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NumPy VS KeyDB

Compare NumPy VS KeyDB and see what are their differences

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NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python

KeyDB logo KeyDB

KeyDB is fast NoSQL database with full compatibility for Redis APIs, clients, and modules.
  • NumPy Landing page
    Landing page //
    2023-05-13
  • KeyDB Landing page
    Landing page //
    2022-06-19

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.

KeyDB features and specs

  • High Performance
    KeyDB offers superior performance over Redis by allowing multi-threading, which utilizes multiple CPU cores efficiently, leading to significant improvements in throughput and latency.
  • Redis Compatibility
    KeyDB is fully compatible with Redis, meaning users can easily switch between Redis and KeyDB without needing to change their existing code or data structures.
  • Active Replication
    It supports multi-primary (active-active) replication, enabling all replicas to accept writes without worrying about conflicts, which increases availability and resilience.
  • Built-in TLS
    KeyDB includes built-in TLS support which enhances security by allowing data encryption in transit, a feature that requires third-party solutions in some Redis setups.
  • Persistence Options
    KeyDB supports both RDB snapshotting and AOF logging, offering flexible persistence strategies to balance between performance and durability.

Possible disadvantages of KeyDB

  • Community Size
    KeyDB, while gaining popularity, has a smaller community compared to Redis, which can lead to less community support and fewer third-party tools or extensions.
  • Maturity
    As a relatively newer project compared to Redis, KeyDB may lack the same level of proven stability and maturity, making it a potentially riskier choice for critical applications.
  • Documentation and Resources
    While KeyDB has extensive documentation, it might not be as comprehensive or complete as Redis, potentially leading to longer project integration times.
  • Potential Compatibility Issues
    Although KeyDB is compatible with Redis, advanced Redis features or unusual configurations might face compatibility issues during migration.
  • Less Architectural Simplicity
    The added complexity of multi-threading and active-active replication modes can increase the operational overhead compared to Redis's simpler single-threaded, master-slave architecture.

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

KeyDB videos

+ Add

KeyDB on FLASH (Redis Compatible)

More videos:

  • Demo - Simple Demo of KeyDB on Flash in under 7 minutes (Drop in Redis Alternative)

Category Popularity

0-100% (relative to NumPy and KeyDB)

NumPy

KeyDB

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
Databases
0 0%
Databases
100% 100
Data Science Tools
100 100%
Data Science Tools
0% 0
Key-Value Database
0 0%
Key-Value Database
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 NumPy and KeyDB

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

KeyDB Reviews

Redis vs. KeyDB vs. Dragonfly vs. Skytable | Hacker News
2. KeyDB: The second is KeyDB. IIRC, I saw it in a blog post which said that it is a "multithreaded fork of Redis that is 5X faster"[1]. I really liked the idea because I was previously running several instances of Redis on the same node and proxying them like a "single-node cluster." Why? To increase CPU utilization. A single KeyDB instance could replace the unwanted...
Source: news.ycombinator.com
Comparing the new Redis6 multithreaded I/O to Elasticache & KeyDB
Because of KeyDB’s multithreading and performance gains, we typically need a much larger benchmark machine than the one KeyDB is running on. We have found that a 32 core m5.8xlarge is needed to produce enough throughput with memtier. This supports throughput for up to a 16 core KeyDB instance (medium to 4xlarge)
Source: docs.keydb.dev
KeyDB: A Multithreaded Redis Fork | Hacker News
"KeyDB works by running the normal Redis event loop on multiple threads. Network IO, and query parsing are done concurrently. Each connection is assigned a thread on accept(). Access to the core hash table is guarded by spinlock. Because the hashtable access is extremely fast this lock has low contention. Transactions hold the lock for the duration of the EXEC command....
Source: news.ycombinator.com

Social recommendations and mentions

Based on our record, NumPy seems to be a lot more popular than KeyDB. While we know about 119 links to NumPy, we've tracked only 10 mentions of KeyDB. 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.

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

KeyDB mentions (10)

  • Redis
    These facts only hold when the size of your payload and the number of connections remain relatively small. This easily jumps out the window with ever-increasing load parameters. The threshold is, unfortunately, rather low at a high number of connections and increased payload sizes. Modern large-scale micro-services will easily have over 100 running instances at medium scale. And since most instances employ some... - Source: dev.to / 3 months ago
  • Introducing LMS Moodle Operator
    The LMS Moodle Operator serves as a meta-operator, orchestrating the deployment and management of Moodle instances in Kubernetes. It handles the entire stack required to run Moodle, including components like Postgres, Keydb, NFS-Ganesha, and Moodle itself. Each of these components has its own Kubernetes Operator, ensuring seamless integration and management. - Source: dev.to / about 1 year ago
  • Dragonfly Is Production Ready (and we raised $21M)
    Congrats on the funding and getting production ready, it's good that KeyDB (and Redis) get some competition. https://docs.keydb.dev/ Open question, how does Dragonfly differ from KeyDB? - Source: Hacker News / about 2 years ago
  • I deleted 78% of my Redis container and it still works
    See: Distroless images[0] This is one of the huge benefits of recent systems languages like go and rust -- they compile to single binaries so you can use things like scatch[1] containers. You may have to fiddle with gnu libc/musl libc (usually when getaddrinfo is involved/dns etc), but once you're done with it, packaging is so easy. Even languages like Node (IMO the most progressive of the scripting languages)... - Source: Hacker News / almost 3 years ago
  • Dragonflydb – A modern replacement for Redis and Memcached
    Interesting project. Very similar to KeyDB [1] which also developed a multi-threaded scale-up approach to Redis. It's since been acquired by Snapchat. There's also Aerospike [2] which has developed a lot around low-latency performance. 1. https://docs.keydb.dev/ 2. https://aerospike.com/. - Source: Hacker News / almost 3 years ago
View more

What are some alternatives?

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

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

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

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

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

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

Apache Ignite - high-performance, integrated and distributed in-memory platform for computing and transacting on...

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