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Elasticsearch vs MeiliSearch: What are the differences?
Introduction
Elasticsearch and MeiliSearch are both powerful search engines that provide efficient searching and indexing capabilities. However, they differ in several key aspects, which are outlined below.
Scalability: Elasticsearch is designed for horizontal scalability, making it suitable for handling large-scale deployments and high data volumes. MeiliSearch, on the other hand, is currently not as scalable as Elasticsearch and is more suitable for smaller applications or use cases.
Full-Text Search: Elasticsearch offers powerful full-text search capabilities out of the box, including support for advanced query features like stemming, fuzzy search, and relevance scoring. MeiliSearch also supports full-text search but does not provide the same level of advanced search functionality that Elasticsearch offers.
Real-Time Updates: Elasticsearch excels at handling real-time updates and near-instant search results. It has built-in support for near real-time indexing and is optimized for fast updates and searches. MeiliSearch, while still capable of providing real-time updates, may not be as performant as Elasticsearch in scenarios where real-time indexing and search are critical.
Ease of Use: MeiliSearch puts a strong emphasis on ease of use and provides a user-friendly API that simplifies indexing, querying, and managing the search index. Elasticsearch, while feature-rich, has a steeper learning curve and requires more configuration and setup for optimal performance.
Community and Ecosystem: Elasticsearch has a thriving community and a mature ecosystem with a wide range of plugins, integrations, and third-party tools available. MeiliSearch, being a relatively new player, has a smaller community and a less extensive ecosystem compared to Elasticsearch.
Use Case Fit: Elasticsearch is well-suited for use cases that require complex search scenarios, such as e-commerce, logging, monitoring, or data analytics. MeiliSearch, on the other hand, is better suited for simpler search requirements, like implementing search functionality in a small website or a blog.
In summary, Elasticsearch stands out in scalability, advanced search capabilities, real-time updates, and a mature ecosystem. MeiliSearch, on the other hand, focuses on ease of use and simplicity, making it a good fit for smaller applications with more straightforward search requirements.
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 Elasticsearch
- Powerful api329
- Great search engine315
- Open source231
- Restful214
- Near real-time search200
- Free98
- Search everything85
- Easy to get started54
- Analytics45
- Distributed26
- Fast search6
- More than a search engine5
- Awesome, great tool4
- Great docs4
- Highly Available3
- Easy to scale3
- Nosql DB2
- Document Store2
- Great customer support2
- Intuitive API2
- Reliable2
- Potato2
- Fast2
- Easy setup2
- Great piece of software2
- Open1
- Scalability1
- Not stable1
- Easy to get hot data1
- Github1
- Elaticsearch1
- Actively developing1
- Responsive maintainers on GitHub1
- Ecosystem1
- Community0
Pros of MeiliSearch
- Saas option1
- Restfull1
- Open source1
- Typo handling1
- Search as you type1
- Useful defaults1
- Easy to deploy1
- Facet search1
- Great long tail search results1
- Fast responses to online chat1
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Cons of Elasticsearch
- Resource hungry7
- Diffecult to get started6
- Expensive5
- Hard to keep stable at large scale4