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memcached VS NumPy

Compare memcached VS NumPy and see what are their differences

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memcached logo memcached

High-performance, distributed memory object caching system

NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python
  • memcached Landing page
    Landing page //
    2023-07-23
  • NumPy Landing page
    Landing page //
    2023-05-13

memcached features and specs

  • High Performance
    Memcached is incredibly fast and efficient at caching data in memory, enabling quick data retrieval and reducing the load on databases. Its in-memory nature significantly reduces latency.
  • Scalability
    Memcached can be easily scaled horizontally by adding more nodes to the caching cluster. This allows it to handle increased loads and large datasets without performance degradation.
  • Simplicity
    Memcached has a simple design and API, making it easy to implement and use. Developers can quickly integrate it into their applications without a steep learning curve.
  • Open Source
    Memcached is free and open-source software, which means it can be used and modified without any licensing fees. This makes it a cost-effective solution for caching.
  • Language Agnostic
    Memcached supports multiple programming languages through various client libraries, making it versatile and suitable for use in diverse tech stacks.

Possible disadvantages of memcached

  • Data Volatility
    Memcached stores data in RAM, so all cached data is lost if the server is restarted or crashes. This makes it unsuitable for storing critical or persistent data.
  • Limited Data Types
    Memcached primarily supports simple key-value pairs. It lacks the rich data types and more complex structures supported by some other caching solutions like Redis.
  • No Persistence
    Memcached does not offer any data persistence features. It cannot save data to disk, so all information is ephemeral and will be lost on system reset.
  • Size Limitation
    Memcached has a memory limit for each instance, thus, large-scale applications may need to manage multiple instances and ensure data is properly distributed.
  • Security
    Memcached does not provide built-in security features such as authentication or encryption. This can be a concern in environments where data privacy and security are critical.

NumPy features and specs

  • Performance
    NumPy operations are executed with highly optimized C and Fortran libraries, making them significantly faster than standard Python arithmetic operations, especially for large datasets.
  • Versatility
    NumPy supports a vast range of mathematical, logical, shape manipulation, sorting, selecting, I/O, and basic linear algebra operations, making it a versatile tool for scientific and numeric computing.
  • Ease of Use
    NumPy provides an intuitive, easy-to-understand syntax that extends Python's ability to handle arrays and matrices, lowering the barrier to performing complex scientific computations.
  • Community Support
    With a large and active community, NumPy offers extensive documentation, tutorials, and support for troubleshooting issues, as well as continuous updates and enhancements.
  • Integrations
    NumPy integrates seamlessly with other libraries in Python's scientific stack like SciPy, Matplotlib, and Pandas, facilitating a streamlined workflow for data science and analysis tasks.

Possible disadvantages of NumPy

  • Memory Consumption
    NumPy arrays can consume large amounts of memory, especially when working with very large datasets, which can become a limitation on systems with limited memory capacity.
  • Learning Curve
    For users new to scientific computing or coming from different programming backgrounds, understanding the intricacies of NumPy's operations and efficient usage can take time and effort.
  • Limited GPU Support
    NumPy primarily runs on the CPU and doesn't natively support GPU acceleration, which can be a disadvantage for extremely compute-intensive tasks that could benefit from parallel processing.
  • Dependency on Python
    Since NumPy is a Python library, it depends on the Python runtime environment. This can be a limitation in environments where Python is not the primary language or isn't supported.
  • Indexing Complexity
    Although NumPy's slicing and indexing capabilities are powerful, they can sometimes be complex or unintuitive, especially for multi-dimensional arrays, leading to potential errors and confusion.

memcached videos

+ Add

Course Preview: Using Memcached and Varnish to Speed Up Your Linux Web App

NumPy videos

+ Add

Learn NUMPY in 5 minutes - BEST Python Library!

More videos:

  • Review - Python for Data Analysis by Wes McKinney: Review | Learn python, numpy, pandas and jupyter notebooks
  • Review - Effective Computation in Physics: Review | Learn python, numpy, regular expressions, install python

Category Popularity

0-100% (relative to memcached and NumPy)

memcached

NumPy

Databases
100 100%
Databases
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
NoSQL Databases
100 100%
NoSQL Databases
0% 0
Data Science Tools
0 0%
Data Science Tools
100% 100

User comments

Share your experience with using memcached and NumPy. For example, how are they different and which one is better?
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Reviews

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

memcached Reviews

Redis vs. KeyDB vs. Dragonfly vs. Skytable | Hacker News
Quick ask: I don’t see “some” of the other offering out there like MemCached… what was the criteria used to select these? I don’t see any source of how the test where run, specs of the systems, how the DB where set up, etc. Would be very valuable to have in order to attempt to re-validate these test on our own platform. I also came back and saw some of your updates...
Source: news.ycombinator.com
Memcached vs Redis - More Different Than You Would Expect
So knowing how the difference between Redis and memcached in-memory usage, lets see what this means. Memcached slabs once assigned never change their size. This means it is possible to poison your memcached cluster and really waste memory. If you load your empty memcached cluster with lots of 1 MB items, then all of the slabs will be allocated to that size. Adding a 80 KB...
Source: engineering.kablamo.com.au
Redis vs. Memcached: In-Memory Data Storage Systems
Memcached itself does not support distributed mode. You can only achieve the distributed storage of Memcached on the client side through distributed algorithms such as Consistent Hash. The figure below demonstrates the distributed storage implementation schema of Memcached. Before the client side sends data to the Memcached cluster, it first calculates the target node of the...
Source: medium.com
Why Redis beats Memcached for caching
Both Memcached and Redis are mature and hugely popular open source projects. Memcached was originally developed by Brad Fitzpatrick in 2003 for the LiveJournal website. Since then, Memcached has been rewritten in C (the original implementation was in Perl) and put in the public domain, where it has become a cornerstone of modern Web applications. Current development of...
Source: www.infoworld.com

