Ray – Simple Explanation for Interviews (with Architecture & Spark Comparison)

1️⃣ What is Ray?

Ray is a distributed computing framework used to:

• Run Python workloads in parallel

• Scale from laptop → cluster

• Support AI, ML, data processing, and agents

In simple words

Ray helps you run Python code in parallel across multiple CPUs, GPUs, and machines.

---

2️⃣ Why Ray was created?

Traditional systems had gaps:

• Threads / multiprocessing → hard to scale beyond one machine

• Spark → great for batch data, not flexible for ML & AI

• Kubernetes → infrastructure, not programming model

👉 Ray fills the gap between Python simplicity and distributed scale.

---

3️⃣ Simple Ray Example

Without Ray (single CPU)

def work(x):
    return x * x

results = [work(i) for i in range(10)]

With Ray (parallel & distributed)

import ray
ray.init()

@ray.remote
def work(x):
    return x * x

results = ray.get([work.remote(i) for i in range(10)])

✔ Same logic
✔ Runs in parallel
✔ Can scale across machines

---

4️⃣ Core Ray Concepts (VERY IMPORTANT)

🔹 Tasks

• Stateless functions

• Run in parallel

@ray.remote
def task():
    pass

---

🔹 Actors

• Stateful workers

• Maintain internal state

@ray.remote
class Counter:
    def __init__(self):
        self.count = 0

---

🔹 Objects

• Data stored in distributed shared memory

• Zero-copy where possible

obj_ref = ray.put(data)

---

5️⃣ Ray Architecture (Simple View)

               ┌───────────────────────────┐
               │        Ray Head Node       │
               │---------------------------│
               │  Global Control Store     │
               │  Scheduler                │
               │  Metadata / Cluster Mgmt  │
               └───────────┬───────────────┘
                           │
          ┌────────────────┴────────────────┐
          │                                   │
┌──────────────────────┐        ┌──────────────────────┐
│     Worker Node 1    │        │     Worker Node 2    │
│----------------------│        │----------------------│
│  Ray Workers         │        │  Ray Workers         │
│  CPU / GPU           │        │  CPU / GPU           │
│  Object Store        │        │  Object Store        │
└──────────────────────┘        └──────────────────────┘

---

6️⃣ How Ray Works (Step-by-Step)

1. Driver program starts (ray.init())

2. Ray connects to head node

3. Tasks / actors are submitted

4. Scheduler decides where to run them

5. Data stored in object store

6. Results returned as object references

👉 You never manage threads or machines directly.

---

7️⃣ Ray Use Cases (Real-World)

AI / ML

• Distributed model training

• Hyperparameter tuning

• Reinforcement learning

LLM & Agent Systems

• Multi-agent execution

• Tool calling

• Parallel reasoning

Data Processing

• Parallel ETL

• Feature engineering

Interview line

Ray is widely used for scalable AI, ML, and agent-based systems.

---

8️⃣ Ray vs Spark (VERY COMMON INTERVIEW QUESTION)

High-Level Comparison

Feature	Ray	Spark
Language	Python-first	Scala / Java / Python
Execution Model	Task & Actor	Batch / DAG
Latency	Low	Higher
ML / AI	Excellent	Limited
Streaming	Not primary	Strong
Flexibility	Very high	Structured
Use Case	AI, agents, ML	Big data analytics

---

Conceptual Difference

Spark

• Data-centric

• Batch-oriented

• Optimized for ETL & analytics

Ray

• Compute-centric

• Task-oriented

• Optimized for parallel Python & AI

---

Simple analogy

• Spark → Big factory processing large data batches

• Ray → Smart coordinator running many small jobs in parallel

---

9️⃣ When to use Ray?

Use Ray when:

• You have Python workloads

• Need low-latency parallelism

• Working on ML, AI, LLMs, agents

• Need flexibility

Use Spark when:

• Heavy data analytics

• SQL-like processing

• Large batch ETL jobs

---

🔟 Ray + Spark together?

Yes ✅

Common pattern:

• Spark → Big data processing

• Ray → ML training on processed data

---

1️⃣1️⃣ Interview One-Liners (MEMORIZE)

• What is Ray?

  Ray is a distributed execution framework for parallel Python workloads.

• How Ray works?

  Ray schedules tasks and actors across a cluster using a shared object store.

• Ray vs Spark?

  Spark is data-centric and batch-oriented, while Ray is compute-centric and flexible for AI workloads.

• Why Ray for AI?

  Ray supports low-latency task execution, actors, and GPU scheduling, making it ideal for AI systems.

---

1️⃣2️⃣ Final Summary

Ray is a flexible, Python-first distributed computing framework designed for scalable AI, ML, and parallel workloads, offering lower latency and more control than traditional data processing engines like Spark.