Exploring the World of Distributed Computing Frameworks: Empowering Scalable and Efficient Computing

In today's data-driven era, the volume and complexity of data continue to grow exponentially, traditional computing approaches often struggle to handle the processing demands. Organizations are increasingly turning to distributed computing frameworks to tackle the challenges posed by big data processing. These frameworks provide a scalable and efficient solution by distributing computational tasks across multiple machines or nodes, allowing for parallel processing and improved performance. In this article, we will delve into the realm of distributed computing frameworks, shedding light on their significance, and benefits, and highlighting some popular frameworks shaping the field.

What?

Distributed computing frameworks:

Distributed computing #frameworks are the fundamental component of distributed computing systems. They provide an essential way to support the efficient processing of big data on clusters or the cloud. The size of big data increases at a pace that is faster than the increase in the #bigdata processing capacity of clusters. Thus, distributed computing frameworks based on the MapReduce computing model are not adequate to support big data analysis tasks which often require running complex analytical algorithms on extremely big data sets in terabytes.

In the era of big data, the need for efficient and scalable processing frameworks has become paramount. #spark, #dask, and #ray are three prominent distributed computing frameworks that have emerged as powerhouses in handling large-scale data processing and analytics. Each framework offers unique features and capabilities that cater to different use cases and workloads. In this article, we will explore Spark, Dask, and Ray, delving into their key features, similarities, and differences, to help you understand which framework may best suit your distributed computing needs.

Apache Spark: Lightning-Fast Data Processing and Analytics

Spark was started in 2009 by Matei Zaharia at UC Berkeley's AMPLab. The goal of the project was to speed up the execution of distributed big data tasks, which at that point in time were handled by #hadoop #mapreduce. Although performance and usability have never been strong points of MapReduce, it was built with scalability and dependability in mind. The main challenge MapReduce faces is the ongoing requirement to save intermediate results on storage. In comparison to MapReduce, Spark significantly reduced latency by introducing the Resilient Distributed Dataset (RDD) model, utilizing in-memory caching, and utilizing lazy evaluation. Due to this, Spark became the de facto industry standard for massive, fault-tolerant, parallel data processing. GraphX (for distributed graph processing), MLlib (for machine learning), SparkSQL (for structured and semi-structured data), and other enhancements improved the project even further.

It's important to note that Spark was originally designed in Scala, with Python and R support added later. As a result, working with it typically doesn't feel Pythonic. It is a widely adopted distributed computing framework known for its lightning-fast data processing and analytics capabilities. Spark's core abstraction, RDDs, enables fault-tolerant parallel processing of data across clusters. It provides a rich set of libraries and APIs for batch processing, real-time stream processing, machine learning, and graph analytics. Spark's in-memory computing model and optimized execution engine deliver exceptional performance, making it suitable for a wide range of use cases.

Dask: Flexible Parallel Computing with Python

Dask is an open-source parallel computing package that was introduced in 2015, making it more recent than Spark. The framework was initially developed at Continuum Analytics (now Anaconda Inc.), which is the creator of many other open-source Python packages, including the popular #anaconda Python distribution.?The original purpose of #dask was simply to parallelize #numpy so that it can take advantage of workstation computers with multiple CPUs and cores.?In contrast to Spark, "invent nothing" was one of the original design tenets used in the development of Dask. The rationale for this choice is that Python-using developers should feel at home using Dask, and there should be little ramp-up time. According to its creators, the design principles of Dask have evolved over the years, and it is now being developed as a general-purpose library for parallel computing.

The original concept of parallel NumPy was expanded upon to include a full-featured, yet lightweight task scheduler that can keep track of dependencies and support the parallelization of huge, multi-dimensional arrays and matrices. #scikitlearn and parallelized Pandas DataFrames eventually received more support. Due to Scikit's computationally intensive grid searches and processes that are too large to fit entirely in memory, this allowed the framework to significantly alleviate some of their primary pain points. Dask can now work easily in a multi-computer, multi-TB problem space, surpassing the intended target of single-machine parallelization thanks to the development of a distributed scheduler.?

One of the infamous features of Python is Global Interpret Lock (GIL). GIL only allows one thread to hold the control of the Python interpret. It limits the performance when the system has more than one core.?Dask is a distributed and dynamic task scheduler. Dask has a scheduler, which can be considered as a job manager node in the #flink, and one or more workers, which are similar to the task manager node of Flink. Workers do the computation work. The computation results are returned back to the scheduler.

Ray: Distributed Execution for Python

Ray is another UC Berkeley initiative with the goal of "simplifying distributed computing." Ray is a high-performance distributed execution framework targeted at large-scale machine learning and reinforcement learning applications. #ray originated with the?RISE Lab at UC #berkeley.?

According to Ion Stoica, is a "distributed computing ecosystem as a service," with a recent focus on "supporting machine learning workloads." He claims it began as a class project at Berkeley in 2016 with the purpose of attaining "distributed training" (data training for machine learning). Berkeley was also the birthplace of Apache Spark, a data processing engine. However, #Stoica claims that they rapidly discovered that Spark was not the greatest solution for deep learning workloads.

Ray is made up of two major components: Ray Core, a distributed computing framework, and Ray Ecosystem, which is a collection of task-specific libraries that come packaged with Ray (e.g.Ray Tune, a hyperparameter optimization framework, RaySGD for distributed deep learning, RayRLib for reinforcement learning, etc).

Ray AIR

• Data: Scalable Datasets for ML
• Train: Distributed Training
• Tune: Scalable Hyperparameter Tuning
• RLlib: Scalable Reinforcement Learning
• Serve: Scalable and Programmable Serving

?Ray Core

• Tasks: Stateless functions executed in the cluster.
• Actors: Stateful worker processes created in the cluster.
• Objects: Immutable values accessible across the cluster.

Reference:

• https://docs.ray.io/en/latest/ray-air/getting-started.html
• https://aws.amazon.com/blogs/startups/scaling-ai-ml-and-accelerating-ai-development-with-anyscale-and-aws/
• https://docs.google.com/document/d/1tBw9A4j62ruI5omIJbMxly-la5w4q_TjyJgJL_jN2fI/preview
• https://thenewstack.io/how-ray-a-distributed-ai-framework-helps-power-chatgpt/#:~:text=According%20to%20Stoica%2C%20Ray%20is,training%E2%80%9D%20(meaning%20training%20of%20data
• https://www.oreilly.com/library/view/learning-ray/9781098117214/ch01.html
• https://www.micahlerner.com/2021/06/27/ray-a-distributed-framework-for-emerging-ai-applications.html
• https://docs.dask.org/en/stable/
• https://en.wikipedia.org/wiki/Dask_(software)