Learn Apache Solr with Big Data and Cloud Computing | Database Design & Development Online Course by Udemy

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Apache Solr, Zookeeper, Clusters, Replication, Cloud, Big data, Search algorithms and Much More

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Learn Apache Solr with Big Data and Cloud Computing

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”

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