Software Alternatives, Accelerators & Startups

pgDash VS FastAPI

Compare pgDash VS FastAPI and see what are their differences

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pgDash logo pgDash

pgDash is a comprehensive monitoring solution designed specifically for PostgreSQL deployments. pgDash shows you information and metrics about every aspect of your PostgreSQL database server, collected using the open-source tool pgmetrics.

FastAPI logo FastAPI

FastAPI is an Open Source, modern, fast (high-performance), web framework for building APIs with Python 3.6+ based on standard Python type hints.
  • pgDash Landing page
    Landing page //
    2022-04-26
  • FastAPI Landing page
    Landing page //
    2023-05-14

pgDash features and specs

  • Real-time Monitoring
    pgDash provides real-time monitoring of PostgreSQL databases, allowing users to track performance metrics and identify issues as they occur.
  • Comprehensive Metrics
    The platform offers a wide range of metrics covering various aspects of PostgreSQL performance, such as query performance, indexing, and cache utilization.
  • User-friendly Interface
    pgDash offers an intuitive and easy-to-navigate interface that helps users of all experience levels to monitor and manage their PostgreSQL databases efficiently.
  • Alerting System
    The tool includes an alerting system that notifies users of potential issues or threshold breaches, enabling proactive database management.
  • Customizable Dashboards
    Users can create and customize dashboards to fit their specific monitoring needs, allowing for a personalized approach to database management.

Possible disadvantages of pgDash

  • Limited to PostgreSQL
    pgDash focuses exclusively on PostgreSQL, which might be a limitation for organizations using multiple types of databases.
  • Cost
    While pgDash provides robust features, there is a cost associated with its use, which might be a consideration for small organizations or individual developers.
  • Learning Curve
    Though the interface is user-friendly, there might be a learning curve for users who are completely new to database monitoring tools.
  • Dependence on External Tool
    Relying on a third-party tool means that users must depend on pgDash's reliability and ongoing support for continuous database monitoring.
  • Integration
    Integrating pgDash with other systems or tools within an organization's IT ecosystem might require additional effort or customization.

FastAPI features and specs

  • High Performance
    Built on Starlette and Pydantic, FastAPI is one of the fastest frameworks for Python, providing high performance due to its asynchronous request handling.
  • Automatic Interactive API Documentation
    FastAPI automatically generates interactive API documentation via Swagger UI and ReDoc, which are very helpful for development and testing.
  • Type Checking and Validation
    With Pydantic models and Python type hints, FastAPI provides automatic data validation and type checking, reducing the chance of runtime errors.
  • Ease of Use
    Its syntax and design make it easy to learn for Python developers, offering a smooth development experience while reducing boilerplate code.
  • Asynchronous Support
    FastAPI supports asynchronous programming, allowing for better performance for I/O-bound operations, making it optimal for handling many simultaneous connections.
  • Extensive Documentation
    It has comprehensive and well-structured documentation, which is very useful for both beginners and advanced users.
  • Community and Ecosystem
    FastAPI has a growing community and ecosystem, with many plugins and integrations available to extend its functionality.

Possible disadvantages of FastAPI

  • Learning Curve for Asynchronous Programming
    Although FastAPI itself is easy to learn, grasping the concepts of asynchronous programming in Python can be challenging for beginners.
  • Complex Dependencies
    Using Pydantic for advanced validation can make the request models complex, requiring a deeper understanding of Pydantic and its functionalities.
  • Early Stage Libraries
    Some third-party libraries and extensions specifically tailored for FastAPI might still be in early stages of development and lack long-term stability.
  • Limited Real-World Examples
    Although the documentation is extensive, there might be limited real-world examples and case studies readily available compared to more mature frameworks.
  • Deployment Complexity
    Deploying FastAPI applications might be more complex in comparison to traditional synchronous frameworks, mainly due to the need for asynchronous server setups.

