Ray vs Spark — The Future of Distributed Computing

In today’s fast-paced technological landscape, the ability to process, analyze, and derive insights from massive sets of data has become a critical competency for both enterprises and researchers alike. This need has ushered in the era of distributed computing — a paradigm where problems are divided across multiple machines (or nodes) to be solved more efficiently. Particularly, as we delve deeper into the complexities of Artificial Intelligence and Machine Learning (AI/ML), the demand for high-performance, flexible, and scalable distributed computing frameworks has never been higher.

Two frameworks have emerged as forerunners in this arena: Apache Spark and Ray. Spark, the elder statesman of the two, has been a go-to choice for a broad array of data processing tasks, ranging from batch processing and stream analytics to machine learning and graph processing. It offers a unified engine and a rich ecosystem of libraries and tools that have gained widespread adoption across various industries.

On the flip side, Ray is a newer contender designed with the modern challenges of AI/ML in mind. It provides universal APIs for distributed computing tasks, actors, and objects, aiming to make distributed parallelism both easy and efficient. It’s particularly geared toward applications requiring low-latency and high-throughput, areas where Spark has shown limitations.

The objective of this blog post is threefold:

1. To offer a comprehensive understanding of both Ray and Spark, outlining their key features, limitations, and use-cases.
2. To compare the two on various fronts such as performance, flexibility, and ecosystem.
3. To explore why Ray may be particularly well-suited for the emerging challenges of modern distributed computing, thereby making it a potentially more future-proof choice.

Whether you’re a CTO deciding the tech stack for your next big project, a researcher pondering over the most suitable framework for your experiment, or a developer curious about the future of distributed computing, this post aims to provide insights that can guide your decision-making process.

Stay tuned as we dive deep into the architectures, capabilities, and differentiators of these two fascinating frameworks in the following sections.

An Overview of Spark

What is Spark?

Apache Spark originated in 2009 as a project within the AMPLab at the University of California, Berkeley. It was designed as a fast, in-memory data processing engine to efficiently execute a wide range of data analytics tasks. Over the years, it has matured into a robust, general-purpose distributed computing framework. Spark has been widely adopted across various industries, from finance and healthcare to retail and telecommunications, for its versatility and power.

One of the unique aspects of Spark is its ability to unify a variety of workloads, including batch processing, real-time streaming, SQL queries, machine learning algorithms, and graph analytics, all under one framework. This unified architecture makes it easier for organizations to build complex data pipelines and get more value out of their data.

Spark’s Key Features

Resilient Distributed Datasets (RDDs)

Spark’s core abstraction, the Resilient Distributed Dataset (RDD), allows for fault-tolerant, parallel data processing. RDDs are immutable, partitioned collections of objects that can be processed across a distributed cluster. This enables Spark to recover lost data through lineage information, making the system more resilient to failures.

Optimizations

Spark employs various optimizations such as predicate pushdown, which filters data before reading it into memory, and Project Tungsten, an initiative that optimizes Spark’s execution engine at the bytecode level for better performance.

High-level APIs: DataFrames and Spark SQL

Building on top of RDDs, Spark introduced DataFrames and Spark SQL to make it easier for developers and data analysts to manipulate data. These high-level APIs come with their own set of optimizations and enable users to express complex queries in a more user-friendly manner.

Spark’s Ecosystem

Libraries

Spark boasts a rich ecosystem of libraries like MLlib for machine learning and GraphX for graph processing, allowing users to perform specialized tasks without having to switch to another engine.

Observability Tools

Spark offers extensive metrics collection, logs, and built-in dashboards for monitoring and debugging, enabling better observability into cluster operations.

Extensibility

Spark provides robust APIs and allows for extensibility through various languages like Python, Java, Scala, and R, making it a versatile tool for different use-cases and programming paradigms.

Spark’s Limitations

Performance Limitations

While Spark is optimized for a range of data analytics tasks, it is not designed for extremely low-latency requirements, making it less suitable for some real-time analytics or machine learning serving tasks.

Complexity and Learning Curve

Spark’s extensive feature set and rich ecosystem, while powerful, can also introduce complexity and a steep learning curve for newcomers.

Emerging Use-cases

Spark was designed for big data batch processing and later adapted to include real-time processing and other tasks. As such, it may not be optimized for emerging applications that require a different kind of performance profile, like low-latency machine learning and reinforcement learning applications.

In summary, Spark has served as the Swiss Army knife of big data processing for many years, offering robust capabilities and a mature ecosystem. However, its design choices also bring limitations, especially as we look towards the demands of modern, fast-paced, data-driven applications. Stay tuned as we explore how Ray aims to address some of these limitations in the next section.

