Solr vs. Elasticsearch: 5 Differences to Know

Solr is entirely open source, while Elasticsearch is maintained by its parent organization.

Solr excels at handling static data, while Elasticsearch is better suited for cloud-based storage and search solutions.

Installation and configuration are easier with Elasticsearch, but Solr offers more versatility in indexing and shard placement.

Big data is the driving force for business and marketing today. To leverage all of that information, companies have been forced to take sides in the Solr vs Elasticsearch date. While both open-source options are practical tools at developing and bolstering your marketing strategies, there are inherent differences that make for a challenging choice. Here are the primary distinctions to help you find what works best for your business.


Because of their similarities, many marketing departments struggle to determine the most effective. Before delving into the battle of Solr vs Elasticsearch, it’s essential to understand the basics when discussing these tools.

Apache Solr

Using the Java library called Lucese, Solr is an open-sourced program that facilitates search functions within that library’s environment. The program has been in use for over a decade and provides automatic load balancing to prevent volume distribution issues and distributed indexing across the entire platform. Current companies that rely on Solr include:

  • Netflix
  • Apple
  • eBay
  • Amazon (Cloud Search)
  • Cisco
  • com

Thanks to its dynamic search structure, businesses can index and search through multiple websites with the program.


Using the Apache 2 license, Elasticsearch is also an open-source search tool that taps into the Lucene library’s versatility. The software relies on a web interface and a distributed full-text search engine to categorize and filter through data. Utilizing a REST API, Elasticsearch allowes for the secure and reliable software transmission standards.

Solr Vs Elasticsearch

While Solr has more presence thanks to being first on the scene, Elasticsearch is keeping pace with its rival thanks to several features that the other lacks. In contrast, there are some aspects to Elasticsearch’s design that may leave some companies wanting. The two platforms distinguish themselves in several ways:

1. Open Source

While both search engines are based on an open source framework, Solr can be entirely available for anyone looking to tinker or add to the platform. On the other hand, Elesticsearch has its roots in the tan open-source environment but is maintained by its parent organization. This means compatibility issues and additional features can be adjusted in-house by your own IT department when using Solr. At the same time, Elasticsearch adopters will have to hope the Elastic development team considers their ideas.

2. Performance

Elasticsearch and Solr are at the top of their game regarding industry performance standards, but that doesn’t mean they work the same way. For example, the former is excellent for handling cloud-based storage and search solutions, while the latter organizes and analyzes static data much more efficiently. In addition, both platforms are scalable but Solr needs support from Apache’s spate of other programs, such as Zookeeper and Solr Cloud, to be most effective.

3. Data Sources

Getting all that data uploaded is just as important as ensuring it’s analyzed correctly. Solr can grab content from a number of sources, including XML, JDBC, and MS Word documents. Some other platforms, including Apache Zepplin or Fume, use Solr as their data set. Elasticsearch’s JSON-based architecture means it can import data from sources such as Beats and Longstash, while other industry programs also grab information from the platform’s database.

4. Shard Placement

When it comes to versatility about constructing the index and shard placement, the platform is designed to shift shards of data around the cluster dynamically. Users can influence this placement through APIs or consciousness tags. In comparison to Elasticsearch, the Solr system is much more static. Rebalancing the data means manually shifting shards within the clusters themselves and then synchronizing information and deleting the unneeded portion.

5. Installation and Configuration

In the contest between Solr and Elasticsearch, the former is definitely not as easy to set up as the latter. Conversely, Elasticsearch is a much beefier platform requiring 1GB of HEAP memory as compared to half that with Solr. The installation package for Solr is much smaller as well, peaking at 192 MB as opposed to its competitor’s 315 MB. In addition, the Solr framework uses schema files to help organize data, while Elasticsearch allows you to define the indexing in-program.

Joining the Data Race

Choosing either Solr or Elasticsearch comes down to the functionality you’re looking for. If information retrieval is most important, Solr may be your best option. However, Elasticsearch wins in the scalability and production department. No matter which you choose, both options provide unique functions that most businesses find invaluable. Look to your company’s needs to determine which option will help make your data analysis projects a huge success!