Four Common Spark Issues and How to Fix Them Quickly and Easily

Spark has become extremely popular because it is easy-to-use, fast, and powerful for large-scale distributed data processing. Spark is developer friendly, and because it works well with many popular data analysis programming languages, such as Python, R, Scala, and Java, everyone from application developers to data scientists can readily take advantage of its capabilities. 

However, Spark doesn’t come without its operational challenges. As a frequent Spark user who works with many other Spark users on a daily basis, I regularly encounter four common issues that tend to unnecessarily waste development time, slow delivery schedules, and complicate operational tasks that impact distributed system performance. 

These issues aren’t related to Spark’s fundamental distributed processing capacity. Instead, they typically result from how Spark is being used. In this article, I will describe these common issues and provide guidance on how to address them quickly and easily so that you can optimize Spark performance and the time you spend configuring and operating Spark installations and jobs.

To set the context, let me describe the three main Spark application entities -- Driver, Cluster Manager, and Cache:

1. The Driver sits on a node in the cluster and runs your main Spark function. It also maintains Spark application information; responds to user input; and analyzes, distributes, and schedules work across executors.
2. The Cluster Manager acts as the liaison between the Spark Driver and executors. Executors are responsible for running tasks and reporting back on their progress. The Cluster Manager can be the default scheduler from Spark, Yarn, Kubernetes, or Mesos.
3. The Cache is shared between all tasks running within the executor.

Now let’s look at some of the ways Spark is commonly misused and how to address these issues to boost Spark performance and improve output. 

Data skew

Data skew is probably the most common mistake among Spark users. Data is skewed when data sets aren’t properly or evenly distributed. Skewed data can impact performance and parallelism. The main reason data becomes skewed is because various data transformations like join, groupBy, and orderBy change data partitioning. This can cause discrepancies in the distribution across a cluster, which prevents Spark from processing data in parallel.

Data skew can cause performance problems because a single task that is taking too long to process gives the impression that your overall Spark SQL or Spark job is slow. Actually, it's only a problem with one task, or more accurately, with skewed data underlying that task. Once the skewed data problem is fixed, processing performance usually improves, and the job will finish more quickly.  

The rule of thumb is to use 128 MB per partition so that tasks can be executed quickly. The associated costs of reading underlying blocks won’t be extravagant if partitions are kept to this prescribed amount. If partitions are kept to this amount, it’s possible to execute large numbers of jobs in parallel, which is ultimately more efficient than trying to overload one particular partition.    

Executor misconfiguration

Executors can run several Spark tasks in parallel. Although conventional logic states that the greater the number of executors, the faster the computation, this isn’t always the case. The reality is that more executors can sometimes create unnecessary processing overhead and lead to slow compute processes.

How does this happen? Although Spark users can create as many executors as there are tasks, this can create issues with cache access. If there are too many executors created. individual executors will need to query the data from the underlying data sources and don’t benefit from rapid cache access. 

The second common mistake with executor configuration is to create a single executor that is too big or tries to do too much. This can create memory allocation issues when all data can’t be read by the single task and additional resources are needed to run other processes that, for example, support running the OS. Dynamic allocation can help, but not in all cases.

The best way to think about the right number of executors is to determine the nature of the workload, data spread, and how clusters can best share resources. Dynamic allocation can help by enabling Spark applications to request executors when there is a backlog of pending tasks and free up executors when idle.

Join/Shuffle

Spark applications require significant memory overhead when they perform data shuffling as part of a group or as part of the join operations. Remember that normal data shuffling is handled by the executor process, and if the execute activity is overloaded, it can’t handle shuffle requests. This issue can be handled with an external shuffle service. Keep in mind that data skew is especially problematic for data sets with joins. Joins can quickly create massive imbalances that can impact queries and performance. 

Cartesian products frequently degrade Spark application performance because they don’t handle joins well. By using nested structures or types, you will be able to declare dealing with fewer numbers of rows at every stage, rather than moving data around.

The key is to fix the data layout. Salting the key to distribute data is the best option. One needs to pay attention to the reduce phase as well, which reduces the algorithm in two stages – first on salted keys, and secondly to reduce unsalted keys. Another strategy is to isolate keys that destroy the performance, and compute them separately.

