Fixing SparkUserAppException: Exit Code 137 Explained

Hey there, fellow data enthusiasts and Spark users! Ever been running your awesome Apache Spark application, only to have it suddenly crash with a cryptic org.apache.spark.SparkUserAppException: User application exited with 137 error? If so, trust me, guys, you’re not alone . This specific Spark error, often manifesting as exit code 137 , is one of those classic head-scratchers that can plague even seasoned developers. It’s essentially Spark telling you, “Hey, something went terribly wrong, and your application was forcefully stopped!” But what does it really mean? Well, most of the time, this particular exit code points fingers directly at memory issues. We’re talking about situations where your Spark application, or one of its critical components like an executor or driver, tried to consume more memory than it was allocated, leading to the operating system or resource manager (like YARN or Kubernetes) stepping in and mercilessly killing the process to prevent system instability. This isn’t just a random crash; it’s a sign that your Spark job is pushing the boundaries of its allocated resources, often memory. Understanding and resolving exit code 137 is crucial for building robust and efficient Spark applications, especially when dealing with large datasets and complex transformations. In this comprehensive guide, we’re going to dive deep into what causes this error, how to effectively diagnose it using the tools at our disposal, and most importantly, equip you with practical, actionable strategies to prevent it from ever happening again. We’ll explore various memory configurations, look into common pitfalls, and share best practices to ensure your Spark jobs run smoothly. So, buckle up, because by the end of this article, you’ll be a pro at tackling this notorious SparkUserAppException !

Understanding SparkUserAppException and Exit Code 137

Let’s kick things off by really understanding what SparkUserAppException and, specifically, exit code 137 are all about. When you see org.apache.spark.SparkUserAppException , it’s Spark’s way of saying that your custom application code (the “user application” part) encountered a critical error and terminated unexpectedly. It’s a generic wrapper exception for any non-zero exit code returned by your application. Now, the exit code 137 part is the key differentiator here, and it’s super important, guys. This isn’t just any old error; exit code 137 is a widely recognized convention in Unix-like operating systems. It typically means that the process was terminated by a signal, and in this specific case, it almost always signifies that the process was killed because it ran out of memory . Think of it this way: your operating system or resource manager (like YARN, Mesos, or Kubernetes) has a guardian angel looking after system stability. When a process starts demanding too much memory, threatening to starve other processes or even crash the entire machine, this guardian angel (often the OOM Killer – Out Of Memory Killer) swoops in and terminates the offending process. So, when your Spark application, be it a driver or an executor, is killed with exit code 137 , it’s a strong indicator that one of its containers exceeded its allocated memory limits. This isn’t necessarily a bug in your code logic itself, but rather a resource management issue. It tells us that the application tried to use more RAM than it was given, leading to an abrupt, unceremonious shutdown. Identifying exit code 137 is often the first crucial step in troubleshooting Spark memory issues because it narrows down the problem significantly, shifting your focus from code logic errors to resource allocation and usage patterns. It’s like a big red flag screaming, “ I need more memory, or I need to use less! ” This understanding is foundational for moving forward and effectively diagnosing the root cause of the memory pressure within your Spark job.

Common Causes of Exit Code 137 in Spark Applications

Alright, now that we know exit code 137 screams “memory, memory, memory!” let’s get into the nitty-gritty of why your Spark application might be hitting these memory limits. There are several usual suspects, and often, it’s a combination of a few. Pinpointing the exact cause is crucial for a lasting solution, so let’s break them down. Each of these scenarios can lead to a SparkUserAppException with exit code 137 , leaving your job in a failed state.

