index anytime. When starting, your application should check if any of the files have been changed or deleted since the application last saved the index. If so, the administrator should be able to have the index updated with the modified file(s).
Note that with HTML, Word, or other types of documents, you would need to extract a plain text version before indexing. That isn't hard, but the search engine is complex enough already. For these projects, limit your search engine to only plain text files (including .txt, .html, and other text files).
The index must be stored on disk, so next time your application starts it can reload its data. The index, list of files, and other data, can be stored in one or more file(s) or in a database. The saved data should be read whenever your application starts. The saved data should be updated (or recreated) when you add, update, or remove documents from your set (of indexed documents), or perhaps just when your application exits. If you use files, the file formats are up to you; have a format that is fast and simple to load and store.
To keep things as simple as possible, in this project you can assume that only a small set of documents will be indexed, and thus the whole index can be kept in memory at once. (That's probably not the case for Google's data!) All you need to do is be able to read the index data from disk at startup into memory, and write it back either when updating the inde ...
Apache LuceneTM is a free open-source , high-performance, full-featured text search engine library that has been written completely in Java. As a technology is best suited for any application that requires full-text search, especially cross-platform.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Le nuove frontiere dell'AI nell'RPA con UiPath Autopilot™UiPathCommunity
In questo evento online gratuito, organizzato dalla Community Italiana di UiPath, potrai esplorare le nuove funzionalità di Autopilot, il tool che integra l'Intelligenza Artificiale nei processi di sviluppo e utilizzo delle Automazioni.
📕 Vedremo insieme alcuni esempi dell'utilizzo di Autopilot in diversi tool della Suite UiPath:
Autopilot per Studio Web
Autopilot per Studio
Autopilot per Apps
Clipboard AI
GenAI applicata alla Document Understanding
👨🏫👨💻 Speakers:
Stefano Negro, UiPath MVPx3, RPA Tech Lead @ BSP Consultant
Flavio Martinelli, UiPath MVP 2023, Technical Account Manager @UiPath
Andrei Tasca, RPA Solutions Team Lead @NTT Data
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
2. Introduction
Lucene Index
Lucene Index data in form of Posting list which are in Inverted Index format.
How does it look ?
Lucene index data in files called segments.
Unlike a database, Lucene has no notion of a fixed global schema
Lucene’s flexible schema also means a single index can hold documents that
rep- resent different entities.
Lucene requires you to flatten, or de-normalize, your content when you index it.
3. A document is Lucene’s atomic unit of indexing and searching. It’s a
container that holds one or more fields, which in turn contain the “real”
content.
To index your raw content sources, you must first translate it into Lucene’s
documents and fields. Then, at search time, it’s the field values that are
searched
Three things Lucene can do with each field:
The value may be indexed
If it’s indexed, the field may also optionally store term vectors,
the field’s value may be stored,
5. Indexing Process
Enriching and Creating the Document
To Index any data, we need to get text of the raw data i.e the form in which Lucene
can ingest the data.
Build Documents are not always simple, when you are indexing from database or
PDF or Website HTML you need to have to do so much, preprocess so that a proper
Document can be build out of it.
Analysis
Method addDocument & addDocuments of IndexWriter Class hand our data off to
Lucene to index.
As a first step Lucene analyzes the text, create tokens out of it and perform analysis
operations like for instance, tokens could be lowercased before indexing, so that it
will help in making search case insensitive.
StemFilter, Synonyms and Stopwords are such examples of analysis
6. Adding to the index
After the analyzed part is done, data is ready to be added to index.
Lucene uses inverted index as the data structure beneath the surface.
Lets see how it works ?
Rather than answering question
“What words are contained in this document?”
it is optimized for providing quick answers to
“Which documents contain word X?”
Lucene index data in the Segments
8. INDEX SEGMENTS
Each segment is a standalone index, holding a subset of all indexed documents.
Index Time : A new segment is created whenever the writer flushes buffered
documents and pending deletions into the directory.
Search time: Each segment is visited separately and the results are combined.
