Amazon SQS vs MySQL

Overview

Amazon SQS

Stacks2.8K

Followers2.0K

Votes171

MySQL

Stacks129.6K

Followers108.6K

Votes3.8K

GitHub Stars11.8K

Forks4.1K

Amazon SQS vs MySQL: What are the differences?

Introduction

Amazon Simple Queue Service (Amazon SQS) and MySQL are two popular technologies used in web development. While Amazon SQS is a managed message queuing service, MySQL is a relational database management system. Both have their own unique features and use cases. Let's explore some key differences between Amazon SQS and MySQL.

Scalability and Performance: One major difference between Amazon SQS and MySQL is scalability and performance. Amazon SQS is designed for high scalability and can handle a large number of messages or requests simultaneously. It is a fully managed service that can automatically scale up or down based on demand. On the other hand, MySQL is a traditional relational database that can be scaled vertically by adding more resources to the server. However, scaling horizontally in MySQL requires additional setup and configuration.
Data Structure and Query Language: Another significant difference between Amazon SQS and MySQL is in their data structure and query languages. Amazon SQS is a message queuing service, where messages are stored in a queue and processed by consumers. It does not provide a built-in query language for manipulating or retrieving data. In contrast, MySQL is a relational database that stores data in tables and allows for complex queries using SQL (Structured Query Language).
Data Persistence: Data persistence is another important distinction between Amazon SQS and MySQL. In Amazon SQS, messages are stored temporarily in queues and are accessible for a limited period of time. Once a message is consumed, it is removed from the queue. On the contrary, MySQL provides persistent storage, where data is stored in tables and remains available until explicitly deleted.
Data Consistency: Amazon SQS and MySQL also differ in terms of data consistency. Amazon SQS guarantees at-least-once delivery, where a message will be delivered to a consumer at least once but may be duplicated. It does not guarantee exactly-once delivery. On the other hand, MySQL provides ACID (Atomicity, Consistency, Isolation, Durability) compliance, which ensures data consistency and reliability.
Concurrency and Locking: Concurrency control and locking mechanisms are handled differently in Amazon SQS and MySQL. In Amazon SQS, multiple consumers can access and process messages from a queue concurrently, without the need for explicit locking. In contrast, MySQL uses locking mechanisms to ensure data integrity and prevent conflicts when multiple users access or modify the same data concurrently.
Data Availability and Reliability: Amazon SQS and MySQL also differ in terms of data availability and reliability. Amazon SQS is a distributed service that replicates messages across multiple data centers, providing high availability and fault tolerance. In case of failures, messages are not lost and can be retried. MySQL requires manual replication and backup configurations to ensure data availability and reliability.

In summary, Amazon SQS and MySQL differ in terms of scalability, data structure, data persistence, data consistency, concurrency control, and data availability. The choice between the two depends on specific use cases and requirements of the application.

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Advice on Amazon SQS, MySQL

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

Ido

Mar 6, 2020

Decided

My data was inherently hierarchical, but there was not enough content in each level of the hierarchy to justify a relational DB (SQL) with a one-to-many approach. It was also far easier to share data between the frontend (Angular), backend (Node.js) and DB (MongoDB) as they all pass around JSON natively. This allowed me to skip the translation layer from relational to hierarchical. You do need to think about correct indexes in MongoDB, and make sure the objects have finite size. For instance, an object in your DB shouldn't have a property which is an array that grows over time, without limit. In addition, I did use MySQL for other types of data, such as a catalog of products which (a) has a lot of data, (b) flat and not hierarchical, (c) needed very fast queries.

