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Solr

  • Course Code: Big Data - Solr
  • Course Dates: Contact us to schedule.
  • Course Category: Big Data & Data Science Duration: 4 Days Audience: This course is geared for those who wants to implement scalable search using Apache Solr

Course Snapshot 

  • Duration: 4 days 
  • Skill-level: Foundation-level Solr skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for those who wants to implement scalable search using Apache Solr  
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation. 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

Whether you’re handling big (or small) data, managing documents, or building a website, it is important to be able to quickly search through your content and discover meaning in it. Apache Solr is your tool: a ready-to-deploy, Lucene-based, open source, full-text search engine. Solr can scale across many servers to enable real-time queries and data analytics across billions of documents. 

Solr teaches you to implement scalable search using Apache Solr. This easy-to-read guide balances conceptual discussions with practical examples to show you how to implement all of Solr’s core capabilities. You’ll master topics like text analysis, faceted search, hit highlighting, result grouping, query suggestions, multilingual search, advanced geospatial and data operations, and relevancy tuning. 

Working in a hands-on learning environment, led by our Solr expert instructor, students will learn about and explore: 

  • Redis expands on the key-value pattern by accepting a wide variety of data types, including hashes, strings, lists, and other structures. 
  •  It provides lightning-fast operations on in-memory datasets, and also makes it easy to persist to disk on the fly. Plus, it’s free and open source. 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • How to scale Solr for big data 
  • Rich real-world examples 
  • Solr as a NoSQL data store 
  • Advanced multilingual, data, and relevancy tricks 
  • Coverage of versions through Solr 4.7 

Audience & Pre-Requisites 

This course is geared for attendees who want to implement scalable search using Apache Solr 

Pre-Requisites:  Students should have  

  • Basic to Intermediate IT Skills 
  • basic knowledge of Java and standard database technology.  
  • No prior knowledge of Solr or Lucene is required. 

