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Plotly VS Apache JMeter

Compare Plotly VS Apache JMeter and see what are their differences

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Contents:
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  3. » Reviews
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Plotly logo Plotly

Low-Code Data Apps

Apache JMeter logo Apache JMeter

Apache JMeter™.
  • Plotly Landing page
    Landing page //
    2023-07-31
  • Apache JMeter Landing page
    Landing page //
    2018-09-29

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.

Apache JMeter features and specs

  • Open Source
    Apache JMeter is free to use, reducing the overall cost of testing and allowing for significant customization by the community.
  • Extensibility
    JMeter is highly extensible with plugins, which can add additional functionalities and capabilities tailored to specific needs.
  • Strong Community Support
    Due to its long history and widespread usage, JMeter benefits from a large, active community that provides tutorials, plugins, and troubleshooting help.
  • Supports Various Protocols
    JMeter supports a wide range of testing protocols, including HTTP, HTTPS, FTP, LDAP, JDBC, and JMS, making it versatile for different types of applications.
  • Continuous Integration
    JMeter can be easily integrated with CI/CD tools like Jenkins, enabling automated performance testing in the development pipeline.
  • Graphical Interface
    The graphical user interface (GUI) makes it easier for testers to design and configure testing scenarios without extensive programming knowledge.

Possible disadvantages of Apache JMeter

  • Resource Intensive
    JMeter can be resource-intensive, especially when simulating high loads, which may require substantial hardware to mimic real-world scenarios.
  • Steep Learning Curve
    Despite its GUI, JMeter can be complex to learn and use effectively, especially for those who are new to performance testing.
  • Limited Reporting
    JMeter's built-in reporting capabilities can be somewhat limited, requiring additional tools or plugins for more advanced reporting and analysis.
  • Not Ideal for UI Testing
    JMeter is not suitable for front-end or UI testing, as it is primarily designed for performance and load testing of backend services.
  • Memory Consumption
    The GUI mode, in particular, can consume a significant amount of memory, impacting performance during large-scale tests.

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.

Plotly videos

+ Add

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

Apache JMeter videos

+ Add

Book Review - Master Apache JMeter - From load testing to DevOps

Category Popularity

0-100% (relative to Plotly and Apache JMeter)

Plotly

Apache JMeter

Data Visualization
100 100%
Data Visualization
0% 0
Website Testing
0 0%
Website Testing
100% 100
Charting Libraries
100 100%
Charting Libraries
0% 0
Software Testing
0 0%
Software Testing
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 Plotly and Apache JMeter

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.
Source: www.technotification.com
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...
Source: www.geeksforgeeks.org
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.
Source: blog.logrocket.com

Apache JMeter Reviews

Best Database Testing Tools in 2025
Apache JMeter is a powerful, open-source database testing tool known for its versatility across various testing scenarios. As a comprehensive, multi‑database IDE, JMeter enables users to design, execute, and analyze complex tests across various protocols including JDBC. It supports load, stress, and functional testing, and is favored by both beginners and advanced users....
Source: www.devart.com
Top 20 Best Automation Testing Tools in 2019 (Comprehensive List)
Apache JMeter is an open-source Java desktop application designed for load testing. It mainly focuses on web applications. This tool can also be employed for unit testing and limited functional testing.
Source: www.softwaretestinghelp.com
Top 20 Best Automation Testing Tools in 2018 (Comprehensive List)
Apache JMeter is an open-source Java desktop application designed for load testing. It mainly focuses on web applications. This tool can also be employed for unit testing and limited functional testing.
Source: www.softwaretestinghelp.com

Social recommendations and mentions

Based on our record, Plotly seems to be a lot more popular than Apache JMeter. While we know about 34 links to Plotly, we've tracked only 2 mentions of Apache JMeter. 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.

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

Apache JMeter mentions (2)

  • Java naming facts
    Before Jakarta EE there was Apache Jakarta which was effectively the group name for Java based projects within the Apache project. Source: over 4 years ago
  • Are servers multithreaded by default?
    If you remove Spring from the equation you need to build the servlets yourself (according to the Sevlet API). You probably package the servlets in a war-file (with some configuration files), the war-file can then be deployed in a servlet server (ie Tomcat,). The sevlet servser usually handles the thread pool and other resources (ie database connection pools) for you, so you "only" have to provide a servlet that... Source: about 5 years ago

What are some alternatives?

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

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.

soapUI - SoapUI Pro is one of the most prominent API testing platforms around, allowing developers to quickly prototype the functions of their apps and get them to market with little hassle.

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

Sauce Labs - Test mobile or web apps instantly across 700+ browser/OS/device platform combinations - without infrastructure setup.

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.

Micro Focus ALM - Learn how Micro Focus’ Application Lifecycle Management (ALM) software tools provide the agility, visibility, and collaboration solutions you need to optimize app development and testing, foster innovation, and improve the user experience.

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