“Solr vs Elasticsearch? Which one reigns supreme? Which one triumphs in speed? Which one excels in scalability? The answers to these questions are not set in stone, as they depend on your unique circumstances. However, it’s beneficial to understand the core features of Solr and Elasticsearch and whether there are any differences between the two’s functionalities.
In this article, we’ll give you an overview of Solr and Elasticsearch and compare their use cases and performance differences. Although there’s no right or wrong search engine, knowing each’s strength and weaknesses will help you make an informed decision.
What is Solr
Solr is an open-source licensed search engine built on the Apache Lucene library. It harnesses the power of HTTP requests to offer a wide range of search capabilities inherited from Apache Lucene. These encompass full-text search, real-time indexing, database integration, hit highlighting, and robust handling of rich documents. Notably, Solr excels in its ability to conduct searches across multiple arrays, facilitate dynamic grouping of results, and support full-time indexing.
Solr offers support for different response types, including JSON and XML. This flexibility enables users to choose the response type that best aligns with their specific requirements. Whether it’s JSON for easy integration with modern web applications or XML for structured data, Solr caters to diverse needs.
Pros of Solr
- Open source licensing: Apache Solr is a truly open-source project, allowing contributions from any SOLR developer.
- Scalability and performance: Solr is designed to scale horizontally.This means you can efficiently handle large datasets and benefit from high-performance search capabilities.
Cons of Solr
- Plummeting community: The decline in community users has affected the robustness and efficiency of product development, as Solr heavily relies on community support.
- Limited API Usability: Solr currently offers limited features through its APIs, which makes integration with agile and automated processes like DevOps more challenging.
- Configuration Changes: Making configuration changes in Solr requires modifying the “solrconfig.xml” file and restarting nodes. This results in downtime and makes it challenging to implement changes in a production environment.
What is Elasticsearch?
Elasticsearch, an open-source licensed search engine, is renowned for its inherent ability to scale horizontally. By utilizing the Apache Lucene library and harnessing a comprehensive range of REST APIs, Elasticsearch empowers users with efficient search and indexing capabilities. Notably, it adopts JSON format as the preferred representation for documents, a choice that has rapidly gained popularity within the community. . This collaborative environment ensures that new features contributed by the community are more likely to be incorporated into the main release, fostering continuous improvement.
Elasticsearch boasts a simplistic design and straightforward architecture, making it easy to configure even with basic knowledge. The solution comes pre-packaged with all the necessary components and other requirements, minimizing the learning curve for users.
Pros of Elasticsearch
- Seamless configuration changes: Most configuration modifications in Elasticsearch can be applied without restarting, ensuring smooth operations.
- Robust API usability: Elasticsearch’s agile APIs integrate easily with demanding processes like DevOps.
- Flexible querying: Elasticsearch allows sophisticated JSON-based queries with full control over logic and result collapsing.
Cons of Elasticsearch
- Limited control over feature requests: Despite being open source, new feature requests in Elasticsearch must go through an approval process by Elastic’s official employees.
- Lack of machine learning: Elasticsearch’s free and open-source editions do not include built-in machine learning capabilities.
Comparing Elasticsearch vs: What Are The Key Differences?
Even though both search solutions sound similar due to the library use, they have key differences that set them apart. Here’s what distinguishes Elasticsearch from Solr:
Both Solr and Elasticsearch leverage the Lucene library for searching. However, they differ in their approach to providing search functionality. Solr focuses on text-oriented searches with highly configurable parsers. It offers extensive customization options for search queries.
Elasticsearch simplifies search implementation by hiding the implementation complexity. While this approach sacrifices some query flexibility, it provides an easier way to implement searches. Additionally, Elasticsearch offers advanced features such as filtering and grouping, expanding beyond text-oriented searches.
Both Solr and Elasticsearch utilize segments in the Lucene index and employ caching mechanisms. Solr maintains global caches, where a single cache instance serves all segments of a specific shard type. However, if a single segment changes, Solr requires invalidating and refreshing the entire cache. This process consumes hardware resources and time.
