Algolia vs Solr: What are the differences?
Developers describe Algolia as "Developer-friendly API and complete set of tools for building search". Our mission is to make you a search expert. Push data to our API to make it searchable in real time. Build your dream front end with one of our web or mobile UI libraries. Tune relevance and get analytics right from your dashboard. On the other hand, Solr is detailed as "An open source enterprise search server based on Lucene search library, with XML/HTTP and JSON APIs, hit highlighting, faceted search, caching, replication etc". Solr is the popular, blazing fast open source enterprise search platform from the Apache Lucene project. Its major features include powerful full-text search, hit highlighting, faceted search, near real-time indexing, dynamic clustering, database integration, rich document (e.g., Word, PDF) handling, and geospatial search. Solr is highly reliable, scalable and fault tolerant, providing distributed indexing, replication and load-balanced querying, automated failover and recovery, centralized configuration and more. Solr powers the search and navigation features of many of the world's largest internet sites.
Algolia and Solr are primarily classified as "Search as a Service" and "Search Engines" tools respectively.
Some of the features offered by Algolia are:
- Database search
- Search as you type
On the other hand, Solr provides the following key features:
- Advanced Full-Text Search Capabilities
- Optimized for High Volume Web Traffic
- Standards Based Open Interfaces - XML, JSON and HTTP
"Ultra fast" is the top reason why over 120 developers like Algolia, while over 33 developers mention "Powerful" as the leading cause for choosing Solr.
Medium, StackShare, and Product Hunt are some of the popular companies that use Algolia, whereas Solr is used by Slack, Coursera, and Zalando. Algolia has a broader approval, being mentioned in 258 company stacks & 54 developers stacks; compared to Solr, which is listed in 140 company stacks and 42 developer stacks.
What is Algolia?
What is Solr?
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"Slack provides two strategies for searching: Recent and Relevant. Recent search finds the messages that match all terms and presents them in reverse chronological order. If a user is trying to recall something that just happened, Recent is a useful presentation of the results.
Relevant search relaxes the age constraint and takes into account the Lucene score of the document — how well it matches the query terms (Solr powers search at Slack). Used about 17% of the time, Relevant search performed slightly worse than Recent according to the search quality metrics we measured: the number of clicks per search and the click-through rate of the search results in the top several positions. We recognized that Relevant search could benefit from using the user’s interaction history with channels and other users — their ‘work graph’."
Although we were using Elasticsearch in the beginning to power our in-app search, we moved this part of our processing over to Algolia a couple of months ago; this has proven to be a fantastic choice, letting us build search-related features with more confidence and speed.
Elasticsearch is only used for searching in internal tooling nowadays; hosting and running it reliably has been a task that took up too much time for us in the past and fine-tuning the results to reach a great user-experience was also never an easy task for us. With Algolia we can flexibly change ranking methods on the fly and can instead focus our time on fine-tuning the experience within our app.
Memcached is used in front of most of the API endpoints to cache responses in order to speed up response times and reduce server-costs on our side.
Back in 2014, I was given an opportunity to re-architect SmartZip Analytics platform, and flagship product: SmartTargeting. This is a SaaS software helping real estate professionals keeping up with their prospects and leads in a given neighborhood/territory, finding out (thanks to predictive analytics) who's the most likely to list/sell their home, and running cross-channel marketing automation against them: direct mail, online ads, email... The company also does provide Data APIs to Enterprise customers.
I had inherited years and years of technical debt and I knew things had to change radically. The first enabler to this was to make use of the cloud and go with AWS, so we would stop re-inventing the wheel, and build around managed/scalable services.
For the SaaS product, we kept on working with Rails as this was what my team had the most knowledge in. We've however broken up the monolith and decoupled the front-end application from the backend thanks to the use of Rails API so we'd get independently scalable micro-services from now on.
Our various applications could now be deployed using AWS Elastic Beanstalk so we wouldn't waste any more efforts writing time-consuming Capistrano deployment scripts for instance. Combined with Docker so our application would run within its own container, independently from the underlying host configuration.