Analysis of FastAPI

Overall verdict

  • FastAPI is widely regarded as a good choice, especially for applications that require high performance, scalability, and modern Python features. It is suitable for both simple and complex projects, making it a versatile tool in the web development ecosystem.

Why this product is good

  • FastAPI is a modern, fast (high-performance), web framework for building APIs with Python 3.7+ based on standard Python type hints. It is built on top of Starlette for web framework capabilities and Pydantic for data validation and settings management. FastAPI is known for its excellent performance, automatic generation of interactive API documentation (with Swagger and Redoc), and support for asynchronous programming. Developers appreciate its ease of use, detailed documentation, and helpful error messages.

Recommended for

  • Developers building RESTful APIs
  • Teams looking for a high-performance ASGI-based web framework
  • Projects that require asynchronous programming capabilities
  • Applications needing automatic generation of interactive API documentation
  • Python developers who prefer utilizing type hints for code clarity and validation

pgDash videos

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FastAPI videos

FastAPI from the ground up

More videos:

  • Tutorial - 30 Days of Python - Day 14 - Web App with Flask, FastAPI, ngrok, and Invictify - Python TUTORIAL
  • Review - [PT] Python - API com FastAPI - Chat | twitch.tv/codeshow

Category Popularity

0-100% (relative to pgDash and FastAPI)
Postgres Tools
100 100%
0% 0
Developer Tools
0 0%
100% 100
Postgres
100 100%
0% 0
API Tools
0 0%
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare pgDash and FastAPI

pgDash Reviews

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FastAPI Reviews

  1. Kurslog team
    ยท Working at Kurslog ยท

    When our backend team needs to build services for data parsing, aggregators, or high-load APIs, FastAPI is our absolute go-to choice. It completely lives up to its name-development speed is outstanding.

    The combination of Pydantic for data validation and built-in async support keeps our shared codebase clean, strictly typed, and reliable. But the biggest highlight for our cross-functional team is the automatic generation of interactive OpenAPI (Swagger) documentation. Our frontend and mobile developers no longer have to wait for backend engineers to manually update API docs; everything stays perfectly in sync automatically. It has drastically improved our team's communication and delivery speed.

    ๐Ÿ Competitors: Django, Flask, ExpressJS, Nest.js, Spring Boot
    ๐Ÿ‘ Pros:    Blazing fast performance (on par with nodejs and go)|Native asynchronous support out of the box|Automatic, interactive documentation generation (swagger/redoc)|Strict typing and data validation with pydantic
    ๐Ÿ‘Ž Cons:    Smaller plug-and-play ecosystem compared to older frameworks like django|Requires our architects to design the project structure and directory layout from scratch

The 20 Best Laravel Alternatives for Web Development
FastAPI, as the name hints, is a swift mover. Built on Starlette, itโ€™s all about speed and performance with Python. Crafting API masterpieces at the speed of light, now thatโ€™s something.
25 Python Frameworks to Master
Since its release in 2018, it has rapidly gained popularity due to its great performance and simplicity. In fact, according to PyPi Stats, FastAPI has over 9 million monthly downloads, surpassing even full-stack frameworks like Django.
Source: kinsta.com
3 Web Frameworks to Use With Python
myapp/ is the main directory of your FastAPI application. It includes all the other files and directories needed for the application.static/ is a directory used to store static assets such as CSS, JavaScript, and image files. These assets are served directly by the web server and are typically used to add visual styling and interactivity to the application.css/, img/, js/...
Best Alternatives to FastAPI App Free for Windows (2021)
FastAPI Alternative โ€“ So many alternatives app to FastAPI that you must to know out there. And, looking for an ideal software was not easy matter. Lucky you, at this page you can find the best replacement app for FastAPI. So what you are waiting for, get the latest FastAPI alternative app for Windows 10 from this page.
Top 5 Back-End Frameworks to Consider for Web Development in 2021
FastAPI is a modern, fast (high-performance), web framework for building APIs with Python 3.6+ based on standard Python type hints. It is fast when compared to other major Python frameworks like Flask and Django. FastAPI gives great flexibility to fulfill businessesโ€™ API needs in todayโ€™s evolving world.

