Amazon RDS for Aurora vs PostgreSQL

Overview

PostgreSQL

Stacks103.0K

Followers83.9K

Votes3.6K

GitHub Stars19.0K

Forks5.2K

Amazon Aurora

Stacks804

Followers744

Votes55

Amazon Aurora vs PostgreSQL: What are the differences?

Amazon Aurora and PostgreSQL are relational database management systems that are different in terms of their architecture, scalability, performance, and managed services. Here are the key differences between Amazon Aurora and PostgreSQL:

Architecture: Amazon Aurora is a cloud-native database engine developed by Amazon Web Services (AWS) that is compatible with PostgreSQL. It is designed for high-performance and scalability, utilizing a distributed storage architecture that separates compute and storage layers. PostgreSQL, on the other hand, is a traditional open-source database management system that follows a monolithic architecture where computing and storage are tightly coupled.
Scalability and Performance: Amazon Aurora surpasses PostgreSQL in scalability and performance. Its shared storage architecture, automatic scaling, and replication capabilities enable efficient handling of high workloads and fast read/write performance. Additional features like read replicas and multi-master capability enhance scalability and availability. Conversely, PostgreSQL can scale vertically with more server resources but requires manual configuration for horizontal scaling.
Managed Services: Amazon Aurora is a managed database service provided by AWS, which means that AWS handles tasks such as software patching, backups, and database maintenance. This offloads the administrative burden from the users, allowing them to focus on application development. PostgreSQL, on the other hand, can be self-managed or used with managed PostgreSQL services offered by cloud providers. Self-managing PostgreSQL requires more effort for tasks like backups, upgrades, and monitoring.
Features and Extensions: PostgreSQL has a rich set of features and extensions, offering advanced indexing, JSON support, and a diverse collection of built-in functions. Its thriving community constantly enriches its ecosystem with additional extensions and plugins. Amazon Aurora, being based on PostgreSQL, shares most of its features and is compatible with PostgreSQL extensions.
Cost: Amazon Aurora is a paid service offered by AWS, and the cost depends on factors such as database instance size, storage usage, and data transfer. While it offers high-performance and managed services, it comes with associated costs. PostgreSQL, on the other hand, being open-source, is free to use. However, if you choose a managed PostgreSQL service from a cloud provider, there will be associated costs based on the service plan and usage.

In summary, Amazon Aurora is a cloud-native, highly scalable, and managed database service with excellent performance, while PostgreSQL is a feature-rich, open-source database management system that offers flexibility but requires more manual management.

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Advice on PostgreSQL, Amazon Aurora

Kyle

Web Application Developer at Redacted DevWorks

Dec 3, 2019

Decidedon

PostGIS

While there's been some very clever techniques that has allowed non-natively supported geo querying to be performed, it is incredibly slow in the long game and error prone at best.

MySQL finally introduced it's own GEO functions and special indexing operations for GIS type data. I prototyped with this, as MySQL is the most familiar database to me. But no matter what I did with it, how much tuning i'd give it, how much I played with it, the results would come back inconsistent.

It was very disappointing.

I figured, at this point, that SQL Server, being an enterprise solution authored by one of the biggest worldwide software developers in the world, Microsoft, might contain some decent GIS in it.

I was very disappointed.

Postgres is a Database solution i'm still getting familiar with, but I noticed it had no built in support for GIS. So I hilariously didn't pay it too much attention. That was until I stumbled upon PostGIS and my world changed forever.

449k views449k

Comments

George

Student

Mar 18, 2020

Needs adviceon

PostgreSQL

Python

Django

Hello everyone,

Well, I want to build a large-scale project, but I do not know which ORDBMS to choose. The app should handle real-time operations, not chatting, but things like future scheduling or reminders. It should be also really secure, fast and easy to use. And last but not least, should I use them both. I mean PostgreSQL with Python / Django and MongoDB with Node.js? Or would it be better to use PostgreSQL with Node.js?

*The project is going to use React for the front-end and GraphQL is going to be used for the API.

Thank you all. Any answer or advice would be really helpful!

620k views620k

Comments

Navraj

CEO at SuPragma

Apr 16, 2020

Needs adviceon

MySQL

PostgreSQL

I asked my last question incorrectly. Rephrasing it here.

