Fix: Corsica DRT Model Crashing Server - OutOfMemoryError

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Troubleshooting Corsica DRT Model Server Crashes: A Deep Dive into OutOfMemoryError

Hey everyone! Today, we're diving into a tricky issue: running the Corsica DRT (Demand Responsive Transit) model on a server and encountering unexpected shutdowns. Specifically, we'll be tackling the dreaded OutOfMemoryError. If you've been scratching your head trying to figure this out, you're in the right place. We'll break down the problem, analyze the logs, and explore potential solutions. Let's get started!

Understanding the Problem: Corsica DRT Model and Server Shutdowns

So, you're trying to run the Corsica DRT model in the background on your server, but it keeps crashing with an OutOfMemoryError. You've noticed that this only happens with the DRT model; other models run just fine. This is a classic scenario where the simulation's memory demands exceed what's available, leading to a server shutdown. The core of the issue revolves around the DRT model's computational intensity, which can be significantly higher than other simulation types. This intensity stems from the complex algorithms required to manage on-demand transportation, including vehicle routing, passenger assignment, and real-time adjustments. When these algorithms process large datasets or intricate scenarios, they can consume substantial memory resources.

To really understand this, let's think about what the DRT model is doing. It's simulating a dynamic system where vehicles are constantly being routed based on demand. This involves a lot of calculations and data storage, especially when dealing with a large number of agents (people) and vehicles. Each agent's journey, each vehicle's route, and the interactions between them all contribute to the memory footprint. Imagine trying to keep track of thousands of taxis and passengers in real-time – that's essentially what the DRT model is doing.

Moreover, the MATSim (Multi-Agent Transport Simulation) framework, often used for such simulations, can generate a large amount of data during a run. This data includes events, plans, and travel times, all of which need to be stored in memory. The larger and more complex the simulation, the more data is generated. This can quickly lead to memory exhaustion if the system isn't properly configured.

In essence, the OutOfMemoryError is a signal that the Java Virtual Machine (JVM) has run out of memory to allocate for the application. This usually means that the heap space, where Java objects are stored, has been filled up. The challenge, then, is to figure out why the DRT model is consuming so much memory and how to mitigate this.

Analyzing the Logs: Key Clues to the OutOfMemoryError

To effectively troubleshoot this issue, digging into the logs is crucial. The provided log snippets offer valuable insights into what's happening behind the scenes. The key lies in identifying the error messages and understanding the sequence of events leading to the crash. Let's dissect the relevant parts of the log:

2025-11-07T03:27:09,646 ERROR AbstractController:225 Mobsim did not complete normally! afterMobsimListeners will be called anyway.
java.lang.OutOfMemoryError: Java heap space

This is the primary indicator of the problem. The java.lang.OutOfMemoryError: Java heap space clearly points to a memory issue. The AbstractController error suggests that the simulation (Mobsim) failed to complete due to insufficient memory. This means that the simulation process was interrupted mid-run because it ran out of the memory it needed to operate.

2025-11-07T03:27:17,632 ERROR MatsimRuntimeModifications:76 Getting uncaught Exception in Thread org.eqasim.ile_de_france.RunIDFLVMTSimulation.main()
java.lang.OutOfMemoryError: Java heap space

This error message reinforces the previous one, indicating that the main simulation thread also encountered an OutOfMemoryError. The mention of org.eqasim.ile_de_france.RunIDFLVMTSimulation.main() suggests that the error occurred within the main simulation execution, further emphasizing the severity of the memory issue. It's like the engine of your car seizing up because it's run out of oil – the main process has stalled due to a lack of resources.

2025-11-07T03:27:17,632  INFO MatsimRuntimeModifications:80 S H U T D O W N   ---   start shutdown.
2025-11-07T03:27:17,632 ERROR MatsimRuntimeModifications:82 ERROR --- This is an unexpected shutdown!
2025-11-07T03:27:17,632 ERROR MatsimRuntimeModifications:85 Shutdown possibly caused by the following Exception:
java.lang.OutOfMemoryError: Java heap space

These lines confirm that the simulation shutdown was unexpected and directly caused by the OutOfMemoryError. The system recognized the critical error and initiated a shutdown to prevent further issues. This is a fail-safe mechanism to ensure that the server doesn't get into a permanently unstable state.

2025-11-07T03:27:27,456 ERROR DrtAnalysisControlerListener:456 writing output ... did not work; probably parameters were such that no such output was generated in the final iteration

These errors from DrtAnalysisControlerListener indicate that various output files could not be written. While these errors are a consequence of the OutOfMemoryError (the simulation didn't complete to generate the outputs), they also highlight the importance of the analysis components within the DRT model. The system tries to write a lot of detailed analysis files, which adds to the memory pressure.

