Single Failure Tolerance (1FT) for HCI Reliability: Myth or Fact?
Storage system reliability starts with properly managing the inevitable storage device failures. Surprisingly, despite the community’s rigorous study of the topic over decades, some popular enterprise systems remain under-configured and are unable to address the significant threat posed by Latent Sector Errors (LSEs). Back in 1988, the RAID paper by Patterson et al.  laid out the fundamental technologies for building a system that can tolerate a whole disk failure. In subsequent rigorous studies, a wealth of data was collected on disk-drive failures in the field from places like supercomputing centers , Google , NetApp [4,5,6], Nutanix , and Backblaze , and the implications on storage system design were considered. The key lessons from these studies are that disk failures may be more common than drive manufacturers’ expected annual failure rates (AFRs) [2,3] and that LSEs can have a profound impact on reliability and should be considered when designing a system [4,6].
This brief delves into the myth and the fact for one system design choice – Single Failure Tolerance (1FT) – in the context of data from the studies mentioned above. Our analysis shows that using 1FT with once-a-month disk scrubbing would imply an extremely high data-loss probability of 0.49% in one year or 1.95% over the 4-year life of a system. Less frequent scrubbing increases the data-loss probability to as high as 18.6% in the fourth year of a system’s life.
The Myth: 1FT Provides Sufficient Reliability
Single Failure Tolerance (1FT) is a product configuration commonly quoted by hyperconvergence vendors that can handle one disk failure. In this configuration, data is typically written to disks in chunks and each chunk is stored twice (on two different disks in the pool). When a drive fails, its chunks are rebuilt from the copies spread across the pool.1 Double Failure Tolerance (2FT) stores three copies of the chunk on three different disks, and data can be rebuilt from any of the copies.
Decades ago, reliability analysis of RAID systems centered on whether data could be rebuilt after a disk failure before a second disk failed. With hot spares, rebuilds could start immediately, and rebuilds could complete quickly. Declustered geometries and distributed spares further sped up
rebuild, so it seemed sufficient to handle one disk failure. But, as disk capacities have soared, and the industry has shifted to larger-capacity drive classes , a new problem emerged: an LSE could make the content needed for rebuilding data unreadable (see below).
The consequence is that data reconstruction fails, which results in at least some data loss, though not the loss of a whole disk. The chances of this happening are frighteningly high. Today, LSEs are a bigger threat to reliability than the chance of a second disk failure during rebuild. In the early 2000s, the industry shift to RAID-6, or double-fault tolerant geometries, was a response to this threat . Double-fault tolerance became standard in enterprise storage systems, not so much to protect against two total disk failures, but to protect against one disk failure and one LSE in a RAID stripe.
Does it really matter if a few chunks aren’t reconstructed properly? The answer is yes, absolutely. A recent study examined the impact of undetected or uncorrected errors on distributed systems like Cassandra, and it found that they are vulnerable to query errors and failures . It is a myth that one mirror is good enough for an enterprise storage system, no matter how fast you can rebuild a drive in a scaled-out manner.
Latent Sector Errors (LSEs)
Sectors on disk drives can become unreadable through a variety of causes, including media imperfections, stray particles, issues when the content was written, etc . These errors are called latent sector errors or simply bad sectors. The errors are called “latent” because they are hidden from the system until an attempt is made to read the data.
One of the authors of this brief co-authored an extensive study of LSEs at NetApp2  that found the following:
- 8.5% of disk drives3 developed LSEs within two years of use.
- The percentage of disk drives with LSEs also increased as the capacity of the drives increased. If the percentage of drives with errors was bad ten years ago for drive capacities, and the trend holds, the percentages will be even worse now.
- About 70% of the drives with at least one LSE have more than one LSE. That is, if a disk develops LSEs, it is very likely that many sectors are affected.
The significance of hitting LSEs during drive rebuild is not a recent discovery; it was identified as one of the primary driving factors necessitating two-drive failure protection more than a decade ago .
We use the following data points in calculating data loss probability:
- The storage system has 30 4TB drives in a single 1FT pool, and data is replicated in chunks across the entire pool. Rebuilds scale perfectly linearly in the pool. When the system is performing a rebuild, the data being rebuilt is read from the remaining 29 drives, and it’s written into spare capacity on each of the surviving drives.
