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Amazon DynamoDB vs Amazon RDS for PostgreSQL: What are the differences?
Introduction
Amazon DynamoDB and Amazon RDS for PostgreSQL are both popular database services provided by Amazon Web Services (AWS). However, they have key differences that make them suitable for different use cases.
Data Model: DynamoDB is a NoSQL database that uses a key-value store data model, allowing for flexible schema designs. On the other hand, Amazon RDS for PostgreSQL is a relational database, following a fixed schema with tables and rows.
Scalability: DynamoDB is designed to scale horizontally by automatically partitioning data across multiple servers. This allows for virtually unlimited storage and throughput as the workload scales. In contrast, Amazon RDS for PostgreSQL can scale vertically by upgrading the hardware running the database but has limitations on storage and throughput based on the chosen instance type.
Query Capabilities: DynamoDB offers fast and efficient querying based on primary key and secondary indexes. It also provides advanced querying capabilities like filtering, sorting, and aggregation. In comparison, Amazon RDS for PostgreSQL supports complex SQL queries, including joins and advanced data manipulations.
Data Consistency: DynamoDB offers eventual consistency by default, where data modifications may take some time to propagate across all replicas. However, it also provides the option for strong consistency, ensuring immediate data consistency. On the other hand, Amazon RDS for PostgreSQL supports ACID transactions and offers strong consistency by default, ensuring immediate data consistency.
Backup and Restore: DynamoDB automatically takes incremental backups and provides point-in-time recovery for up to 35 days. It also offers cross-region replication for disaster recovery. In contrast, Amazon RDS for PostgreSQL provides automated backups with adjustable retention periods and supports cross-region automated backups. It also allows manual snapshots for longer-term backups.
Pricing Model: DynamoDB pricing is based on provisioned throughput, storage, and data transfer. Users pay for the consistent throughput they provision and the storage they consume. Amazon RDS for PostgreSQL pricing is based on instance types, storage, and data transfer. Users pay for the size of the chosen instance type, allocated storage, and data transfer.
In summary, Amazon DynamoDB and Amazon RDS for PostgreSQL differ in their data models, scalability options, query capabilities, data consistency models, backup and restore features, and pricing models. These differences make each service suitable for different application requirements and use cases.
We are building a social media app, where users will post images, like their post, and make friends based on their interest. We are currently using Cloud Firestore and Firebase Realtime Database. We are looking for another database like Amazon DynamoDB; how much this decision can be efficient in terms of pricing and overhead?
Hi, Akash,
I wouldn't make this decision without lots more information. Cloud Firestore has a much richer metamodel (document-oriented) than Dynamo (key-value), and Dynamo seems to be particularly restrictive. That is why it is so fast. There are many needs in most applications to get lightning access to the members of a set, one set at a time. Dynamo DB is a great choice. But, social media applications generally need to be able to make long traverses across a graph. While you can make almost any metamodel act like another one, with your own custom layers on top of it, or just by writing a lot more code, it's a long way around to do that with simple key-value sets. It's hard enough to traverse across networks of collections in a document-oriented database. So, if you are moving, I think a graph-oriented database like Amazon Neptune, or, if you might want built-in reasoning, Allegro or Ontotext, would take the least programming, which is where the most cost and bugs can be avoided. Also, managed systems are also less costly in terms of people's time and system errors. It's easier to measure the costs of managed systems, so they are often seen as more costly.
Considering moving part of our PostgreSQL database infrastructure to the cloud, however, not quite sure between AWS, Heroku, Azure and Google cloud. Things to consider: The main reason is for backing up and centralize all our data in the cloud. With that in mind the main elements are: -Pricing for storage. -Small team. -No need for high throughput. -Support for docker swarm and Kubernetes.
Good balance between easy to manage, pricing, docs and features.
DigitalOcean's offering is pretty solid. Easy to scale, great UI, automatic daily backups, decent pricing.
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 Amazon RDS for PostgreSQL
- Easy setup, backup, monitoring25
- Geospatial support13
- Master-master replication using Multi-AZ instance2
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Cons of Amazon DynamoDB
- Only sequential access for paginate data4
- Scaling1
- Document Limit Size1
Cons of Amazon RDS for PostgreSQL
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What is Amazon DynamoDB?
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