An Overview of Ray

What is Ray?

Emerging from the Berkeley RISELab, Ray was developed to be a general-purpose distributed computing framework with a specific focus on meeting the requirements of modern machine learning and artificial intelligence (AI/ML) workloads. It was crafted to offer the performance, flexibility, and ease-of-use necessary for complex AI/ML models and simulations, especially those requiring low latency and high throughput.

Ray places a strong emphasis on enabling distributed parallelism. It aims to make the complexities of distributed systems as transparent as possible, thereby providing an ideal environment for emerging applications, such as machine learning training, reinforcement learning, and AI-driven simulations.

Ray’s Key Features

Universal APIs: tasks, actors, objects

One of the strongest points of Ray is its universal API that encompasses tasks, actors, and objects. This enables a more straightforward and cohesive programming model that makes distributed computing more accessible and efficient.

Microsecond Task Latency and High Throughput

Ray boasts microsecond task latency and can achieve millions of tasks per second, particularly well-suited for applications that require real-time responsiveness, such as machine learning inference or financial simulations.

Decentralized Metadata Management

Ray’s decentralized approach to metadata management enhances both performance and reliability. Most of the metadata is managed locally, allowing for better scalability and fewer performance bottlenecks.

Ray’s Ecosystem

Emerging Libraries and Tools

Ray has a burgeoning ecosystem of libraries and tools tailored for various AI/ML tasks. While still young, it has been quickly adopted by researchers and practitioners who are building on top of it.

Cloud-Native and GPU Capabilities

Ray is designed with cloud-native architecture in mind, which allows it to seamlessly scale across distributed cloud resources. Furthermore, Ray has first-class support for GPUs, thereby accelerating tasks that are compute-intensive.

Integration with Existing Data Storage Systems

Ray’s storage system-agnostic design enables it to integrate easily with a variety of data storage solutions, from traditional databases to modern data lakes.

Ray’s Limitations

Less Mature Ecosystem

Being relatively new, Ray’s ecosystem is not as mature or extensive as Spark’s. While it is rapidly growing, it doesn’t yet offer the same breadth of community support, tools, and libraries.

Trade-offs for Performance and Simplicity

Ray opts for simplicity, performance, and flexibility over a broader range of optimizations. This means it may lack certain features that are available in more mature frameworks like Spark, which had the luxury of evolving over a longer period.

In summary, Ray offers a compelling package for modern distributed applications, especially those centered around AI/ML. Its design philosophy focuses on the requirements of low-latency and high-throughput tasks, making it a powerful tool for cutting-edge projects. However, its youth means that it has certain limitations and a smaller ecosystem compared to more established players like Spark. As we’ll explore in the next section, when we place these two frameworks side-by-side, it’s clear that each has unique advantages and constraints that make them suited for different types of workloads and future challenges.

Comparing Spark and Ray

In this section, we will go head-to-head, comparing Apache Spark and Ray on multiple dimensions that are crucial for modern distributed computing frameworks. The aim is to provide a balanced perspective that helps potential users make informed decisions based on their specific needs and workloads.

Performance: Task Latency and Throughput

1. Spark: Spark is optimized for high-throughput batch processing. However, it’s generally not designed for extremely low-latency requirements. Task latencies are typically in the millisecond range, making it less suitable for real-time analytics or machine learning serving tasks.
2. Ray: Ray shines in scenarios requiring low-latency and high-throughput. With microsecond task latency and the ability to handle millions of tasks per second, Ray is well-suited for real-time machine learning, simulations, and financial models.

Metadata Management: Centralized vs Decentralized

1. Spark: Spark uses a more centralized approach for metadata management, which can sometimes become a bottleneck for very large-scale applications.
2. Ray: On the other hand, Ray uses a decentralized metadata management system that improves both performance and reliability. This design enables Ray to scale more efficiently and eliminates single points of failure.

Resource Management: Fine-Grained Control vs Established Scheduling Mechanisms

1. Spark: Spark has mature resource scheduling capabilities with features like dynamic resource allocation. It can be run on various cluster managers like YARN, Mesos, and Kubernetes.
2. Ray: Ray offers more fine-grained control over resources, allowing users to specify exact resource requirements. It also provides the ability to create custom resource types, offering more flexibility in heterogeneous cluster settings.

Flexibility and Composability: Custom Solutions

1. Spark: Spark offers a wide range of built-in libraries for various tasks like machine learning, graph processing, and SQL queries, encouraging a more unified but somewhat rigid workflow.
2. Ray: Ray, being designed with flexibility in mind, allows users to compose custom solutions more easily. Its universal API enables diverse tasks, actors, and objects to be combined in a flexible manner.

