Learn Apache Solr with Big Data and Cloud Computing

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Learn Apache Solr with Big Data and Cloud Computing

Course Description
Solr is the popular, blazing fast open source enterprise search platform from the Apache LuceneTMproject. 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.

Solr is written in Java and runs as a standalone full-text search server within a servlet container such as Jetty. Solr uses the Lucene Java search library at its core for full-text indexing and search, and has REST-like HTTP/XML and JSON APIs that make it easy to use from virtually any programming language. Solr’s powerful external configuration allows it to be tailored to almost any type of application without Java coding, and it has an extensive plugin architecture when more advanced customization is required.

Solr Features

Solr is a standalone enterprise search server with a REST-like API. 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.

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
Linearly scalable, auto index replication, auto failover and recovery
Near Real-time indexing
Flexible and Adaptable with XML configuration
Extensible Plugin Architecture
Solr Uses the LuceneTM Search Library and Extends it!

A Real Data Schema, with Numeric Types, Dynamic Fields, Unique Keys
Powerful Extensions to the Lucene Query Language
Faceted Search and Filtering
Geospatial Search with support for multiple points per document and geo polygons
Advanced, Configurable Text Analysis
Highly Configurable and User Extensible Caching
Performance Optimizations
External Configuration via XML
An AJAX based administration interface
Monitorable Logging
Fast near real-time incremental indexing and index replication
Highly Scalable Distributed search with sharded index across multiple hosts
JSON, XML, CSV/delimited-text, and binary update formats
Easy ways to pull in data from databases and XML files from local disk and HTTP sources
Rich Document Parsing and Indexing (PDF, Word, HTML, etc) using Apache Tika
Apache UIMA integration for configurable metadata extraction
Multiple search indices
Detailed Features

Schema

Defines the field types and fields of documents
Can drive more intelligent processing
Declarative Lucene Analyzer specification
Dynamic Fields enables on-the-fly addition of new fields
CopyField functionality allows indexing a single field multiple ways, or combining multiple fields into a single searchable field
Explicit types eliminates the need for guessing types of fields
External file-based configuration of stopword lists, synonym lists, and protected word lists
Many additional text analysis components including word splitting, regex and sounds-like filters
Pluggable similarity model per field
Query

HTTP interface with configurable response formats (XML/XSLT, JSON, Python, Ruby, PHP, Velocity, CSV, binary)
Sort by any number of fields, and by complex functions of numeric fields
Advanced DisMax query parser for high relevancy results from user-entered queries
Highlighted context snippets
Faceted Searching based on unique field values, explicit queries, date ranges, numeric ranges or pivot
Multi-Select Faceting by tagging and selectively excluding filters
Spelling suggestions for user queries
More Like This suggestions for given document
Function Query – influence the score by user specified complex functions of numeric fields or query relevancy scores.
Range filter over Function Query results
Date Math – specify dates relative to “NOW” in queries and updates
Dynamic search results clustering using Carrot2
Numeric field statistics such as min, max, average, standard deviation
Combine queries derived from different syntaxes
Auto-suggest functionality for completing user queries
Allow configuration of top results for a query, overriding normal scoring and sorting
Simple join capability between two document types
Performance Optimizations
Core

Dynamically create and delete document collections without restarting
Pluggable query handlers and extensible XML data format
Pluggable user functions for Function Query
Customizable component based request handler with distributed search support
Document uniqueness enforcement based on unique key field
Duplicate document detection, including fuzzy near duplicates
Custom index processing chains, allowing document manipulation before indexing
User configurable commands triggered on index changes
Ability to control where docs with the sort field missing will be placed
“Luke” request handler for corpus information
Caching

Configurable Query Result, Filter, and Document cache instances
Pluggable Cache implementations, including a lock free, high concurrency implementation
Cache warming in background
When a new searcher is opened, configurable searches are run against it in order to warm it up to avoid slow first hits. During warming, the current searcher handles live requests.
Autowarming in background
The most recently accessed items in the caches of the current searcher are re-populated in the new searcher, enabling high cache hit rates across index/searcher changes.
Fast/small filter implementation
User level caching with autowarming support
SolrCloud

