Software Alternatives, Accelerators & Startups

Moleculer VS Plotly

Compare Moleculer VS Plotly and see what are their differences

Moleculer logo Moleculer

Fast & modern microservices framework for Node.js.

Plotly logo Plotly

Low-Code Data Apps
  • Moleculer Landing page
    Landing page //
    2021-12-21
  • Plotly Landing page
    Landing page //
    2023-07-31

Moleculer features and specs

  • Microservices Architecture
    Moleculer provides an efficient microservices framework which allows developers to build robust and scalable distributed systems effortlessly.
  • Out-of-the-Box Features
    Moleculer offers an extensive array of built-in features such as service discovery, load balancing, fault tolerance, and more, reducing the need for third-party integrations.
  • Ease of Use
    Its straightforward API and comprehensive documentation make it easy to learn and implement, even for developers who are new to microservices.
  • Pluggable Transport Layer
    Supports different transporters such as NATS, MQTT, Kafka, and Redis, giving flexibility in how services communicate with each other.
  • Performance
    Designed for high performance, Moleculer can handle a large number of requests efficiently, making it suitable for production-level applications.

Possible disadvantages of Moleculer

  • Complexity in Large Systems
    As with any microservices framework, managing a large number of services can become complex and may require robust monitoring and orchestration tools.
  • Learning Curve
    While Moleculer is easy to start with, mastering it and understanding all its features and best practices may require time.
  • Community and Ecosystem
    Compared to more established frameworks, Moleculer may have a smaller community and ecosystem which can affect the availability of third-party plugins or modules.
  • Dependency Management
    Ensuring compatibility between different versions of services and third-party libraries can be challenging, especially when services are updated independently.
  • Debugging and Error Handling
    Distributed systems can be more complex to debug, and although Moleculer provides tools for this, it may still require extra effort compared to monolithic applications.

Plotly features and specs

  • Interactivity
    Plotly offers highly interactive plots that allow users to pan, zoom, and hover over data points for more information. This enhances the user experience and provides deeper insights.
  • High-quality visualizations
    It provides aesthetically pleasing and highly customizable charts, making it suitable for publication-quality visuals.
  • Versatility
    Plotly supports multiple chart types including line charts, scatter plots, bar charts, and 3D plots, making it suitable for a wide range of applications.
  • Python integration
    Plotly is well-integrated with Python and works seamlessly with other popular data science libraries like Pandas, NumPy, and Scikit-learn.
  • Web-based
    The plots can be easily embedded in web applications or dashboards, making it ideal for sharing insights over the internet.
  • Open-source
    Plotly offers an open-source version, which allows users to create and share visualizations without any cost.

Possible disadvantages of Plotly

  • Performance
    Rendering very large datasets can sometimes be slow, which may not be suitable for real-time data visualization requirements.
  • Learning curve
    Even though the library is well-documented, the extensive range of features can have a steep learning curve for beginners.
  • Cost for advanced features
    While the basic functionality is free, more advanced features, such as export to certain formats and additional customizable options, require a paid subscription.
  • Dependency management
    Plotly has a number of dependencies that need to be managed properly, which can sometimes complicate the setup process.
  • Complexity
    For simple visualizations, Plotly might be overkill and simpler libraries like Matplotlib or Seaborn could be more appropriate.

Analysis of Plotly

Overall verdict

  • Overall, Plotly is a strong choice for those looking to create dynamic and interactive data visualizations, thanks to its range of features and ease of integration with web technologies.

Why this product is good

  • Plotly is considered good because it offers a comprehensive suite of tools for creating interactive visualizations that can be used in web applications, reports, and dashboards. It supports many different types of plots, is easy to use for both beginners and experienced developers, and integrates well with popular programming languages like Python, R, and JavaScript.

Recommended for

    Plotly is recommended for data scientists, analysts, and developers who need to create interactive and visually appealing data visualizations. It's particularly useful for those who work with Python or R and want the ability to embed their visualizations in web applications or dashboards.

Moleculer videos

MoleculeR review

Plotly videos

Create Real-time Chart with Javascript | Plotly.js Tutorial

More videos:

  • Review - Introducing plotly.py 3.0
  • Review - Is Plotly The Better Matplotlib?
  • Tutorial - Plotly Tutorial 2021
  • Review - Data Visualization as The First and Last Mile of Data Science Plotly Express and Dash | SciPy 2021

Category Popularity

0-100% (relative to Moleculer and Plotly)
Developer Tools
100 100%
0% 0
Data Visualization
0 0%
100% 100
Web Frameworks
100 100%
0% 0
Charting Libraries
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 Moleculer and Plotly

