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Hosted search-as-a-service platform delivering real-time, full-text search for apps and websites
Apache Solr
Blazing-fast multi-modal search platform built on Apache Lucene
Enterprise search platform powering full-text, vector, and geospatial search capabilities. Built on Apache Lucene, Solr delivers high-performance information retrieval for the world's largest organizations.
Overview
Enterprise-Grade Search Platform
Apache Solr is a production-ready search platform that combines full-text search, vector search, and geospatial capabilities in a single solution. Built on the proven Apache Lucene foundation, Solr delivers the performance and scalability required by enterprise organizations managing massive data volumes.
Flexible Deployment Options
Solr adapts to modern infrastructure with native support for Docker containers and Kubernetes orchestration through the official Solr Operator. Whether you're running a single-node development instance or a distributed SolrCloud cluster, the platform scales to meet your needs. The comprehensive Reference Guide walks teams through deployment scenarios from proof-of-concept to production.
Developer-Friendly Architecture
The platform includes multiple example configurations—from schema-less indexing to comprehensive techproducts demonstrations—that accelerate development cycles. Teams can start with sensible defaults and progressively customize as requirements evolve. Built with Java and Gradle, Solr integrates naturally into JVM-based technology stacks while offering REST APIs for polyglot environments.
Highlights
Multi-modal search supporting full-text, vector, and geospatial queries
Native Kubernetes support with official Solr Operator for cloud-native deployments
SolrCloud mode for distributed, fault-tolerant search clusters
Schema-less indexing with dynamic field inference during data ingestion
Pros
- Proven at scale by major organizations worldwide
- Comprehensive deployment options including Docker and Kubernetes
- Rich example configurations accelerate time-to-value
- Active Apache Software Foundation community and governance
Considerations
- Java-based stack requires JVM expertise for optimization
- Steeper learning curve compared to simpler search solutions
- Resource-intensive for small-scale deployments
- Configuration complexity increases with advanced features
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Fit guide
Great for
- Enterprise organizations requiring production-grade search infrastructure
- Teams building applications with diverse search modalities (text, vector, geo)
- Organizations already invested in JVM-based technology stacks
- Projects needing distributed, fault-tolerant search clusters
Not ideal when
- Small applications with basic keyword search requirements
- Teams without Java or JVM infrastructure experience
- Projects requiring minimal resource footprint or embedded search
- Rapid prototypes needing managed search-as-a-service
How teams use it
E-commerce Product Catalog Search
Enable customers to find products through full-text queries, faceted navigation, and geospatial filtering for location-based inventory
Enterprise Knowledge Management
Index documents, wikis, and internal resources with relevance tuning and access controls for organization-wide information retrieval
Semantic Search with Vector Embeddings
Implement AI-powered search using vector representations to surface conceptually similar content beyond keyword matching
Real-time Analytics Dashboard
Aggregate and query time-series data with faceting and grouping for interactive business intelligence visualizations
Tech snapshot
Java95%
Kotlin1%
Python1%
HTML1%
Shell1%
CSS1%
Frequently asked questions
What's the difference between Solr and Elasticsearch?
Both are built on Lucene, but Solr emphasizes configurability and traditional search use cases, while Elasticsearch focuses on analytics and log aggregation. Solr offers stronger SQL support and more mature faceting capabilities.
Can Solr handle vector search for AI applications?
Yes, Solr supports vector search capabilities, enabling semantic search and similarity matching using embeddings from machine learning models alongside traditional full-text search.
How does SolrCloud differ from standalone Solr?
SolrCloud provides distributed indexing and querying with automatic failover, replication, and load balancing across multiple nodes. Standalone Solr runs on a single server without built-in clustering.
What infrastructure is needed to run Solr in production?
Solr requires a JVM environment (Java 11+). For production, consider SolrCloud with ZooKeeper for coordination, adequate RAM for caching, and SSD storage. Docker and Kubernetes deployments are officially supported.
Does Solr support schema-less indexing?
Yes, Solr can infer schema from incoming data during indexing, allowing rapid prototyping. You can also define explicit schemas for production use cases requiring strict data validation and optimization.
Project at a glance
Active- Stars
- 1,552
- Watchers
- 1,552
- Forks
- 805
LicenseApache-2.0
Repo age4 years old
Last commit16 hours ago
Primary languageJava
Last synced 2 hours ago