Centralized Apache ZooKeeper based configuration
Automated distributed indexing/sharding – send documents to any node and it will be forwarded to correct shard
Near Real-Time indexing with immediate push-based replication (also support for slower pull-based replication)
Transaction log ensures no updates are lost even if the documents are not yet indexed to disk
Automated query failover, index leader election and recovery in case of failure
No single point of failure
Admin Interface

Comprehensive statistics on cache utilization, updates, and queries
Interactive schema browser that includes index statistics
Replication monitoring
SolrCloud dashboard with graphical cluster node status
Full logging control
Text analysis debugger, showing result of every stage in an analyzer
Web Query Interface w/ debugging output
Parsed query output
Lucene explain() document score detailing
Explain score for documents outside of the requested range to debug why a given document wasn’t ranked higher.
What are the requirements?
Internet
OS X, Windows or Linux
What am I going to get from this course?
Integrate Search functionality into any web or mobile app
Understand Cloud
Solve Search problem of big data
You can build your own search engine
What is the target audience?
Developers
Engineers
Data Scientists

Curriculum

Section 1: Introduction
Introduction
06:29
“SOLR” Pronunciation
00:36
Section 2: Big Data Fundamentals
What is Big Data
03:10
What Big Data problems Apache Solr solves?
07:06
Section 3: Cloud Computing Fundamentals
What is Cloud Computing?
02:14
How does Solr fit into Cloud?
01:48
Section 4: Fundamentals of Solr
Apache Solr Architecture
04:02
Downloading and Installing Solr
04:21
Solr basic Files
02:20
Basic solr concepts
02:41
Starting up Solr
02:13
HTTP Requests and Responses with Solr
01:36
Solr Admin UI
05:21
Section 5: Search Algorithms
Inverted Index
06:10
Forward Index
02:51
Section 6: Creating a Core
Creating a Core via Admin Panel
03:42
Understanding Structure of Schema.xml
04:37
Define fieldType
08:08
Define field
03:48
Field properties
17:37
copyfield
02:15
dynamicfield
04:58
unique fields
06:43
docvalues vs fieldcache
07:21
Analyzers, Tokenizers and Filters
09:48
Character Filters
02:18
Section 7: Indexing Documents
Adding documents
06:54
Commit and Optimize
05:55
Deleting Documents
06:31
Updating document Values
08:09
Section 8: Querying Documents
Search Fundamentals
05:42
Filter, Fields, Debug and Time Allowed
05:59
Understanding search components and request handlers in solrconfig.xml
06:05
q Parameter in depth
11:45
Range searching
01:31
Function Queries
03:43
Faceting
06:19
Hignlighting
05:46
Spell Checking
19:42
Auto Suggester
05:19
Morelikethis
06:34
Result grouping
03:49
Spatial search, terms component, stats component and query elevation component
06:33
Section 9: Modifying schema
Modifying Schema.xml
06:51
Section 10: Miscellaneous
Solr Logging
03:20
Solr Security
07:26
Section 11: Clustering and Replication
SolrCloud Concepts
Article
Clustering
05:29
Replication
02:27
Section 12: ZooKeeper
Understanding need of Zookeeper
02:53
Setting up ZooKeeper
22:24
Adding More Configs and Collections
00:57
Section 13: SolrCloud
Setting Up Solr Cloud
11:43
Section 14: Final Thoughts
Conclusion
01:10
Exercise Files
1 page

Instructor Biography
QScutter Tutorials, a place to learn technology
QScutter is a Indian based company that offers an ever growing range of high quality eLearning solutions that teach using studio quality narrated videos backed-up with practical hands-on examples. The emphasis is on teaching real life skills that are essential in today’s commercial environment. We provide tutorials for almost all IT topics.

Course Features

  • Lectures
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