Insufficient Driver Memory

The Spark driver is like the brain of your application, guys. It orchestrates the entire process, analyzes, schedules tasks, and collects results. If the driver itself runs out of memory, your whole application comes crashing down with exit code 137 . This often happens when you’re collecting a very large result set back to the driver (e.g., using collect() , toPandas() , or show() on a massive DataFrame without limits), or when performing large broadcast variables that exceed the driver’s capacity. Additionally, if your application involves complex graph computations, extensive local aggregations, or uses UDFs that allocate significant memory on the driver side, it can quickly exhaust spark.driver.memory . Imagine you’re trying to fit an elephant into a shoebox – that’s what happens when your driver tries to hold too much data. To tackle this, you’ll want to increase spark.driver.memory . This parameter defines the amount of memory allocated to the driver process. For example, if you’re running on YARN, you might set --driver-memory 8g for 8 gigabytes. But be careful, simply bumping up this value isn’t always the silver bullet. Sometimes, the real solution is to rethink your data collection strategy. Can you save results directly to a distributed storage system like HDFS or S3 instead of collecting them all to the driver? Can you sample data, or use take() instead of collect() for debugging? Remember, the driver shouldn’t be a data processing workhorse; its main job is coordination. Over-relying on the driver for heavy data operations is a common anti-pattern that invariably leads to exit code 137 errors, particularly for jobs that scale up in data volume. Always check your Spark UI’s Environment tab to see the current driver memory settings and consider increasing them incrementally while observing performance.

Insufficient Executor Memory

Now, if the driver is the brain, the executors are the muscles. They’re the ones doing the heavy lifting – processing data, running tasks, and performing computations across your cluster. When an executor runs out of memory, it’s usually one specific task, or a small group of tasks on that executor, that’s causing the problem, leading to that dreaded exit code 137 for the entire executor container. This is perhaps the most common cause of this error. It can occur due to various reasons: processing a particularly large partition of data (data skew), performing complex aggregations or joins that create many intermediate objects, or caching too much data within the executor’s memory. If you’re seeing several executors dying with exit code 137 , you’ve almost certainly got an executor memory crunch. The primary way to address this is by adjusting spark.executor.memory . This parameter controls the amount of memory allocated to each executor process. A typical starting point might be --executor-memory 4g , but you might need to go higher depending on your workload. However, it’s not just about the absolute value; it’s also about how this memory is used . Spark divides spark.executor.memory into Storage, Execution, and User memory. If one of these pools gets overwhelmed, exit code 137 comes knocking. Beyond simply increasing spark.executor.memory , consider optimizing your code. Are you using efficient data structures? Are you accidentally creating data duplication? Are you over-caching RDDs or DataFrames that aren’t strictly necessary? These subtle factors can quietly lead to excessive memory consumption on your executors, ultimately resulting in the dreaded out-of-memory kill. Always monitor the Executors tab in the Spark UI to spot which executors are struggling and their memory utilization, along with garbage collection activity, to get a clearer picture of the issue. Sometimes, just a slight tweak to spark.executor.memory can make all the difference, but often it requires a deeper dive into task behavior.

Too Many Cores Per Executor / Not Enough Executors

This one is a bit more nuanced, but equally important, guys. The number of cores per executor ( spark.executor.cores ) dictates how many tasks an executor can run concurrently. While more cores might seem like a good idea for parallelism, there’s a catch: each concurrent task within an executor shares that executor’s memory . If you assign too many cores to an executor, each task gets a smaller slice of the spark.executor.memory pie. This can lead to tasks quickly running out of memory, even if the total spark.executor.memory seems sufficient for a single task. For example, if you have spark.executor.memory=8GB and spark.executor.cores=8 , each task conceptually only has 1GB of dedicated memory, plus some shared space. If a single task needs more than 1GB for its intermediate computations, boom, exit code 137 . Conversely, not having enough executors ( spark.num.executors ) can force your existing executors to process larger chunks of data or handle more tasks, increasing their memory pressure. A common recommendation is to set spark.executor.cores to between 2-5. This provides a good balance between parallelism and memory allocation per task, reducing the likelihood of OOM errors. It’s often better to have more smaller executors with fewer cores (e.g., 50 executors with 3 cores and 6GB memory each) than fewer larger executors with many cores (e.g., 5 executors with 30 cores and 60GB memory each). The former approach offers better fault isolation (if one executor dies, you lose less progress) and better memory allocation per task. This optimization, specifically tailoring the spark.executor.cores and spark.num.executors parameters to your specific cluster and workload, can significantly reduce the chances of encountering exit code 137 due to memory contention within individual executor processes. It’s a strategic decision that balances resource utilization with task stability and throughput. Experimentation with these parameters, alongside spark.executor.memory , is often necessary to find the sweet spot for your particular Spark application and data volume.