Storage-wise, we went with Amazon S3 and ditched any pre-existing local or network storage people used to deal with in our legacy systems. On the database side: Amazon RDS / MySQL initially. Ultimately migrated to Amazon RDS for Aurora / MySQL when it got released. Once again, here you need a managed service your cloud provider handles for you.
Future improvements / technology decisions included:
Caching: Amazon ElastiCache / Memcached CDN: Amazon CloudFront Systems Integration: Segment / Zapier Data-warehousing: Amazon Redshift BI: Amazon Quicksight / Superset Search: Elasticsearch / Amazon Elasticsearch Service / Algolia Monitoring: New Relic
As our usage grows, patterns changed, and/or our business needs evolved, my role as Engineering Manager then Director of Engineering was also to ensure my team kept on learning and innovating, while delivering on business value.
One of these innovations was to get ourselves into Serverless : Adopting AWS Lambda was a big step forward. At the time, only available for Node.js (Not Ruby ) but a great way to handle cost efficiency, unpredictable traffic, sudden bursts of traffic... Ultimately you want the whole chain of services involved in a call to be serverless, and that's when we've started leveraging Amazon DynamoDB on these projects so they'd be fully scalable.
Maxime is a big supporter of Product Hunt, recognizing the continual request that I add search to the product from others in the community. Having seen many frustrating search implementations on other sites, I assumed it would be hard to integrate and provide something useful. Algolia proved me wrong (see the results here: http://producthunt.co/search).
I'm impressed with the speed and amazing support from the Algolia team. The dashboard analytics and management are incredibly useful, providing insight into how people are using the product and ability to act on those learning without changing a line of code. I would highly recommend it.
Having a great search engine is extremely important for our app store. We find that users love to search, not only when they know what they are looking for but also to discover content around different themes.
In a very rushed period with lots of things to do in parallel, we found that Algolia offered a high quality solution that perfectly solved our problem and we had a first version working great in less than a day.
We also enjoyed getting their feedbacks and ideas to help us improve our search and we are now using Algolia in our internal tools as well. We strongly recommend them!
We were looking for a better search solution for GrowthHackers.com for a couple months. All the options we looked at were either too complicated to setup, didn't have the features we needed, or were too expensive. Algolia hit the right balance for us. It's super fast and easy to customize, and the documentation and examples for getting started are great. Most importantly though, their support rocks. It's always a pleasure talking with their team.
I'm Antonio, TVShow Time's CEO, a startup that has more than 100k+ active users.
Before Algolia, we were using Elastic Search that was costing a lot (hosted on 2 big EC2 instances) and with results that weren't that relevant.
Then we switched to Algolia, in 1 hour. We were blown away by how easy the integration was for such a good relevance in results and high performance.
We tried a lot of services at Socialcam to handle our massive user base. All of them couldn't handle that number of users.
Algolia handles it without any problem but on top of that, it does it at full speed: we get results back in under a few milliseconds. Last but not least, it does it with error handling, which is great as typos are very frequent on mobile...
elastic search 와 함께 유명한 검색 엔진 오픈 소스 중 하나 이다. 처음 설정할 것이 많은데, 어플리케이션의 이해가 없다면 잦은 수정이 필요하다. Solr Client 로 제어 할 수 없고 Server 에서 설정해 줘야하는 것들이 있어 서버 설정하는 부분이 중요하다. 서버 설정만 잘 되있다면, Client 쪽 소스는 별게 없다.
중요한 건 형태소 분석기....
Algolia helps us search across disparate pieces of information in our staff portal, and allows customers to easily jump around the portal between devices, support conversations, and documentation.
This is the bedrock of kako.pk - it not only serves the JSON data, it doubles as a (very fast) web-server if you connect to the client JS widget libraries
We use algolia to power our product search / filtering (https://www.shoesofprey.com/shoes).
We started with Algolia, but switched to our home-backed full-text search solution. It's serverless, based on Lunr.js
Full text search is provided by a SOLR cluster. This is done on Master/Slave replication with Varnish as a cache.