FIO Hangs With 112 HDDs: Troubleshooting & IO Limits

by Felix Dubois 53 views

Hey guys,

Ever run into a situation where your storage testing tool, FIO, just throws its hands up in the air and says, "I'm out!"? Well, that's exactly what happened when we tried to push 112 HDDs to their limits. Let's break down the issue, the environment, and how we're tackling this performance puzzle.

The Bug: FIO Job Startup Hung

So, here's the deal: we've got this massive system loaded with 112 hard drives. Our goal? To see how it handles some serious I/O action using FIO (Flexible I/O Tester). We're talking about simulating real-world workloads to ensure everything runs smoothly.

When we fired up FIO with the following parameters, things went south:

fio --name=${job_name} --filename=/dev/"$DEV" --ioengine=libaio --direct=1 --thread=1 --numjobs=32 --iodepth=32 --rw=write --bs=128k --runtime=3600 --time_based=1 --size=100% --group_reporting --log_avg_msec=1000 --bwavgtime=1000

Basically, we're telling FIO to run 32 jobs (--numjobs=32), each with an I/O depth of 32 (--iodepth=32), writing 128KB blocks (--bs=128k) to the drives for an hour (--runtime=3600 --time_based=1). Sounds like a solid test, right?

Wrong. FIO just sat there, blinking, and then threw this error:

fio: job startup hung? exiting.

Ouch. It's like trying to start a car with a dead battery. Nothing's happening.

But here's the kicker: when we dialed back the number of jobs to 16 (--numjobs=16), everything worked like a charm. So, what gives? Is there a limit to how many I/O operations FIO can handle concurrently? That's the million-dollar question we're trying to answer.

Diving Deep into FIO's I/O Handling

To really understand this, let's break down what these parameters mean and how they impact FIO's operation.

  • --numjobs: This is the number of independent I/O streams FIO will run concurrently. Think of it as the number of workers you've got hammering away at the drives.
  • --iodepth: This is the number of I/O operations each job can have outstanding at any given time. It's like how many tasks each worker can juggle simultaneously.
  • --ioengine=libaio: This specifies the I/O engine FIO uses. libaio is an asynchronous I/O engine, meaning it can queue up multiple I/O requests without waiting for each one to complete. This is crucial for high-performance testing.
  • --direct=1: This tells FIO to bypass the operating system's buffer cache and write directly to the drives. This gives us a more accurate picture of the drive's performance.
  • --thread=1: This means each job runs in its own thread, allowing for parallel execution.

So, when we set --numjobs=32 and --iodepth=32, we're asking FIO to manage 32 * 32 = 1024 outstanding I/O operations. That's a lot! It's possible that our system, or FIO itself, is hitting some kind of limit.

Potential Bottlenecks: Where Could Things Be Going Wrong?

  1. System Resource Limits: The operating system might have limits on the number of file descriptors or threads that can be open simultaneously. Each FIO job requires file descriptors for the drives and threads for execution. If we exceed these limits, things can grind to a halt.
  2. libaio Limits: The libaio engine itself might have limitations on the number of concurrent I/O operations it can handle. While it's designed for high performance, there could be internal constraints we're hitting.
  3. Hardware Limitations: Our Broadcom SAS Expander and HBA (Host Bus Adapter) might have their own limits on the number of I/O requests they can process concurrently. These components are the gatekeepers between the CPU and the drives, so they're critical to performance.
  4. Drive Saturation: It's also possible that we're simply overwhelming the drives themselves. Even though we have 112 HDDs, each one has a finite bandwidth capacity. If we're pushing too much data, the drives might become saturated, leading to performance bottlenecks.

Environment: The Hardware and Software Stack

To get a clearer picture, let's look at the environment where this is happening:

  • Broadcom SAS Expander: 11.06.06.03
  • HBA: 9600-24i FW: 8.13.2.0 Driver: 8.13.1.0.0
  • CPU Type: Intel(R) Xeon(R) 6519P-C
  • Memory: Samsung M321R8GA0EB2-CCPWC, 64GB * 32 (That's a whopping 2TB of RAM!)
  • HDD Type: Seagate ST32000NM004K-3U FW: SE02 * 112 (2TB drives, so we've got plenty of storage)
  • OS: Debian 10.11
  • FIO Version: 4.40

We've got a pretty beefy system here. The Intel Xeon 6519P-C is a powerful CPU, and 2TB of RAM should be more than enough. The Seagate drives are enterprise-grade, designed for heavy workloads. So, the hardware should be able to handle a lot.

Key Components and Their Potential Impact

  1. HBA and SAS Expander: These are crucial for connecting the drives to the system. The HBA acts as the interface between the CPU and the drives, while the SAS expander allows us to connect a large number of drives to a single HBA. If these components are not performing optimally, they can become a bottleneck.
  2. Operating System: Debian 10.11 is a solid, stable OS, but it's essential to ensure it's configured correctly for high-performance I/O. This includes things like kernel parameters, file system settings, and resource limits.
  3. FIO Version: FIO 4.40 is a relatively recent version, so it should have the latest features and bug fixes. However, it's always possible that there's a bug in FIO itself that's causing the issue.

