PostgreSQL vs RabbitMQ

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PostgreSQL vs RabbitMQ: What are the differences?

PostgreSQL: A powerful, open source object-relational database system. 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; RabbitMQ: A messaging broker - an intermediary for messaging. RabbitMQ gives your applications a common platform to send and receive messages, and your messages a safe place to live until received.

PostgreSQL belongs to "Databases" category of the tech stack, while RabbitMQ can be primarily classified under "Message Queue".

"Relational database" is the primary reason why developers consider PostgreSQL over the competitors, whereas "It's fast and it works with good metrics/monitoring" was stated as the key factor in picking RabbitMQ.

PostgreSQL and RabbitMQ are both open source tools. RabbitMQ with 5.88K GitHub stars and 1.73K forks on GitHub appears to be more popular than PostgreSQL with 5.38K GitHub stars and 1.79K GitHub forks.

According to the StackShare community, PostgreSQL has a broader approval, being mentioned in 2701 company stacks & 2098 developers stacks; compared to RabbitMQ, which is listed in 921 company stacks and 532 developer stacks.

What is 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.

What is RabbitMQ?

RabbitMQ gives your applications a common platform to send and receive messages, and your messages a safe place to live until received.
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Jobs that mention PostgreSQL and RabbitMQ as a desired skillset
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What are some alternatives to PostgreSQL and RabbitMQ?
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.
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.
Oracle
Oracle Database is an RDBMS. An RDBMS that implements object-oriented features such as user-defined types, inheritance, and polymorphism is called an object-relational database management system (ORDBMS). Oracle Database has extended the relational model to an object-relational model, making it possible to store complex business models in a relational database.
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.
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.
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Decisions about PostgreSQL and RabbitMQ
Anton Sidelnikov
Anton Sidelnikov
Backend Developer at Beamery · | 5 upvotes · 9K views
MongoDB
MongoDB
PostgreSQL
PostgreSQL

In my opinion PostgreSQL is totally over MongoDB - not only works with structured data & SQL & strict types, but also has excellent support for unstructured data as separate data type (you can store arbitrary JSONs - and they may be also queryable, depending on one of format's you may choose). Both writes & reads are much faster, then in Mongo. So you can get best on Document NoSQL & SQL in single database..

Formal downside of PostgreSQL is clustering scalability. There's not simple way to build distributed a cluster. However, two points:

1) You will need much more time before you need to actually scale due to PG's efficiency. And if you follow database-per-service pattern, maybe you won't need ever, cause dealing few billion records on single machine is an option for PG.

2) When you need to - you do it in a way you need, including as a part of app's logic (e.g. sharding by key, or PG-based clustering solution with strict model), scalability will be very transparent, much more obvious than Mongo's "cluster just works (but then fails)" replication.

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RabbitMQ
RabbitMQ
Kafka
Kafka

The question for which Message Queue to use mentioned "availability, distributed, scalability, and monitoring". I don't think that this excludes many options already. I does not sound like you would take advantage of Kafka's strengths (replayability, based on an even sourcing architecture). You could pick one of the AMQP options.

I would recommend the RabbitMQ message broker, which not only implements the AMQP standard 0.9.1 (it can support 1.x or other protocols as well) but has also several very useful extensions built in. It ticks the boxes you mentioned and on top you will get a very flexible system, that allows you to build the architecture, pick the options and trade-offs that suite your case best.

For more information about RabbitMQ, please have a look at the linked markdown I assembled. The second half explains many configuration options. It also contains links to managed hosting and to libraries (though it is missing Python's - which should be Puka, I assume).

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Alex A
Alex A
Founder at PRIZ Guru · | 6 upvotes · 8.4K views
atPRIZ GuruPRIZ Guru
PostgreSQL
PostgreSQL
MySQL
MySQL

One of our battles at the very beginning of the road was choosing the right database. In fact, our first prototype was built on MySQL and back then nothing else was even under a consideration (don't ask me why). At some point, I was working on a project which was running on PostgreSQL and it is only then I understood the full power of it. We have over a billion of records in production instance, and we are able to optimize it to run fast and reliable. Well, now my default DB is PostgreSQL :)

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Tim Nolet
Tim Nolet
Founder, Engineer & Dishwasher at Checkly · | 8 upvotes · 61.2K views
atChecklyHQChecklyHQ
Amazon DynamoDB
Amazon DynamoDB
MongoDB
MongoDB
Node.js
Node.js
Heroku
Heroku
PostgreSQL
PostgreSQL

PostgreSQL Heroku Node.js MongoDB Amazon DynamoDB

When I started building Checkly, one of the first things on the agenda was how to actually structure our SaaS database model: think accounts, users, subscriptions etc. Weirdly, there is not a lot of information on this on the "blogopshere" (cringe...). After research and some false starts with MongoDB and Amazon DynamoDB we ended up with PostgreSQL and a schema consisting of just four tables that form the backbone of all generic "Saasy" stuff almost any B2B SaaS bumps into.

