Did you know that $300B is lost yearly from bad online search experiences? Meanwhile, 43% of consumers would pay more if they could find what they’re looking for in just a few clicks.
The ability to efficiently search through vast amounts of data is more crucial today than ever before, as the volume of information on the internet is constantly growing.
Solr, a powerful open-source search platform, has emerged as a solution to this problem. Let’s delve into what makes Solr a standout choice for search and indexing, its key features, and use cases.
Apache Solr is an enterprise search platform, built on Apache Lucene.
It provides powerful full-text search capabilities, real-time indexing, and highly scalable architecture, making it ideal for handling large volumes of data. Solr is designed to deliver high performance and is used by many of the world’s largest internet sites, such as eBay, to power their search. This is how Apache Solr works in simple terms:
The tool first converts the documents into a machine-readable format.
Solr analyzes the user’s query, which can include keywords and phrases.
Next, it aligns the user’s query with the documents in the database to identify relevant matches.
Finally, it prioritizes the search results based on their relevance to the query and delivers it to the user.
01. Full-text search
Solr’s primary strength is its robust full-text search functionality. It offers advanced matching capabilities, including support for phrases, wildcards, joins, and grouping. It also supports highlighting and spell checkers.
02. Faceted search
It has faceted search capabilities, meaning that search results are organized into multiple dimensions (facets), such as date, author, or product category, making it easier for users to navigate and delve deeper into the data.
03. Scalability
Solr is designed to scale horizontally, making it suitable for applications with growing data needs. It achieves this through:
Distributing index data across multiple nodes.
Copying data across multiple servers to ensure high availability and fault tolerance.
04. Real-time indexing
The search solution provides near real-time indexing capabilities, allowing new or updated documents to be searchable almost immediately. This is essential for applications that require up-to-date search results.
05. Rich document parsing
Solr is among the few search engines capable of reading rich content documents, such as PDFs, Word files, XML, and plain text.
This makes it an ideal solution for projects that require searching through extensive collections of files on a website, including contracts, resumes, learning materials, and ebooks.
06. Extensibility and customization
Solr’s modular architecture allows developers to extend and customize its functionalities to meet specific business requirements.
07. Distributed search
The tool supports distributed search, which allows querying across multiple Solr servers and combining results into a single response. This is particularly useful for large-scale applications and websites.
08. Big data integration
Solr integrates well with big data ecosystems, including Hadoop, enabling it to handle massive datasets and complex queries efficiently.
09. Multilingual support
Its multilingual search capabilities ensure that users can find relevant content in their preferred language. Solr can automatically detect the language of the incoming query and provide relevant search results.
While Solr is powerful and feature-rich, there are some alternative search engines, including Elasticsearch, Sphinx, and Algolia.
Let’s take a look at how they compare in terms of benefits, drawbacks, and ideal use cases.
Apache Solr’s robust features, scalability, and extensibility make it suitable for applications ranging from small websites to large enterprise systems. It integrates seamlessly with any CMS, including Drupal.
Modules like Search API Solr enable easy integration, configuration, and management of Solr within the Drupal environment. It’s no surprise that Drupal uses this powerful search engine on their websites, Drupal.org and Drupal Wiki.
At Attico International, we also employ Apache Solr on several internal and external projects.
Attico.io
Attico.io plans to release Apache Solr on our corporate website shortly. This update will introduce enhanced search functionalities and performance improvements, further optimizing the user experience and search capabilities.
Search index
For example, the Search index settings allow us to select entities for indexing: content, taxonomy terms, users, files, redirects, and paragraphs.
Rendered HTML output
The Rendered HTML output setting allows Solr to index the entire content of a web page as it appears to users after being fully rendered by a web browser. This includes dynamic content generated by JavaScript, which traditional crawlers might miss. By indexing the fully rendered HTML, Solr can deliver more accurate and relevant search results that reflect the complete user experience.
Spellcheck
By configuring the Spellcheck component in the Aggregated field settings, we further enhance user search experiences by providing suggestions for correcting misspelled queries. When users make typographical errors, for instance, write “drupil” instead of “drupal”, they will see “Do you mean Drupal?”
Prioritizing certain types of content
One of the key features of Solr is its ability to prioritize certain types of content. This way, for Attico's website search results, we will prioritize Services first, followed by Cases, and then Insights.
Artec 3D
Our client, Artec 3D, is a global leader in 3D scanning technology. To provide a superior search experience for their users, Artec 3D has implemented Apache Solr. This implementation enhances various aspects of their website, including product discovery, documentation search, and customer support.
Autocomplete
The autocomplete search function on their website provides users with real-time suggestions as they type their queries. For instance, if a user starts typing “global”, suggestions like “Artek 3D to participate in global industries Lyon” will appear. Clicking any of these suggestions will redirect the user to the corresponding page.
As demonstrated by the examples above, Solr proves to be an invaluable tool for organizations aiming to improve their search infrastructure.
By leveraging Solr’s advanced features, businesses can ensure that users find what they are looking for quickly and efficiently, ultimately driving better outcomes and higher satisfaction.