Language Support: Python-centric vs Scala/Java Ecosystem

1. Spark: Initially built on the JVM, Spark has native support for Scala and Java. While it does support Python, the support is somewhat secondary and may come with performance trade-offs.
2. Ray: Ray is built with a Python-first approach, making it more friendly for the rapidly growing community of Python developers in data science and machine learning.

Ecosystem and Community: Size and Momentum

1. Spark: Spark benefits from a large, mature ecosystem with extensive community contributions, a wide array of third-party tools, and robust enterprise support.
2. Ray: Ray’s ecosystem is nascent but growing rapidly. It is quickly gaining traction, especially among researchers and organizations focused on AI/ML.

Why Ray may be the Future

As we delve into the new horizons of machine learning, Internet of Things (IoT), and other emerging technologies, the requirements for a distributed computing framework are evolving. Below, we examine why Ray, despite being the younger framework, is particularly well-suited for these modern needs and could very well become the go-to choice for future applications.

Designed for Modern Needs

Low Latency and High Throughput for ML Training and Serving

Machine learning models are becoming increasingly complex, requiring faster data processing for both training and inference. Ray’s architecture, which supports microsecond task latency and high throughput, is perfectly aligned with these needs, making it a strong contender for the new wave of AI/ML applications.

Scalability from Laptops to Clusters

Ray’s seamless scaling abilities are ideal for organizations that have diverse hardware setups — from individual researchers working on laptops to large clusters in data centers. This scalability makes Ray extraordinarily versatile.

Alignment with Emerging Tech

Focus on AI/ML Applications

Ray is purpose-built for modern AI/ML applications, offering tailored libraries and features that accelerate the development in this fast-evolving field. This gives Ray a distinct edge as more organizations look to integrate machine learning into their operations.

Optimized for GPUs and Cloud-Native Solutions

As the world moves towards more specialized hardware for computing, like GPUs, and adopts cloud-native architectures, Ray is ahead of the curve. It provides first-class support for GPUs and is designed to be cloud-native, making it a future-proof solution.

Development Momentum

High Development Velocity and Focus on Fundamentals

The Ray community has exhibited a swift development pace, focusing on essential features and performance enhancements. This quick iteration allows Ray to adapt rapidly to user needs and emerging technologies.

Less Legacy Burden Compared to Spark

Being a younger project, Ray is less encumbered by legacy code and outdated design philosophies. This lean approach allows the project to focus solely on providing the best performance and features for modern distributed computing tasks.

Future-Proofing

Designed for Emerging Fields

Ray’s architecture makes it a strong fit for emerging fields like AI, IoT, and spatial computing. As these areas grow, the demand for a flexible, high-performance distributed computing framework will increase, making Ray an excellent long-term choice.

Promising to Commoditize Distributed Computing

Much like how CUDA commoditized GPU computing, Ray has the potential to do the same for distributed computing. Its focus on performance, simplicity, and flexibility could make it the industry standard in the coming years, as more organizations look to leverage distributed systems for complex computing tasks.

Conclusion

As we’ve explored in this deep dive, both Apache Spark and Ray have their own unique sets of features, advantages, and limitations. Spark, with its mature ecosystem and wide array of supported tasks, remains a robust and versatile choice for many traditional big data applications. However, when it comes to meeting the demands of modern, high-performance applications, particularly in the realms of AI and machine learning, Ray clearly has the edge. Its focus on low-latency, high-throughput operations, and its scalability from a single laptop to a large cluster make it especially compelling for forward-looking organizations and researchers.

Caveats

It’s essential to note that Spark is unlikely to disappear or become obsolete in the near future. Its rich feature set, extensive community, and compatibility with a wide range of computing environments make it an enduring tool for specific use-cases. In areas like ETL pipelines, batch processing, and certain types of analytics, Spark still shines and will likely continue to do so.

Final Thoughts

Choosing between Ray and Spark isn’t merely a technical decision; it’s a strategic one that could influence your project’s future scalability, adaptability, and overall success. If your focus is on real-time analytics, AI, or machine learning, or if you need a framework that is optimized for cloud and GPU acceleration, then Ray presents a compelling case. Its alignment with the emerging needs of distributed computing makes it a future-proof alternative that warrants serious consideration.

Call to Action

We encourage you to take Ray for a spin for your next distributed computing project, especially if you’re diving into AI/ML or other cutting-edge technologies. Given its promising trajectory and growing community support, now is the perfect time to experiment with its capabilities and perhaps become a part of its burgeoning ecosystem.

By understanding the strengths and weaknesses of both Spark and Ray, you can make an informed decision that best serves your immediate needs while also setting you up for future success. Thank you for taking the time to explore this topic with us, and happy coding!