2025-11-07T03:27:27,480 ERROR MatsimRuntimeModifications:91 Exception during shutdown:
java.lang.RuntimeException: java.nio.file.NoSuchFileException: ... 0.eqasim_drt_passenger_rides.csv

This error, occurring during shutdown, indicates that a file (0.eqasim_drt_passenger_rides.csv) could not be found. This is likely because the simulation didn't complete successfully due to the OutOfMemoryError, and the file was never created. This shows how a memory issue early in the process can cascade into other problems.

2025-11-07T03:28:09,645  INFO MemoryObserver:42 used RAM: 13276 MB  free: 16931 MB  total: 30208 MB
... (Repeated memory observations)

These lines provide a snapshot of memory usage. While they don't directly cause the error, they show that a significant amount of RAM is being used (13276 MB), with a substantial amount still free (16931 MB). However, the JVM's heap space might be configured to a smaller limit within this total RAM, leading to the OutOfMemoryError even though the server has more physical memory available. This highlights the distinction between the total system memory and the memory allocated to the Java process.

In summary, the logs paint a clear picture: the Corsica DRT model is running out of Java heap space, causing the simulation to crash and leading to various secondary errors during shutdown. The next step is to explore potential solutions to this memory bottleneck.

Solutions and Strategies to Tackle the OutOfMemoryError

Okay, so we've pinpointed the problem: the DRT model is hitting the OutOfMemoryError wall. Now, let's arm ourselves with strategies to break through it! There are several avenues we can explore, ranging from tweaking JVM settings to optimizing the model itself. Here’s a breakdown of the most effective solutions:

1. Increasing JVM Heap Space

The most straightforward approach is to increase the amount of memory allocated to the JVM. This gives the simulation more room to breathe and can often resolve the OutOfMemoryError. You've already attempted this with the JAVA_OPTS environment variable, which is a good start. Let's revisit that command and ensure it's set correctly:

JAVA_OPTS="-Xms40G -Xmx150G -XX:+UseG1GC -XX:+ParallelRefProcEnabled"

Here's what each part means:

  • -Xms40G: This sets the initial heap size to 40GB. The JVM will start with this amount of memory allocated.
  • -Xmx150G: This sets the maximum heap size to 150GB. The JVM can grow its memory usage up to this limit.
  • -XX:+UseG1GC: This enables the Garbage-First Garbage Collector (G1GC), which is designed for large heaps and aims to minimize pauses during garbage collection.
  • -XX:+ParallelRefProcEnabled: This enables parallel processing of reference objects, which can improve garbage collection performance.

Key Considerations:

  • Ensure Sufficient Physical Memory: Make sure your server actually has 150GB of RAM available. Setting -Xmx higher than the available memory will lead to other issues.
  • Optimal -Xms Value: Setting -Xms to a value close to -Xmx can prevent the JVM from frequently resizing the heap, which can be a performance bottleneck.
  • Alternative Garbage Collectors: While G1GC is generally recommended, you could experiment with other garbage collectors like Concurrent Mark Sweep (CMS) if you encounter issues. However, G1GC is usually the best choice for large heaps.

Actionable Steps:

  1. Double-check that the JAVA_OPTS environment variable is correctly set in your shell environment.
  2. Verify that your server has at least 150GB of RAM.
  3. Monitor memory usage during the simulation to see if the increased heap size is sufficient.

2. Analyzing Memory Usage with a Profiler

If simply increasing the heap size doesn't completely resolve the issue, it's time to dig deeper and understand how the memory is being used. A Java profiler can help you identify memory leaks, inefficient data structures, and other memory-hogging culprits.

Popular Profiling Tools:

  • VisualVM: A free tool bundled with the JDK, VisualVM provides a wealth of information about JVM performance, including memory usage, CPU usage, and thread activity.
  • JProfiler: A commercial profiler with a user-friendly interface and advanced features for memory and CPU profiling.
  • YourKit Java Profiler: Another commercial option known for its in-depth memory analysis capabilities.

Profiling Steps:

  1. Connect the Profiler: Start your chosen profiler and connect it to the running JVM process of your simulation.
  2. Monitor Memory Usage: Observe the heap usage over time. Look for patterns of increasing memory consumption that don't decrease, which could indicate a memory leak.
  3. Identify Memory-Intensive Objects: The profiler will show you which classes and objects are consuming the most memory. This is crucial for pinpointing areas in the code that need optimization.
  4. Analyze Garbage Collection: Examine the garbage collection activity. Frequent garbage collections can indicate that the heap is under pressure.

Example Scenario:

Let's say the profiler reveals that a particular data structure used in the DRT routing algorithm is consuming a large amount of memory. This might suggest that the data structure is inefficient or that it's storing more data than necessary. You can then focus your optimization efforts on this specific area.

