How to Prevent Spark Executor Memory Failures on YARN

·September 22, 2025

·10 min read

How to Prevent Spark Executor Memory Failures on YARN — Image Source: pexels

You want your Spark jobs to work well on YARN, but memory failures can stop them. If you see Spark Executor OOM errors, there is a problem with memory settings. Many jobs fail when executor memory reaches a limit, like 16.1 GB. Sometimes, YARN memory use gets too high, even up to 90%. Look for error messages like java.lang.OutOfMemoryError: Java heap space or containers being killed. To keep things working, always check your memory settings. Watch how much memory your jobs use.

Key Takeaways

Set memory settings the right way to stop Spark Executor OOM errors. Begin with 8 to 16 GB for executor memory. Make sure memory overhead is at least 10%.
Find a good balance for executors and memory. Too many executors can make the cluster too busy. Too few executors can make jobs slow. Figure out the best number by looking at node memory.
Make your queries better so they use less memory. Do not use collect and joins too much. Use persist for data frames you need often. This helps things run faster.
Watch your jobs often and check logs for errors. Look for signs of memory problems. Change settings if you see issues to keep things working well.
Try your settings in a test environment before using them for real. Use Spark UI to look at memory use and how tasks run. This helps you find problems early.

Prevention Strategies

Memory Configuration

Setting memory right helps stop Spark Executor OOM errors. Each executor needs enough memory for its job. If memory is too low, jobs crash. If it is too high, you waste resources.

Here’s a table to help pick settings:

Parameter	Recommended Setting
spark.executor.memory	Set for what your job needs
spark.yarn.executor.memoryOverhead	Usually 10% of executor memory or more

Memory overhead matters a lot. It covers extra needs like JVM overhead and native code. If you skip it, Spark Executor OOM errors can happen even with enough memory.

Tip: Start with 8–16 GB for executor memory. Set memory overhead to at least 10%. If your job is big or uses native libraries, increase overhead.

Let’s see how these settings help your job:

Parameter	Impact on Stability
spark.executor.memory	Controls heap size. It affects how much data can be cached and shuffle data size.
spark.yarn.executor.memoryOverhead	Added to executor memory. It sets total memory for YARN and affects stability.

Tuning these settings means fewer Spark Executor OOM errors and more stable jobs.

Resource Allocation

You need to balance memory and executors. Too many executors overload the cluster. Too few make jobs slow and may fail.

Here’s how to pick the right number:

Divide node memory by memory for each executor.
For example, with 32 cores and 63 GB per node, run about 6 executors per node.
On a 6-node cluster, you get 36 executors. Leave one for Application Master, so use 35.
If your job needs 10 GB per executor, run 3 executors per node. That gives 29 executors.

You can change executor memory and number to help jobs:

Raise spark.executor.memory from 4 GB to 8 GB to lower disk spills.
Add more executors, like going from 10 to 20, for better parallelism.
Fix data skew by repartitioning or raising spark.sql.shuffle.partitions.
Rerun your job and check Spark UI to see if disk spills drop and tasks run smoother.

Note: Always save some memory for YARN and background tasks. Using all resources causes more Spark Executor OOM errors.

Here’s a table with more tuning ideas:

Parameter	Description
executor.memory	Memory for each executor. It affects how many tasks run at once.
executor.cores	CPU cores for each executor. It affects parallel task execution.
spark.dynamicAllocation.enabled	Lets Spark change executor number based on workload. It helps use resources better.
spark.sql.shuffle.partitions	Sets number of partitions for shuffling data. It affects joins and aggregations.

Query Optimization

You can use less memory by writing better queries. Using collect or joins too much uses lots of memory. Try these steps to keep jobs running:

Set memory overhead to 10% of executor memory or at least 328 MB.
Remove extra driver cores to save resources.
Set number of executors but leave space for YARN and other tasks.
In cluster mode, add one executor for the driver.
Check your code for collect, joins, and repartitioning. These use lots of memory.
Use persist for data frames you use often. This saves memory and makes jobs faster.
Run steps in spark-shell to find slow parts.

