The document provides an introduction to Apache Solr, an open source enterprise search platform. It outlines the objectives of the training, which are to understand the need for enterprise search, how indexing and searching works in Lucene and Solr, Solr features like faceting and highlighting, and job opportunities for Solr developers. The training will cover topics such as Solr architecture, indexing, querying, analysis, and configuration using solrconfig.xml.
A presentation given at the Lucene/Solr Revolution 2014 conference to show Solr and Elasticsearch features side by side. The presentation time was only 30 minutes, so only the core usability features were compared. The full video is embedded on the last slide.
Building Your Data Streams for all the IoTDevOps.com
Apache NIFI and OPC-UA have been game changers in the world of IoT, allowing you to automate the flow of data from IoT sensors to just about anywhere you want. In this presentation, Craig Hobbs of InfluxData will demonstrate how you can collect and process data streams of millions of values per second using an OPC-UA server and automate into an InfluxDB stream processing engine using Nifi. You'll learn how to setup your workflow to gain a highly-available, fast, scalable, and reliable data stream as a real-world application.
A closer look at the MySQL and PostgreSQL compatible relational database built for the cloud that combines the performance and availability of high-end commercial databases with the simplicity and cost-effectiveness of open source databases. We’ll explore how Amazon Aurora uses the AWS cloud to provide high reliability, high durability, and high throughput.
Spark + Parquet In Depth: Spark Summit East Talk by Emily Curtin and Robbie S...Spark Summit
What if you could get the simplicity, convenience, interoperability, and storage niceties of an old-fashioned CSV with the speed of a NoSQL database and the storage requirements of a gzipped file? Enter Parquet.
At The Weather Company, Parquet files are a quietly awesome and deeply integral part of our Spark-driven analytics workflow. Using Spark + Parquet, we’ve built a blazing fast, storage-efficient, query-efficient data lake and a suite of tools to accompany it.
We will give a technical overview of how Parquet works and how recent improvements from Tungsten enable SparkSQL to take advantage of this design to provide fast queries by overcoming two major bottlenecks of distributed analytics: communication costs (IO bound) and data decoding (CPU bound).
Apache Solr is a popular, open source enterprise search platform built on the Java based search engine library Apache Lucene. It powers the search and navigation features of many of the world's largest companies like Netflix, Instagram, LinkedIn, Twitter and eBay, etc.
A presentation given at the Lucene/Solr Revolution 2014 conference to show Solr and Elasticsearch features side by side. The presentation time was only 30 minutes, so only the core usability features were compared. The full video is embedded on the last slide.
Building Your Data Streams for all the IoTDevOps.com
Apache NIFI and OPC-UA have been game changers in the world of IoT, allowing you to automate the flow of data from IoT sensors to just about anywhere you want. In this presentation, Craig Hobbs of InfluxData will demonstrate how you can collect and process data streams of millions of values per second using an OPC-UA server and automate into an InfluxDB stream processing engine using Nifi. You'll learn how to setup your workflow to gain a highly-available, fast, scalable, and reliable data stream as a real-world application.
A closer look at the MySQL and PostgreSQL compatible relational database built for the cloud that combines the performance and availability of high-end commercial databases with the simplicity and cost-effectiveness of open source databases. We’ll explore how Amazon Aurora uses the AWS cloud to provide high reliability, high durability, and high throughput.
Spark + Parquet In Depth: Spark Summit East Talk by Emily Curtin and Robbie S...Spark Summit
What if you could get the simplicity, convenience, interoperability, and storage niceties of an old-fashioned CSV with the speed of a NoSQL database and the storage requirements of a gzipped file? Enter Parquet.
At The Weather Company, Parquet files are a quietly awesome and deeply integral part of our Spark-driven analytics workflow. Using Spark + Parquet, we’ve built a blazing fast, storage-efficient, query-efficient data lake and a suite of tools to accompany it.
We will give a technical overview of how Parquet works and how recent improvements from Tungsten enable SparkSQL to take advantage of this design to provide fast queries by overcoming two major bottlenecks of distributed analytics: communication costs (IO bound) and data decoding (CPU bound).