Memory Overheads (Off-Heap Memory, OS, JVM)

Here’s a common trap that even experienced Spark users sometimes fall into: forgetting about memory overhead . When you allocate spark.driver.memory or spark.executor.memory , you’re primarily specifying the heap memory available for your JVM application. However, a Java process needs more than just heap memory. It also uses off-heap memory for things like JVM itself, garbage collection metadata, custom off-heap data structures, Python processes (if you’re using PySpark), and operating system buffers. If you don’t account for this, the total memory requested by your container (e.g., in YARN or Kubernetes) might be less than what the actual process needs, leading to the OOM killer showing up and giving you exit code 137 . Spark provides parameters to account for this overhead: spark.executor.memoryOverhead and spark.driver.memoryOverhead . These values are added to spark.executor.memory and spark.driver.memory respectively, to determine the total container memory requested from the resource manager. By default, Spark calculates an overhead of max(384MB, 0.10 * spark.executor.memory) . While this default is often sufficient, complex PySpark applications, applications with large broadcast variables, or those using off-heap memory libraries might need a larger memoryOverhead . If you’re seeing exit code 137 even when spark.executor.memory seems generous, try explicitly setting spark.executor.memoryOverhead to a higher value, for example, spark.executor.memoryOverhead=1g or even 2g . This tells the resource manager to allocate more physical RAM for your container beyond just the JVM heap, giving your Spark process breathing room for its non-heap memory needs. This configuration is absolutely critical in containerized environments like Kubernetes, where strict memory limits are enforced at the container level. Ignoring memory overhead is a guaranteed path to exit code 137 , as the system will simply kill any container that exceeds its total allocated memory, regardless of how much heap memory is being used. Properly configuring spark.executor.memoryOverhead is a vital step in preventing these kinds of subtle but impactful out-of-memory errors that can frustrate even the most seasoned Spark developers.

Data Skew and Large Partitions

Data skew is a silent killer, guys, and a very common reason for exit code 137 on individual executors. Imagine you have a massive dataset, and after a transformation (like a join or groupByKey ), a disproportionately large amount of data ends up in a single partition . When an executor gets assigned this super-sized partition, it has to process all that data with its limited spark.executor.memory . Even if your overall memory configuration is fine for the average partition, this one monstrous partition can easily overwhelm a single executor, causing it to run out of memory and get killed by the OOM killer, leading to the dreaded exit code 137 . Identifying data skew often requires looking at the Spark UI’s Stages tab, specifically at the “Input Size” and “Records” for tasks within a stage. You’ll see a massive difference between the min, average, and max values. The good news is, Spark has several strategies to combat data skew. One common approach is to repartition your data strategically using repartition() or coalesce() before or after operations that might introduce skew. For joins, techniques like “salting” (adding a random key to skewed keys to distribute them) or using broadcast joins (if one DataFrame is small enough to fit in the driver’s memory and broadcast to all executors) can be incredibly effective. Spark’s Adaptive Query Execution (AQE) in Spark 3.0+ is a game-changer here, as it can dynamically handle skew by repartitioning data on the fly during join operations. Make sure AQE is enabled ( spark.sql.adaptive.enabled=true ). By proactively identifying and mitigating data skew, you can prevent those individual executor failures that often result in exit code 137 , ensuring a more balanced and stable execution of your Spark jobs across the cluster. Ignoring data skew is like playing Russian roulette with your memory resources – eventually, that one big partition will hit, and your application will crash.

