There has never been a better time to be a search and open source enthusiast than 2017. Far behind are the old days of information retrieval being a field only available for academia and experts.
Now we have plenty of search engines that allow us not only to search, but also navigate and discover our information. We are going to be focusing on two of the leading search engines that happed to be open source projects: Elasticsearch and Solr.
Comparison (Solr and Elasticsearch)
Organisations and Support
Solr is an Apache sub-project developed parallelly along Lucene. Thanks to this it has synchronized releases and benefits directly from any new Lucene feature.
Lucidworks (previously Lucid Imagination) is the main company supporting Solr. They provide development resources for Solr, commercial support, consulting services, technical training and commercial software around Solr. Lucidworks is based in San Francisco and offer their services in the USA and all rest of the world through strategic partners. Lucidworks have historically employed around a third of the most active Solr and Lucene committers, contributing most of the Solr code base and organizing the Lucene/Revolution conference every year.
Elasticsearch is an open-source product driven by the company Elastic (formerly known as Elasticsearch). This approach creates a good balance between the open-source community contributing to the product and the company making long term plans for future functionality as well as ensuring transparency and quality.
Elastic comprehends not only Elasticsearch but a set of open-source products called the Elastic stack: Elassticsearch, Kibana, Logstash, and Beats. The company offers support over the whole Elastic stack and a set of commercial products called X-Pack, all included, in different tiers of subscriptions. They offer trainings every second week around the world and organize the ElasticON user conferences.
Solr is an Apache project and by being so it benefits from a large variety of apache projects that can be used along with it. The first and foremost example is its Lucene core (http://lucene.apache.org/core/) that is released on the same schedule and from which it receives all its main functionalities. The other main project is Zookeper that handles SolrCloud clusters configuration and distribution.
When it comes to process information, there are no end to Apache projects, the most commonly used alongside Solr are Mahout for machine learning, Tika for document text and metadata extraction and Spark for data processing.
The big advantage lies in the big data management and storage, with the highly popular Hadoop library as well as Hive, HBase, and Cassandra databases. Solr has support to store the index in a Hadoop Highly Distributed File System for high resilience.
Elasticsearch is owned by the Elastic company that drives and develops all the products on its ecosystem, which makes it very easy to use together.
The main open-source products of the Elastic stack along Elasticsearch are Beats, Logstash and Kibana. Beats is a modular platform to build different lightweight data collectors. Logstash is a data processing pipeline. Kibana is a visualization platform where you can build your own data visualization, but already has many build-in tools to create dashboards over your Elasticsearch data.
Elastic also develop a set of products that are available under subscription: X-Pack. Right now, X-Pack includes five producs: Security, Alerting, Monitoring, Reporting, and Graph. They all deliver a layer of functionality over the Elastic Stack that is described by its name. Most of them are included as a part of Elasticsearch and Kibana.
- Many interfaces, many clients, many languages.
- A query is as simple as solr/select?q=query.
- Easy to preconfigure.
- Base product will always be complete in functionality, commercial is an addon.
- Everything can be done with a JSON HTTP request.
- Optimized for time-based information.
- Tightly coupled ecosystem.
Base product will contain the base and is expandable, commercial are additional features.
Conclusion – Solr or Elasticsearch?
If you are already using one of them and do not explicitly need a feature exclusive of the other, there is no big incentive in making a migration.
In any case, as the common answer when it comes to hardware sizing recommendations for any of them: “It depends.” It depends on the amount of data, the expected growth, the type of data, the available software ecosystem around each, and mostly the features that your requirements and ambitions demand; just to name a few.
At Findwise we can help you make a Platform evaluation study to find the perfect match for your organization and your information.
Written by: Daniel Gómez Villanueva – Findability and Search Expert