Moleculer Reviews

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Plotly Reviews

Best 8 Redash Alternatives in 2023 [In Depth Guide]
Plotly is specifically designed for companies who want to build and deploy analytic applications like dashboards using Python, Julia, or R without needing DevOps or Javascript developers.
Source: www.datapad.io
5 Best Python Libraries For Data Visualization in 2023
Plotly is a web-based data visualization toolkit that comes with unique functionalities such as dendrograms, 3D charts, and also contour plots, which is not very common in other libraries. It has a great API offering scatter plots, line charts, bar charts, error bars, box plots, and other visualizations. Plotly can even be accessed from a Python Notebook.
Top 8 Python Libraries for Data Visualization
Plotly is a free open-source graphing library that can be used to form data visualizations. Plotly (plotly.py) is built on top of the Plotly JavaScript library (plotly.js) and can be used to create web-based data visualizations that can be displayed in Jupyter notebooks or web applications using Dash or saved as individual HTML files. Plotly provides more than 40 unique...
5 top picks for JavaScript chart libraries
Plotly is a graphing library that’s available for various runtime environments, including the browser. It supports many kinds of charts and graphs that we can configure with a variety of options.

Social recommendations and mentions

Based on our record, Plotly should be more popular than Moleculer. It has been mentiond 34 times since March 2021. 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.

Moleculer mentions (14)

  • Make microservices look like monoliths
    My goto for this kind of task is moleculer: https://moleculer.services/ Fast, battle tested, vue2-like approach, great documentation, good community. The automatic indipendent-scalability as an option is usually the main selling point of these solutions, but honestly I think the real pro is the "composition" approach, which is essential if you want to keep a clean and well-organized codebase. On this regard, I... - Source: Hacker News / about 3 years ago
  • How to Import/Reference a Microservice from another one
    If you’re using k8s, check out https://moleculer.services and this would likely solve what you’re looking for. Source: over 3 years ago
  • Node JS Microservice Frameworks for Developing Scalable Web Apps.
    Molecular – Progressive Microservices Framework for Node.js. Source: over 3 years ago
  • First time building microservice-based application
    While you’re delving into microservices, check out Moleculer https://moleculer.services. Source: over 3 years ago
  • if Nodejs does not meant for CPU intensive tasks so I think it's better to avoid it from the beginning
    I almost can’t believe I haven’t seen it mentioned here before, but adding Moleculer into your node project (if it’s clustered/k8s’d) will literally solve many single threaded problems, not to mention tons of other scalability issues. https://moleculer.services/. Source: about 4 years ago
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Plotly mentions (34)

  • How to Analyze 47 Million Hacker News Posts: A Data Scientist's Dream Dataset Just Got Better
    Let's dive into some practical examples. First, you'll need to set up your environment with the right tools. I recommend using pandas for data manipulation and plotly for visualization. - Source: dev.to / 6 months ago
  • Python for Data Visualization: Best Tools and Practices
    Plotly is perfect for interactive visualizations. You can create interactive charts and graphs that allow users to hover, click, and zoom in. Plotly is also great for web-based visuals, making it easy to share your findings online. - Source: dev.to / over 1 year ago
  • Generative AI Powered QnA & Visualization Chatbot
    Front End: A React application that leverages React-Chatbotify library to easily integrate a chatbot GUI. It also uses the Plotly library to display the charts/visualizations. The generative AI implementation and details are entirely abstracted from the front end. The front-end application depends on a single REST endpoint of the backend application. - Source: dev.to / over 1 year ago
  • Build a Stock Dashboard in less than 40 lines of Python code!🤓
    In this tutorial, Mariya Sha will guide you through building a stock value dashboard using Taipy, Plotly, and a dataset from Kaggle. - Source: dev.to / almost 2 years ago
  • Essential Deep Learning Checklist: Best Practices Unveiled
    How to Accomplish: Utilize visualization libraries like Matplotlib, Seaborn, or Plotly in Python to create histograms, scatter plots, and bar charts. For image data, use tools that visualize images alongside their labels to check for labeling accuracy. For structured data, correlation matrices and pair plots can be highly informative. - Source: dev.to / about 2 years ago
View more

What are some alternatives?

When comparing Moleculer and Plotly, you can also consider the following products

Nest.js - A progressive Node.js framework for building efficient, reliable and scalable server-side applications.

D3.js - D3.js is a JavaScript library for manipulating documents based on data. D3 helps you bring data to life using HTML, SVG, and CSS.

Loopback by RogueAmoeba - Get all the power of a high-end studio mixing board, right inside your Mac!

RAWGraphs - RAWGraphs is an open source app built with the goal of making the visualization of complex data...

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

Tableau - Tableau can help anyone see and understand their data. Connect to almost any database, drag and drop to create visualizations, and share with a click.