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Apache Solr

Apache Solr


What is Apache Solr?

Apache Solr is an open-source enterprise search server.

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Solr, an open-source search platform, has been widely used across various industries for its versatile capabilities. Users have found Solr …
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What is Apache Solr?

Apache Solr is an open-source enterprise search server.

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Elasticsearch is an enterprise search tool from Elastic in Mountain View, California.

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What is Apache Solr?

Apache Solr Technical Details

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Community Insights

TrustRadius Insights are summaries of user sentiment data from TrustRadius reviews and, when necessary, 3rd-party data sources. Have feedback on this content? Let us know!

Solr, an open-source search platform, has been widely used across various industries for its versatile capabilities. Users have found Solr to be an effective indexing mechanism for complex product data and user profiles, enabling rapid delivery of search results with minimal strain on hardware configurations. For example, a CTO at a real estate company implemented Solr in a Drupal powered e-commerce environment, resulting in remarkable success with the open-source integration. Additionally, Solr has been chosen as a NoSQL database for real estate search engines, managing vast amounts of classifieds and doubling the number of unique users.

Solr's functionality extends beyond e-commerce applications. It serves as a reliable search server in diverse projects such as city-wide websites, enterprise search functions, and internal company portals. By utilizing Solr, these platforms are able to deliver quick, accurate, and relevant search results to their users. Furthermore, Solr has transformed the retail domain by solving type-ahead search challenges and offering various faceting options that allow customers to quickly isolate their desired products.

The versatility of Solr is also evident in its application within the realm of venue data analysis and management. By implementing Solr in web portals that contain large collections of venue data, users can benefit from geospatial filtering and ease of use when searching and analyzing this information. In addition, Solr has proven to be an invaluable tool in e-commerce applications for product display, sorting, and management by facilitating easy querying and fetching of desired results.

Overall, Solr's ability to handle complex data indexing tasks and deliver fast search results has made it a popular choice for organizations across industries. Its versatility makes it suitable for various use cases ranging from enterprise search functions to retail domains and beyond.

Fast Performance: Many reviewers have praised the platform for its fast performance. They have found it impressive and appreciated the ability to rapidly grow their environments to meet expanding business needs.

Flexibility of Solr: Users have mentioned that Solr is highly flexible and can be customized to meet specific business needs. They have been able to make Solr bend to their requirements, which they found advantageous.

Useful Functionality: Reviewers have emphasized the usefulness of Solr's faceted navigation and field collapsing/grouping functionalities. These features allow them to filter and obtain quick results for their websites, resulting in good and efficient outcomes for their customers.

Difficult Configuration: Several users have reported that they found the platform to be challenging to configure, particularly in terms of its ease of use. They have encountered difficulties while setting up and customizing the platform according to their needs.

Lack of Developer Community Understanding: Some reviewers have mentioned that they faced challenges in finding talent or developers who were well-versed with the platform. The lack of understanding within the developer community has made it harder for users to find suitable resources or support for their projects.

Slow Development on Drupal 8 Integration APIs: Users have expressed frustration with the slow progress in developing APIs for integrating the platform with Drupal 8. Specifically, they highlighted issues related to Apache Solr usage and integration, which impacted their ability to leverage the full potential of both platforms effectively.

Users have provided several recommendations for using Solr in various business aspects. One of the most common recommendations is to leverage Solr's capabilities for storing and indexing data from different sources, allowing users to easily search and retrieve information. Another common recommendation is to take advantage of the quick learning curve, easy client-side coding, and straightforward setup of Solr. Users also recommend using Solr for searching massive amounts of data as it offers efficient performance and scalability. In addition, some users suggest using the latest version of Solr to benefit from its improved features and enhancements. Furthermore, users recommend considering both Elasticsearch and Solr to determine which one best suits their specific access methods and query requirements. Lastly, users find Solr useful for real-time search functionality, including word or partial word search, word synonyms, and the cost-effectiveness it offers.


