Apache Solr Reviews and Ratings

Rating: 8.9 out of 10

Score

8.9 out of 10

Community insights

TrustRadius Insights for Apache Solr are summaries of user sentiment data from TrustRadius reviews and, when necessary, third party data sources.

Pros

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.

Reviews

11 Reviews

Super Easy Sorting and Filtering with Apache Solr

Rating: 9 out of 10

Incentivized

April 26, 2022

Use Cases and Deployment Scope

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.

Pros

Sorting on products for display.
Fetching filtered results.
Creating ranges, facets as per requirement.
Querying in Apache Solr is easy to learn.

Cons

Training material should be easily available.
Features with examples for developers to use.
Better documentation.

Likelihood to Recommend

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.

Verified User

Team Lead in Information Technology (10,001+ employees)

Vetted Review

Powerful, flexible search tool

Rating: 9 out of 10

Incentivized

July 30, 2021

Use Cases and Deployment Scope

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.

Pros

Full-text search capabilities
Ranking the results
Fuzzy search
High scalability

Cons

Authentication and authorization
Cross-data-center replication
CPU consumption can be high in some cases

Likelihood to Recommend

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.

Verified User

Engineer in Information Technology (5001-10,000 employees)

Vetted Review

Powerful, fast and flexible search tool with a rich query API set

Rating: 8 out of 10

Incentivized

June 12, 2019

Use Cases and Deployment Scope

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.

Pros

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.

Cons

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.

Likelihood to Recommend

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.

trang nguyen

Software Engineer in Information Technology at FPT software (5001-10,000 employees)

Vetted Review

Apache Solr and Drupal 8 are a match made in heaven!

Rating: 7 out of 10

Incentivized

May 31, 2019

Use Cases and Deployment Scope

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.

Pros

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.

Cons

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.

Likelihood to Recommend

<div>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.

</div><div>

</div><div>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.

</div>

Richard Davies

Web Application Developer in Information Technology at City of Portland (5001-10,000 employees)

Vetted Review

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Apache Solr Has Changed the Search Game

Rating: 9 out of 10

Incentivized

November 27, 2018

Use Cases and Deployment Scope

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.

Pros

Provide an organized search platform that is easily customizable.
Provides easy-to-use documentation.
Offers several different methods of indexing and parsing search data.

Cons

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.

Likelihood to Recommend

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.

Robyne Gallacher

Associate Software Tester in Information Technology at Overstock.com (1001-5000 employees)

Vetted Review

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Solr has Flexible Query API

Rating: 8 out of 10

Incentivized

March 3, 2018

Use Cases and Deployment Scope

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.

Pros

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.

Cons

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.

Likelihood to Recommend

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.

Kelvin Yeo

Senior Search Engineer in Information Technology at GIC (1001-5000 employees)

Vetted Review

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Powerful, Flexible Search Tool

Rating: 9 out of 10

Incentivized

November 30, 2017

Use Cases and Deployment Scope

<div class="row"><ul><li>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.</li><li>Information Retrieval, Search Engines prototype, Query suggestion Systems</li><li>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.</li><li>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. </li></ul></div>

Pros

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.

Cons

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.

Likelihood to Recommend

<ul><li> 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.</li><li>Managing the Solr instances was proving to be a pain.

</li></ul>

Bharadwaj (Brad) Chivukula

Sr.Technical Manager/Delivery Manager in Information Technology at Nisum Technologies, Inc. (10,001+ employees)

Vetted Review

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Apache Solr is a Win With Drupal

Rating: 9 out of 10

Incentivized

April 3, 2017

Use Cases and Deployment Scope

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.

Pros

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.

Cons

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.

Likelihood to Recommend

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.

Peter Feddo

Senior Front End Developer in Engineering at GSATi (11-50 employees)

Vetted Review

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Apache Solr - Performance, Reliability, and No Headaches!

Rating: 9 out of 10

Incentivized

March 29, 2017

Use Cases and Deployment Scope

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.

Pros

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.

Cons

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.

Likelihood to Recommend

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.

Verified User

Engineer in Engineering (51-200 employees)

Vetted Review

Apache Solr - Searching and matching efficiency

Rating: 8 out of 10

Incentivized

September 1, 2016

Use Cases and Deployment Scope

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).

Pros

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)

Cons

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.

Likelihood to Recommend

It is well suited for classified search and filtering, and high volume data matching.

Philippe Kozak

Senior Consultant in Information Technology at Equancy (51-200 employees)

Vetted Review

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Reviews and Ratings

Apache Solr - Searching and matching efficiency

Rating: 8 out of 10

Incentivized

September 1, 2016

Use Cases and Deployment Scope

Pros

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)

Cons

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.

Likelihood to Recommend

It is well suited for classified search and filtering, and high volume data matching.

Philippe Kozak

Senior Consultant in Information Technology at Equancy (51-200 employees)

Vetted Review

View profile