Apache Solr is a popular, open source enterprise search platform built on the Java based search engine library Apache Lucene. It powers the search and navigation features of many of the world's largest companies like Netflix, Instagram, LinkedIn, Twitter and eBay, etc.
This presentation describes how to efficiently load data into Hive. I cover partitioning, predicate pushdown, ORC file optimization and different loading schemes
A Thorough Comparison of Delta Lake, Iceberg and HudiDatabricks
Recently, a set of modern table formats such as Delta Lake, Hudi, Iceberg spring out. Along with Hive Metastore these table formats are trying to solve problems that stand in traditional data lake for a long time with their declared features like ACID, schema evolution, upsert, time travel, incremental consumption etc.
ORC files were originally introduced in Hive, but have now migrated to an independent Apache project. This has sped up the development of ORC and simplified integrating ORC into other projects, such as Hadoop, Spark, Presto, and Nifi. There are also many new tools that are built on top of ORC, such as Hive’s ACID transactions and LLAP, which provides incredibly fast reads for your hot data. LLAP also provides strong security guarantees that allow each user to only see the rows and columns that they have permission for.
This talk will discuss the details of the ORC and Parquet formats and what the relevant tradeoffs are. In particular, it will discuss how to format your data and the options to use to maximize your read performance. In particular, we’ll discuss when and how to use ORC’s schema evolution, bloom filters, and predicate push down. It will also show you how to use the tools to translate ORC files into human-readable formats, such as JSON, and display the rich metadata from the file including the type in the file and min, max, and count for each column.
Building robust CDC pipeline with Apache Hudi and DebeziumTathastu.ai
We have covered the need for CDC and the benefits of building a CDC pipeline. We will compare various CDC streaming and reconciliation frameworks. We will also cover the architecture and the challenges we faced while running this system in the production. Finally, we will conclude the talk by covering Apache Hudi, Schema Registry and Debezium in detail and our contributions to the open-source community.
Automate Your Kafka Cluster with Kubernetes Custom Resources confluent
(Sam Obeid, Shopify) Kafka Summit SF 2018
At Shopify we manage multiple Apache Kafka clusters in multiple locations in Google’s cloud platform. We deploy our Kafka clusters as Kubernetes StatefulSets, and we use other K8s workloads to implement different tasks. Automating critical and repetitive operational tasks is one of our top priorities.
In this talk we’ll discuss how we leveraged Kubernetes Custom Resources and Controllers to automate some of the key cluster operational tasks, to detect clusters configuration changes and react to these changes with required actions. We will go through actual examples we implemented at Shopify, how we solved the problem of cluster discovery and how we automated topics creation across different clusters with zero human intervention and safety controls.
ApacheCon 2022: From Column-Level to Cell-Level_ Towards Finer-grained Encryp...XinliShang1
This talk is about Apache Parquet cell-level encryption feature. It allows encryption can happen at the cell(intersection of column and row) level, which is finer-grained than the column level.
This presentation describes how to efficiently load data into Hive. I cover partitioning, predicate pushdown, ORC file optimization and different loading schemes
A Thorough Comparison of Delta Lake, Iceberg and HudiDatabricks
Recently, a set of modern table formats such as Delta Lake, Hudi, Iceberg spring out. Along with Hive Metastore these table formats are trying to solve problems that stand in traditional data lake for a long time with their declared features like ACID, schema evolution, upsert, time travel, incremental consumption etc.
ORC files were originally introduced in Hive, but have now migrated to an independent Apache project. This has sped up the development of ORC and simplified integrating ORC into other projects, such as Hadoop, Spark, Presto, and Nifi. There are also many new tools that are built on top of ORC, such as Hive’s ACID transactions and LLAP, which provides incredibly fast reads for your hot data. LLAP also provides strong security guarantees that allow each user to only see the rows and columns that they have permission for.
This talk will discuss the details of the ORC and Parquet formats and what the relevant tradeoffs are. In particular, it will discuss how to format your data and the options to use to maximize your read performance. In particular, we’ll discuss when and how to use ORC’s schema evolution, bloom filters, and predicate push down. It will also show you how to use the tools to translate ORC files into human-readable formats, such as JSON, and display the rich metadata from the file including the type in the file and min, max, and count for each column.