(1-10 of 10)
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Score 9 out of 10
Vetted Review
Verified User
I am an e-commerce website developer in my organization and our application uses mainly Apache Solr for the display, sorting, and managing of products on our website. With the Apache Solr in use products sorting, filtering with ranges, fetching filtered results, and display of products on list pages is possible. With a simple query, we can fetch desired results, and the query in Apache Solr is easy to learn.
  • Sorting on products for display.
  • Fetching filtered results.
  • Creating ranges, facets as per requirement.
  • Querying in Apache Solr is easy to learn.
  • Training material should be easily available.
  • Features with examples for developers to use.
  • Better documentation.
Apache Solr is very useful in e-commerce websites. Apache Solr can be used for major features like sorting and filtering on websites. Querying in Apache Solr is easy. With the use of the Apache Solr website's response, time can be improved as results fetched using Apache Solr are faster compared to the database.
Score 9 out of 10
Vetted Review
Verified User
Apache Solr is used to index and store billions of activity events as a document containing information such as time of activity, the user performing the activity, type of activity, and a few business-relevant fields. The data is queried and served as part of the user activity request. It's used within the department and it addresses searching for specific user activity events among billions of events.
  • Full-text search capabilities
  • Ranking the results
  • Fuzzy search
  • High scalability
  • Authentication and authorization
  • Cross-data-center replication
  • CPU consumption can be high in some cases
Apache Solr is a very quick and easy way to search for keywords within a document; the ability to add weightings to search terms to increase the outcome of search relevance gives it great flexibility. It is simple to access its data via REST API. The flexibility in adjusting the query in terms of boosting, faceting, and more is very useful.
Score 8 out of 10
Vetted Review
Verified User
We use Solr as a search server in our eCommerce project which our product data is stored as documentation. Solr works as both a data store server and search indexer. It provides a very high-speed query to respond almost immediately in milliseconds, allowing us to support the TypeAhead function to our end user.
  • Flexible and powerful query language allows us to build a various and complex query to retrieve data.
  • High-speed response query.
  • Good documentation and great community support.
  • Cluster mode with separate master and slave so we could scale each type base on we need to increase input data or response speed.
  • It does not support authentication and authorization so we need to place it inside a private network.
  • Working with Solr cloud require additional Zookeeper.
  • Master node requires reconfiguration if it down.
Solr is suitable in almost every case for Indexing and Searching data, but If you need to expose your Solr server to the external network, you need to integrate with another authentication solution. Reviewing your business on input and response data to define a good strategy for Master and Slaver Nodes will save cost and increase system performance.

Solr would not fit if you want a data warehouse for storing data in binary or unstructured data.
Richard Davies | TrustRadius Reviewer
Score 7 out of 10
Vetted Review
Verified User
We use Solr as the search engine for our new city-wide website that we're building using the Drupal open source content management system. We want to make it easy for citizens to find the city services and information that they're seeking and search is a critical feature. We need it to deliver quick, accurate, and relevant results.
  • Solr is very flexible and can be customized to meet your specific needs and requirements.
  • Solr is fast at returning search results.
  • Solr is pretty much an industry standard so there's a good chance other software programs have modules designed to work with it.
  • It has a steep learning curve. It's not intuitive how to configure it or customize it.
  • It doesn't include a web crawler. Indexing your website requires adding a separate web crawler or using their API to add information to the index.
  • Debugging and troubleshooting query issues can be a difficult task.
Solr is an open source product so it's available for free. It's a good choice if you want to keep costs down but need a powerful and flexible search engine. But it will require some time to learn how to use it effectively. Also, you will either need to maintain it on your own server (which takes time and expertise) or pay to have someone else maintain it on a hosted server.

It's not the best choice if you want a turnkey solution that's quick to implement and easy to configure. But those type of solutions are usually very expensive.
Robyne Gallacher | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Apache Solr helps us create a dynamic and engaging search engine interface for our customers. It addresses the challenge of delivering accurate and fast search results to the users of our website. It allows our development teams to customize the search experience to our unique needs.
  • Provide an organized search platform that is easily customizable.
  • Provides easy-to-use documentation.
  • Offers several different methods of indexing and parsing search data.
  • The admin UI is good, but could be a bit more user-friendly. The field names are not very intuitive and require a learning curve.
Solr is great if you are interested in customizing and configuring a search experience for your users. Without configuration, the search experience is fairly basic and would not be suitable for a large website, but if you are willing to put in the time and development, it can become a very powerful tool that will engage your consumers.
Kelvin Yeo | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User
Apache Solr is the underlying search engine servicing our Enterprise Search functions. Our instance indexes both structured data like Oracle records and unstructured data like PDF files. Its ability to return relevancy ranked results from textual search terms enabled my organization to find information faster.
  • It is simple to access its data via REST API. The flexibility in adjusting the query in terms of boosting, faceting and more is very useful.
  • It can scale horizontally by splitting shards, making it practically limitless in size.
  • It has been stable in an operational production environment.
  • It would be good to handle authentication natively in an enterprise scale. Currently it has to be wrapped by another service that does authentication.
  • Support querying across multiple collections.
  • Improve stability in Cross-Data-Center-Replication.
It is suitable for indexing varying types of content and in applying a consistent search experience across all the content. It is less suitable for environments where indexing new content comes at higher frequencies.
Bharadwaj (Brad) Chivukula | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
  • Solved Type-ahead search and provide various faceting options to keep the customer engaged in a Retail domain so that he/she can isolate their products in less than 3 clicks.
  • Information Retrieval, Search Engines prototype, Query suggestion Systems
  • We are utilizing Solr with our Google Search Appliances for the search functionality within out internal company portal. Solr helps us query other databases for personnel and location information.
  • We needed to improve the relevance and performance of our existing search, and by using Solr did both of these with the added benefit of reducing the load on our Oracle Database. We improved our relevance and customers immediately noticed the difference. That was with the 'out-of'the'box' settings, we can see how this can be improved further.
  • Solr is a very quick and easy way to search for keywords within a document, the ability to add weightings to search terms to increase the outcome of search relevance gives it great flexibility.
  • The customisability of the Solr is so good that anyone can set it up and customise it for his/her needs.
  • Solr allows you to build your own custom request handlers and allows you to import data from pretty much any datasource.
  • Version 4.3-4.10, the documentation for Solr was very lacking.
  • No monitoring for Solr available built-in; have to be dependent on CDH or HDP if you are implementing thru them.
  • CPU consumption can be high in some cases.
  • Type ahead search - The application needs to build a short list of match words based on users' input as they typing into a search field. The search field is unified search field, that means multiple data types to be searched. For example, the field can be built for search of a "clothing type" like "Jeans" and you get options like "Jeans Slim" etc.
  • Managing the Solr instances was proving to be a pain.
Score 9 out of 10
Vetted Review
Verified User
At my company we use Apache Solr to help improve customer experience when it comes to searching and analyzing our large collections venue data on our web portal. Apache Solr allows us to restrict data based on geospatial features, organizing data into coupled groups using pivots, and provides the performance and ease of use that makes it great for developing with. It took only a few hours to have a successful proof of concept up and running and very few changes were needed out-of-the-box. The other benefit to working with Apache Solr is the active and large developer community and the well-maintained and easy to read resources.
  • Easy to get started with Apache Solr. Whether it is tackling a setup issue or trying to learn some of the more advanced features, there are plenty of resources to help you out and get you going.
  • Performance. Apache Solr allows for a lot of custom tuning (if needed) and provides great out of the box performance for searching on large data sets.
  • Maintenance. After setting up Solr in a production environment there are plenty of tools provided to help you maintain and update your application. Apache Solr comes with great fault tolerance built in and has proven to be very reliable.
  • Indexing of data can sometimes be a slog, meaning it can sometimes take a while to get a large collection up and running if you have many fields that need to be indexed.
  • Elasticsearch offers better support and flexibility.
Very effective for end-user searching applications and for generating search results. Also very well suited to those looking for high reliability and performance. If [you're doing] fuzzy searching or if you are working on a smaller end-user application or an internal application that does not require high performance and flexible/adapting searching then it may not be necessary to use Solr.
Peter Feddo | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
We utilize Solr as an indexing mechanism for complex product data and user profiles. It allows us to rapidly deliver search results with minimum strain on our hardware configurations.

