Amazon S3 vs PostgreSQL

Overview

Amazon S3

Stacks55.1K

Followers40.2K

Votes2.0K

PostgreSQL

Stacks103.0K

Followers83.9K

Votes3.6K

GitHub Stars19.0K

Forks5.2K

Amazon S3 vs PostgreSQL: What are the differences?

Amazon S3 is a scalable object storage service while PostgreSQL is a powerful open-source relational database management system. Let's explore the key differences between them.

Data Storage Model: Amazon S3 is a cloud-based storage service primarily used for storing and retrieving any amount of data from anywhere. It provides object storage, where each file is treated as an object with a unique identifier. On the other hand, PostgreSQL is a relational database management system (RDBMS) that organizes data into tables with predefined schemas, enforcing relationships between them.
Data Consistency: Amazon S3 is eventually consistent, which means that once an object is stored, subsequent retrieval might not immediately reflect the latest write. This characteristic is suitable for scenarios where eventual consistency can be accepted, such as storing backups or non-critical data. In contrast, PostgreSQL follows the ACID (Atomicity, Consistency, Isolation, Durability) properties, ensuring strict consistency and reliability of data.
Query Language: Amazon S3 does not have a built-in query language. It treats files as objects and does not provide facilities for structured queries. However, it offers simple APIs to manage objects and their metadata. On the other hand, PostgreSQL uses Structured Query Language (SQL) as its standard query language. It supports complex queries, joins, and various relational operations, making it suitable for handling structured data.
Scalability: Amazon S3 is highly scalable and can handle massive amounts of data. It is designed to be distributed across multiple servers, providing high availability and durability. As the data size or access demands increase, Amazon S3 scales automatically to accommodate the workload. PostgreSQL also offers scalability features like horizontal scaling with the help of sharding, but it requires manual configuration and management to achieve optimal scalability.
Data Integrity: Amazon S3 ensures data durability by automatically storing multiple copies of each object across multiple devices in multiple data centers. It protects data from hardware failures and maintains 99.999999999% (11 nines) durability. PostgreSQL, with its transactional capabilities and data validation rules, provides data integrity within the database. It uses mechanisms like write-ahead logging (WAL) and crash recovery to maintain consistency and prevent data corruption.
Data Access Control: Amazon S3 offers access control mechanisms through Access Control Lists (ACLs) and bucket policies. These allow users to define granular permissions for buckets and objects, restricting access based on different criteria like user identity or IP address. In PostgreSQL, access control is managed through authentication and authorization mechanisms. It provides robust user management and privileges management at the database, schema, table, and column levels.

In summary, Amazon S3 is ideal for storing unstructured data like images, videos, and documents, offering high scalability and durability. In contrast, PostgreSQL is a relational database suited for structured data storage, providing powerful querying capabilities and ACID compliance for transactional integrity.

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Advice on Amazon S3, PostgreSQL

