PostgreSQL vs Sequel Pro: What are the differences?
Developers describe PostgreSQL as "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. On the other hand, Sequel Pro is detailed as "MySQL database management for Mac OS X". Sequel Pro is a fast, easy-to-use Mac database management application for working with MySQL databases.
PostgreSQL and Sequel Pro are primarily classified as "Databases" and "Database" tools respectively.
"Relational database" is the primary reason why developers consider PostgreSQL over the competitors, whereas "Free" was stated as the key factor in picking Sequel Pro.
PostgreSQL and Sequel Pro are both open source tools. It seems that Sequel Pro with 6.73K GitHub stars and 589 forks on GitHub has more adoption than PostgreSQL with 5.44K GitHub stars and 1.8K GitHub forks.
Uber Technologies, Spotify, and Netflix are some of the popular companies that use PostgreSQL, whereas Sequel Pro is used by Movielala, Algorithmia, and Punchh. PostgreSQL has a broader approval, being mentioned in 2741 company stacks & 2176 developers stacks; compared to Sequel Pro, which is listed in 46 company stacks and 23 developer stacks.
I am going to work on a real estate project and have to decide on a database. Now, SQL databases can be very efficient if appropriately designed. More relations between the data and less redundancy. But with a #NoSQL database, the development time is reduced, and it is easy to query. Since this is my first time working on the real estate domain, I would like to pick a database that would be efficient in the long run.
I recommend PostgreSQL as it’s the most powerful out of the 3 databases you mentioned. It supports JSON objects so you can mimic the MongoDB functionality, but I would also argue that SQL is actually quite powerful and in many cases significantly easier to work with than with NoSQL databases.
Stay away from foreign keys, keep it fast and simple. Define your data structures well in advance. Try to model your data structures based on your system’s vision; based on where it’s going and not based solely on what you currently need it to do. This will help you avoid drastic changes to your database after your system is launched. Populate the database with fake data and run tests. PostgreSQL allows you to create Views from multiple tables. Try to create those views and make sure you can easily create useful views from multiple tables. Run an Explain on those view queries to make sure you created your indexes correctly. Make sure it’s fast!
Any of those three databases are going to be efficient, scalable, and reliable in the long term if you configure and use them correctly. They all also have solid hosting solutions.
All things being equal, I would agree with other posters that Postgres is my preference among the three, but there are caveats.
MongoDB and MySQL have better support for mutli-region replication in your big three cloud environments. Azure recently bought Citus Data, which was a best-in-class Postgres replication solution, so they might be the only one I trust to provide cross-region replication at the moment.
If you have a single region deployment and are on AWS, I can't recommend Aurora Postgres highly enough. It's a very good implementation and extremely performant.
That really depends of where do you see you application in the long run. On any application, any of those choices are excellent. You could argue about good support on JSON binaries, but even MySQL has an excellent support for that on the latest versions.
On the long run, when your application gets hundreds of thousands of requests per second, you might start thinking about how many inputs you will have in the database compared to the outputs. PostgresSQL it’s excellent at giving you outputs, but table corruption can happen when you start receiving this massive number of inputs (Which was the reason Uber switched from Postgres to MySQL)
On our OPS Platform at CTO.ai , we decided to use Postgres, because we need a reliable and agile way to send the output to our users, so that was out best choice in the long run for our product.
I'll second another piece of advice. Postgresql's JSON columns are a dream when it comes to productivity and I use them frequently with our Rails application. In these cases, no migration is required to change schema. We store payloads with dozens or hundreds of keys and performance has not been an issue. We also have a lot of relational tables, so the joins we get with SQL are very important to us and hard to replicate with a NoQL solution.
I am one of those who believes that MongoDB can be used for everything, this thanks to the advertising of MongoDB.
We are creating an e-commerce platform, we know that it has many relationships, but with MongoDB we can avoid some, but in the end, some relationships have to exist.
A single developer to create two native applications in Flutter, a web application with React, create the backend with multiple microservices hosted with Google Cloud Run. PostgreSQL can be heavy because it should be used with an ORM, on the contrary, with MongoDB you can avoid some relationships and avoid ORM / ODM.
We need advice from someone who has the experience and has had to choose between these two databases for an e-commerce site.
The real question here is not about the technology but rather your real needs and your data. Do you need to manage data that has core concepts and relations ? (such as a family, with parents and children) or do you need to manage a basic collection of similar data (such as blog entries)? PostgreSQL is definitely a relational database for managing entities and their relationships whereas MongoDB (I may be strongly opinionated here ;-) ) is more targeted at managing collection of entities (such as the blog entries). For an e-commerce site (with some products, products categories, user ratings and comments, prices, bundles...) I would go for PostgreSQL as it will support/guide you in creating a structured data set with all your products, organized in categories and with user ratings/comments attached to them. HTH
Had exactly the same question when selecting data storage for our new product. Not e-commerce though, rather interactive and content-focused HR SaaS for SME.
The key arguments for PostgreSQL
It gives you the opportunity to use relationships where you really need it and just go with key-value tables where you don't.