Building robust CDC pipeline with Apache Hudi and DebeziumTathastu.ai
We have covered the need for CDC and the benefits of building a CDC pipeline. We will compare various CDC streaming and reconciliation frameworks. We will also cover the architecture and the challenges we faced while running this system in the production. Finally, we will conclude the talk by covering Apache Hudi, Schema Registry and Debezium in detail and our contributions to the open-source community.
Automate Your Kafka Cluster with Kubernetes Custom Resources confluent
(Sam Obeid, Shopify) Kafka Summit SF 2018
At Shopify we manage multiple Apache Kafka clusters in multiple locations in Google’s cloud platform. We deploy our Kafka clusters as Kubernetes StatefulSets, and we use other K8s workloads to implement different tasks. Automating critical and repetitive operational tasks is one of our top priorities.
In this talk we’ll discuss how we leveraged Kubernetes Custom Resources and Controllers to automate some of the key cluster operational tasks, to detect clusters configuration changes and react to these changes with required actions. We will go through actual examples we implemented at Shopify, how we solved the problem of cluster discovery and how we automated topics creation across different clusters with zero human intervention and safety controls.
ApacheCon 2022: From Column-Level to Cell-Level_ Towards Finer-grained Encryp...XinliShang1
This talk is about Apache Parquet cell-level encryption feature. It allows encryption can happen at the cell(intersection of column and row) level, which is finer-grained than the column level.
Solr is the popular, blazing fast open Source Enterprise search platform from the Apache LuceneTM project. Its major features include powerful full-text search, hit highlighting, faceted search, near real-time indexing, dynamic clustering, database integration, rich document (e.g., Word, PDF) handling, and geospatial search. Solr is highly reliable, scalable and fault tolerant, providing distributed indexing, replication and load-balanced querying, automated failover and recovery, centralized configuration and more. Solr powers the search and navigation features of many of the world's largest internet sites like (Aol, Yahoo, Buy.com, Cnet, CitySearch, Netflix, Zappos, Stubhub!, digg, eTrade, Disney, Apple, NASA and MTV).
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.
SolrTM is the popular, blazing fast open Source Enterprise search platform from the Apache LuceneTM project. Its major features include powerful full-text search, hit highlighting, faceted search, near real-time indexing, dynamic clustering, database integration, rich document (e.g., Word, PDF) handling, and geospatial search. Solr is highly reliable, scalable and fault tolerant, providing distributed indexing, replication and load-balanced querying, automated failover and recovery, centralized configuration and more. Solr powers the search and navigation features of many of the world's largest internet sites like (Aol, Yahoo, Buy.com, Cnet, CitySearch, Netflix, Zappos, Stubhub!, digg, eTrade, Disney, Apple, NASA and MTV).
Apache Solr is the popular, blazing fast open source enterprise search platform; it uses
Lucene as its core search engine. Solr’s major features include powerful full-text search, hit
highlighting, faceted search, dynamic clustering, database integration, and complex queries.
Solr is highly scalable, providing distributed search and index replication, and it powers the
search and navigation features of many of the world's largest internet sites.
Apache Solr is the popular, blazing fast open source enterprise search platform; it uses
Lucene as its core search engine. Solr’s major features include powerful full-text search, hit
highlighting, faceted search, dynamic clustering, database integration, and complex queries.
Solr is highly scalable, providing distributed search and index replication, and it powers the
search and navigation features of many of the world's largest internet sites.
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
Drupal and Apache Solr Search Go Together Like Pizza and Beer for Your Sitenyccamp
The Apache Solr Search Integration module provides integration with the (free, open-source) Apache Solr server. This great combination of Drupal with a powerful and flexible search server will make your site irresistible to visitors by providing advanced search features like faceting filtering and by delivering the most relevant search results from your site. The module has been re-written for Drupal 7 to integrate with Facet API and those changes have been backported to a new Drupal 6 branch. Thus, you can use this module for all your projects, as well as setting up a shared search index that allows you to search across different Drupal 6 and Drupal 7 sites. This talk will focus on explaining configurations options in the admin UI to help you quickly and confidently configure the facets, pages, related content blocks, and other features for your site. Highlights may include:
- What are the key Solr concepts you need to understand to get the most out of Solr integration?
