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Algolia vs Typesense: What are the differences?
Introduction
In this Markdown code, I will provide the key differences between Algolia and Typesense.
Data Scalability: Algolia is a search-as-a-service platform that offers high scalability, allowing businesses to handle millions of queries per second across a large dataset. On the other hand, Typesense is a lightweight search engine that is designed to handle smaller datasets and lower query volumes.
Focused Use Case: Algolia is ideal for complex search use cases that require advanced features such as faceting, filtering, and typo-tolerance. It provides various customizable options for search relevance and ranking. On the contrary, Typesense is designed for simpler search requirements, focusing on delivering instant search results without the need for extensive configurations or customizations.
Ease of Deployment: Algolia offers a fully-hosted solution where businesses can use Algolia's infrastructure to power their search functionality. This eliminates the need for setting up and maintaining search servers. In contrast, Typesense can be self-hosted, allowing businesses to have more control over their search infrastructure and data.
Query Performance: Algolia prioritizes fast response times, aiming for sub-50ms latency for search queries. It achieves this by optimizing the search infrastructure and leveraging distributed systems. Typesense also aims for low latency but may have slightly higher response times compared to Algolia due to its lightweight nature.
Pricing Model: Algolia follows a usage-based pricing model, where businesses are billed based on the number of operations performed, including the number of indexed records and search queries. Typesense, on the other hand, offers a fixed pricing model based on the number of nodes in the cluster, regardless of the number of records or search queries.
Community and Ecosystem: Algolia has a larger and more mature community, which means there are more resources, libraries, and integrations available. It also has better documentation and support options. Typesense is relatively newer and may have a smaller community and less extensive ecosystem, although it is growing rapidly.
In Summary, the key differences between Algolia and Typesense lie in their scalability, use cases, deployment options, query performance, pricing model, and community/ecosystem size.
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.
Thank you!
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.
Pros of Algolia
- Ultra fast126
- Super easy to implement95
- Modern search engine73
- Excellent support71
- Easy setup, fast and relevant70
- Typos handling46
- Search analytics40
- Distributed Search Network31
- Designed to search records, not pages31
- Multiple datacenters30
- Smart Highlighting10
- Search as you type9
- Multi-attributes8
- Instantsearch.js8
- Super fast, easy to set up6
- Amazing uptime5
- Database search5
- Highly customizable4
- Great documentation4
- Github-awesome-autocomple4
- Realtime4
- Powerful Search3
- Places.js3
- Beautiful UI3
- Ok to use2
- Integrates with just about everything2
- Awesome aanltiycs and typos hnadling2
- Developer-friendly frontend libraries1
- Smooth platform1
- Fast response time1
- Github integration1
- Nooo0
- Fuck0
- Giitera0
- Is it fool0
Pros of Typesense
- Free5
- Facet search4
- Easy to deploy4
- Out-of-the-box dev experience3
- Ultra fast3
- Search as you type3
- Typo handling3
- Open source3
- Near real-time search2
- Super easy to implement2
- InstantSearch integration2
- Modern search engine2
- Restful1
- Great documentation1
- SaaS option1
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Cons of Algolia
- Expensive11
Cons of Typesense
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