Pro Tip: Test your settings in staging before production. Use Spark UI to watch memory, task time, and shuffle activity. If you see Spark Executor OOM errors, check query logic and memory settings first.

Here’s a table with more best practices:

Parameter	Description	Tuning Recommendations
Num-executors	Sets total Executor processes for the Spark job.	Set to 50-100 to use resources well without overloading the cluster.
Executor-memory	Sets memory for each Executor process.	Use 4G to 8G, depending on resource queue limits.
Executor-cores	Sets CPU cores for each Executor process.	Use 2-4 cores for good parallel tasks without using too many resources.
Driver-memory	Sets memory for the Driver process.	Set to about 1G, but use more if using collect to avoid OOM errors.
Spark.default.parallelism	Sets default number of tasks per stage.	Set between 500 to 1000 for better job speed and to avoid using too many resources.

If you follow these steps, you’ll see fewer Spark Executor OOM errors and jobs will finish faster. Start small, watch your jobs, and scale up when needed.

Diagnosing Spark Executor OOM Errors

Common Causes

Spark Executor OOM errors happen for a few main reasons. Most times, executors or the driver do not get enough heap memory. If your job gets a lot more data, memory use can go up fast. This can crash your executors. You should check for odd data patterns or duplicates. These can make memory use jump quickly.

Here’s a table with the most common causes:

Root Cause	Description
Insufficient JVM Heap Memory	Executors or drivers do not have enough memory to process tasks.
Sudden Data Spikes	Large or unexpected data increases can overwhelm memory.
Data Skew	Some tasks get much bigger data chunks than others.
Large Shuffle Operations	Group by or join operations move lots of data and use extra memory.

There are other reasons too. Data skew can make some tasks handle huge rows. This can overload executors. Heavy shuffling, like joins, can push NodeManager past its memory limit. If you give too many cores to each executor, memory problems can happen. Very large dataframes can also cause Spark Executor OOM errors.

Tip: Try to keep your data balanced. Avoid big spikes. Watch for tasks that look much bigger than others.

Error Messages

When Spark Executor OOM errors happen, you will see signs in your logs. Here are the most common messages:

java.lang.OutOfMemoryError: Java heap space
Container killed by YARN for exceeding memory limits
Warnings about virtual memory limits being exceeded
Suggestions to boost spark.yarn.executor.memoryOverhead

You may also see advice to add more executor memory. Be careful though. Just adding memory can waste resources if your job is not tuned well.

Here is what these messages mean:

OutOfMemoryError means your executor used too much memory.
If YARN kills your container, you went past the memory limit.
Virtual memory warnings mean you should check YARN settings.
If executors keep getting terminated, they are running out of memory again and again.

Note: If you keep seeing these errors, check your memory settings. Look for big shuffle operations or data skew.

Log Analysis

You can find out why Spark Executor OOM errors happen by checking your logs. Start with YARN container logs and driver logs. Look for lines like:

java.lang.OutOfMemoryError: Java heap space
Container killed by YARN for exceeding memory limits
Application terminated due to exceeding virtual memory limits

Watch for garbage collection warnings too. Long GC times or lots of collections mean executors are having memory trouble. In Spark UI, check shuffle metrics for big sizes or many disk spills.

Here is a checklist to help you look at logs:

Look for OutOfMemoryError messages in executor logs.
Check if containers are killed for memory limits.
Watch for executors getting terminated over and over.
Monitor shuffle metrics for big spikes.
Review garbage collection logs for long pauses.
Make sure you give at least 80% of system RAM to executors. Leave enough for YARN and other services.

If you run your job in yarn-client mode, you can spot driver memory issues faster. The driver runs on your machine, so you can check its logs right away.

Sometimes, you see messages about virtual memory limits. You can fix this by turning off virtual memory checks in yarn-site.xml or by raising memory overhead settings.

Set yarn.nodemanager.vmem-check-enabled to false if you keep hitting virtual memory errors.
Raise spark.yarn.executor.memoryOverhead and spark.yarn.driver.memoryOverhead to give jobs more room.

Pro Tip: Always check your logs after a job fails. Error messages and warnings will help you find the real problem.