Diagnosing and Troubleshooting Exit Code 137

Alright, you’ve hit exit code 137 . Now what? Diagnosis is key, guys. You can’t fix what you don’t understand, and luckily, Spark gives us a lot of tools to figure out exactly where the memory crunch is happening. It’s like being a detective, piecing together clues from various sources to pinpoint the culprit behind the SparkUserAppException .

Leveraging Spark UI for Insights

The Spark UI (usually available at http://<driver-ip>:4040 during job execution or as a history server) is your best friend when troubleshooting exit code 137 . It provides a wealth of information about your application’s performance and resource usage. Here’s what to look for:

1. Stages Tab: This is often the first place to check. Look for failed stages or tasks. If a stage fails, click on it and then look at the Tasks table. Sort by Input Size , Shuffle Write , or Duration to spot any tasks that are significantly larger or taking much longer than others. If you see a large disparity between min, average, and max values (especially for Input Size or Shuffle Write bytes), you’re likely dealing with data skew on those specific tasks, which could lead to an executor running out of memory. Tasks that are consistently failing are huge indicators.
2. Executors Tab: This tab provides a summary of all your active (and sometimes dead) executors. Pay close attention to the Active Tasks , Total Task Time , GC Time , and Memory Usage columns. If an executor has a very high GC Time percentage (e.g., 20% or more), it means the JVM is spending too much time trying to reclaim memory, a classic sign of memory pressure. Look for executors that died prematurely or have inconsistent memory usage patterns. The Storage Memory and Used Memory percentages are critical. An executor consistently hovering near 100% Used Memory is a ticking time bomb for an exit code 137 error. This is where you can confirm if your spark.executor.memory settings are adequate or if you need to bump them up. The details provided here are invaluable for understanding the resource consumption of individual workers in your cluster and directly link to the SparkUserAppException you’re seeing.
3. Storage Tab: If you’re caching RDDs or DataFrames, this tab shows you how much memory they’re consuming. If you’re caching too much data, or large objects are being retained longer than necessary, it can quickly exhaust spark.executor.memory (specifically the storage fraction), leading to OOM issues and exit code 137 . Ensure your caching strategy is efficient and only cache what’s truly beneficial for performance.

By diligently checking these tabs, you can often pinpoint which stage or executor is failing and why it’s running out of memory, providing clear direction for your debugging efforts against SparkUserAppException .

Analyzing YARN/Mesos/Kubernetes Logs

While the Spark UI gives you a high-level overview, the actual logs from your cluster manager are where you find the smoking gun for exit code 137 . These logs contain the direct output from the resource manager about why a container was terminated. When a Spark executor or driver dies with exit code 137 , it’s typically because the resource manager (YARN, Mesos, Kubernetes) killed its container. You need to access the logs for the specific failed container. Here’s what to look for, depending on your environment:

• YARN: Use yarn logs -applicationId <application_id> to fetch all logs, or yarn logs -applicationId <application_id> -containerId <container_id> for a specific container. In the logs, search for phrases like “ Container killed by YARN for exceeding memory limits ”, “ OOMKilled ”, or messages indicating a specific memory threshold breach. YARN often provides details on the requested vs. actual memory usage at the time of the kill, which is immensely helpful for fine-tuning spark.executor.memory and spark.executor.memoryOverhead .
• Kubernetes: Use kubectl logs <pod-name> -n <namespace> . If a pod (which houses your driver or executor) was OOMKilled , kubectl describe pod <pod-name> will show State: Terminated , Reason: OOMKilled . The logs will then give you more details leading up to the kill. You’ll often see explicit messages from the Kubelet about the container exceeding its memory limits or requests defined in your Spark Kubernetes configuration. Understanding these messages is critical for adjusting your Kubernetes resource limits for your Spark pods and resolving the SparkUserAppException .
• Mesos: Check the Mesos agent logs on the host where the task ran. Look for messages related to container termination due to resource constraints. The exact message might vary, but the context will point to memory over-consumption.