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

Amazon S3	PostgreSQL
Amazon Simple Storage Service provides a fully redundant data storage infrastructure for storing and retrieving any amount of data, at any time, from anywhere on the web	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.
Write, read, and delete objects containing from 1 byte to 5 terabytes of data each. The number of objects you can store is unlimited.;Each object is stored in a bucket and retrieved via a unique, developer-assigned key.;A bucket can be stored in one of several Regions. You can choose a Region to optimize for latency, minimize costs, or address regulatory requirements. Amazon S3 is currently available in the US Standard, US West (Oregon), US West (Northern California), EU (Ireland), Asia Pacific (Singapore), Asia Pacific (Tokyo), Asia Pacific (Sydney), South America (Sao Paulo), and GovCloud (US) Regions. The US Standard Region automatically routes requests to facilities in Northern Virginia or the Pacific Northwest using network maps.;Objects stored in a Region never leave the Region unless you transfer them out. For example, objects stored in the EU (Ireland) Region never leave the EU.;Authentication mechanisms are provided to ensure that data is kept secure from unauthorized access. Objects can be made private or public, and rights can be granted to specific users.;Options for secure data upload/download and encryption of data at rest are provided for additional data protection.;Uses standards-based REST and SOAP interfaces designed to work with any Internet-development toolkit.;Built to be flexible so that protocol or functional layers can easily be added. The default download protocol is HTTP. A BitTorrent protocol interface is provided to lower costs for high-scale distribution.;Provides functionality to simplify manageability of data through its lifetime. Includes options for segregating data by buckets, monitoring and controlling spend, and automatically archiving data to even lower cost storage options. These options can be easily administered from the Amazon S3 Management Console.;Reliability backed with the Amazon S3 Service Level Agreement.	-
Statistics
GitHub Stars -	GitHub Stars 19.0K
GitHub Forks -	GitHub Forks 5.2K
Stacks 55.1K	Stacks 103.0K
Followers 40.2K	Followers 83.9K
Votes 2.0K	Votes 3.6K
Pros & Cons
Pros 590 Reliable 492 Scalable 456 Cheap 329 Simple & easy 83 Many sdks Cons 7 Permissions take some time to get right 6 Requires a credit card 6 Takes time/work to organize buckets & folders properly 3 Complex to set up	Pros 765 Relational database 511 High availability 439 Enterprise class database 383 Sql 304 Sql + nosql Cons 10 Table/index bloatings

What are some alternatives to Amazon S3, PostgreSQL?

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.

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.

InfluxDB

InfluxDB is a scalable datastore for metrics, events, and real-time analytics. It has a built-in HTTP API so you don't have to write any server side code to get up and running. InfluxDB is designed to be scalable, simple to install and manage, and fast to get data in and out.

Related Comparisons

Amazon S3 vs PostgreSQL: What are the differences?

Amazon S3 is a scalable object storage service while PostgreSQL is a powerful open-source relational database management system. Let's explore the key differences between them.

Data Storage Model: Amazon S3 is a cloud-based storage service primarily used for storing and retrieving any amount of data from anywhere. It provides object storage, where each file is treated as an object with a unique identifier. On the other hand, PostgreSQL is a relational database management system (RDBMS) that organizes data into tables with predefined schemas, enforcing relationships between them.
Data Consistency: Amazon S3 is eventually consistent, which means that once an object is stored, subsequent retrieval might not immediately reflect the latest write. This characteristic is suitable for scenarios where eventual consistency can be accepted, such as storing backups or non-critical data. In contrast, PostgreSQL follows the ACID (Atomicity, Consistency, Isolation, Durability) properties, ensuring strict consistency and reliability of data.
Query Language: Amazon S3 does not have a built-in query language. It treats files as objects and does not provide facilities for structured queries. However, it offers simple APIs to manage objects and their metadata. On the other hand, PostgreSQL uses Structured Query Language (SQL) as its standard query language. It supports complex queries, joins, and various relational operations, making it suitable for handling structured data.
Scalability: Amazon S3 is highly scalable and can handle massive amounts of data. It is designed to be distributed across multiple servers, providing high availability and durability. As the data size or access demands increase, Amazon S3 scales automatically to accommodate the workload. PostgreSQL also offers scalability features like horizontal scaling with the help of sharding, but it requires manual configuration and management to achieve optimal scalability.
Data Integrity: Amazon S3 ensures data durability by automatically storing multiple copies of each object across multiple devices in multiple data centers. It protects data from hardware failures and maintains 99.999999999% (11 nines) durability. PostgreSQL, with its transactional capabilities and data validation rules, provides data integrity within the database. It uses mechanisms like write-ahead logging (WAL) and crash recovery to maintain consistency and prevent data corruption.
Data Access Control: Amazon S3 offers access control mechanisms through Access Control Lists (ACLs) and bucket policies. These allow users to define granular permissions for buckets and objects, restricting access based on different criteria like user identity or IP address. In PostgreSQL, access control is managed through authentication and authorization mechanisms. It provides robust user management and privileges management at the database, schema, table, and column levels.

Amazon S3 vs PostgreSQL

Overview