Each segment is consist of various types of files :
_X.<ext> where X is the segment’s name and ext is extension
There are separate files to hold the different parts of the index
You can use compound file format so that most of these index files are collapsed into a
single compound file in extension .cfs
segements file is the file which contains references of all live segments named
segments_<N>
9. Types of Index files and formats:
Name Extension Brief Description
Segments File segments.gen, segments_N Stores information about segments
Lock File write.lock The Write lock prevents multiple IndexWriters from writing to
same file.
Compound File .cfs An optional "virtual" file consisting of all the other index files for
systems that frequently run out of file handles.
Fields .fnm Stores information about the fields
Field Index .fdx Contains pointers to field data
Field Data .fdt The stored fields for documents
Term Infos .tis Part of the term dictionary, stores term info
Term Info Index .tii The index into the Term Infos file
Frequencies .frq Contains the list of docs which contain each term along with
frequency
Positions .prx Stores position information about where a term occurs in the
index
Norms .nrm Encodes length and boost factors for docs and fields
Term Vector Index .tvx Stores offset into the document data file
Term Vector Documents .tvd Contains information about each document that has term
Term Vector Fields .tvf The field level info about term vectors
Deleted Documents .del Info about what files are deleted
10. Indexing Utils
Indexing Operations
Adding documents
addDocument(Document) Adds the document using the default analyze
addDocuments(List<Document>) Adds the document using the default analyze in a block
Deleting documents
IndexWriter provides various methods to remove documents from an index:
deleteDocuments(Term)
deleteDocuments(Term[])
deleteDocuments(Query)
deleteDocuments(Query[])
As with added documents, you must call commit() or close() on your writer to commit the changes to the index.
hasDeletions() method to check if an index contains any documents marked for deletion.
After optimize the deleted docs got removed from index
11. Indexing Operations
Updating documents
updateDocument(Term, Document) first deletes all documents containing the
provided term and then adds the new document using the writer’s default analyzer.
updateDocument(Term, Document, Analyzer) does the same but uses provided
analyzer instead of the writer’s default analyzer.
12. Optimize Index
When you index documents, especially many documents or using multiple
sessions with IndexWriter, you’ll invariably create an index that has many
separate segments.
When you search the index, Lucene must search each segment separately
then combine the results.
This has a tradeoff as the large no of segments the large no of seprate search
and more the merge would be.
An optimized index also consumes fewer file descriptors during searching.
Optimizing only improves searching speed, not indexing speed.
13. Optimize Index
IndexWriter exposes four methods to optimize:
forceMerge(int maxNumSegments): Forces merge policy to merge segments until
there are <= maxNumSegments.
forceMerge(int maxNumSegments, boolean doWait): Just like forceMerge(int),
except you can specify whether the call should block until all merging completes.
forceMergeDeletes() : Forces merging of all segments that have deleted
14. Index Commits
A new index commit is created whenever you invoke one of IndexWriter’s
commit methods.
Commits all pending changes (added and deleted documents, segment
merges, added indexes, etc.) to the index, and syncs all referenced index files,
such that a reader will see the changes and the index updates will survive an
or machine crash or power loss.
The steps IndexWriter takes during commit:
Flush any buffered documents and deletions.
Sync all newly created files, including newly flushed files
Write and sync the next segments_N file.
Remove old commits by calling on IndexDeletionPolicy to remove old com- mits.
15. Index Merging
When an index has too many segments, IndexWriter selects some of the segments
and merges them into a single, large segment
There are various merge policies like : LogMergePolicy , LogDocMergePolicy etc
Concurrency, thread safety, and locking issues
Any number of read-only IndexReaders may be open at once on a single index.
Only a single writer may be open on an index at once. Lucene uses a write lock
to enforce this
IndexReaders may be open even while an IndexWriter is making changes to the
index. Each IndexReader will always show the index as of the point in time that it
was opened. It won’t see any changes being done by the IndexWriter until the
commits and the reader is reopened.
16. Concurrency, thread safety, and locking issues
The Lucene index only blocks concurrent write operations on the index.
Various implementations of Lock are :
NoLockFactory
SimpleFSLockFactory
SingleInstanceLockFactory
VerifyingLockFactory
17. Boosting documents and fields
Index-time boosts are not supported anymore. As a replacement, index-time
scoring factors should be indexed into a doc value field combined at query
time using eg. FunctionScoreQuery.