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Amazon DynamoDB vs Elasticsearch: What are the differences?
Introduction:
Amazon DynamoDB and Elasticsearch are both popular database technologies used in the industry. Despite being used for similar purposes, they have key differences in terms of their architecture and functionalities.
Data Model: Amazon DynamoDB is a NoSQL database with a flexible schema where each item can have different attributes, while Elasticsearch is schema-free and document-oriented, allowing for complex nested data structures.
Query Language: DynamoDB uses a simple query language with limited complex querying capabilities, whereas Elasticsearch provides a powerful search DSL (Domain Specific Language) allowing for advanced queries like full-text search, aggregations, and filtering.
Scalability: Amazon DynamoDB is fully managed and scales automatically by adjusting read and write capacity units, while Elasticsearch requires manual scaling by adding more nodes to the cluster to handle increased workloads.
Use Cases: DynamoDB is optimized for fast and predictable performance on small to large-scale applications with high availability requirements, while Elasticsearch is designed for full-text search and analytics use cases, making it a go-to solution for log analysis and real-time data monitoring.
Data Replication: In DynamoDB, data replication is handled by the service itself across multiple Availability Zones for high durability, while Elasticsearch provides the ability to set up data replication and sharding configurations for improved performance and fault tolerance.
Indexing Capabilities: DynamoDB does not provide indexing capabilities beyond primary and secondary indexes, whereas Elasticsearch has a sophisticated indexing mechanism that includes inverted indexes, allowing for fast and efficient search operations on large data sets.
In Summary, Amazon DynamoDB and Elasticsearch differ in their data model flexibility, query capabilities, scalability options, use case suitability, data replication mechanisms, and indexing 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 DynamoDB
- Predictable performance and cost62
- Scalable56
- Native JSON Support35
- AWS Free Tier21
- Fast7
- No sql3
- To store data3
- Serverless2
- No Stored procedures is GOOD2
- ORM with DynamoDBMapper1
- Elastic Scalability using on-demand mode1
- Elastic Scalability using autoscaling1
- DynamoDB Stream1
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
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Cons of Amazon DynamoDB
- Only sequential access for paginate data4
- Scaling1
- Document Limit Size1
Cons of Elasticsearch
- Resource hungry7
- Diffecult to get started6
- Expensive5
- Hard to keep stable at large scale4