Social recommendations and mentions

Based on our record, FastAPI seems to be a lot more popular than pgDash. While we know about 311 links to FastAPI, we've tracked only 3 mentions of pgDash. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

pgDash mentions (3)

  • PostgresBench: A Reproducible Benchmark for Postgres Services
    This is a great initiative. Benchmarking managed services is notoriously tricky due to varying configurations and 'black-box' optimizations. For anyone looking to run these benchmarks on their own or wanting to dive deeper into the why behind the numbers, pgmetrics (https://pgmetrics.io) is a fantastic open-source tool. It collects a massive amount of internal PG stats into a structured JSON format, making... - Source: Hacker News / 3 months ago
  • High memory usage in Postgres is good
    Great write-upโ€”the distinction between healthy OS caching and actual memory pressure is often misunderstood. To get a granular view of where your memory is actually going (shared buffers, cache hits, etc.) without the overhead of heavy agents, pgmetrics (https://pgmetrics.io) is very effective. If you need to track these metrics over time to catch when 'good' caching turns into 'bad' pressure, pgDash... - Source: Hacker News / 3 months ago
  • Top ๐Ÿ˜๐Ÿ‘€ Postgres Monitoring Tools ๐Ÿงฐ and Best Practices in 2024 ๐Ÿ”
    PgDash has a similar feature set and pricing point to pganalyze. Pgdash looks less polished. On the other hand, pgDash offers self-hosted option for all plans, whereas pganalyze only offers self-hosted option for the Enterprise plan. - Source: dev.to / over 1 year ago

FastAPI mentions (311)

  • Shipping Sovereign SDK: Cryptographic Forensic Receipts and the End of the AI "Prose Tax"
    The Sovereign SDK is a Python-native framework designed to minimize prose overhead while generating ironclad, cryptographic execution receipts for AI agents, complete with drop-in FastAPI/Starlette ASGI middleware. - Source: dev.to / about 1 month ago
  • 5-Minute AI Jobs and Closed Tabs โ€” Why We Built Replay-Then-Tail SSE
    We had a feature in production where a single user request could run for five-plus minutes โ€” fetch documents, chunk them, hit an LLM per chunk, synthesize a final answer. We did the obvious thing first: a FastAPI handler that ran the pipeline and streamed progress back to the browser over Server-Sent Events. - Source: dev.to / 2 months ago
  • FastAPI With LangChain and MongoDB
    FastAPI is a Python framework for building APIs quickly, efficiently, and with very little code. - Source: dev.to / 3 months ago
  • I Built A " CrowdSense AI " : A Scalable, Context-Aware Platform for Smart Stadiums
    Backend: Python-based FastAPI for its asynchronous I/O capabilities and rapid JSON serialization. - Source: dev.to / 3 months ago
  • Async PyMongo in FastAPI
    FastAPI is a high-performance web framework that is production-ready and designed for building APIs in python, with roots embedded in asynchronous programming. It embraces Pythonโ€™s asyncio model as its core principle rather than treating it as optional. This design choice gives FastAPI leverage for I/O bound workloads such as db access, external API calls and real-time data streaming. - Source: dev.to / 3 months ago
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What are some alternatives?

When comparing pgDash and FastAPI, you can also consider the following products

pganalyze - PostgreSQL performance monitoring installed within minutes

Flask - a microframework for Python based on Werkzeug, Jinja 2 and good intentions.

PgHero - Rails database insights made easy. Add the gem, get a dashboard with long running queries, cache hit rate, and more.

ExpressJS - Sinatra inspired web development framework for node.js -- insanely fast, flexible, and simple

Postgres Monitor - A better way to monitor and debug your Postgres database. Real-time health dashboards, query insights, dynamic recommendations and more.

Django - The Web framework for perfectionists with deadlines