With Jsonb datatype you can store documents/objects/arrays as JSON then use JSON elements in queries and even indexes.
There are more tools/integrations working with PostgreSQL which you can use out of the box, e.g. Hasura
I am in your spot, exactly. A few months ago, I had decided to use Postgres because since its version 9 it showed a lot of progress for being a high-availability database. However, frankly, I didn't want to model statically all data, since I have several distinct schemas (like for different product types) and I wanted some flexibility to add or remove as I saw fit. One of the main challenges with analyzing a NoSQL database being familiar in the SQL ways, is that it's easy to look for "analogies" for what makes SQL useful, like relationship enforcing, transactions and the cascading effect on deletes, updates and inserts, and that limit your vision a lot when analyzing a tool like Mongo, especially in a micro-services pattern. Now-a-days, I really found my solution in Mongo. Not just because of it being NoSQL, but because all of the support I find in the NodeJS community through packages and utilities that make it dead easy to use it for several use-cases. Whatever Postgres offers, Mongo does it a little easier and better, like text search and geo-queries. What you need to see is to model your data in a way that makes sense with Mongo. For instance, I've got a User service that has all auth related information of a user. But then, I have the same user in the Profile service, with the same id, but totally different fields. You have two de facto ways to connect data, by reference and embedding, which in Ecommerce, both have big uses. Like using references to relate a User to a Profile, and an embed to relate a Product to an Order. There's even a third, albeit a little more "manual" implementation here, the graph relationship in which you can model data, in which you can easily model event-driven documents, like a Purchase that goes from "a customer" to "a store", which you can later use for much easier and deep analytics than with the classical SQL stance. MariaDB has it readily available, and also has many improvements over MySQL and Postgres, especially for NoSQL features and scalability. Sadly it is just seen as a MySQL clone, but it offers more than that (although its documentation could be improved). Using Mongo in a micro-service environment is even better because your models can be smaller, meaning less burden on relationships, although you do compensate with a bit of duplication, but a well-designed schema will have minimal impact on that. Whatever tool might do the job, but I want to cheer on the newer generation. Hope it helps.
I am trying to design an online ordering app similar to Doordash or Uber Eats. I'm having a hard time trying to finalise on what database (or mixture of databases) to use. I'm leaning towards using a relational database like MySQL or PostgreSQL. But, when the application grows, I don't want to join on 20 tables to get a data. Any help would be greatly appreciated. Thank you for your time.
Hello Suhas , We build our product www.voilacabs.com which is in the same lines as yours but we have used a combination of Mysql and MongoDB. When using MySQL, i would recommend doing the following: 1. Use Mysql only for storage only and for realtime updates we recommend MongoDB. 2. Don't try to Join more than 3 tables. ( the moment you reach 3 join stop there and try to un-normalized database. 3. Never or very rarely use Auto-increments. ( we recommend using UUIDS ) . Use UUIDS always for Auto increments for MYSQL. If you using Postgre SQL then i would suggest you to please check this https://instagram-engineering.com/sharding-ids-at-instagram-1cf5a71e5a5c There is a stored procedure that generated unique keys instead of auto-increment keys and that will help you sharding or clustering database without sync errors. 4. Also For MongoDB if you can put a layer of REDIS Cache then that will boost your api performance under large loads. 5. Use Node.js programing language as that function asynchronously .
Let me know if you still need any suggestion's . Thanks & Regards Rupen Makhecha CTO @ Voila Cab's www.voilacabs.com
I would recommend a mixture of MySQL and MongoDB. Using MongoDB for the Content Distribution Network (CDN) will make it easy to store high volume incoming data. MySQL is recommended to be used for business logic. PostgreSQL is not recommended since you will be faced with inefficient database replication features and constant migration from one PostgreSQL version to another.
We have chosen Tibero over Oracle because we want to offer a PL/SQL-as-a-Service that the users can deploy in any Cloud without concerns from our website at some standard cost. With Oracle Database, developers would have to worry about what they implement and the related costs of each feature but the licensing model from Tibero is just 1 price and we have all features included, so we don't have to worry and developers using our SQLaaS neither. PostgreSQL would be open source. We have chosen Tibero over Oracle because we want to offer a PL/SQL that you can deploy in any Cloud without concerns. PostgreSQL would be the open source option but we need to offer an SQLaaS with encryption and more enterprise features in the background and best value option we have found, it was Tibero Database for PL/SQL-based applications.
The Gentlent Tech Team made lots of updates within the past year. The biggest one being our database:
We decided to migrate our #PostgreSQL -based database systems to a custom implementation of #Cassandra . This allows us to integrate our product data perfectly in a system that just makes sense. High availability and scalability are supported out of the box.
MySQL has a lot of strengths working for it. It's simple and easy to set up and use. It's JSON engine is also really good these days. Mongo is also simple to setup and use, and it's speed as a document-object storage engine is first class.
Where Postgres has both beat is in it's combining of all of the features that make both MySQL and Mongo great, while adding on enterprise grade level scalability and replication. It's Postgres' stability and robustness, while still fulfilling the roles of it's contemporaries extremely well that edge Postgre for me.
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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.
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.
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.
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.
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.