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Amazon DocumentDB vs Elasticsearch: What are the differences?
- Data Model: Amazon DocumentDB is a fully managed document database service that supports MongoDB workloads, with JSON-like documents. Elasticsearch, on the other hand, is a search engine that indexes and searches unstructured content. While DocumentDB is specifically designed for document-oriented data, Elasticsearch is more oriented towards full-text search and analytics tasks.
- Query Language: DocumentDB uses the MongoDB query language, which is intuitive for developers familiar with MongoDB. Elasticsearch uses a query language based on JSON, which allows for complex queries including fuzzy searches, wildcard searches, and more. The query language in Elasticsearch offers more advanced search capabilities compared to DocumentDB.
- Scalability: Amazon DocumentDB is designed for horizontal scalability where users can add read replicas to distribute read traffic. Elasticsearch is designed for horizontal scalability for large-scale data, with support for sharding and replication to distribute data across nodes in a cluster. Elasticsearch is more suited for scenarios where massive data processing is required.
- Analytics Capabilities: Elasticsearch is built for real-time analytics, log analysis, and full-text search use cases, providing powerful aggregations, filtering, and sorting capabilities. Amazon DocumentDB lacks some advanced analytical features provided by Elasticsearch, making it less suitable for advanced analytics workloads.
- Indexing and Searching: Elasticsearch includes built-in indexing and searching features that are optimized for speed and efficiency. Amazon DocumentDB relies on indexes to improve query performance, but may not provide the same level of advanced search capabilities as Elasticsearch. Elasticsearch excels in indexing and searching documents across large datasets efficiently.
- Community Support: Elasticsearch has a large and active open-source community, offering a wide range of plugins, integrations, and support resources. Amazon DocumentDB, being a managed service, may have limited community support compared to Elasticsearch, which can impact the availability of resources and expertise for troubleshooting.
In Summary, Amazon DocumentDB is tailored for document-oriented workloads with MongoDB compatibility, while Elasticsearch is focused on real-time analytics, search, and scalability with advanced indexing and searching capabilities.
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 Amazon DocumentDB
- Storage elasticity0
- Scalable0
- Easy Setup0
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
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Cons of Amazon DocumentDB
Cons of Elasticsearch
- Resource hungry7
- Diffecult to get started6
- Expensive5
- Hard to keep stable at large scale4
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