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

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

Q: 1. How does Spark determine the number of partitions in parallelize()?

Spark decides the default number of partitions using the available CPU cores across your cluster. This default may not fit your data or processing needs. You can override it by passing a second parameter to sc.parallelize(). For example, sc.parallelize(data, 8) forces eight partitions. This flexibility is crucial when learning how to parallelise in Spark correctly. Balancing partition count ensures tasks are evenly distributed and resources are fully used.

Q: 2. Why does reduceByKey() sometimes slow down my Spark job?

reduceByKey() involves shuffling data across partitions based on keys. If some keys appear too frequently, those partitions get overloaded. This skew causes uneven workload and slow performance. You can mitigate this by pre-aggregating or using combineByKey(). These reduce shuffling and improve load balance. Always inspect the Spark UI to understand task timing and shuffle stages. It's key to optimising how to parallelise in Spark.

Q: 3. Can Spark parallelise large files effectively?

Yes, but only if the file format supports block splitting. Formats like CSV, Parquet, and ORC work well. Spark reads these files in chunks and distributes them across tasks. Compressed formats like GZIP can’t be split, creating a single-threaded bottleneck. For efficient parallelism, always choose split-friendly formats. Knowing your file structure is essential to master how to parallelise in Spark.

Q: 4. How do I check if RDD partitions are balanced?

You can use rdd.glom().map(len).collect() to inspect how many elements each partition holds. If several partitions return zero, your workload isn’t balanced. This causes idle workers and slow tasks. Also, check Spark UI for task distribution insights. Use repartition() or coalesce() to fix partition imbalance. Understanding and managing partition distribution is critical when learning how to parallelise in Spark.

Q: 5. What’s the difference between sc.parallelize() and sc.textFile()?

sc.parallelize() distributes in-memory collections manually. It's useful for small data or tests. sc.textFile() reads from external storage and splits the input automatically. It’s ideal for production-scale processing. Learning how to parallelise in Spark requires understanding both methods. Choose based on your data source and performance needs.

Q: 6. Can I do broadcast joins with RDDs?

RDDs don’t support native broadcast joins like DataFrames. But you can manually broadcast small RDDs. Collect the RDD to the driver and use it in a map function. This only works if the dataset is small enough to fit in memory. For large-scale joins, switch to DataFrames and use Spark's broadcast() function. Efficient joins are crucial when learning how to parallelise in Spark workflows.

Q: 7. Why do I get memory errors while processing RDDs?

These errors usually happen when RDDs are too large or transformations are poorly managed. Avoid chaining multiple wide operations without checkpointing. Also, don’t cache everything blindly, cache only reused RDDs. Monitor the storage tab in Spark UI for memory usage insights. Optimising memory use is key to successful RDD processing and parallelisation in Spark.

Q: 8. Can recursive or nested logic be parallelised in Spark?

Not directly. Spark transformations are functional and flat. They don’t support internal loops or recursion. You need to break recursion into separate stages or handle it at the driver level. For graph processing, consider Spark's GraphX. When exploring how to parallelise in Spark, focus on stateless, distributed-friendly logic for best results.

Q: 9. Should I use Spark for small datasets?

No, Spark’s overhead makes it inefficient for tiny datasets. Local tools like pandas work better. But you can still use sc.parallelize() for testing workflows. It helps simulate transformations before running them on large data. This practice is great when exploring how to parallelise in Spark in safe, small-scale environments.

Q: 10. How do I aggregate data across partitions with minimal overhead?

Use aggregate(), treeAggregate(), or aggregateByKey() to combine results efficiently. These perform local aggregation first, then merge partial results. This avoids unnecessary shuffles and reduces memory pressure. They're useful for computing large-scale metrics. Learning these operations is essential if you're serious about how to parallelise in Spark effectively.

By Rohit Sharma

Updated on Jul 07, 2025 | 9 min read | 8.75K+ views

Did you know? Spark can process data up to 100x faster than Hadoop using in-memory parallelization. This is because Spark keeps data in memory for quicker access and execution. This reduces time spent on reading and writing between tasks during data 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.

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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:

Ticket ID	Agent ID	City	Resolution Time (min)	Satisfaction Score
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

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

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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.

City	Avg. Resolution Time (mins)	Consistency (Est. Std. Dev.)
Mumbai	75.0	High (≈ 21.2)
Delhi	57.5	High (≈ 31.8)
Bangalore	45.0	– (Single value)
Chennai	30.0	– (Single value)

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

Agent ID	Avg. Customer Satisfaction
A101	3.1
A102	2.65
A103	4.5
A104	4.0

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.

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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:

Challenge	Solution
Uneven Data Distribution	Use repartition() or coalesce() to balance data across partitions evenly.
Expensive Shuffles During Joins or Aggregations	Optimize with broadcast joins, partitioning, or reduce shuffles via pre-aggregation.
Too Many Small Files or Tasks	Use proper file formats (like Parquet) and batch smaller files when loading.
Memory Bottlenecks with Large RDDs	Persist selectively with persist() or cache(), and monitor storage levels.
Debugging in a Distributed Environment	Use Spark UI, logs, and glom() to inspect partitions and execution plans.

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.

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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.

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