I am looking for the most secure open source database for my project I'm starting: https://github.com/SuPragma/SuPragma/wiki

Which database is more secure? MySQL or PostgreSQL? Are there others I should be considering? Is it possible to change the encryption keys dynamically?

Thanks,

Raj

401k views401k

Comments

Detailed Comparison

PostgreSQL	Amazon Aurora
PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.	Amazon Aurora is a MySQL-compatible, relational database engine that combines the speed and availability of high-end commercial databases with the simplicity and cost-effectiveness of open source databases. Amazon Aurora provides up to five times better performance than MySQL at a price point one tenth that of a commercial database while delivering similar performance and availability.
-	High Throughput with Low Jitter;Push-button Compute Scaling;Storage Auto-scaling;Amazon Aurora Replicas;Instance Monitoring and Repair;Fault-tolerant and Self-healing Storage;Automatic, Continuous, Incremental Backups and Point-in-time Restore;Database Snapshots;Resource-level Permissions;Easy Migration;Monitoring and Metrics
Statistics
GitHub Stars 19.0K	GitHub Stars -
GitHub Forks 5.2K	GitHub Forks -
Stacks 103.0K	Stacks 804
Followers 83.9K	Followers 744
Votes 3.6K	Votes 55
Pros & Cons
Pros 765 Relational database 511 High availability 439 Enterprise class database 383 Sql 304 Sql + nosql Cons 10 Table/index bloatings	Pros 14 MySQL compatibility 12 Better performance 10 Easy read scalability 9 Speed 7 Low latency read replica Cons 2 Vendor locking 1 Rigid schema
Integrations
No integrations available	MySQL

What are some alternatives to PostgreSQL, Amazon Aurora?

MongoDB

MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.

MySQL

The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.

Amazon RDS

Amazon RDS gives you access to the capabilities of a familiar MySQL, Oracle or Microsoft SQL Server database engine. This means that the code, applications, and tools you already use today with your existing databases can be used with Amazon RDS. Amazon RDS automatically patches the database software and backs up your database, storing the backups for a user-defined retention period and enabling point-in-time recovery. You benefit from the flexibility of being able to scale the compute resources or storage capacity associated with your Database Instance (DB Instance) via a single API call.

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

SQLite

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

Cassandra

Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.

Memcached

Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.

MariaDB

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

RethinkDB

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.

ArangoDB

A distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.

Related Comparisons

Architecture: Amazon Aurora is a cloud-native database engine developed by Amazon Web Services (AWS) that is compatible with PostgreSQL. It is designed for high-performance and scalability, utilizing a distributed storage architecture that separates compute and storage layers. PostgreSQL, on the other hand, is a traditional open-source database management system that follows a monolithic architecture where computing and storage are tightly coupled.
Scalability and Performance: Amazon Aurora surpasses PostgreSQL in scalability and performance. Its shared storage architecture, automatic scaling, and replication capabilities enable efficient handling of high workloads and fast read/write performance. Additional features like read replicas and multi-master capability enhance scalability and availability. Conversely, PostgreSQL can scale vertically with more server resources but requires manual configuration for horizontal scaling.
Managed Services: Amazon Aurora is a managed database service provided by AWS, which means that AWS handles tasks such as software patching, backups, and database maintenance. This offloads the administrative burden from the users, allowing them to focus on application development. PostgreSQL, on the other hand, can be self-managed or used with managed PostgreSQL services offered by cloud providers. Self-managing PostgreSQL requires more effort for tasks like backups, upgrades, and monitoring.
Features and Extensions: PostgreSQL has a rich set of features and extensions, offering advanced indexing, JSON support, and a diverse collection of built-in functions. Its thriving community constantly enriches its ecosystem with additional extensions and plugins. Amazon Aurora, being based on PostgreSQL, shares most of its features and is compatible with PostgreSQL extensions.
Cost: Amazon Aurora is a paid service offered by AWS, and the cost depends on factors such as database instance size, storage usage, and data transfer. While it offers high-performance and managed services, it comes with associated costs. PostgreSQL, on the other hand, being open-source, is free to use. However, if you choose a managed PostgreSQL service from a cloud provider, there will be associated costs based on the service plan and usage.

Amazon RDS for Aurora vs PostgreSQL