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Amazon DynamoDB vs Amazon RDS for PostgreSQL: What are the differences?
Developers describe Amazon DynamoDB as "Fully managed NoSQL database service". All data items are stored on Solid State Drives (SSDs), and are replicated across 3 Availability Zones for high availability and durability. With DynamoDB, you can offload the administrative burden of operating and scaling a highly available distributed database cluster, while paying a low price for only what you use. On the other hand, Amazon RDS for PostgreSQL is detailed as "* Set up, operate, and scale PostgreSQL deployments in the cloud*". Amazon RDS manages complex and time-consuming administrative tasks such as PostgreSQL software installation and upgrades, storage management, replication for high availability and back-ups for disaster recovery. With just a few clicks in the AWS Management Console, you can deploy a PostgreSQL database with automatically configured database parameters for optimal performance. Amazon RDS for PostgreSQL database instances can be provisioned with either standard storage or Provisioned IOPS storage. Once provisioned, you can scale from 10GB to 3TB of storage and from 1,000 IOPS to 30,000 IOPS.
Amazon DynamoDB can be classified as a tool in the "NoSQL Database as a Service" category, while Amazon RDS for PostgreSQL is grouped under "PostgreSQL as a Service".
Some of the features offered by Amazon DynamoDB are:
- Automated Storage Scaling – There is no limit to the amount of data you can store in a DynamoDB table, and the service automatically allocates more storage, as you store more data using the DynamoDB write APIs.
- Provisioned Throughput – When creating a table, simply specify how much request capacity you require. DynamoDB allocates dedicated resources to your table to meet your performance requirements, and automatically partitions data over a sufficient number of servers to meet your request capacity. If your throughput requirements change, simply update your table's request capacity using the AWS Management Console or the Amazon DynamoDB APIs. You are still able to achieve your prior throughput levels while scaling is underway.
- Fully Distributed, Shared Nothing Architecture – Amazon DynamoDB scales horizontally and can seamlessly scale a single table over hundreds of servers.
On the other hand, Amazon RDS for PostgreSQL provides the following key features:
- Monitoring and Metrics –Amazon RDS provides Amazon CloudWatch metrics for you DB Instance deployments at no additional charge.
- DB Event Notifications –Amazon RDS provides Amazon SNS notifications via email or SMS for your DB Instance deployments.
- Automatic Software Patching – Amazon RDS will make sure that the PostgreSQL software powering your deployment stays up-to-date with the latest patches.
"Predictable performance and cost" is the top reason why over 53 developers like Amazon DynamoDB, while over 22 developers mention "Easy setup, backup, monitoring" as the leading cause for choosing Amazon RDS for PostgreSQL.
Lyft, New Relic, and Sellsuki are some of the popular companies that use Amazon DynamoDB, whereas Amazon RDS for PostgreSQL is used by Instacart, Tictail, and DSTLD. Amazon DynamoDB has a broader approval, being mentioned in 429 company stacks & 173 developers stacks; compared to Amazon RDS for PostgreSQL, which is listed in 164 company stacks and 27 developer stacks.
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.
DigitalOcean's offering is pretty solid. Easy to scale, great UI, automatic daily backups, decent pricing.
Good balance between easy to manage, pricing, docs and features.
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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