- How is the module admin UI organized?
- How do I configure facets, sorts, and content recommendation blocks?
- How can I use additional modules to index file attachments?
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: moinz.lair@gmail.com
What to learn during the 21 days Lockdown | EdurekaEdureka!
Register Here: https://resources.edureka.co/21-days-learning-plan-webinar/
In light of the complete national lockdown for 21 days, we invite you to join a FREE webinar by renowned Mentor and Advisor, Nitin Gupta as he helps you create a 21-day learning gameplan to maximize returns for your career.
The webinar will help freshers and experienced professionals to capitalize on these 21 days and figure out the best technologies to learn while confined to home.
You will also get all your questions and doubts resolved in real-time.
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Top 10 Dying Programming Languages in 2020 | EdurekaEdureka!
YouTube Link: https://youtu.be/LSM7hD6GM4M
Get Edureka Certified in Trending Programming Languages: https://www.edureka.co
In this highly competitive IT industry, everyone wants to learn programming languages that will keep them ahead of the game. But knowing what to learn so you gain the most out of your knowledge is a whole other ball game. So, we at Edureka have prepared a list of Top 10 Dying Programming Languages 2020 that will help you to make the right choice for your career. Meanwhile, if you ever wondered about which languages are slated for continuing uptake and possible greatness, we have a list for that, too.
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Top 5 Trending Business Intelligence Tools | EdurekaEdureka!
YouTube Link: https://youtu.be/eEwq_mPd1iI
Edureka BI Certification Training Courses: https://www.edureka.co/bi-and-visualization-certification-courses
Receiving insights and finding trends is absolutely critical for businesses to scale and adapt as the years go on. This is exactly what business intelligence does and the best thing about these software solutions is that their potential uses are practically unlimited.
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Tableau Tutorial for Data Science | EdurekaEdureka!
YouTube Link:https://youtu.be/ZHNdSKMluI0
Edureka Tableau Certification Training: https://www.edureka.co/tableau-certification-training
This Edureka's PPT on "Tableau for Data Science" will help you to utilize Tableau as a tool for Data Science, not only for engagement but also comprehension efficiency. Through this PPT, you will learn to gain the maximum amount of insight with the least amount of effort.
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YouTube Link:https://youtu.be/CVv8zhYEjUE
Edureka Python Certification Training: https://www.edureka.co/data-science-python-certification-course
This Edureka PPT on 'Python Programming' will help you learn Python programming basics with the help of interesting hands-on implementations.
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Get Edureka Certified in Trending Project Management Certifications: https://www.edureka.co/project-management-and-methodologies-certification-courses
Whether you want to scale up your career or are trying to switch your career path, Project Management Certifications seems to be a perfect choice in either case. So, we at Edureka have prepared a list of Top 5 Project Management Certifications that you must check out in 2020 for a major career boost.
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Top Maven Interview Questions in 2020 | EdurekaEdureka!
YouTube Link: https://youtu.be/5iTcAR4fScM
**DevOps Certification Courses - https://www.edureka.co/devops-certification-training***
This video on 'Maven Interview Questions' discusses the most frequently asked Maven Interview Questions. This PPT will help give you a detailed explanation of the topics which will help you in acing the interviews.
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** Linux Administration Certification Training - https://www.edureka.co/linux-admin **
Linux Mint is the first operating system that people from Windows or Mac are drawn towards when they have to switch to Linux in their work environment. Linux Mint has been around since the year 2006 and has grown and matured into a very user-friendly OS. Do watch the PPT till the very end to see all the demonstrations.
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How to Deploy Java Web App in AWS| EdurekaEdureka!
YouTube Link:https://youtu.be/Ozc5Yu_IcaI
** Edureka AWS Architect Certification Training - https://www.edureka.co/aws-certification-training**
This Edureka PPT shows how to deploy a java web application in AWS using AWS Elastic Beanstalk. It also describes the advantages of using AWS for this purpose.
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*** Edureka Digital Marketing Course: https://www.edureka.co/post-graduate/digital-marketing-certification***
This Edureka PPT on "Top 10 Reasons to Learn Digital Marketing" will help you understand why you should take up Digital Marketing
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** RPA Training: https://www.edureka.co/robotic-process-automation-training**
This PPT on RPA in 2020 will provide a glimpse of the accomplishments and benefits provided by RPA. Also, it will list out the new changes and technologies that will collaborate with RPA in 2020.