- The chance of a disk drive failing in a year was 0.76% in the Nutanix dataset , 2-4% in the supercomputing dataset , about 2% in the Backblaze dataset , and as much as 8.6% in the third year of use in the Google dataset . We picked 2% as a reasonable “mid-point” for enterprise-grade drives.4
- The probability of a disk drive developing a latent sector error in 2 years is 8.5%. We estimated from the data in  that a drive with LSEs had an average of about 14 LSEs. While LSEs tend to
be clustered, more than 50% of them are more than 1 MB apart in logical address 5; so, we assume that they could affect 7 different chunks of data.
- One way to reduce the chances of encountering a latent sector error during rebuild is constantly “scrubbing” the disk drives, that is, reading and verifying the content and rebuilding lost sectors6. The reduction in data-loss probability depends on the scrubbing frequency. Scrubbing at very high frequency without affecting customer workloads is nearly impossible due to increasing drive capacities and the 24/7 nature of many workloads. We assumed that disks are scrubbed once a month. A full scrub of the 30-drive pool once a month would consume 45 MB/s disk bandwidth 24×77. Some HCI storage systems implement scrubbing 8.
Based on the assumptions, we arrived at these results (see Appendix for the detailed math):
- The probability of at least one drive in the pool failing in one year is 1-(1-0.02)30 = 45%.
- In the absence of disk scrubbing, the probability that we will encounter at least one LSE during rebuild is about 41%. In total, there is a 45% * 41% = 18.6% chance of data loss in the third year after LSEs have accumulated over time.
- With a full scrub of every sector (i.e., 120 TB of data) once a month, the probability that we will encounter at least one LSE during rebuild is about 1.1%. In total, there is a 45% * 1.1% =0.49% chance of data loss in a year or a 1.95% chance of data loss over the 4-year lifetime of the system with 1FT, even with scrubbing. That number is astonishingly high for an enterprise product.
The math here is conservative: the LSE data used in the above math is for drive sizes that were an order of magnitude smaller than they are now, and LSE rates are known to increase with drive size increase; the error rates also increase significantly as the number of drives in a single 1FT pool increases; errors are known to have correlations (e.g., we are not including the case where the system is rebuilding a large number of drives at the same time that the server hosting the drives goes down). Furthermore, it is entirely possible that many storage systems do not detect corrupt data during rebuild because of inadequate end-to-end and referential data integrity checks . Lack of such checks would hide data loss/corruption issues.
Storing three copies of data with 2FT (examples include RAID 6 – arrays, RF3 – Nutanix, FTT2 – VMware VSAN) or using erasure-coding techniques that tolerate two failures improves reliability significantly. To lose data in a system that tolerates two drive failures, there must be either three simultaneous drive failures, two drive failures and an LSE, or one drive failure and LSEs in both redundant copies of the same chunk. All of these are extremely improbable events, and the chance of data loss is reduced by many orders of magnitude .
In conclusion, IT departments should be very wary of solutions using 1FT or similar offerings that handle only one drive failure, not because a second drive might fail before reconstruction completes, but because there is a good chance of discovering a Latent Sector Error (LSE) during reconstruction. Such an LSE can cause reconstruction of a portion of the data to fail, resulting in data loss or corruption. Systems need to be able to handle one drive failure plus one LSE. If they can’t, the probability of data loss is simply too high: 1.95% chance of data loss over system lifetime even with once-a-month scrubbing of all data. Infrastructure purchasers should insist on systems configured with 2FT (or use erasure coding that supports 2-drive failure tolerance). Our findings are in line with the qualitative examination of the topic by industry analyst Gartner; its report on HCI underscores the same data-loss concerns that we discuss in this brief, stating, “Data loss can only be prevented by using higher protection levels.”  Anything less is rolling the dice. Two nines of durability is just not good enough.
 Patterson et al. A case for redundant arrays of inexpensive disks (RAID). Proceedings of SIGMOD ‘88.
 Schroeder and Gibson. Disk failures in the real world: What does an MTTF of 1,000,000 hours mean to you? Proceedings of FAST’07.
 Pinheiro et al. Failure Trends in a Large Disk Drive Population. Proceedings of FAST’07.
 Bairavasundaram et al. An Analysis of Latent Sector Errors in Disk Drives. Proceedings of SIGMETRICS’07.