In a nutshell:cPostgreSQL Heroku Node.js MongoDB Amazon DynamoDB

When I started building Checkly, one of the first things on the agenda was how to actually structure our SaaS database model: think accounts, users, subscriptions etc. Weirdly, there is not a lot of information on this on the "blogopshere" (cringe...). After research and some false starts with MongoDB and Amazon DynamoDB we ended up with PostgreSQL and a schema consisting of just four tables that form the backbone of all generic "Saasy" stuff almost any B2B SaaS bumps into.

In a nutshell:

  • We use Postgres on Heroku.
  • We use a "one database, on schema" approach for partitioning customer data.
  • We use an accounts, memberships and users table to create a many-to-many relation between users and accounts.
  • We completely decouple prices, payments and the exact ingredients for a customer's plan.

All the details including a database schema diagram are in the linked blog post.

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Sebastian Gębski
Sebastian Gębski
CTO at Shedul/Fresha · | 5 upvotes · 32K views
atFresha EngineeringFresha Engineering
Redis
Redis
RabbitMQ
RabbitMQ
PostgreSQL
PostgreSQL

Initially we had just 1 monolithic application with a PostgreSQL database (picked for performance, community & flexibility to work with GIS data), but as we've developed more features, it was clear that some stuff is relatively independent from the rest of the platform - it made sense to split the application into loosely coupled, asynchronously communicated services. As a communication broker we've used RabbitMQ (wrapped in our custom, ProtoBuff-based wrapper). To reduce some excessive inter-process (& inter-dyno) communication, we've applied Redis as a tool to keep short-lived, not-persistent information (but not as a cheap caching workaround for any kind of performance issues ;>).

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Łukasz Korecki
Łukasz Korecki
CTO & Co-founder at EnjoyHQ · | 12 upvotes · 38.6K views
atEnjoyHQEnjoyHQ
PostgreSQL
PostgreSQL
MongoDB
MongoDB
RethinkDB
RethinkDB

We initially chose RethinkDB because of the schema-less document store features, and better durability resilience/story than MongoDB In the end, it didn't work out quite as we expected: there's plenty of scalability issues, it's near impossible to run analytical workloads and small community makes working with Rethink a challenge. We're in process of migrating all our workloads to PostgreSQL and hopefully, we will be able to decommission our RethinkDB deployment soon.

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Mauro Bennici
Mauro Bennici
CTO at You Are My GUide · | 7 upvotes · 10.4K views
atYou Are My GUideYou Are My GUide
MongoDB
MongoDB
TimescaleDB
TimescaleDB
PostgreSQL
PostgreSQL

PostgreSQL plus TimescaleDB allow us to concentrate the business effort on how to analyze valuable data instead of manage them on IT side. We are now able to ingest thousand of social shares "managed" data without compromise the scalability of the system or the time query. TimescaleDB is transparent to PostgreSQL , so we continue to use the same SQL syntax without any changes. At the same time, because we need to manage few document objects we dismissed the MongoDB cluster.

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Tor Hagemann
Tor Hagemann
at Socotra · | 2 upvotes · 2.2K views
atSocotraSocotra
Amazon DynamoDB
Amazon DynamoDB
PostgreSQL
PostgreSQL
MySQL
MySQL

Much of our data model is relational, which makes MySQL or PostgreSQL (and family) fit the API's we need to build, in order to meet the needs of our customers.

Sometimes the flexibility of a NoSQL store like Amazon DynamoDB is very useful, but the lack of consistency really impacts usability and performance long-term, compared with viable alternatives. At our current scale, we've seen huge benefits from moving some of our tables out of Dynamo and doing more in SQL.

There will always be use cases for NoSQL and key-values stores, but if your model is well understood in your business/industry: relational databases are the way to go after finding product-market fit. Always understand the trade-offs (and a few intimate details) of any data store before you add to your company's stack!

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Joseph Irving
Joseph Irving
DevOps Engineer at uSwitch · | 8 upvotes · 7.3K views
atuSwitchuSwitch
Go
Go
PostgreSQL
PostgreSQL
MySQL
MySQL
Kubernetes
Kubernetes
Vault
Vault

At uSwitch we use Vault to generate short lived database credentials for our applications running in Kubernetes. We wanted to move from an environment where we had 100 dbs with a variety of static passwords being shared around to a place where each pod would have credentials that only last for its lifetime.

