How to Fix MongoDB Segmentation Fault on Azure VM

Troubleshooting Guide: MongoDB Segmentation Fault on Azure VM

As a Senior DevOps Engineer, encountering a Segmentation Fault (or SIGSEGV) from your mongod process is a critical incident that demands immediate attention. On an Azure Virtual Machine, this often points to specific resource constraints or configuration mismatches that are common in cloud environments. This guide will help you diagnose and resolve this issue professionally and directly.

1. The Root Cause: Why MongoDB Segfaults on Azure VMs

A segmentation fault indicates an attempt by a program (mongod in this case) to access a memory location that it is not allowed to access, or to access it in an illegal way. For MongoDB on an Azure VM, the common culprits typically fall into these categories:

  • Insufficient System Resources:
    • RAM Exhaustion: MongoDB is memory-intensive. If the VM is undersized, or if its working set exceeds available RAM, the kernel might aggressively swap, leading to performance degradation and eventually mongod crashing due to memory access issues.
    • Disk I/O Bottlenecks: Azure’s default disk options (Standard HDD/SSD) might not provide the necessary IOPS and throughput for demanding MongoDB workloads. High I/O wait times, especially during writes, can cause mongod to mismanage its internal buffers and potentially corrupt memory structures, leading to a crash.
    • Ephemeral OS Disk Usage: Installing MongoDB data files (dbPath) on the ephemeral OS disk (which is often smaller, less performant, and prone to data loss on VM deallocation/reboot) is a common mistake that can lead to data corruption and segmentation faults.
  • Corrupt Data or Indexes: If mongod crashes mid-write, or if there’s an underlying disk issue, data files or indexes can become corrupted. When mongod attempts to read or write to these corrupt structures, it can trigger a SIGSEGV.
  • System Limits (ulimits): MongoDB requires a large number of open file descriptors (nofile) and user processes (nproc). Default Linux ulimit settings on some Azure VM images might be too low, preventing mongod from operating correctly under load.
  • Kernel Parameters: Default kernel settings (vm.swappiness, dirty_ratio, dirty_expire_centisecs) might not be optimized for database workloads, leading to suboptimal memory management and potential issues under stress.
  • MongoDB Software Bugs: While less common in stable releases, specific versions or interactions with the underlying OS kernel can expose bugs within the MongoDB daemon itself.
  • Storage Engine Configuration: Incorrect WiredTiger cache sizing or other storage engine misconfigurations can lead to memory pressure.

2. Quick Fix (CLI)

Before diving deep, perform these immediate checks and actions:

  1. Examine MongoDB Logs: The mongod.log is your primary source of truth. Look for Segmentation fault messages, stack traces, or any other errors leading up to the crash.

    sudo tail -n 200 /var/log/mongodb/mongod.log | grep -iE "segmentation fault|SIGSEGV|exception|error"
# Alternatively, view the entire log
sudo less /var/log/mongodb/mongod.log

    Look for clues about what MongoDB was doing immediately before the crash.

  2. Check System Resources:

    • Memory Usage: 
      free -h
# Look for high `used` memory, low `available`, and significant `swap` usage.
    • Disk Space: 
      df -h /path/to/mongodb/data # Replace with your actual dbPath
# Ensure there's ample free space.
    • Disk I/O (during periods of activity): 
      iostat -xz 1 # Install if missing: sudo apt install sysstat or sudo yum install sysstat
# Look at %util, r/s, w/s, rkB/s, wkB/s, and especially avgrq-sz and avgqu-sz for the disk where MongoDB data resides. High %util and avgqu-sz can indicate I/O bottlenecks.
    • CPU Usage: 
      htop # Or top - If CPU is consistently high, it might point to workload issues.

  3. Check ulimit for the mongod process: First, identify the mongod PID (if it’s running, otherwise check systemd unit):

    ps aux | grep mongod | grep -v grep
# Let's assume PID is 12345
cat /proc/12345/limits

    Alternatively, check the limits defined in the systemd service unit or /etc/security/limits.conf.

  4. Attempt a Controlled Restart: If the crash was transient, a clean restart might resolve it temporarily.

    sudo systemctl stop mongod
sudo systemctl start mongod
sudo systemctl status mongod

  5. Data Repair (Last Resort / If logs indicate corruption): WARNING: This operation can be destructive if not used carefully and should only be attempted after backing up your data and only if logs explicitly point to corruption issues. mongod --repair rebuilds indexes and may remove corrupted data.