Elasticsearch takes a different approach by maintaining individual caches per segment. When a segment changes, Elasticsearch only requires invalidating and refreshing the affected cache portion, making the cache update process less resource-intensive.
Rebalancing and shared splitting
When it comes to Elasticsearch vs Solr, both share the Shards system feature. It is nothing but the partitioning unit for the Linux index. The user can distribute his/her index by placing the shards in a cluster on different machines. Since 2013, Solr has supported shard splitting which allows the user to create more shards from the existing shards. Elasticsearch doesn’t have this feature.
However, in order to make the current system ready for sharding and addition of more machines, the user needs to have multiple shards in that machine by splitting the index based upon the estimated count of machines needed in the future. Here, the advantage is that all the machines would be having multiple shards and when the requirement for the addition of new machines comes, Elasticsearch automatically balances the load and relocates the shards to the newly formed nodes in the cluster. The automatic shard rebalancing feature doesn’t exist in Solr.
Clustering and node discovery
Elasticsearch incorporates Zen, a built-in feature that enables easy horizontal scaling. It simplifies clustering of multiple nodes without requiring manual intervention during cluster rebuilding after failures or when adding new nodes. Elastic recommends having at least three dedicated master nodes to ensure complete fault tolerance within the cluster.
In contrast, Solr lacks native clustering capabilities and relies on an additional service like Apache ZooKeeper for cluster coordination. Adding a new node to the Solr cluster requires manual intervention and coordination with the existing cluster.
JSON vs XML
Solr utilizes XML for returning HTTP responses, which is outdated. However, recent versions of Solr also support JSON, providing a more flexible design. In contrast, Elasticsearch has native support for JSON, allowing for both response and request handling via its REST APIs. This native support enhances customization and usability of the solution.
Elasticsearch uses its cluster handling mechanism through an inbuilt coordination mechanism, whereas Solr uses Zookeeper. This means in order to work with SolrCloud, the user needs a Zookeeper quorum setup. People who are already using the components of Hadoop ecosystem won’t have any problem as they most likely would be having a Zookeeper quorum setup already.
In Solr, search results are obtained by querying the defined request handlers and passing query criteria parameters via an HTTP GET request. The response format can be chosen, such as JSON, XML, or JavaBin, based on convenience and application requirements. The Solr API extends beyond querying, allowing access to search component statistics and control over Solr’s behavior, including collection creation.
In contrast, Elasticsearch exposes a REST API that supports DELETE, POST, HTTP GET, and PUT methods. It enables not only document querying and deletion but also index creation, management, analysis control, and retrieval of various metrics. The REST API is widely used for logs and events ingestion and retrieval in platforms like Sematext Cloud.
While Solr supports multiple response formats, Elasticsearch exclusively responds in JSON format. Another significant difference lies in querying. Solr relies on URL parameters to pass query parameters, whereas Elasticsearch structures queries as JSON objects. This JSON-based query structure in Elasticsearch offers more control over understanding and shaping the query, influencing the returned results.
No-flat data handling
If you have complex, nested data structures within your MongoDB JSON objects and want to index them without flattening the data, Elasticsearch is an ideal choice. It offers robust support for handling nested objects, nested documents, and parent-child relationships. This means you can index your MongoDB JSON objects as they are and still perform efficient full-text searches.
Solr, meanwhile, may not be the most suitable option in this scenario. However, it’s worth noting that Solr does support parent-child and nested documents when indexing both JSON and XML. Furthermore, Solr provides a crucial advantage – query time joins within and across different collections. This means you’re not restricted to handling parent-child relationships only during the indexing process.
Solr consists of a broad, open-source community and, hence, stands ahead in the Elasticsearch vs Solr battle. Anyone who wants to contribute to Solr can do it without any hassle, and the election of new Solr developers or code committers is held based on merit only.
