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  1. Stackups
  2. Application & Data
  3. Databases
  4. Databases
  5. FaunaDB vs PostgreSQL

FaunaDB vs PostgreSQL

OverviewDecisionsComparisonAlternatives

Overview

PostgreSQL
PostgreSQL
Stacks103.1K
Followers83.9K
Votes3.6K
GitHub Stars19.0K
Forks5.2K
Fauna
Fauna
Stacks112
Followers153
Votes27

FaunaDB vs PostgreSQL: What are the differences?

Introduction:

FaunaDB and PostgreSQL are both popular database management systems that offer different features and functionalities. While PostgreSQL is a traditional relational database, FaunaDB is a distributed, multi-model database.

1. Scalability: FaunaDB is designed to be highly scalable and can handle massive amounts of data and high traffic loads. It provides built-in sharding, automatic data repartitioning, and automatic scaling, making it ideal for applications that need to handle rapid growth and frequent scale changes. In contrast, PostgreSQL requires manual partitioning and scaling techniques to handle large datasets and high traffic loads.

2. Data Models: FaunaDB supports multiple data models, including relational, document, and graph models, allowing developers to choose the most suitable model for their application. It provides seamless integration between these models, allowing complex data structures to be efficiently managed. On the other hand, PostgreSQL primarily focuses on the relational data model, making it suitable for applications that heavily rely on structured data.

3. Global Data Distribution: FaunaDB has built-in support for global data distribution, allowing data to be replicated across multiple regions for improved performance and fault tolerance. It automatically handles data synchronization and conflict resolution, ensuring consistency even in the presence of network partitions. PostgreSQL, on the other hand, requires external mechanisms like database replication or sharding to achieve global data distribution.

4. Security: FaunaDB provides advanced security features such as built-in encryption at rest and in transit, fine-grained access control with attribute-level security, and automatic enforcement of access policies. It also offers features like temporal queries and bi-temporal data storage for auditing and compliance purposes. While PostgreSQL also provides security features like SSL encryption and access control, it may require additional configurations and extensions for advanced security requirements.

5. Serverless Architecture: FaunaDB has a serverless architecture, meaning that developers do not need to manage server instances or infrastructure. It automatically handles scaling, monitoring, and backups, allowing developers to focus on application development. PostgreSQL, on the other hand, requires manual management of server instances and infrastructure, which can add complexity and maintenance overhead.

6. Developer Experience: FaunaDB provides a modern and developer-friendly experience with features like native GraphQL support, real-time data streaming, and seamless integration with popular frameworks and platforms. It offers a flexible schema that allows schema changes without downtime or performance degradation. PostgreSQL, while being a mature and widely used database, may require more manual configuration and management, especially when it comes to integrating with modern application development practices.

In Summary, FaunaDB and PostgreSQL differ in terms of scalability, data models, global data distribution, security, serverless architecture, and developer experience, making each suitable for different use cases and application requirements.

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Advice on PostgreSQL, Fauna

Kyle
Kyle

Web Application Developer at Redacted DevWorks

Dec 3, 2019

DecidedonPostGISPostGIS

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
George

Student

Mar 18, 2020

Needs adviceonPostgreSQLPostgreSQLPythonPythonDjangoDjango

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
Navraj

CEO at SuPragma

Apr 16, 2020

Needs adviceonMySQLMySQLPostgreSQLPostgreSQL

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
PostgreSQL
Fauna
Fauna

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.

Escape the boundaries imposed by legacy databases with a data API that is simple to adopt, highly productive to use, and offers the capabilities that your business needs, without the operational pain typically associated with databases.

-
Native support for GraphQL and others. Easily access any data with any API. No middleware necessary.; Access all data via a data model that best suits your needs - relational, document, graph or composite.; A unique approach to indexing makes it simpler to write efficient queries that scale with your application.; Build SaaS apps more easily with native multi-tenancy and query-level QoS controls to prevent workload collisions.; Eliminate data anomalies with multi-region ACID transactions that don't limit number of keys or documents.; Data-driven RBAC that combines with SSL to offers reliable protection, and yet is simple to understand and codify.; Travel back in time with temporal querying. Run queries at a point-in-time or as change feeds. Track how your data evolved.; Dynamically replicates your data to global locations, so that your queries run fast no matter where your users are.; Easily deploy a FaunaDB cluster on your workstation accompanied by a powerful shell and tools to simplify your workflow.;
Statistics
GitHub Stars
19.0K
GitHub Stars
-
GitHub Forks
5.2K
GitHub Forks
-
Stacks
103.1K
Stacks
112
Followers
83.9K
Followers
153
Votes
3.6K
Votes
27
Pros & Cons
Pros
  • 765
    Relational database
  • 511
    High availability
  • 439
    Enterprise class database
  • 383
    Sql
  • 304
    Sql + nosql
Cons
  • 10
    Table/index bloatings
Pros
  • 5
    100% ACID
  • 4
    Removes server provisioning or maintenance
  • 4
    Generous free tier
  • 3
    Also supports SQL, CQL
  • 3
    No more n+1 problems (+ GraphQL)
Cons
  • 1
    Log stack traces to avoid improper exception handling
  • 1
    Susceptible to DDoS (& others) use timeouts throttling
  • 1
    Must keep app secrets encrypted

What are some alternatives to PostgreSQL, Fauna?

MongoDB

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

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

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

SQLite

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

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

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

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

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

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

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.

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