    # 1. STOP MONGODB
sudo systemctl stop mongod

# 2. (CRITICAL) BACKUP YOUR DATA DIRECTORY
sudo cp -rp /path/to/mongodb/data /path/to/backup/mongodb_data_$(date +%F_%H%M)

# 3. RUN REPAIR (replace /path/to/mongodb/data with your actual dbPath)
sudo mongod --repair --dbpath /path/to/mongodb/data

# 4. RESTART MONGODB
sudo systemctl start mongod

3. Configuration Check

Review and modify the following configuration files and settings on your Azure VM:

  1. MongoDB Configuration (/etc/mongod.conf or similar):

    • storage.dbPath: Crucial. Ensure this points to a dedicated Azure data disk (e.g., /mnt/mongodb/data), not the OS disk (/ or /var). The data disk should be Premium SSD or Ultra Disk for optimal performance.
    • systemLog.path: Also ensure logs are on a persistent disk, ideally a data disk.
    • storage.journal.enabled: Should be true (default). This ensures data integrity on crashes.
    • wiredTiger.engineConfig.cacheSizeGB: Configure this based on your VM’s RAM. A common recommendation is 50% of available RAM, but never more than 80% to leave room for the OS and other processes. 
      storage:
  dbPath: /mnt/mongodb/data
  journal:
    enabled: true
  wiredTiger:
    engineConfig:
      cacheSizeGB: <Calculated_Value_GB> # e.g., 8 for a 16GB RAM VM
systemLog:
  destination: file
  path: /mnt/mongodb/log/mongod.log
  logAppend: true

  2. System Limits (ulimits): For systemd managed services (common on modern Linux distributions like Ubuntu, RHEL/CentOS):

    • Create a systemd override file:

      sudo systemctl edit mongod.service

      Add the following lines (adjust values as needed; 64000 is a common recommendation for nofile):

      [Service]
LimitNOFILE=64000
LimitNPROC=64000
# For MongoDB Enterprise, if using mlockall to prevent swapping
# LimitMEMLOCK=infinity

      Save and exit.

    • Reload systemd and restart MongoDB:

      sudo systemctl daemon-reload
sudo systemctl restart mongod

    • Verify: Check /proc/<mongod_pid>/limits again.

    Alternative (and older method) via /etc/security/limits.conf:

    sudo nano /etc/security/limits.conf

    Add (or ensure) these lines for the mongodb user:

    mongodb soft nofile 64000
mongodb hard nofile 64000
mongodb soft nproc 64000
mongodb hard nproc 64000
# mongodb soft memlock unlimited # If using mlockall
# mongodb hard memlock unlimited # If using mlockall

    Note: For limits.conf to take effect for a systemd service, UsePAM=yes needs to be enabled in /etc/systemd/system/mongod.service or its override, and PAM modules configured correctly. The systemctl edit method is generally more reliable for services.

  3. Kernel Parameters (/etc/sysctl.conf): Optimize for database workloads.

    sudo nano /etc/sysctl.conf

    Add or modify:

    # Disable swappiness for MongoDB
vm.swappiness=1

# Optimize dirty page caching (adjust based on RAM and workload)
vm.dirty_ratio=15             # Percentage of system memory that can be filled with dirty pages (default is often 20)
vm.dirty_background_ratio=5   # Percentage of system memory that can be filled with dirty pages before background flush starts (default is often 10)
vm.dirty_expire_centisecs=10000 # How long dirty pages can live in cache before being written to disk (100 seconds)
vm.dirty_writeback_centisecs=1500 # How often kupdate flushes dirty pages (15 seconds)

# Increase maximum file descriptors
fs.file-max=200000

    Apply changes:

    sudo sysctl -p

  4. Azure Data Disk Mounting Options (/etc/fstab): Ensure your MongoDB data disk is mounted with optimal options.

    sudo nano /etc/fstab

    Example for xfs filesystem (recommended for MongoDB) and Azure disks:

    # Example for an XFS disk mounted at /mnt/mongodb
UUID=<YOUR_DISK_UUID> /mnt/mongodb xfs defaults,noatime,nofail 0 2
    • noatime: Prevents updating file access times, reducing I/O operations.
    • nofail: Allows the system to boot even if this disk mount fails.
    • For ext4, you might use defaults,noatime,journal_async_commit.

4. Verification

After applying changes, rigorously verify the stability and performance of your MongoDB instance:

  1. Restart MongoDB:

    sudo systemctl stop mongod
sudo systemctl start mongod
sudo systemctl status mongod

    Ensure it starts without errors.

  2. Monitor Logs Continuously:

    sudo tail -f /var/log/mongodb/mongod.log

    Look for any warnings or errors.

  3. Connect and Query: Connect to your MongoDB instance using the mongo shell or your application. Perform some typical queries to ensure basic functionality.

    mongo
> show dbs
> db.adminCommand({ ping: 1 })

  4. Monitor System Resources (Post-Configuration): Keep an eye on free -h, htop, and iostat -xz 1 for a sustained period, especially during peak load.

    • Check if swap usage remains low or non-existent.
    • Ensure disk I/O metrics are within acceptable limits for your Azure disk type.
    • Verify ulimit values for the mongod process are now correctly applied.

  5. Load Testing (If Applicable): If possible, run your standard load tests or observe your application’s interaction with MongoDB under typical traffic patterns to confirm stability.

By systematically addressing these potential causes, you should be able to identify and resolve the MongoDB Segmentation Fault on your Azure VM, ensuring a more robust and reliable database deployment.

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