Also, note that a Spark external shuffle often initiates an auxiliary service which will act as an external shuffle service. The NodeManager memory is about 1 GB, and apps that do a lot of data shuffling are liable to fail due to the NodeManager using up memory capacity. This brings up issues of configuration and memory, which we’ll look at next.

Memory issues

Spark users will invariably get an out-of-memory condition at some point in their development, which is not unusual. Spark is based on a memory-centric architecture. These memory issues are typically observed in the driver node, executor nodes, and in the NodeManager. 

Note that Spark’s in-memory processing is directly tied to its performance and scalability. In order to get the most out of your Spark applications and data pipelines, there are a few things you should try when you encounter memory issues. 

First off, driver shuffles are to be avoided at all costs. ReduceByKey should be used over GroupByKey, everything that goes into the shuffle memory of the executor, so avoid that all the time at all costs. TreeReduce is any day better than standard Reduce. Complex and Nested Structures should be used over Cartesian Joins. Ordering of data particularly for historical data. 

When you get an error message about being out of memory, it’s usually the result of a driver failure. Spark users may encounter this frequently, but it’s a fixable issue. Keep in mind that Spark distributes workloads among various machines, and that a driver is an orchestrator of that distribution. As a result, a driver is not provisioned with the same amount of memory as executors, so it’s critical that you do not rely too heavily on the driver.  

There are a number of other issues Spark users encounter, including modernizing the data science infrastructure and planning to run Kubernetes. These, and others, are big topics, and we will take them up in a later post in detail.

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Frequently Asked Questions (FAQs)

1. What causes data skew in Apache Spark and how can I fix it?

Data skew in Spark happens when data is unevenly distributed across partitions—usually due to joins, groupBy, or orderBy operations. This results in some tasks taking much longer than others. To fix it, try using salting techniques, ensure partitions are sized around 128 MB, and avoid overloading a single partition with too much data.

2. Why is my Spark job running slow despite having many executors?

Adding more executors doesn’t always mean faster performance. If executors are misconfigured—either too many or too large—it can cause overhead, memory issues, or inefficient cache usage. Tune your executor size based on workload characteristics and consider using dynamic allocation for better resource balance.

3. How can I avoid out-of-memory errors in Spark?

Out-of-memory (OOM) issues are common in Spark due to its in-memory architecture. Avoid expensive operations like GroupByKey, use ReduceByKey instead, and minimize driver memory usage. Also, steer clear of large driver shuffles and optimize data structures to prevent memory spikes during execution.

4. What is Spark shuffle and how do I reduce shuffle-related performance issues?

Spark shuffle is the data transfer that occurs between executors during operations like joins or groupBy. It can slow down jobs significantly if not managed well. To reduce shuffle overhead, use efficient joins, enable external shuffle services, and avoid Cartesian products when possible.

5. How do I fix slow joins in Spark?

Joins can be slow due to data skew or shuffle inefficiencies. Use broadcast joins for small datasets, apply salting techniques to distribute data more evenly, and restructure nested data to reduce the size of join inputs. Monitor join behavior to identify bottlenecks.

6. What is the ideal partition size in Spark for optimal performance?

A good rule of thumb is to keep partition sizes around 128 MB. This helps Spark process tasks in parallel efficiently without overloading memory or causing skewed execution times. Always monitor partition distribution when scaling jobs.

7. Why is my Spark driver failing with memory errors?

Driver memory errors often occur when the driver is asked to process or coordinate too much data. Spark drivers aren't meant to handle heavy data loads directly—delegate work to executors, and avoid operations that push data through the driver, such as large shuffles or collect operations.

8. What is dynamic allocation in Spark and when should I use it?

Dynamic allocation allows Spark to request or release executors based on workload demand. It's useful when workloads are variable or unpredictable. It helps optimize cluster usage and prevent over-provisioning. However, it's not a silver bullet—make sure your shuffle service is configured correctly.

9. How does Acceldata help with Spark performance issues?

Acceldata’s AI-powered observability platform provides real-time monitoring and intelligent insights into Spark jobs. It detects performance issues like skewed data, misconfigured executors, or inefficient joins, and helps data teams proactively resolve them using automated observability agents.

10. What is Agentic Data Management and how can it improve Spark reliability?

Agentic Data Management (ADM) from Acceldata uses AI agents to monitor, detect, and resolve data and infrastructure issues in real time. These agents ensure Spark jobs run reliably by spotting memory leaks, skewed partitions, or poor data distribution—before they impact performance.