We chose vault because:

  • It had built in Kubernetes support so we could use service accounts to permission which pods could access which database.

  • A terraform provider so that we could configure both our RDS instances and their vault configuration in one place.

  • A variety of database providers including MySQL/PostgreSQL (our most common dbs).

  • A good api/Go -sdk so that we could build tooling around it to simplify development worfklow.

  • It had other features we would utilise such as PKI

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Daniel Quinn
Daniel Quinn
Senior Developer at Workfinder · | 2 upvotes · 22.1K views
atThe Paperless ProjectThe Paperless Project
PostgreSQL
PostgreSQL
SQLite
SQLite

SQLite is a tricky beast. It's great if you're working single-threaded, but a Terrible Idea if you've got more than one concurrent connection. You use it because it's easy to setup, light, and portable (it's just a file).

In Paperless, we've built a self-hosted web application, so it makes sense to standardise on something small & light, and as we don't have to worry about multiple connections (it's just you using the app), it's a perfect fit.

For users wanting to scale Paperless up to a multi-user environment though, we do provide the hooks to switch to PostgreSQL .

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Robert Zuber
Robert Zuber
CTO at CircleCI · | 22 upvotes · 161.3K views
atCircleCICircleCI
Amazon S3
Amazon S3
GitHub
GitHub
Redis
Redis
PostgreSQL
PostgreSQL
MongoDB
MongoDB

We use MongoDB as our primary #datastore. Mongo's approach to replica sets enables some fantastic patterns for operations like maintenance, backups, and #ETL.

As we pull #microservices from our #monolith, we are taking the opportunity to build them with their own datastores using PostgreSQL. We also use Redis to cache data we’d never store permanently, and to rate-limit our requests to partners’ APIs (like GitHub).

When we’re dealing with large blobs of immutable data (logs, artifacts, and test results), we store them in Amazon S3. We handle any side-effects of S3’s eventual consistency model within our own code. This ensures that we deal with user requests correctly while writes are in process.

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Martin Johannesson
Martin Johannesson
Senior Software Developer at IT Minds · | 10 upvotes · 15.1K views
atIT MindsIT Minds
AMP
AMP
PWA
PWA
React
React
MongoDB
MongoDB
Next.js
Next.js
GraphQL
GraphQL
Apollo
Apollo
PostgreSQL
PostgreSQL
TypeORM
TypeORM
Node.js
Node.js
TypeScript
TypeScript
#Serverless
#Backend
#B2B

At IT Minds we create customized internal or #B2B web and mobile apps. I have a go to stack that I pitch to our customers consisting of 3 core areas. 1) A data core #backend . 2) A micro #serverless #backend. 3) A user client #frontend.

For the Data Core I create a backend using TypeScript Node.js and with TypeORM connecting to a PostgreSQL Exposing an action based api with Apollo GraphQL

For the micro serverless backend, which purpose is verification for authentication, autorization, logins and the likes. It is created with Next.js api pages. Using MongoDB to store essential information, caching etc.

Finally the frontend is built with React using Next.js , TypeScript and @Apollo. We create the frontend as a PWA and have a AMP landing page by default.

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Jelena Dedovic
Jelena Dedovic
MSSQL
MSSQL
PostgreSQL
PostgreSQL
AIOHTTP
AIOHTTP
asyncio
asyncio
Tornado
Tornado

Investigating Tortoise ORM and GINO ORM...

I need to introduce some async ORM with the current stack: Tornado with asyncio loop, AIOHTTP, with PostgreSQL and MSSQL. This project revolves heavily around realtime and due to the realtime requirements, blocking during database access is not acceptable.

Considering that these ORMs are both young projects, I wondered if anybody had some experience with similar stack and these async ORMs?

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Nicolas Apx
Nicolas Apx
CEO - FullStack Javascript at Apx Development Limited · | 14 upvotes · 17.2K views
atAPX DevelopmentAPX Development
PostgreSQL
PostgreSQL
MongoDB
MongoDB
Node.js
Node.js
Python
Python

I am planning on building a micro-service eCommerce back-end to be easy to reuse in any project as we need. I would like to use both Python and Node.js and MongoDB & PostgreSQL , in your opinion which one would best suited for the following services:

  • Users-service
  • Products-service
  • Auth-service
  • Inventory-service
  • Order-service
  • Payment-service
  • Sku-service
  • And more not yet defined....

Thanks

Nicolas

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Michael Mota
Michael Mota
CEO & Founder at AlterEstate · | 4 upvotes · 12.1K views
atAlterEstateAlterEstate
Django
Django
RabbitMQ
RabbitMQ
Celery
Celery

Automations are what makes a CRM powerful. With Celery and RabbitMQ we've been able to make powerful automations that truly works for our clients. Such as for example, automatic daily reports, reminders for their activities, important notifications regarding their client activities and actions on the website and more.

