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 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
• Support for Dynamic Faceted Browsing 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 Snapshot Distribution
• Distributed search with sharded index on multiple hosts
• XML and CSV/delimited-text update formats
• Easy ways to pull in data from databases and XML files from local disk and HTTP sources
• 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)
• 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 and explicit queries
• Spelling suggestions for user queries
• More Like This suggestions for given document
• Constant scoring range and prefix queries - no idf, coord, or lengthNorm factors, and no restriction on the number of terms the query matches.
• Function Query - influence the score by a function of a field's numeric value or ordinal
• Date Math - specify dates relative to "NOW" in queries and updates
• Performance Optimizations

Core

• Pluggable query handlers and extensible XML data format
• Document uniqueness enforcement based on unique key field
• Batches updates and deletes for high performance
• User configurable commands triggered on index changes
• Searcher concurrency control
• Correct handling of numeric types for both sorting and range queries
• 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
• 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, enabing 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 via rsync transport
• Pull strategy allows for easy addition of searchers
• Configurable distribution interval allows tradeoff between timeliness and cache utilization

Admin Interface

• Comprehensive statistics on cache utilization, updates, and queries
• 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.