NumPy Reviews

25 Python Frameworks to Master
SciPy provides a collection of algorithms and functions built on top of the NumPy. It helps to perform common scientific and engineering tasks such as optimization, signal processing, integration, linear algebra, and more.
Source: kinsta.com
Top 8 Image-Processing Python Libraries Used in Machine Learning
Scipy is used for mathematical and scientific computations but can also perform multi-dimensional image processing using the submodule scipy.ndimage. It provides functions to operate on n-dimensional Numpy arrays and at the end of the day images are just that.
Source: neptune.ai
Top Python Libraries For Image Processing In 2021
Numpy It is an open-source python library that is used for numerical analysis. It contains a matrix and multi-dimensional arrays as data structures. But NumPy can also use for image processing tasks such as image cropping, manipulating pixels, and masking of pixel values.
Source: www.analyticsvidhya.com
4 open source alternatives to MATLAB
NumPy is the main package for scientific computing with Python (as its name suggests). It can process N-dimensional arrays, complex matrix transforms, linear algebra, Fourier transforms, and can act as a gateway for C and C++ integration. It's been used in the world of game and film visual effect development, and is the fundamental data-array structure for the SciPy Stack,...
Source: opensource.com

Social recommendations and mentions

Based on our record, NumPy should be more popular than memcached. It has been mentiond 119 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.

memcached mentions (36)

  • MySQL Performance Tuning Techniques
    Memcached can help when lightning-fast performance is needed. These tools store frequently accessed data, such as session details, API responses, or product prices, in RAM. This reduces the laid on your primary database, so you can deliver microsecond response times. - Source: dev.to / 2 months ago
  • 10 Best Practices for API Rate Limiting in 2025
    In-memory tools like Redis or Memcached for fast Data retrieval. - Source: dev.to / 3 months ago
  • Outgrowing Postgres: Handling increased user concurrency
    A caching layer using popular in-memory databases like Redis or Memcached can go a long way in addressing Postgres connection overload issues by being able to handle a much larger concurrent request load. Adding a cache lets you serve frequent reads from memory instead, taking pressure off Postgres. - Source: dev.to / 3 months ago
  • API Caching: Techniques for Better Performance
    Memcached — Free and well-known for its simplicity, Memcached is a distributed and powerful memory object caching system. It uses key-value pairs to store small data chunks from database calls, API calls, and page rendering. It is available on Windows. Strings are the only supported data type. Its client-server architecture distributes the cache logic, with half of the logic implemented on the server and the other... - Source: dev.to / 7 months ago
  • story of upgrading rails 5.x to 7.x
    The app depends on several packages to run, so I need to install them locally too. I used a combination of brew and orbstack / docker for installing packages. Some dependencies for this project are redis, mongodb and memcache. - Source: dev.to / 8 months ago
View more

NumPy mentions (119)

  • Building an AI-powered Financial Data Analyzer with NodeJS, Python, SvelteKit, and TailwindCSS - Part 0
    The AI Service will be built using aiohttp (asynchronous Python web server) and integrates PyTorch, Hugging Face Transformers, numpy, pandas, and scikit-learn for financial data analysis. - Source: dev.to / 3 months ago
  • F1 FollowLine + HSV filter + PID Controller
    This library provides functions for working in domain of linear algebra, fourier transform, matrices and arrays. - Source: dev.to / 7 months ago
  • Intro to Ray on GKE
    The Python Library components of Ray could be considered analogous to solutions like numpy, scipy, and pandas (which is most analogous to the Ray Data library specifically). As a framework and distributed computing solution, Ray could be used in place of a tool like Apache Spark or Python Dask. It’s also worthwhile to note that Ray Clusters can be used as a distributed computing solution within Kubernetes, as... - Source: dev.to / 8 months ago
  • Streamlit 101: The fundamentals of a Python data app
    It's compatible with a wide range of data libraries, including Pandas, NumPy, and Altair. Streamlit integrates with all the latest tools in generative AI, such as any LLM, vector database, or various AI frameworks like LangChain, LlamaIndex, or Weights & Biases. Streamlit’s chat elements make it especially easy to interact with AI so you can build chatbots that “talk to your data.”. - Source: dev.to / 9 months ago
  • A simple way to extract all detected objects from image and save them as separate images using YOLOv8.2 and OpenCV
    The OpenCV image is a regular NumPy array. You can see it shape:. - Source: dev.to / 9 months ago
View more

What are some alternatives?

When comparing memcached and NumPy, you can also consider the following products

Redis - Redis is an open source in-memory data structure project implementing a distributed, in-memory key-value database with optional durability.

Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.

MongoDB - MongoDB (from "humongous") is a scalable, high-performance NoSQL database.

Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.

Aerospike - Aerospike is a high-performing NoSQL database supporting high transaction volumes with low latency.

OpenCV - OpenCV is the world's biggest computer vision library

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