We use Celery basically for everything that needs to be scheduled for the future, and using RabbitMQ as our Queue-broker is amazing since it fully integrates with Django and Celery storing on our database results of the tasks done so we can see if anything fails immediately.

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Interest over time
Reviews of PostgreSQL and RabbitMQ
Review ofRabbitMQRabbitMQ

I developed one of the largest queue based medical results delivery systems in the world, 18,000+ queues and still growing over a decade later all using MQSeries, later called Websphere MQ. When I left that company I started using RabbitMQ after doing some research on free offerings.. it works brilliantly and is incredibly flexible from small scale single instance use to large scale multi-server - multi-site architectures.

If you can think in queues then RabbitMQ should be a viable solution for integrating disparate systems.

How developers use PostgreSQL and RabbitMQ
Avatar of AngeloR
AngeloR uses PostgreSQLPostgreSQL

We use postgresql for the merge between sql/nosql. A lot of our data is unstructured JSON, or JSON that is currently in flux due to some MVP/interation processes that are going on. PostgreSQL gives the capability to do this.

At the moment PostgreSQL on amazon is only at 9.5 which is one minor version down from support for document fragment updates which is something that we are waiting for. However, that may be some ways away.

Other than that, we are using PostgreSQL as our main SQL store as a replacement for all the MSSQL databases that we have. Not only does it have great support through RDS (small ops team), but it also has some great ways for us to migrate off RDS to managed EC2 instances down the line if we need to.

Avatar of Cloudcraft
Cloudcraft uses PostgreSQLPostgreSQL

PostgreSQL combines the best aspects of traditional SQL databases such as reliability, consistent performance, transactions, querying power, etc. with the flexibility of schemaless noSQL systems that are all the rage these days. Through the powerful JSON column types and indexes, you can now have your cake and eat it too! PostgreSQL may seem a bit arcane and old fashioned at first, but the developers have clearly shown that they understand databases and the storage trends better than almost anyone else. It definitely deserves to be part of everyone's toolbox; when you find yourself needing rock solid performance, operational simplicity and reliability, reach for PostgresQL.

Avatar of Brandon Adams
Brandon Adams uses PostgreSQLPostgreSQL

Relational data stores solve a lot of problems reasonably well. Postgres has some data types that are really handy such as spatial, json, and a plethora of useful dates and integers. It has good availability of indexing solutions, and is well-supported for both custom modifications as well as hosting options (I like Amazon's Postgres for RDS). I use HoneySQL for Clojure as a composable AST that translates reliably to SQL. I typically use JDBC on Clojure, usually via org.clojure/java.jdbc.

Avatar of ReviewTrackers
ReviewTrackers uses PostgreSQLPostgreSQL

PostgreSQL is responsible for nearly all data storage, validation and integrity. We leverage constraints, functions and custom extensions to ensure we have only one source of truth for our data access rules and that those rules live as close to the data as possible. Call us crazy, but ORMs only lead to ruin and despair.

Avatar of Jeff Flynn
Jeff Flynn uses PostgreSQLPostgreSQL

Tried MongoDB - early euphoria - later dread. Tried MySQL - not bad at all. Found PostgreSQL - will never go back. So much support for this it should be your first choice. Simple local (free) installation, and one-click setup in Heroku - lots of options in terms of pricing/performance combinations.

Avatar of Cloudify
Cloudify uses RabbitMQRabbitMQ

The poster child for scalable messaging systems, RabbitMQ has been used in countless large scale systems as the messaging backbone of any large cluster, and has proven itself time and again in many production settings.

Avatar of Chris Saylor
Chris Saylor uses RabbitMQRabbitMQ

Rabbit acts as our coordinator for all actions that happen during game time. All worker containers connect to rabbit in order to receive game events and emit their own events when applicable.

Avatar of Clarabridge Engage
Clarabridge Engage uses RabbitMQRabbitMQ

Used as central Message Broker; off-loading tasks to be executed asynchronous, used as communication tool between different microservices, used as tool to handle peaks in incoming data, etc.

Avatar of Analytical Informatics
Analytical Informatics uses RabbitMQRabbitMQ

RabbitMQ is the enterprise message bus for our platform, providing infrastructure for managing our ETL queues, real-time event notifications for applications, and audit logging.

Avatar of Packet
Packet uses RabbitMQRabbitMQ

RabbitMQ is an all purpose queuing service for our stack. We use it for user facing jobs as well as keeping track of behind the scenes jobs.

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