575k views575k

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 SQS	MySQL
Transmit any volume of data, at any level of throughput, without losing messages or requiring other services to be always available. With SQS, you can offload the administrative burden of operating and scaling a highly available messaging cluster, while paying a low price for only what you use.	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.
A queue can be created in any region.;The message payload can contain up to 256KB of text in any format. Each 64KB ‘chunk’ of payload is billed as 1 request. For example, a single API call with a 256KB payload will be billed as four requests.;Messages can be sent, received or deleted in batches of up to 10 messages or 256KB. Batches cost the same amount as single messages, meaning SQS can be even more cost effective for customers that use batching.;Long polling reduces extraneous polling to help you minimize cost while receiving new messages as quickly as possible. When your queue is empty, long-poll requests wait up to 20 seconds for the next message to arrive. Long poll requests cost the same amount as regular requests.;Messages can be retained in queues for up to 14 days.;Messages can be sent and read simultaneously.;Developers can get started with Amazon SQS by using only five APIs: CreateQueue, SendMessage, ReceiveMessage, ChangeMessageVisibility, and DeleteMessage. Additional APIs are available to provide advanced functionality.	-
Statistics
GitHub Stars -	GitHub Stars 11.8K
GitHub Forks -	GitHub Forks 4.1K
Stacks 2.8K	Stacks 129.6K
Followers 2.0K	Followers 108.6K
Votes 171	Votes 3.8K
Pros & Cons
Pros 62 Easy to use, reliable 40 Low cost 28 Simple 14 Doesn't need to maintain it 8 It is Serverless Cons 2 Difficult to configure 2 Proprietary 2 Has a max message size (currently 256K) 1 Has a maximum 15 minutes of delayed messages only	Pros 800 Sql 679 Free 562 Easy 528 Widely used 490 Open source Cons 16 Owned by a company with their own agenda 3 Can't roll back schema changes

What are some alternatives to Amazon SQS, MySQL?

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.

PostgreSQL

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.

Kafka

Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.

RabbitMQ

RabbitMQ gives your applications a common platform to send and receive messages, and your messages a safe place to live until received.

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.

Related Comparisons

Amazon SQS vs MySQL: What are the differences?

Introduction

Scalability and Performance: One major difference between Amazon SQS and MySQL is scalability and performance. Amazon SQS is designed for high scalability and can handle a large number of messages or requests simultaneously. It is a fully managed service that can automatically scale up or down based on demand. On the other hand, MySQL is a traditional relational database that can be scaled vertically by adding more resources to the server. However, scaling horizontally in MySQL requires additional setup and configuration.
Data Structure and Query Language: Another significant difference between Amazon SQS and MySQL is in their data structure and query languages. Amazon SQS is a message queuing service, where messages are stored in a queue and processed by consumers. It does not provide a built-in query language for manipulating or retrieving data. In contrast, MySQL is a relational database that stores data in tables and allows for complex queries using SQL (Structured Query Language).
Data Persistence: Data persistence is another important distinction between Amazon SQS and MySQL. In Amazon SQS, messages are stored temporarily in queues and are accessible for a limited period of time. Once a message is consumed, it is removed from the queue. On the contrary, MySQL provides persistent storage, where data is stored in tables and remains available until explicitly deleted.
Data Consistency: Amazon SQS and MySQL also differ in terms of data consistency. Amazon SQS guarantees at-least-once delivery, where a message will be delivered to a consumer at least once but may be duplicated. It does not guarantee exactly-once delivery. On the other hand, MySQL provides ACID (Atomicity, Consistency, Isolation, Durability) compliance, which ensures data consistency and reliability.
Concurrency and Locking: Concurrency control and locking mechanisms are handled differently in Amazon SQS and MySQL. In Amazon SQS, multiple consumers can access and process messages from a queue concurrently, without the need for explicit locking. In contrast, MySQL uses locking mechanisms to ensure data integrity and prevent conflicts when multiple users access or modify the same data concurrently.
Data Availability and Reliability: Amazon SQS and MySQL also differ in terms of data availability and reliability. Amazon SQS is a distributed service that replicates messages across multiple data centers, providing high availability and fault tolerance. In case of failures, messages are not lost and can be retried. MySQL requires manual replication and backup configurations to ensure data availability and reliability.

Amazon SQS vs MySQL

Overview

Amazon SQS vs MySQL: What are the differences?