Optimizing persistent disk performance


Persistent disks give you the performance described in the disk type chart if the VM drives usage that is sufficient to reach the performance limits. After you size your persistent disk volumes to meet your performance needs, your app and operating system might need some tuning.

In the following sections, we describe a few key elements that can be tuned for better performance and how to apply some of them to specific types of workloads.

I/O queue depth

  • Use a high I/O queue depth

    Persistent disks are network-attached storage devices, so they provide very high IOPS and throughput, but latency is higher compared to physically attached disks like traditional hard drives and local SSDs. To reach the maximum IOPS and throughput limits of your persistent disk, you must issue I/O requests with enough parallelism that latency does not bottleneck your application. Tune the settings on your operating system or application so that you're using an I/O queue depth of 32 or higher.

    Suppose the latency of your SSD persistent disk is 800 microseconds. If you issue I/O operations sequentially, you can reach at most 1,250 IOPS:

    800 microseconds per 1 I/O = 1 microsecond per 0.00125 I/Os = 1,250 I/Os per second

    This is far below the 30,000 IOPS maximum limit of the disk.

I/O size

  • Use large I/O size

    To ensure IOPS limits and latency don't bottleneck your application performance, use a minimum I/O size of 256 KB or higher.

    Use large stripe sizes for distributed file system applications. A random I/O workload using large stripe sizes (4 MB or larger) achieves great performance on standard persistent disks due to how closely the workload mimics multiple sequential stream disk access.

  • Make sure your application is generating enough I/O

    Make sure your application is generating enough I/Os to fully utilize the IOPS and throughput limits of the disk. To better understand your workload I/O pattern, review persistent disk usage and performance metrics in Cloud Monitoring.

  • Make sure there is enough available CPU on the instance that is generating the I/O

    If your VM instance is starved for CPU, your app won't be able to manage the IOPS described earlier. We recommend that you have one available CPU for every 2,000–2,500 IOPS of expected traffic.

Limit heavy I/O loads to a 50 TB span

Heavy I/O loads achieve maximum performance when limited to a 50 TB span. Spans on separate persistent disks that add up to 50 TB or less can be considered equal to a single 50 TB span for performance purposes. A span refers to a contiguous range of logical block addresses on a single physical disk.

Disable lazy initialization and enable DISCARD commands

Persistent disks support DISCARD or TRIM commands, which allow operating systems to inform the disks when blocks are no longer in use. DISCARD support allows the OS to mark disk blocks as no longer needed, without incurring the cost of zeroing out the blocks.

On most Linux operating systems, you enable DISCARD when you mount a persistent disk to your instance. Windows Server 2012 R2 instances enable DISCARD by default when you mount a persistent disk.

Enabling DISCARD can boost general runtime performance, and it can also speed up the performance of your disk when it is first mounted. Formatting an entire disk volume can be time consuming, so "lazy formatting" is a common practice. The downside of lazy formatting is that the cost is often then paid the first time the volume is mounted. By disabling lazy initialization and enabling DISCARD commands, you can get fast format and mount.

  • Disable lazy initialization and enable DISCARD during format by passing the following parameters to mkfs.ext4:

    -E lazy_itable_init=0,lazy_journal_init=0,discard
    

    The lazy_journal_init=0 parameter does not work on instances with CentOS 6 or RHEL 6 images. For those instances, format persistent disks without that parameter.

    -E lazy_itable_init=0,discard
    
  • Enable DISCARD commands on mount by passing the following flag to the mount command:

    -o discard
    

Persistent disks work well with the discard option enabled. However, you can optionally run fstrim periodically in addition to, or instead of using the discard option. If you do not use the discard option, run fstrim before you create a snapshot of your disk. Trimming the file system lets you create smaller snapshot images, which reduces the cost of storing snapshots.

Readahead cache

To improve I/O performance, operating systems employ techniques such as readahead, where more of a file than was requested is read into memory with the assumption that subsequent reads are likely to need that data. Higher readahead increases throughput at the expense of memory and IOPS. Lower readahead increases IOPS at the expense of throughput.

On Linux systems, you can get and set the readahead value with the blockdev command:

$ sudo blockdev --getra /dev/[DEVICE_ID]
$ sudo blockdev --setra [VALUE] /dev/[DEVICE_ID]

The readahead value is <desired_readahead_bytes> / 512 bytes.

For example, for an 8-MB readahead, 8 MB is 8388608 bytes (8 * 1024 * 1024).

8388608 bytes / 512 bytes = 16384

You set blockdev to 16384:

$ sudo blockdev --setra 16384 /dev/[DEVICE_ID]

Free CPUs

Reading and writing to persistent disk requires CPU cycles from your VM. To achieve very high, consistent IOPS levels, you must have CPUs free to process I/O.

IOPS-oriented workloads

Databases, whether SQL or NoSQL, have usage patterns of random access to data. Google recommends the following values for IOPS-oriented workloads:

  • I/O queue depth values of 1 per each 400–800 IOPS, up to a limit of 64 on large volumes

  • One free CPU for every 2,000 random read IOPS and 1 free CPU for every 2,500 random write IOPS

Lower readahead values are typically suggested in best practices documents for MongoDB, Apache Cassandra, and other database applications.

Throughput-oriented workloads

Streaming operations, such as a Hadoop job, benefit from fast sequential reads, and larger I/O sizes can increase streaming performance.

  • Use an I/O size of 256 KB or larger.

  • On standard persistent disks, use 8 or more parallel sequential I/O streams when possible. Standard persistent disks are designed to optimize I/O performance for sequential disk access, similar to a physical HDD hard drive.

  • Make sure your app is optimized for a reasonable temporal data locality on large disks

    If your app accesses data that is distributed across different parts of a disk over a short period of time (hundreds of GB per vCPU), you won't achieve optimal IOPS. For best performance, optimize for temporal data locality, weighing factors like the fragmentation of the disk and the randomness of accessed parts of the disk.

  • On SSD persistent disks, make sure the I/O scheduler in the OS is configured to meet your specific needs

    On Linux-based systems, you can set the I/O scheduler to noop to achieve the highest number of IOPS on SSD-backed devices.

Review persistent disk performance metrics

You can review persistent disk performance metrics in Cloud Monitoring, Google Cloud's integrated monitoring solution.

Several of these metrics are useful for understanding if and when your disks are being throttled. Throttling is intended to smooth out bursty I/Os. With throttling, bursty I/Os can be spread over a period of time such that the performance limits of your disk can be met but not exceeded at any given instant.

To learn more, see Reviewing persistent disk performance metrics.

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