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**DevOps Certification Courses - https://www.edureka.co/devops-certification-training **
This PPT shows how to configure Jenkins to receive email notifications. It also includes a demo that shows how to do it in 6 simple steps in the Windows machine.
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EA Algorithm in Machine Learning | EdurekaEdureka!
YouTube Link: https://youtu.be/DIADjJXrgps
** Machine Learning Certification Training: https://www.edureka.co/machine-learning-certification-training **
This Edureka PPT on 'EM Algorithm In Machine Learning' covers the EM algorithm along with the problem of latent variables in maximum likelihood and Gaussian mixture model.
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PGP in AI and Machine Learning (9 Months Online Program): https://www.edureka.co/post-graduate/machine-learning-and-ai
This Edureka PPT on "Cognitive AI" explains cognitive computing and how it helps in making better human decisions at work. Also, it explains the differences between cognitive computing and artificial intelligence.
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Edureka AWS Architect Certification Training - https://www.edureka.co/aws-certification-training
This Edureka PPT on AWS Cloud Practitioner will provide a complete guide to your AWS Cloud Practitioner Certification exam. It will explain the exam details, objectives, why you should get certified and also how AWS certification will help your career.
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Blue Prism Top Interview Questions | EdurekaEdureka!
YouTube Link: https://youtu.be/ykbRdUNIbyQ
** RPA Training: https://www.edureka.co/robotic-process-automation-certification-courses**
This PPT on Blue Prism Interview Questions will cover the Top 50 Blue Prism related questions asked in your interviews.
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This PPT will help you in understanding how AWS deals smartly with Big Data. It also shows how AWS can solve Big Data challenges with ease.
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A star algorithm | A* Algorithm in Artificial Intelligence | EdurekaEdureka!
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** Artificial Intelligence and Deep Learning: https://www.edureka.co/ai-deep-learni... **
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Apache Solr-Webinar
1. www.edureka.co/apache-solr
Introduction to APACHE SOLR
View Apache Solr course details at www.edureka.co/apache-solr
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2. Slide 2
LIVE Online Class
Class Recording in LMS
24/7 Post Class Support
Module Wise Quiz
Project Work
Verifiable Certificate
www.edureka.co/apache-solr
How it Works?
3. Objectives
At the end of this module, you will be able to:
Understand the need for search engine for enterprise grade applications
Understand the objectives & challenges of search engine
What is Indexing & Searching & Why do you need them ?
What is Lucene & its overview?
How is Indexing & Searching Handled in Lucene
What is Solr & its features?
What is Solr schema & its structure?
Understand how to achieve Bigdata/NoSQL needs using SolrCloud
Explore job opportunity for Solr Developers
Slide 3 www.edureka.co/apache-solr
5. What is Lucene ?
Lucene is a powerful Java search library that lets you easily add search or Information Retrieval (IR) to applications
Used by LinkedIn, Twitter, … and many more (see http://wiki.apache.org/lucene-java/PoweredBy )
Scalable & High-performance Indexing
Powerful, Accurate and Efficient Search Algorithms
Cross-Platform Solution
» Open Source & 100% pure Java
» Implementations in other programming languages available that are index-compatible
Doug Cutting “Creator”