3. Optimizing the DRT Model and Configuration

Sometimes, the issue isn't just the amount of memory, but how efficiently the model uses it. There are several ways to optimize the DRT model and its configuration to reduce memory consumption:

  • Reduce Scenario Size: If possible, try running the simulation with a smaller scenario (e.g., fewer agents, a smaller network). This will naturally reduce the memory footprint. You can then gradually increase the scenario size to find the limit.
  • Optimize Data Structures: Review the data structures used in the DRT model. Are there any that can be replaced with more memory-efficient alternatives? For example, using HashMap with a large initial capacity can avoid resizing overhead.
  • Control Data Retention: The simulation might be storing data that isn't needed for analysis. Identify and remove any unnecessary data retention to free up memory. This often involves tweaking configuration settings related to event handling and data output.
  • Adjust DRT Parameters: Certain DRT parameters, such as the number of vehicles, the fleet size, and the request acceptance criteria, can impact memory usage. Experiment with different parameter values to see if you can reduce memory consumption without significantly affecting simulation results.
  • Review Event Handling: MATSim simulations generate a large number of events. If you're not using all the event data, consider filtering or disabling certain event handlers to reduce memory overhead. This might involve adjusting the configuration to only record events that are critical for your analysis.

Practical Tips:

  • Start with the smallest possible scenario and gradually increase complexity.
  • Regularly review and clean up data structures.
  • Profile the application to identify memory hotspots.

4. MATSim Configuration Tweaks

The MATSim configuration plays a crucial role in memory management. Several configuration settings can be adjusted to optimize memory usage:

  • global Section: Check the global section in your MATSim configuration file. Settings like numberOfThreads can impact memory usage. Using too many threads can increase memory consumption due to thread-local data.
  • strategy Section: The choice of replanning strategies and their parameters can affect memory usage. Complex strategies might require more memory. Experiment with simpler strategies or adjust parameters like the maximum number of plans per agent.
  • qsim Section: The qsim (Queue Simulation) settings can impact memory. For example, the snapshotStyle setting determines how much simulation state is retained. Using a less detailed snapshot style can reduce memory usage.
  • controler Section: Settings related to output data and analysis can be adjusted. For example, you can control which events are written to the output file or disable certain analysis modules.

Example Configuration Snippet:

<module name="global">
 <param name="numberOfThreads" value="4"/> 
</module>

<module name="qsim">
 <param name="snapshotStyle" value="minimal"/>
</module>

<module name="controler">
 <param name="writeEventsInterval" value="0"/> <!-- Disable event writing -->
</module>

In this example, we've reduced the number of threads, used a minimal snapshot style in the qsim, and disabled event writing to reduce memory overhead.

5. Code-Level Optimizations

If all else fails, it might be necessary to dive into the code and perform more targeted optimizations. This requires a deeper understanding of the MATSim framework and the DRT model implementation.

  • Memory Leaks: Identify and fix any memory leaks. A memory leak occurs when objects are no longer needed but are still being referenced, preventing them from being garbage collected.
  • Inefficient Algorithms: Review the algorithms used in the DRT model, especially those related to routing and scheduling. Are there any opportunities to use more efficient algorithms or data structures?
  • Object Pooling: Consider using object pooling for frequently created and destroyed objects. Object pooling can reduce garbage collection overhead by reusing objects instead of creating new ones.

Example Scenario:

Suppose you identify a routing algorithm that creates a large number of temporary objects. You could refactor the code to reuse these objects or use a more memory-efficient algorithm.

Wrapping Up: Conquering the OutOfMemoryError

The OutOfMemoryError can be a frustrating roadblock, but by systematically analyzing the problem and applying the right solutions, you can overcome it. Remember, it's often a combination of factors that contribute to the issue, so a multi-pronged approach is usually the most effective. Start with the easiest solutions (like increasing heap size) and gradually move towards more complex optimizations if needed.

To recap, here’s a quick checklist of the strategies we’ve discussed:

  1. Increase JVM Heap Space: Use -Xms and -Xmx to allocate more memory.
  2. Analyze Memory Usage with a Profiler: Use tools like VisualVM or JProfiler to pinpoint memory bottlenecks.
  3. Optimize the DRT Model and Configuration: Reduce scenario size, optimize data structures, and control data retention.
  4. MATSim Configuration Tweaks: Adjust settings in the global, strategy, qsim, and controler sections.
  5. Code-Level Optimizations: Fix memory leaks, use efficient algorithms, and consider object pooling.

By implementing these strategies, you'll be well-equipped to tackle the OutOfMemoryError and successfully run your Corsica DRT model. Good luck, and happy simulating!