We're currently implementing Solr in a Drupal powered e-commerce environment and enjoying amazing success with the open source integration.
  • Speed -- This is a very fast platform.
  • Scalability -- We can rapidly grow our environments to meet expanding business needs.
  • Flexibility -- We can make Solr bend to our business needs and not the other way around.
  • Ease of use -- this is not always the easier platform to configure.
  • Developer Community -- There are not a whole lot of folks out there that understand this stuff so finding talent is difficult.
  • Drupal 8 -- We've been frustrated with slow development on APIs for Drupal 8 integration.
Solr spins up nicely and works effectively for small enterprise environments providing helpful mechanisms for fuzzy searches and facetted searching.

For larger enterprises with complex business solutions you'll find the need to hire an expert Solr engineer to optimize the powerful platform to your needs.

Internationalization is tricky with Solr and many hosting solutions may limit you to a latin character set.
Philippe Kozak | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User
I worked as a CTO for a pure play company in real estate activity. We had to design and to build five websites for the customers of real estate agencies. We manage about 2 millions classifieds. This area is highly competitive. An the same time, we doubled our unique number of users. So we (the people I managed and myself) decided to use a NoSQL database for our search engine. Our choice went to Apache Solr 4. This DB redesign was done at the same time as a PHP code redesign with Zend Framework. All our five websites were redesigned over a period of 2.5 years. We did a proof of concept with Apache Solr when we needed to redesign our registered customers searches (match 500k searches with 2M classifieds).
  • Faceted navigation and field collapsing/grouping : filtering and quick results were what we needed for our websites. Our customers needed to have this functionalities for good and efficient results.
  • We tested them with our customers' registered searches (they received all new goods matching with their registered searches by emails and/or mobile push). Results were incredible by comparison with our old system (old MySQL requests).
  • Note : we didn't put all our data in Solr. Just what we need for searching uses. Other data stayed in our MySQL database.
  • Auto-suggest : our old auto-suggest wasn't performing well. With Apache Solr, our new one was worked really well ! The suggestions came quickly and suggestions were good.
  • We also extended auto-suggestion with geo-spatial data and it worked well.
  • Hit highlighting : we used this functionality and we didn't have problem and nasty surprise.
  • Keep all data status during data upgrading (see next details for improvements)
  • These examples are due to the way we use Apache Solr. I think we have had the same problems with other NoSQL databases (but perhaps not the same solution). High data volumes of data and a lot of users were the causes.
  • We have lot of classifications and lot of data for each classification. This gave us several problems:
  • First: We couldn't keep all our data in Solr. Then we have all data in our MySQL DB and searching data in Solr. So we need to be sure to update and match the 2 databases in the same time.
  • Second: We needed several load balanced Solr databases.
  • Third: We needed to update all the databases and keep old data status.
  • If I don't speak about problems due to our lack of experience, the main Solr problem came from frequency of updates vs validation of several database. We encountered several locks due to this (our ops team didn't want to use real clustering, so all DB weren't updated). Problem messages were not always clear and we several days to understand the problems.
It is well suited for classified search and filtering, and high volume data matching.
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