Course Agenda / Topics 

  1. INTRODUCTION TO SOLRFREE 
  • WHY DO I NEED A SEARCH ENGINE? 
  • WHAT IS SOLR? 
  • WHY SOLR? 
  • FEATURES OVERVIEW 
  1. GETTING TO KNOW SOLR 
  • GETTING STARTED 
  • SEARCHING IS WHAT IT’S ALL ABOUT 
  • TOUR OF THE SOLR ADMINISTRATION CONSOLE 
  • ADAPTING THE EXAMPLE TO YOUR NEEDS 
  1. KEY SOLR CONCEPTSFREE 
  • SEARCHING, MATCHING, AND FINDING CONTENT 
  • RELEVANCY 
  • PRECISION AND RECALL 
  • SEARCHING AT SCALE 
  1. CONFIGURING SOLR 
  • OVERVIEW OF SOLRCONFIG.XML 
  • QUERY REQUEST HANDLING 
  • MANAGING SEARCHERS 
  • CACHE MANAGEMENT 
  • REMAINING CONFIGURATION OPTIONS 
  1. INDEXING 
  • EXAMPLE MICROBLOG SEARCH APPLICATION 
  • DESIGNING YOUR SCHEMA 
  • DEFINING FIELDS IN SCHEMA.XML 
  • FIELD TYPES FOR STRUCTURED NONTEXT FIELDS 
  • SENDING DOCUMENTS TO SOLR FOR INDEXING 
  • UPDATE HANDLER 
  • INDEX MANAGEMENT 
  1. TEXT ANALYSIS 
  • ANALYZING MICROBLOG TEXT 
  • BASIC TEXT ANALYSIS 
  • DEFINING A CUSTOM FIELD TYPE FOR MICROBLOG TEXT 
  • ADVANCED TEXT ANALYSIS 
  1. PERFORMING QUERIES AND HANDLING RESULTS 
  • THE ANATOMY OF A SOLR REQUEST 
  • WORKING WITH QUERY PARSERS 
  • QUERIES AND FILTERS 
  • THE DEFAULT QUERY PARSER (LUCENE QUERY PARSER) 
  • HANDLING USER QUERIES (EDISMAX QUERY PARSER) 
  • OTHER USEFUL QUERY PARSERS 
  • RETURNING RESULTS 
  • SORTING RESULTS 
  • DEBUGGING QUERY RESULTS 
  1. FACETED SEARCH 
  • NAVIGATING YOUR CONTENT AT A GLANCE 
  • SETTING UP TEST DATA 
  • FIELD FACETING 
  • QUERY FACETING 
  • RANGE FACETING 
  • FILTERING UPON FACETED VALUES 
  • MULTISELECT FACETING, KEYS, AND TAGS 
  • BEYOND THE BASICS 
  1. HIT HIGHLIGHTING 
  • OVERVIEW OF HIT HIGHLIGHTING 
  • HOW HIGHLIGHTING WORKS 
  • IMPROVING PERFORMANCE USING FASTVECTORHIGHLIGHTER 
  • POSTINGSHIGHLIGHTER 
  1. QUERY SUGGESTIONS 
  • SPELL-CHECK 
  • AUTOSUGGESTING QUERY TERMS 
  • SUGGESTING DOCUMENT FIELD VALUES 
  • SUGGESTING QUERIES BASED ON USER ACTIVITY 
  1. RESULT GROUPING/FIELD COLLAPSING 
  • RESULT GROUPING VS. FIELD COLLAPSING 
  • SKIPPING DUPLICATE DOCUMENTS 
  • RETURNING MULTIPLE DOCUMENTS PER GROUP 
  • GROUPING BY FUNCTIONS AND QUERIES 
  • PAGING AND SORTING GROUPED RESULTS 
  • GROUPING GOTCHAS 
  • EFFICIENT FIELD COLLAPSING WITH THE COLLAPSING QUERY PARSER 
  1. TAKING SOLR TO PRODUCTION 
  • DEVELOPING A SOLR DISTRIBUTION 
  • DEPLOYING SOLR 
  • HARDWARE AND SERVER CONFIGURATION 
  • DATA ACQUISITION STRATEGIES 
  • SHARDING AND REPLICATION 
  • SOLR CORE MANAGEMENT 
  • MANAGING CLUSTERS OF SERVERS 
  • QUERYING AND INTERACTING WITH SOLR 
  • MONITORING SOLR’S PERFORMANCE 
  • UPGRADING BETWEEN SOLR VERSIONS 
  1. SOLRCLOUD 
  • GETTING STARTED WITH SOLRCLOUD 
  • CORE CONCEPTS 
  • DISTRIBUTED INDEXING 
  • DISTRIBUTED SEARCH 
  • COLLECTIONS API 
  • BASIC SYSTEM-ADMINISTRATION TASKS 
  • ADVANCED TOPICS 
  1. CH 14. MULTILINGUAL SEARCH 
  • WHY LINGUISTIC ANALYSIS MATTERS 
  • STEMMING VS. LEMMATIZATION 
  • STEMMING  
  • HANDLING EDGE CASES 
  • AVAILABLE LANGUAGE LIBRARIES IN SOLR 
  • SEARCHING CONTENT IN MULTIPLE LANGUAGES 
  • LANGUAGE IDENTIFICATION 
  1. COMPLEX QUERY OPERATIONS 
  • FUNCTION QUERIES 
  • GEOSPATIAL SEARCH 
  • PIVOT FACETING 
  • REFERENCING EXTERNAL DATA 
  • CROSS-DOCUMENT AND CROSS-INDEX JOINS 
  • BIG DATA ANALYTICS WITH SOLR 
  1. MASTERING RELEVANCY 
  • THE IMPACT OF RELEVANCY TUNING 
  • DEBUGGING THE RELEVANCY CALCULATION 
  • RELEVANCY BOOSTING 
  • PLUGGABLE SIMILARITY CLASS IMPLEMENTATIONS 
  • PERSONALIZED SEARCH AND RECOMMENDATIONS 
  • CREATING A PERSONALIZED SEARCH EXPERIENCE 
  • RUNNING RELEVANCY EXPERIMENTS 
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