Introduction to The Solr Enterprise Search Server

Solr is a standalone enterprise search server with a web-services like API. You put documents in it (called "indexing") via XML over HTTP. You query it via HTTP GET and receive XML 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
• Scalability - Efficient Replication to other Solr Search Servers
• Flexible and Adaptable with XML configuration
• Extensible Plugin Architecture

• A Real Data Schema, with Numeric Types, Dynamic Fields, Unique Keys
• Powerful Extensions to the Lucene Query Language
• Faceted Search and Filtering
• Advanced, Configurable Text Analysis
• Highly Configurable and User Extensible Caching
• Performance Optimizations
• External Configuration via XML
• An Administration Interface
• Monitorable Logging
• Fast Incremental Updates and Index Replication
• Highly Scalable Distributed search with sharded index across multiple hosts
• 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
• Multiple search indices

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

Query

• HTTP interface with configurable response formats (XML/XSLT, JSON, Python, Ruby, PHP, Velocity, binary)
• Sort by any number of 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, or date ranges
• 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
• Allow configuration of top results for a query, overriding normal scoring and sorting
• 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

Replication

• Efficient distribution of index parts that have changed
• Pull strategy allows for easy addition of searchers
• Configurable distribution interval allows tradeoff between timeliness and cache utilization
• Replication and automatic reloading of configuration files

Admin Interface

• Comprehensive statistics on cache utilization, updates, and queries
• Interactive schema browser that includes index statistics
• Replication monitoring
• 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.