What is 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, 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 a tool in the Search Tools category of a tech stack.
Who uses Solr?
Companies
222 companies reportedly use Solr in their tech stacks, including Slack, Accenture, and Coursera.
Developers
517 developers on StackShare have stated that they use Solr.
Solr Integrations
Datadog, Netdata, Lucene, StreamSets, and Server Density are some of the popular tools that integrate with Solr. Here's a list of all 8 tools that integrate with Solr.
Pros of Solr
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Decisions about Solr
Here are some stack decisions, common use cases and reviews by companies and developers who chose Solr in their tech stack.
Raja Gc
I have 9TB of documents that need to be indexed. which of the above will suit to handle this much amount of data?
I have client-specific documents. So I would need to create 200 number of indices if 200 clients are there.
what other criteria should I check before choosing Azure Cognitive Search vs Solr?
Solr's Features
- 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 Alternatives & Comparisons
What are some alternatives to Solr?
Lucene
Lucene Core, our flagship sub-project, provides Java-based indexing and search technology, as well as spellchecking, hit highlighting and advanced analysis/tokenization capabilities.
Algolia
Our mission is to make you a search expert. Push data to our API to make it searchable in real time. Build your dream front end with one of our web or mobile UI libraries. Tune relevance and get analytics right from your dashboard.
Elasticsearch
Elasticsearch is a distributed, RESTful search and analytics engine capable of storing data and searching it in near real time. Elasticsearch, Kibana, Beats and Logstash are the Elastic Stack (sometimes called the ELK Stack).
Apache Spark
Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
MongoDB
MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.
