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.
Hey everybody! (1) I am developing an android application. I have data of around 3 million record (less than a TB). I want to save that data in the cloud. Which company provides the best cloud database services that would suit my scenario? It should be secured, long term useable, and provide better services. I decided to use Firebase Realtime database. Should I stick with Firebase or are there any other companies that provide a better service?
(2) I have the functionality of searching data in my app. Same data (less than a TB). Which search solution should I use in this case? I found Elasticsearch and Algolia search. It should be secure and fast. If any other company provides better services than these, please feel free to suggest them.
Hi Rana, good question! From my Firebase experience, 3 million records is not too big at all, as long as the cost is within reason for you. With Firebase you will be able to access the data from anywhere, including an android app, and implement fine-grained security with JSON rules. The real-time-ness works perfectly. As a fully managed database, Firebase really takes care of everything. The only thing to watch out for is if you need complex query patterns - Firestore (also in the Firebase family) can be a better fit there.
To answer question 2: the right answer will depend on what's most important to you. Algolia is like Firebase is that it is fully-managed, very easy to set up, and has great SDKs for Android. Algolia is really a full-stack search solution in this case, and it is easy to connect with your Firebase data. Bear in mind that Algolia does cost money, so you'll want to make sure the cost is okay for you, but you will save a lot of engineering time and never have to worry about scale. The search-as-you-type performance with Algolia is flawless, as that is a primary aspect of its design. Elasticsearch can store tons of data and has all the flexibility, is hosted for cheap by many cloud services, and has many users. If you haven't done a lot with search before, the learning curve is higher than Algolia for getting the results ranked properly, and there is another learning curve if you want to do the DevOps part yourself. Both are very good platforms for search, Algolia shines when buliding your app is the most important and you don't want to spend many engineering hours, Elasticsearch shines when you have a lot of data and don't mind learning how to run and optimize it.
Rana - we use Cloud Firestore at our startup. It handles many million records without any issues. It provides you the same set of features that the Firebase Realtime Database provides on top of the indexing and security trims. The only thing to watch out for is to make sure your Cloud Functions have proper exception handling and there are no infinite loop in the code. This will be too costly if not caught quickly.
For search; Algolia is a great option, but cost is a real consideration. Indexing large number of records can be cost prohibitive for most projects. Elasticsearch is a solid alternative, but requires a little additional work to configure and maintain if you want to self-host.
Hope this helps.
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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.