ElasticSearch vs Apache SolrCloud

 

Elasticsearch and Apache SolrCloud are both powerful, distributed search engines built on top of Apache Lucene.

Both Elasticsearch and Solr are released under the Apache Software License, however, Solr is truly open-source – Community over code. Solr code is not always beautiful, but once the feature is there it usually stays there and is not removed from the code base. Anyone can contribute to Solr.

On the other hand, Elasticsearch is technically open source, but less so in spirit. Anyone can see the source on Github, anyone can change it and offer a contribution, but only employees of Elastic can make changes to Elasticsearch, 

Apache SolrCloud:

Schema-Based Search: Solr is schema-based and allows you to define a schema for your data, including fields, field types, and analysis chain.

Distributed Search Platform: Apache SolrCloud is a distributed search platform built on top of Apache Lucene. It's known for its scalability and fault tolerance.

RESTful API and Native Clients: Similar to Elasticsearch, Solr provides a RESTful API for indexing and querying data. It also offers native clients for Java, Python, and other programming languages.

 

Dynamic Clustering and Load Balancing: SolrCloud dynamically distributes indexed data across a cluster of nodes and provides built-in load balancing and failover capabilities.

 

Rich Query Syntax: Solr supports a rich query syntax including full-text search, filtering, faceting, highlighting, spatial search, and more.

 

Apache Project: Solr is an Apache Software Foundation project with a large and active open-source community.

 

Built-in Replication and Security: SolrCloud provides built-in replication for data redundancy and security features like authentication and authorization.

 

ElasticSearch:

Distributed Document Store: Elasticsearch is a distributed document-oriented search engine that stores data in JSON format.

 

Real-time Search and Analytics: It's optimized for near real-time search and analytics, making it suitable for various use cases including log and event data analysis.

 

RESTful API: Elasticsearch provides a RESTful API for indexing and querying data, making it easy to integrate with various programming languages and frameworks.

 

Built-in Scalability and Resilience: Elasticsearch is designed to scale horizontally, allowing you to add nodes to the cluster to handle increasing data and query loads. It also provides built-in resilience features like automatic shard replication and failover.

 

Rich Query DSL: It offers a powerful query DSL (Domain Specific Language) for constructing complex queries including full-text search, aggregations, filtering, and more.

 

Ecosystem and Plugins: Elasticsearch has a rich ecosystem with various plugins available for extending its functionality, including plugins for monitoring, security, and data visualization.

 

Owned by Elastic NV: Elasticsearch is developed and maintained by Elastic NV, the same company behind the Elastic Stack (formerly ELK Stack), which includes Elasticsearch, Logstash, and Kibana.

 

Here is a tabular comparison between Solr/SolrCloud and Elasticsearch, highlighting their key differences and similarities:

Feature/Aspect	Solr/SolrCloud	Elasticsearch
Developer	Apache Software Foundation	Elastic NV
Initial Release	2006	2010
Primary Use Case	Full-text search, faceted search, enterprise search	Full-text search, log and event data analysis
Architecture	Standalone (Solr) and distributed (SolrCloud)	Distributed
Indexing	Requires schema definition, can auto-detect	Schema-free, flexible mappings
Query Language	Solr Query Language (based on Lucene)	Query DSL (based on JSON)
Scalability	SolrCloud supports sharding and replication	Built-in sharding and replication
Data Storage	Supports various formats (XML, JSON, CSV)	JSON format primarily
Faceting and Aggregation	Advanced faceting and filtering capabilities	Powerful aggregations and metrics
Community and Support	Large open-source community, commercial support available via Lucidworks	Large community, commercial support via Elastic
Installation and Setup	Can be more complex, especially for SolrCloud	Generally easier to set up and configure
Cluster Management	Managed via Zookeeper in SolrCloud	Managed via built-in APIs, Elastic Stack tools (Kibana, etc.)
Plugins and Extensibility	Wide range of plugins, extensible via Java	Extensive plugin ecosystem, extensible via REST API
Performance	High performance for read-heavy workloads, optimized for search use cases	High performance for both search and analytics, optimized for large-scale data
Security	Basic authentication and authorization, SSL, Kerberos (via plugins or external solutions)	Built-in security features, including TLS, RBAC, and more (with X-Pack)
Monitoring and Management	Tools available (e.g., Solr Admin UI), less integrated monitoring	Integrated monitoring and management tools (e.g., Kibana)
Data Visualization	Limited to third-party tools (e.g., Banana, Grafana)	Native support via Kibana, seamless integration
Updates and Versioning	Regular updates, but can be less frequent	Frequent updates and releases, active development

 Happy learning!