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Elasticsearch vs MongoDB Atlas: What are the differences?
- Deployment Method: Elasticsearch is typically self-hosted on-premises or on cloud infrastructure like AWS, while MongoDB Atlas is a fully managed database service provided by MongoDB, running on the cloud with automatic scaling and backups.
- Data Model: Elasticsearch is optimized for full-text search and complex queries, while MongoDB Atlas is a document-oriented database that stores data in a JSON-like format, making it easy to work with dynamic schemas.
- Querying Capabilities: Elasticsearch utilizes its own query DSL (Domain Specific Language) for powerful and flexible search queries, including fuzzy searches and aggregations, while MongoDB Atlas supports query operations through its MongoDB Query Language.
- Scalability: Elasticsearch is designed for horizontal scalability, allowing easy distribution of data across nodes for improved performance, while MongoDB Atlas offers automatic scaling for data storage as well as read and write operations.
- Indexing Strategy: Elasticsearch indexes all fields by default, making it suitable for search applications like logging and monitoring, whereas MongoDB Atlas requires manual indexing to optimize query performance for specific fields in a collection.
- Performance Optimization: Elasticsearch provides powerful indexing and search capabilities for large datasets, making it ideal for applications where search speed is crucial, while MongoDB Atlas excels in general-purpose applications with predictable workloads due to its schema flexibility and ease of data manipulation.
In Summary, Elasticsearch and MongoDB Atlas differ significantly in deployment methods, data models, querying capabilities, scalability options, indexing strategies, and performance optimizations.
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 api328
- 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
- Great docs4
- Awesome, great tool4
- Highly Available3
- Easy to scale3
- Potato2
- Document Store2
- Great customer support2
- Intuitive API2
- Nosql DB2
- Great piece of software2
- Reliable2
- Fast2
- Easy setup2
- Open1
- Easy to get hot data1
- Github1
- Elaticsearch1
- Actively developing1
- Responsive maintainers on GitHub1
- Ecosystem1
- Not stable1
- Scalability1
- Community0
Pros of MongoDB Atlas
- MongoDB SaaS for and by Mongo, makes it so easy10
- Amazon VPC peering6
- Granular role-based access controls4
- MongoDB atlas is GUItool through you can manage all DB4
- Use it anywhere3
- Cloud instance to be worked with3
- Built-in data browser3
- Simple and easy to integrate1
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Cons of Elasticsearch
- Resource hungry7
- Diffecult to get started6
- Expensive5
- Hard to keep stable at large scale4