How to Parallelise in Spark? Everything You Need to Know About RDD Processing

Parallelisation is at the core of what makes Apache Spark so powerful for big data processing. Instead of processing data sequentially, Spark splits work across multiple cores or machines, saving time and effort.

Let’s say you’re analyzing 10 million customer transactions to detect unusual spending patterns. On a single machine, this task might take hours, or even crash due to memory limits. With Spark, you can break the workload into parts and process them all at once. That means faster insights, smoother execution, and smarter use of your system resources.

In this blog, you’ll learn exactly how to parallelise in Spark using RDDs, with clear examples and best practices.

How to Parallelise in Spark? A Step-by-Step Guide

Parallelising in Spark means splitting large datasets across multiple cores or machines to process faster. Instead of handling data one record at a time, Spark distributes work across a cluster using RDDs (Resilient Distributed Datasets). This massively reduces processing time, especially with big data. 

You gain speed, scalability, and fault tolerance. You don’t even need to write complex multi-threaded code. Learning how to parallelise in Spark helps you unlock its real performance advantage.

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Let’s take the example of a customer support performance analysis using parallelise in Spark. You manage a support center with teams across Delhi, Mumbai, Bangalore, and Chennai. Each support agent handles dozens of queries daily. 

Your goal is to find which cities have high resolution time and why. You want to analyze support ticket data for the last 30 days. This includes city, agent ID, resolution time, and satisfaction score.

Here’s a sample from your dataset:

Also Read: 6 Game Changing Features of Apache Spark [How Should You Use]

Step 1: Create a SparkSession

This kicks off your Spark app.

from pyspark.sql import SparkSession

spark = SparkSession.builder \
    .appName("Support Performance Analysis") \
    .getOrCreate()

Spark now manages your parallel tasks and memory.

Step 2: Load and Parallelise the Data

Define the data in a Python list.

data = [
    ("T001", "A101", "Delhi", 35, 4.2),
    ("T002", "A102", "Mumbai", 60, 3.8),
    ("T003", "A103", "Bangalore", 45, 4.5),
    ("T004", "A101", "Delhi", 80, 2.0),
    ("T005", "A104", "Chennai", 30, 4.0),
    ("T006", "A102", "Mumbai", 90, 1.5)
]

Now parallelise the list.

rdd = spark.sparkContext.parallelize(data)

Spark splits the data across executors. Each node works on a chunk independently.

When using sc.parallelize(data), Spark automatically splits the data into partitions based on your system's core count. However, this default isn't always ideal, especially if your data size or cluster setup demands finer control.

You can control the number of partitions upfront by specifying it directly:

rdd = sc.parallelize(data, 6)  # Creates 6 partitions

If you need to rebalance partitions later (say, after filtering or joining), use rdd.repartition(numPartitions):

rdd = rdd.repartition(4)  # Redistributes data across 4 new partitions

This ensures tasks are evenly distributed across your cluster, which is vital for performance when learning how to parallelise in Spark effectively.

Step 3: Assess Average Resolution Time by City

You want to find which city takes the longest to resolve issues.

city_time = rdd.map(lambda x: (x[2], (x[3], 1))) \
    .reduceByKey(lambda a, b: (a[0]+b[0], a[1]+b[1])) \
    .mapValues(lambda x: x[0]/x[1])

• x[2] = City
• x[3] = Resolution Time
• This returns the average resolution time per city.

Step 4: Analyze Satisfaction by Agent

Let’s find which agent is causing customer dissatisfaction.

agent_score = rdd.map(lambda x: (x[1], (x[4], 1))) \
    .reduceByKey(lambda a, b: (a[0]+b[0], a[1]+b[1])) \
    .mapValues(lambda x: x[0]/x[1])

This gives an average satisfaction score per agent.

Step 5: Combine Insights

Let’s now identify agents in cities with low satisfaction and high resolution time.

# Join both datasets
results = agent_score.collect()
for agent, score in results:
    print(f"Agent {agent} Avg Satisfaction: {score:.2f}")

Sample Output:

Agent A101 Avg Satisfaction: 3.10
Agent A102 Avg Satisfaction: 2.65
Agent A103 Avg Satisfaction: 4.50
Agent A104 Avg Satisfaction: 4.00

You can add filters to find agents scoring below 3.0.

low_perf = agent_score.filter(lambda x: x[1] < 3.0).collect()

Step 6: Interpretation and Action

After running your analysis in Spark, here’s a snapshot of your key insights.

Note: For Chennai and Bangalore, since there’s only one value, std. dev. is undefined or 0.

Now that you've processed the data, it’s time to make sense of what it shows.

Here’s what you discovered:

• Mumbai has the highest average resolution time among all the cities analyzed.
• Agent A102 consistently receives low satisfaction scores across multiple tickets.
• Delhi shows unpredictable resolution times, swinging between quick and very slow responses.

These findings are not just numbers, they’re signals that point to deeper operational issues.

Here’s what you can do with this insight:

• Schedule coaching sessions for Agent A102 focused on communication and time management skills.
• Review ticket logs from Mumbai to find patterns, like peak hours or ticket type delays.
• Analyze shift-wise data in Delhi to see if certain teams are causing inconsistent performance.
• Create performance benchmarks based on Bangalore’s more balanced scores as a baseline.
• Implement dynamic routing, where critical tickets are redirected to high-performing agents automatically.

By turning data into action, you don’t just measure performance, you improve it strategically. This is where Spark’s parallelisation shines: it gives you the clarity to act at scale.

Why This Matters?

• Spark processed all this without loops or database calls.
• You just parallelised insights from a multi-city, multi-agent dataset.
• If this was real production data, you’d be dealing with millions of rows.
• Spark makes it fast, scalable, and fault-tolerant, all with minimal effort.

Also Read: Top 10 Apache Spark Use Cases Across Industries and Their Impact in 2025

Now that you know how to parallelise in Spark, let’s look at some of the challenges you might face and how you can overcome them.

Common Pitfalls When You Parallelise in Spark — And How to Fix Them?

While Spark’s parallel processing capabilities are powerful, they come with learning curves and hidden bottlenecks. Recognizing these early can help you build faster and more efficient data pipelines without running into scalability issues.

Below is a table of five key challenges and their practical solutions:

Each of these issues can slow your job or even crash your cluster if ignored. But Spark also offers the right tools and tuning capabilities to handle them with care.

Also Read: DataFrames in Spark: A Simple How-To Guide for 2025

Next, let’s look at how upGrad can help you parallelise in Spark.

upGrad Can Help You Learn How to Parallelise in Spark!

Parallelising data is what makes Apache Spark fast, scalable, and efficient for big data tasks. In real-world use, you deal with millions of records that need to be processed quickly. Without parallelisation, your code becomes slow, costly, and hard to maintain at scale. That’s why understanding RDD processing is a must-have skill for modern data engineers.

With upGrad, you learn how Spark handles data distribution, execution, and in-memory computation. You will also have the opportunity to learn other important programming languages and coding concepts. Each course is built around practical problems and real projects from leading data teams.

In addition to the programs covered above, here are some courses that can enhance your learning journey:

• Learn Basic Python Programming
• Core Java Basics
• Java Object-oriented Programming
• JavaScript Basics from Scratch
• Advanced JavaScript for All

If you're unsure where to begin or which area to focus on, upGrad’s expert career counselors can guide you based on your goals. You can also visit a nearby upGrad offline center to explore course options, get hands-on experience, and speak directly with mentors! 

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Reference:
https://www.ibm.com/think/insights/hadoop-vs-spark