These container logs are critical because they provide the definitive proof that the exit code 137 was indeed a memory-related kill, and sometimes even give you specific byte counts for requested vs. used memory. This direct feedback from the underlying infrastructure is the most reliable way to confirm an OOM scenario and guide your resource adjustments to fix the SparkUserAppException for good.

Monitoring System-Level Metrics

While Spark UI and container logs are excellent, sometimes exit code 137 can be a symptom of broader system-level issues, not just within your Spark application. This is where monitoring external system metrics comes in handy. If you have access to the underlying nodes (physical or virtual machines) where your Spark cluster is running, monitoring tools can provide crucial context. Look at:

• Total Node Memory Usage: Is the entire node close to 100% memory utilization? If so, even if your Spark containers are within their allocated limits, the host operating system might be under stress, leading to OOM Killer activating at a system level, potentially affecting other services or even Spark containers that are technically within their container-specific limits but are part of a larger, overallocated node. This scenario can result in SparkUserAppException due to an exit code 137 that’s harder to trace purely within Spark.
• Swap Space Usage: Heavy swapping (the OS moving data from RAM to disk) is a huge red flag. It indicates severe memory pressure, leading to drastically reduced performance and often pre-empting an OOM kill with exit code 137 . If you see swap space being heavily utilized, it means your nodes are simply under-provisioned for the total workload.
• CPU Usage and Disk I/O: While less directly related to exit code 137 , unusually high CPU usage or disk I/O could indirectly contribute to memory issues if the system becomes unresponsive or creates bottlenecks that lead to memory build-up. For example, if disk I/O is very slow, tasks might buffer more data in memory while waiting for I/O operations, consuming more RAM than anticipated.

Monitoring these system-level metrics helps you understand if your memory issues are localized to your Spark app or indicative of a larger infrastructure problem. It complements the Spark-specific diagnostics, giving you a holistic view and helping you address exit code 137 from all angles. Sometimes, a simple scaling up of your cluster nodes might be the underlying fix, rather than just tweaking Spark config parameters.

Best Practices to Prevent Spark Exit Code 137

Prevention is always better than cure, especially when it comes to the dreaded exit code 137 and SparkUserAppException . By adopting some best practices, you can significantly reduce the chances of your Spark applications crashing due to memory issues. It’s all about being smart with your resources and understanding how Spark works under the hood. Let’s make sure those memory errors become a thing of the past for your jobs, guys!

1. Incremental Resource Allocation: Don’t just throw arbitrary amounts of memory at your application from the get-go. Start with reasonable defaults for spark.driver.memory , spark.executor.memory , spark.executor.cores , and spark.executor.memoryOverhead . Then, incrementally increase these values while closely monitoring the Spark UI, logs, and system metrics. This iterative approach helps you find the sweet spot without over-provisioning resources and wasting cluster capacity. For example, if you start with 4GB executor memory and hit exit code 137 , try 6GB, then 8GB, and observe the GC Time and Memory Usage in the Spark UI. Always consider the total memory available on your cluster nodes and how many executors you plan to run simultaneously. It’s a balancing act: too little memory leads to exit code 137 , too much wastes money and resources. This careful, data-driven approach to resource allocation is a cornerstone of preventing SparkUserAppException related to memory constraints. Furthermore, remember that the total memory requested by an executor is spark.executor.memory + spark.executor.memoryOverhead . Ensure that this sum doesn’t exceed the physical memory available for an individual container on your cluster, especially in YARN or Kubernetes, where strict limits are enforced. Don’t forget that the number of cores per executor also dictates how much of that allocated memory is shared amongst parallel tasks, so a higher core count might necessitate a higher spark.executor.memory value to prevent individual task OOMs, even if the executor itself isn’t technically out of memory.

2. Efficient Data Handling and Transformations: Your code plays a massive role in memory consumption. Always strive for efficient data processing. Avoid actions like collect() on large DataFrames; instead, write results directly to persistent storage like S3, HDFS, or a database using `write.mode(