Apache Lucene – Apache Solr.
Solr is the popular, blazing fast open source enterprise search platform from the Apache Lucene project. Its major features include powerful full-text search, hit highlighting, faceted search, dynamic clustering, database integration, rich document (e.g., Word, PDF) handling, and geospatial search. Solr is highly scalable, providing distributed search and index replication, and it 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 Tomcat. 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.
See the complete feature list for more details.
For more information about Solr, please see the Solr wiki.
Solr is a standalone enterprise search server with a REST-like API. You put documents in it (called “indexing”) via XML, JSON or binary over HTTP. You query it via HTTP GET and receive XML, JSON, 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
- Scalability – Efficient Replication to other Solr Search Servers
- Flexible and Adaptable with XML configuration
- Extensible Plugin Architecture
Solr Uses the Lucene 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
- 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
- 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
- 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
- HTTP interface with configurable response formats (XML/XSLT, JSON, Python, Ruby, PHP, Velocity, 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, and numeric 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 for completing user queries
- Allow configuration of top results for a query, overriding normal scoring and sorting
- Performance Optimizations
- 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
- 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
- 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
- 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.
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