Solr vs Elasticsearch

Difference Between Solr vs Elasticsearch

Solr vs Elasticsearch both are an open-source search engine, Solr(pronounced as solar), built on Apache Lucene Library, which is in Java. It is developed in Java. It consists of HTTP/XML web API interfaces. Solr queries are in the form of JSON documents. Elasticsearch is highly scalable analytics and search engine. It is developed in Java and is basically a wrapper on Apache Lucene Library. It consists of an HTTP web API interface. It has no schema with JSON documents where all the data is stored.

Head to Head Comparison between Solr vs Elasticsearch (Infographics)

Below are the top 9 comparisons between Solr vs Elasticsearch:

Key differences between Solr vs Elasticsearch

Let us discuss some key differences between Solr vs Elasticsearch in the following points:

• Solr has been in the industry since 2004, and it is safe to say that it is a mature product, and it has a huge community. Elasticsearch is relatively new to the industry. Solr is truly open source in the sense that anyone can contribute to the product, whereas Elastic is slightly restricted. All the contributions made makes it to the product only when the development team of elasticsearch approves it.
• Elasticsearch talks in terms of JSON. Solr also supports JSON, but this feature was added recently. When Solr was built initially, its main language was XML.
• Solr’s a focus is mainly on text search and related operations. Elasticsearch is a step ahead in this by analyzing logs and visualization along with basic text queries.
• Solr has a special feature, facet search. Faceting is a process that enables users to narrow down the query results. It can be considered as guided navigation.
• Search engines, by default, work with huge data sets. To improve efficiency, it is recommended that they are modular and scalable. Elasticsearch is scalable and is very flexible when it comes to data clusters. Solr itself is not very scalable, but SolrCloud, managed by a zookeeper, does the job.
• Solr allows the addition of shards on the go based on the requirements of the applications. Elastic search actually discourages this practice. It has a fixed number of primary shards that cannot be altered. This is done to increase the query efficiency as the number of shards/indexes increase, latency in fetching the results increase.
• Elastic search exposes multiple metrics to the users for monitoring, visualization, etc. Solr also exposes metrics and APIs but not as extensive as Elasticsearch.
• Caching plays a major role in search engines for faster responses. Elastic search caches at a segment level, which mean changes in a segment don’t affect others. Solr caches at a global level. Whenever there is a change, the whole shard instance becomes invalid, and indexing with respect to new data should be done again. Each has its own advantages based on the application.

Comparison Table of Solr vs Elasticsearch

The table below summarizes the comparisons between Solr vs Elasticsearch:

Studies on the performance of both the search engines are ongoing. The results from the existing studies are not concrete. That being said, some studies have shown that both engines are almost similar when it comes to performance.

Conclusion

Overall, Solr and Elasticsearch seem similar in working but are different in a few areas like scalability, functionalities, ease of deployment, etc. Choosing either one is entirely dependent on the organizations and their applications. A detailed description of the pros and cons is given above to get a brief understanding of both entities.

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This is a guide to Solr vs Elasticsearch. Here we discuss the Solr vs Elasticsearch key differences with infographics and comparison table. You may also have a look at the following articles to learn more –

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