Elasticsearch can be called as an open-source platform, but not completely. All its contributors have access to the source code. The users can make changes and contribute to them as well. But the final changes are confirmed and done by the employees of Elastic (the company behind Elasticsearch). This makes it clear that Elasticsearch is driven more by a single company rather than a whole community.
Apache Solr has a strong development and user community. It has dominated the search engine space since its open source release in 2006. It offers rich functionality beyond basic text indexing, including faceting, grouping, filtering, and pluggable components.
In contrast, Elasticsearch emerged around 2010 as a newer option with a focus on modern use cases and easier handling of large indices and high query rates. Despite being less stable and lacking the same feature depth and brand recognition as Solr, Elasticsearch introduced sought-after functionalities like Near Real-Time Search earlier.
Elasticsearch attracted those who hadn’t yet adopted a search engine, as well as those dealing with large volumes of data and needing easy scalability. Additionally, some were drawn to Elasticsearch simply for its novelty.
Fast forward to 2022, Elasticsearch has closed the feature gap with Solr and generated more buzz. Both projects are now mature and stable. However, Elasticsearch clusters tend to face more issues due to factors such as easy setup without deep understanding, scalability demands, and the dynamic nature of data movement within the cluster.
While Solr has traditionally focused on text search, Elasticsearch aims to handle analytical queries as well, albeit with potential drawbacks. Despite these considerations, many organizations, including ours, rely on Elasticsearch alongside Solr for different products.
Solr offers extensive data analysis capabilities, starting with facets that allow slicing and dicing data for better understanding. It also introduced JSON facets, which provide similar features but with improved speed and reduced memory usage. Additionally, Solr offers streaming expressions, allowing the combination of data from multiple sources (such as SQL, Solr, and facets) and the application of various expressions for data decoration, sorting, extracting, and counting significant terms.
On the other hand, Elasticsearch provides a powerful aggregations engine that goes beyond the one-level data analysis offered by traditional Solr facets. It supports nested data analysis, enabling calculations like average price for each product category in each shop division. Elasticsearch also allows analysis on top of aggregation results, facilitating functionalities like moving averages calculation. While marked as experimental, Elasticsearch offers support for matrix aggregation, which computes statistics over a set of fields. This adds another layer of data analysis capability.
Solr vs. Elastic: Differences Recap
|Full Text Search Features||Language analysis based on Lucene||Language analysis based on Lucene|
|Node Discovery||Zen, built into Elasticsearch itself||Apache Zookeeper, mature and battle-tested|
|Caches||Per segment, better for dynamically changing data||Global, invalidated with each segment change|
|Analytics||Sophisticated and highly flexible aggregations||Facets and powerful streaming aggregations|
|Non-flat Data Handling||Nested documents and parent-child support||Natural support with nested and object types|
|DevOps Friendliness||Very good APIs||Not fully there yet, but coming|
|Query DSL||JSON (limited), XML (limited), or URL parameters||JSON|
|Index/Collection Leader Control||Leader placement control and leader rebalancing||Not possible|
|Machine Learning||Commercial feature, focused on anomalies and outliers||Built-in – on top of streaming aggregations|
|Ecosystem||Rich – Kibana, Grafana, with large entities support and big user base||Modest – Banana, Zeppelin with community support|
At a glance, it may appear that Elasticsearch is the clear winner for modern applications and use cases. It excels in terms of flexibility, ease of use, scalability, and meeting essential enterprise environment requirements.
The popularity of Elasticsearch can be attributed to its accessibility for new users, seamless scalability, and superior querying and analytics capabilities compared to Solr. Both databases are capable of searching full-text and handling rich documents using the Apache Tika library. Ultimately, it is important to consider your team’s priorities and specific needs in order to choose the most suitable tool that will maximize your business’s data potential.
We hope that this breakdown has been helpful in guiding your decision-making process and enabling you to select a tool that aligns with your organization’s goals.