 Bairavasundaram et al. An Analysis of Data Corruption in the Storage Stack. Proceedings of FAST’08.
 Schroeder et al. Understanding Latent Sector Errors and How to Protect Against Them. Proceedings of FAST’10.
 Cano et al. Characterizing Private Clouds: A Large-Scale Empirical Analysis of Enterprise Clusters. Proceedings of SoCC’16.
 Andy Klein (2017). Hard Drive Stats for Q2 2017. Retrieved September 12, 2017, from https://www.backblaze.com/blog/hard-drive-failure-stats-q2-2017/
 Anderson et al. More than an interface – SCSI vs. ATA. Proceedings of FAST’03.
 Corbett et al. Row-Diagonal Parity for Double Disk Failure Correction. Proceedings of FAST’04.
 Ganesan et al. Redundancy Does Not Imply Fault Tolerance: Analysis of Distributed Storage Reactions to Single Errors and Corruptions. Proceedings of FAST’17.
 Poitras (2017). The Nutanix Bible. Retrieved August 25, 2017, from https://nutanixbible.com/
 Krioukov et al. Parity Lost and Parity Regained. Proceedings of FAST’08.
 Jerry Rozeman. (July 26, 2017). Key Differences Between Nutanix, SimpliVity and VxRail HCIS Appliances, Part 2: Data Protection, Capacity Optimization and Failure Analysis. Gartner Report, ID: G00334429.
Appendix: Detailed Math
1 This setup is similar to RAID-1, except that the copies are spread across multiple nodes. One could also have an erasure-coded variant that is similar to RAID-5, but it tolerates just a single disk failure as well.
2 The study won the 10-year Test-of-Time Award from ACM SIGMETRICS, with many follow-on studies that included additional results, and new techniques for handling disk failures and latent sector errors.
3 These numbers are for “nearline” or enterprise-quality SATA disk drives, the kind used widely in the industry now.
4 Given the diverse datasets and in the interest of brevity, we used some approximations in picking error probabilities. Drive-failure probabilities are dependent on factors such as the specific drive models, batches of drives, the age of the drives, etc. Picking 2% has the
benefit of better matching the studies using enterprise-grade drives [2,6].
5 The 50% estimate is conservative. The study  simply reflects the smallest distance (not the typical distances between errors that we need for the math here); that is, if there are two clusters of two LSEs each, we leaned towards counting it as one chunk affected instead of two.
6 The other way to find an LSE is when reading data to serve user requests; however, a significant part of the data is only rarely read by workloads, and scrubbing is usually the main method for detecting LSEs .
7 Scrubbing once a week would consume 198 MB/s 24×7, which would significantly affect regular workloads.
8 The Nutanix Bible  states that scrubbing is performed when the disk utilization is not high, and it does not indicate a scrubbing frequency.
9 It is 1/N and not 1/(N-1) since 1 drive worth of capacity is needed as spare space for rebuilding.
10 In this formula, we treated L as if it is evenly spread throughout the two years for average results; the first year would be better than our numbers indicate, and the second year would be worse because LSE rates increase with age.
About the Authors
Lakshmi N. Bairavasundaram is a Member of Technical Staff at Datrium. Lakshmi has worked in the file and storage systems domain for the last 14 years, including a Ph.D. from the University of Wisconsin-Madison on the topic of disk failures, and working at NetApp and Datrium. He has received four Best Paper awards for his conference publications, and his paper on Latent Sector Error characteristics received the ACM SIGMETRICS 10-year Test-of-Time Award.
Zhe Wang is a Member of Technical Staff at Datrium, working on the storage pool. Before that, he worked on content-based search systems at Princeton University, where he received his Ph.D. His paper on efficient indexing for similarity search received the VLDB 10-year Test-of-Time Award.
R. Hugo Patterson is the CTO, VP of Engineering, and Co-Founder at Datrium. Prior to Datrium, Hugo was an EMC Fellow serving as CTO of the EMC Backup Recovery Systems Division, and the Chief Architect and CTO of Data Domain (acquired by EMC in 2009), where he built the first deduplication storage system product. Before that, he was the engineering lead at NetApp, developing SnapVault, the first snap-and-replicate disk-based backup product. Hugo has a Ph.D. from Carnegie Mellon. His paper on snap-and-replicate received the USENIX FAST 10-year Test-of-Time Award.