Troubleshooting

Quick Fixes

You want to fix Spark Executor OOM errors quickly. Start with a simple checklist. First, check which container is getting killed. Look at your logs to see if it’s the executor, driver, or application master. If it’s the executor, use the container ID in NodeManager logs to find the problem. Next, open the Spark UI and go to the Environment tab. Check the memory values used. Look for causes like data skew, not enough partitions, or memory leaks. Try these quick fixes. Increase spark.yarn.executor.memoryOverhead to give executors more room. Raise executor memory if needed. For shuffle errors, set enough shuffle partitions. Use this formula:

num-shuffle-partitions = num-executors * executor-cores * 2

Check your code for collect or show operations that use lots of memory.

If you see Spark Executor OOM errors, don’t just add more memory. Make sure your job is tuned well.

Monitoring Tools

You need good tools to watch memory use in your YARN cluster. Here are some popular choices. Ganglia tracks node-level metrics. It helps you spot overloaded nodes, but it’s harder to see executor-level memory. VisualVM works for standalone apps and can help in cluster mode. Real-time monitoring solutions track job progress, resource use, and performance issues. These tools send alerts if a node gets overloaded or if memory use spikes. NodeManager keeps an eye on CPU, memory, and disk use for each node. You can spot problems early.

Use these tools to catch Spark Executor OOM errors before they crash your jobs.

Long-Term Solutions

You want your jobs to run smoothly for a long time. Try these steps. Balance resources. Don’t give too much memory, or garbage collection will slow things down. Set memory overhead for Spark on YARN to at least 7% of executor memory. Analyze job abnormalities often. Look for big data shuffles or tasks that use too much memory. Break up big jobs by writing intermediate results to S3 in Parquet format. This lowers memory use and boosts fault tolerance. Make these configuration changes for better stability: 1. Set spark.yarn.maxAppAttempts to 4. 2. Use spark.yarn.am.attemptFailuresValidityInterval set to 1 hour. 3. Adjust spark.yarn.max.executor.failures to 8 times the number of executors. 4. Set spark.yarn.executor.failuresValidityInterval to 1 hour.

Keep monitoring and tuning your jobs. You’ll see fewer Spark Executor OOM errors and better performance.

You can make your Spark jobs work well on YARN. Change memory settings to fit your job’s needs. Balance resources so nothing gets overloaded. Check logs often to find problems early. Always watch your jobs and fix settings before issues happen. If you want to learn more, here are some helpful resources about Spark optimization:

Resource Title	Description
Optimization of Spark applications in Hadoop YARN	This article shares Spark ideas and tips for using resources wisely in Hadoop YARN.
YARN's Impact on Spark and Hive Performance Optimization	Talks about how YARN helps Spark apps run faster by managing resources well.
A Glance at Apache Spark optimization techniques	Gives a quick look at popular ways to make Spark run better.
Apache Spark Optimization Techniques for High-performance Data Processing	Explains how to use persist() and cache() for saving data and splitting data to speed up jobs.

Try new things and keep testing. Your jobs will run faster and have fewer memory problems!

FAQ

What causes Spark Executor OOM errors most often?

You usually see OOM errors when your executors do not have enough memory. Big data spikes, heavy joins, or too many collect actions can also cause problems. Always check your memory settings and job logic.

How can I quickly check if my job ran out of memory?

Look for messages like java.lang.OutOfMemoryError or “Container killed by YARN for exceeding memory limits” in your logs. The Spark UI also shows failed tasks and memory usage.

Should I just keep adding more memory to fix OOM errors?

No, just adding memory does not always help. You should tune your job, fix data skew, and optimize queries. Sometimes, better code or more partitions works better than more memory.

What is memory overhead in Spark, and why does it matter?

Memory overhead is extra memory for things outside the JVM heap, like native code or Python processes. If you set it too low, your executors can crash even if you have enough heap memory.

Can I prevent OOM errors by changing YARN settings?

Yes, you can. Try raising spark.yarn.executor.memoryOverhead or turning off virtual memory checks in yarn-site.xml. Always test changes first to make sure your jobs stay stable.