Slide 5 www.edureka.co/apache-solr
6. Why Indexing ?
Search engine indexing collects, parses, and stores data to facilitate fast and
accurate information retrieval
The purpose of storing an index is to optimize speed and performance in
finding relevant documents for a search query
Without an index, the search engine would scan every document in the
corpus, which would require considerable time and computing power
For example, while an index of 10,000 documents can be queried within
milliseconds, a sequential scan of every word in 10,000 large documents could
take hours
Slide 6 www.edureka.co/apache-solr
7. Indexing: Flow
Tokens Inverted Index
Document analysis indexing
We can get a better idea of the flow of indexing from the following example:
“edureka”
Position:0
Offset:0
Length:7
“hadoop”
Position:1
Offset:8
Length:6
“edureka hadoop” tokenization
“Term Vector” “Term Vector”
Slide 7 www.edureka.co/apache-solr
8. Lucene: Writing to Index
Document
Field
Field
Field
Field
Analyzer IndexWriter Directory
Classes used when indexing documents with Lucene
Slide 8 www.edureka.co/apache-solr
9. Lucene: Searching In Index
Query Parser translates a textual expression from the end into an arbitrarily complex query for searching
Expression Query object
QueryParser
IndexSearcher Text fragments
Analyzer
Slide 9 www.edureka.co/apache-solr
10. Lucene: Inverted Indexing Technique
1 1 1
3
1 1 1
3
1 1 1
3
1 1 1
3
1 1
9
Indexing uses Inverted Index technique
(Ex: Book Index). Because indexes are
faster to read documents
Write a new segment for each new
document insertion
Merge the segments when too many of
them into the index. (Merge-sort
technique to merge the index in to the
store.)
Single updates are costly, preferred bulk
updates due to merging
Slide 10 www.edureka.co/apache-solr
11. Lucene: Storage Schema
Like “databases” Lucene does not have common global schema
Lucene has indexes, which contains documents
Each document can have multiple fields
Each document can have different fields for every document
Fields can be only used to index & search or store it for retrieval
You can add new fields at any point of time
Document-1
<Field1>
<Field2>
<Field3>
Document-2
<Field2>
<Field3>
<Field4>
Index-1
Slide 11 www.edureka.co/apache-solr
12. Analyzers
Analyzers handle the job of analyzing text into tokens or keywords to be searched / indexed
An Analyzer builds TokenStreams, which analyze text and represents a policy for extracting index terms from
text
There are few default Analyzers provided by Lucene, which can be used at the time of indexing or querying
Analyzers are provided to parse & analyze different languages like (Chinese, Japanese etc.,)
Reader Tokenizer TokenFilter TokenFilter TokenFilter Tokens
Slide 12 www.edureka.co/apache-solr
15. Scoring: Score Boosting
Document’s weight / score can be changed from default, which is called as boosting
Lucene allows influencing search results by "boosting" at different times:
Scoring
Index Time
Query Time
Index-time boost by calling Field.setBoost() before
a document is added to the index
Query-time boost by setting a boost on a query clause,
calling Query.setBoost()
Slide 15 www.edureka.co/apache-solr
16. Key Features
Faceting
Highlighting
Grouping
Joins
Spatial Search
Apache Tika Support
Slide 16 www.edureka.co/apache-solr
18. Search Engine: Why do I need them?
1. Text Based Search
2. Filter
3. Documents
1
2
3
Slide 18 www.edureka.co/apache-solr
19. Solr: Introduction
Solr is an open source enterprise search server / web application
Solr Uses the Lucene Search Library and extends it
Solr exposes lucene Java API’s as REST-Full services
You put documents in it (called "indexing") via XML, JSON, CSV or binary over HTTP
You query it via HTTP GET and receive XML, JSON, CSV or binary results
Slide 19 www.edureka.co/apache-solr
20. Solr: History
In 2004, Solr was created by “Yonik Seeley” at CNET Networks as an in-house project to add
search capability for the company website
In January 2006, CNET Networks decided to openly publish the source code by donating it to
the Apache Software Foundation under the Lucene top-level project
In September 2008, Solr 1.3 was released with many enhancements including distributed
search capabilities and performance enhancements among many others
In October 2012 Solr version 4.0 was released, including the new SolrCloud feature
Yonik Seeley
Slide 20 www.edureka.co/apache-solr
21. Solr: Key Features
Advanced Full-Text Search Capabilities
Optimized for High Volume Web Traffic
Standards Based Open Interfaces - XML, JSON and HTTP
Comprehensive HTML Administration Interfaces
Server statistics exposed over JMX for monitoring
Near Real-time indexing and Adaptable with XML Configuration
Linearly scalable, auto index replication, auto, Extensible Plugin Architecture
Slide 21 www.edureka.co/apache-solr
24. Solr
Instance
Solr: Schema Hierarchy
Core/Index
Documents
Field Field
Core/Index Core/Index
Indexing & Querying
Schema.xml
Slide 24 www.edureka.co/apache-solr
25. Solr: Core
Solr Core: Also referred to as just a "Core"
This is a running instance of a Lucene index along with all the Solr configuration (SolrConfigXml, SchemaXml, etc...)