Reproduction Steps: How to Recreate the Issue

If you want to try and reproduce this yourself, here's the magic formula:

  1. Set up a system with a similar configuration: lots of HDDs, a SAS expander, and a decent HBA.
  2. Install Debian 10.11 (or a similar Linux distribution).
  3. Install FIO 4.40.
  4. Run the following command:
fio --name=${job_name} --filename=/dev/"$DEV" --ioengine=libaio --direct=1 --thread=1 --numjobs=32 --iodepth=32 --rw=write --bs=128k --runtime=3600 --time_based=1 --size=100% --group_reporting --log_avg_msec=1000 --bwavgtime=1000

If you see the "fio: job startup hung? exiting." error, you've successfully reproduced the bug.

Minimizing the Command for Clarity

To really nail down the issue, it's crucial to minimize the command to the bare essentials. This helps us isolate the problem and avoid red herrings.

For example, we could try removing the logging options (--log_avg_msec, --bwavgtime) to see if they're contributing to the problem. We could also reduce the runtime (--runtime) to a smaller value to speed up testing.

The goal is to find the simplest command that still triggers the bug. This makes it much easier to debug.

Digging Deeper: Troubleshooting Strategies

So, we've got a bug, we've got a reproduction case, and we've got a good understanding of the environment. Now, it's time to roll up our sleeves and start troubleshooting.

Here are some strategies we're considering:

  1. System Resource Monitoring: We'll use tools like top, htop, and iostat to monitor system resource usage (CPU, memory, I/O) while FIO is running. This can help us identify if we're hitting any resource limits.
  2. File Descriptor Limits: We'll check the operating system's file descriptor limits and make sure they're set high enough. The ulimit -n command can show the current limit, and we can adjust it in /etc/security/limits.conf if needed.
  3. libaio Configuration: We'll investigate if there are any configurable parameters for libaio that might affect its performance. This might involve digging into the libaio documentation or source code.
  4. HBA and SAS Expander Firmware: We'll check for firmware updates for the HBA and SAS expander. Sometimes, firmware bugs can cause performance issues, and updates can resolve them.
  5. Drive-Level Monitoring: We'll use tools like smartctl to monitor the health and performance of the individual drives. This can help us identify if any drives are acting as bottlenecks.
  6. FIO Debugging: We'll explore FIO's debugging options to get more insight into what's happening internally. This might involve using the --debug flag or looking at FIO's log files.
  7. strace: The strace utility is our friend. Monitoring system calls can shed light on where FIO is getting stuck.
  8. Reduce the Number of Devices: Try running the test with fewer drives to see if the issue is related to the sheer number of devices.

The Scientific Method: Testing Hypotheses

Troubleshooting is all about forming hypotheses and testing them systematically. We'll change one variable at a time, run the test, and see if the results change.

For example, we might hypothesize that the number of outstanding I/O operations (numjobs * iodepth) is the limiting factor. To test this, we could try reducing --iodepth while keeping --numjobs at 32. If the test runs successfully, that would support our hypothesis.

How Much IO Does FIO Support? The Quest for the Answer

So, the core question remains: how much I/O can FIO really handle? Is there a magic number of jobs or I/O depth that we should stick to?

The answer, as with many things in the world of performance tuning, is "it depends." It depends on:

  • Your Hardware: The capabilities of your CPU, memory, storage devices, HBAs, and SAS expanders all play a role.
  • Your Operating System: The OS's resource limits and I/O scheduling algorithms can impact performance.
  • Your Workload: The type of I/O operations you're performing (reads vs. writes, sequential vs. random), the block size, and the I/O depth all matter.
  • FIO's Configuration: The I/O engine, the number of jobs, the I/O depth, and other FIO parameters can all influence performance.

Benchmarking: Finding the Sweet Spot

The best way to find the optimal I/O configuration for your system is to benchmark. Start with a baseline configuration and then gradually increase the load until you hit a bottleneck. This will help you identify the limiting factors in your system.

We'll be experimenting with different values for --numjobs and --iodepth to see how they affect performance. We'll also be looking at metrics like throughput (MB/s), latency (ms), and CPU utilization to get a comprehensive picture.

Conclusion: The Journey to High-Performance Storage

This FIO "job startup hung" issue is a classic example of a performance puzzle. It's a reminder that optimizing storage performance is not just about throwing hardware at the problem; it's about understanding the entire system and how its components interact.

We're committed to getting to the bottom of this. We'll keep you updated on our progress as we dig deeper, test hypotheses, and explore potential solutions.

Stay tuned, guys! We're on a quest for high-performance storage, and we're not giving up until we find the answer.