required to use it
A single Solr application can contain 0 or more cores
Cores are run largely in isolation but can communicate with each other if necessary via the CoreContainer
Solr initially only supported one index, and the SolrCore class was a singleton for coordinating the low-level functionality
at the "core" of Solr
Slide 25 www.edureka.co/apache-solr
26. Solr: Documents & Fields
Solr's basic unit of information is a document, which is a set of data that describes something
Documents are composed of fields, which are more specific pieces of information
Fields can contain different kinds of data. A name field, for example, is text (character data)
The field type tells Solr how to interpret the field and how it can be queried
Slide 26 www.edureka.co/apache-solr
27. Solr: Indexing Data
A Solr index can accept data from many different sources, including XML files, comma-separated value (CSV) files, data
extracted from tables in a database, and files in common file formats such as Microsoft Word or PDFs
Here are the three most common ways of loading data into a Solr index:
Uploading XML files by sending HTTP requests to the Solr
Using Index Handlers to Import from databases
Using the Solr Cell framework
Writing a custom Java application to ingest data through Solr's Java Client
Slide 27 www.edureka.co/apache-solr
28. Analysis
Analyzers
Tokenizers
Filters
Solr: Analysis
There are three main concepts in analysis: analyzers, tokenizers, and filters
Analyzers are used both during, when a document is indexed, and at query
time
» The same analysis process need not be used for both operations
» An analyzer examines the text of fields and generates a token stream
» Analyzers may be a single class or they may be composed of a series
of tokenizer and filter classes
Tokenizers break field data into lexical units, or tokens
Filters examine a stream of tokens and keep them, transform or discard
them, or create new ones
Slide 28 www.edureka.co/apache-solr
29. Solr: solrconfig.xml
Lib directives
indicates where
Solr can find JAR
files for extensions
Register event handlers
for searcher events;
for example queries
To execute to warm
new searchers
Activates version-dependent
features in Lucene
Index management
settings
Enable JMX
instrumentation of
Solr MBeans
Update
handler for
indexing
documents
Cache-management
settings
Slide 29 www.edureka.co/apache-solr
30. Solr: Search Process
qt: selects a RequestHandler for a query using/select(by default ,the DisMaxRequestHandler is used)
Request
Handler
defType : selects a query parser for the query
(by default, uses whatever has been
configured for the RequestHandler)
Query Parser
Response
Writer
qf: selects which fields to query
in the index(by default, all fields
are required)
Index
wt: selects a response writer
for formatting the query
response
fq: filters query by applying an additional query to
the initial query’s results, caches the results
Rows:
specifies the
number of rows
to be displayed
at one time
Start: specifies an
offset(by default 0)
into the query results
where the returned
response should begin
Slide 30 www.edureka.co/apache-solr
31. Solr Features
Faceting
Highlighting
Spell Checking
Query-Re-ranking
Transforming
Suggestors
More Like This
Pagination
Grouping & Clustering
Spatial Search
Components
Real time (Get & Update)
LABS
Slide 31 www.edureka.co/apache-solr
33. SolrCloud Introduction
Apache Solr includes the ability to set up a cluster of Solr servers that combines fault tolerance and high availability
called SolrCloud
SolrCloud is flexible distributed search and indexing, without a master node to allocate nodes, shards and replicas
Solr uses ZooKeeper to manage these locations, depending on configuration files and schemas
Documents can be sent to any server and ZooKeeper will figure it out
Slide 33 www.edureka.co/apache-solr
34. Features
Horizontal Scaling (For Sharding & Replication)
Elastic Scaling
High Availability
Distributed Indexing
Distribution Searching
Central Configuration For Entire Cluster
Automatic Load Balancing
Automatic Failover For Queries
Zookeeper Integration For Coordination & Configurations
Slide 34 www.edureka.co/apache-solr
38. Disclaimer
Criteria and guidelines mentioned in this presentation may change. Please visit our website for
latest and additional information on Apache Solr
Slide 38 www.edureka.co/apache-solr