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For database systems, data durability is not optional—it's the fundamental promise that separates a database from a cache. RAID's primary purpose is ensuring that hardware failures, which are inevitable in any storage system, do not result in data loss.
This page examines RAID reliability with mathematical rigor, moving beyond simple 'tolerates N failures' statements to understand the probabilistic realities of storage system reliability. We'll analyze failure models, calculate expected reliability, and explore the real-world factors that determine whether your data survives hardware failures.
By the end of this page, you will understand how to calculate and compare RAID reliability mathematically, recognize the limitations of simple fault tolerance metrics, and design storage systems that meet specific durability requirements for database applications.
Understanding RAID reliability requires understanding how drives fail. The classical model assumes constant failure rates, but real-world data reveals more complex patterns.
Mean Time Between Failures (MTBF):
Manufacturers specify MTBF as a reliability metric. An MTBF of 1,000,000 hours doesn't mean the drive will last 114 years—it represents the average time between failures across a large population of drives, assuming most failures occur early (infant mortality) or late (wear-out).
Annualized Failure Rate (AFR):
AFR is more intuitive: the probability a drive fails within one year. For enterprise drives:
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// Converting between MTBF and AFR // MTBF to AFR:// AFR = 1 - e^(-8760/MTBF) [8760 = hours per year]// For small AFR, approximately: AFR ≈ 8760/MTBF // Examples:// MTBF = 1,000,000 hours → AFR ≈ 8760/1,000,000 ≈ 0.876%// MTBF = 500,000 hours → AFR ≈ 8760/500,000 ≈ 1.75%// MTBF = 250,000 hours → AFR ≈ 8760/250,000 ≈ 3.5% // AFR to MTBF:// MTBF ≈ 8760/AFR // Examples:// AFR = 2% → MTBF ≈ 8760/0.02 = 438,000 hours// AFR = 5% → MTBF ≈ 8760/0.05 = 175,200 hours // Key insight: Manufacturer MTBFs are measured in idealized conditions// Real-world AFRs are typically 2-6× higher than specifications suggestThe Bathtub Curve:
Drive failures follow a 'bathtub curve' pattern:
Infant Mortality (0-6 months): Manufacturing defects cause early failures. AFR may be 2-3× average.
Useful Life (6 months - 3 years): Failures are random, relatively rare. AFR at its lowest.
Wear-Out (3+ years): Mechanical components degrade. AFR increases, especially for HDDs.
This pattern has implications for RAID planning:
| Drive Age | HDD AFR (Observed) | SSD AFR (Observed) | Notes |
|---|---|---|---|
| Year 1 | 2.0-3.0% | 0.5-1.0% | Includes infant mortality |
| Year 2 | 1.5-2.5% | 0.5-1.0% | Steady state |
| Year 3 | 2.0-3.0% | 0.5-1.5% | Beginning of wear-out |
| Year 4 | 3.0-5.0% | 1.0-2.0% | Accelerating failures |
| Year 5+ | 5.0-10.0% | 2.0-4.0% | Significant wear-out |
SSDs have no mechanical components, eliminating seek-related wear. However, NAND flash cells have finite write endurance (program/erase cycles). Enterprise SSDs include wear leveling and over-provisioning to extend life, but heavily-written SSDs approach end-of-life faster than HDDs in some workloads. Monitor SSD wear indicators.
MTTDL (Mean Time To Data Loss) is the key reliability metric for RAID arrays. It represents the expected time before enough drives fail simultaneously to cause irreversible data loss. MTTDL calculations reveal the dramatic differences in reliability between RAID levels.
MTTDL Calculation Approach:
The calculation models the race condition between:
Data loss occurs when too many drives fail before rebuilds complete.
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// MTTDL Calculations// MTBF = Mean Time Between Failures (single drive)// MTTR = Mean Time To Repair (rebuild time)// N = Number of drives in array // RAID 0 (no redundancy):// Any single failure causes data loss// MTTDL = MTBF / N//// Example: 8 drives, MTBF = 500,000 hours// MTTDL = 500,000 / 8 = 62,500 hours ≈ 7.1 years// But: This is average time to *first* failure, which is data loss // RAID 1 (mirror, 2 drives):// Both drives must fail within MTTR to lose data// MTTDL = MTBF² / (2 × MTTR)//// Example: MTBF = 500,000 hours, MTTR = 24 hours// MTTDL = 500,000² / (2 × 24) = 5,208,333,333 hours ≈ 594,564 years // RAID 5 (N drives, tolerates 1 failure):// After first failure, a second failure during rebuild causes data loss// MTTDL = MTBF² / (N × (N-1) × MTTR)//// Example: 8 drives, MTBF = 500,000 hours, MTTR = 24 hours// MTTDL = 500,000² / (8 × 7 × 24) = 186,011,905 hours ≈ 21,234 years // RAID 6 (N drives, tolerates 2 failures):// After second failure, third failure during rebuild causes data loss// MTTDL = MTBF³ / (N × (N-1) × (N-2) × MTTR²)//// Example: 8 drives, MTBF = 500,000 hours, MTTR = 24 hours// MTTDL = 500,000³ / (8 × 7 × 6 × 24²) = 64,484,127,000,000 hours ≈ 7.36 billion years // RAID 10 (N/2 mirror pairs, striped):// Data loss requires both drives in any single pair to fail within MTTR// MTTDL = MTBF² / ((N/2) × 2 × MTTR) = MTBF² / (N × MTTR)//// Example: 8 drives (4 pairs), MTBF = 500,000 hours, MTTR = 8 hours// MTTDL = 500,000² / (8 × 8) = 3,906,250,000 hours ≈ 446,005 yearsCritical Insight: MTTR Dominates Modern Calculations
Notice how MTTR appears in the denominator of MTTDL formulas. As drive capacities have grown:
The exponential increase in capacity has not been matched by proportional I/O speed increases, dramatically extending rebuild times and vulnerability windows.
| Drive Size | Estimated MTTR | RAID 5 MTTDL (8 drives) | Risk Assessment |
|---|---|---|---|
| 500 GB | 2 hours | 930,059,523 hours (106,170 years) | Low risk |
| 2 TB | 8 hours | 232,514,880 hours (26,542 years) | Moderate risk |
| 8 TB | 32 hours | 58,128,720 hours (6,636 years) | Elevated risk |
| 16 TB | 56 hours | 33,216,411 hours (3,792 years) | High risk |
| 18 TB | 72 hours | 25,835,986 hours (2,949 years) | Very high risk |
MTTDL calculations assume independent, random failures. Real-world factors—batch failures, correlated stress during rebuild, latent sector errors—significantly reduce actual MTTDL. Production systems should assume 10× to 100× lower reliability than calculated MTTDL suggests.
Saying 'RAID 5 tolerates 1 failure' is technically correct but misleadingly simple. Several factors complicate real-world fault tolerance.
Latent Sector Errors (LSE) / Unrecoverable Read Errors (URE):
Modern drives have an Unrecoverable Read Error rate specification, typically:
During RAID rebuild, the controller reads the entire content of all surviving drives. With large drives, the probability of hitting a URE during rebuild becomes significant.
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// URE Probability During RAID 5 Rebuild// URE specification: 1 in 10^14 bits (consumer drive) // Scenario: 8-drive RAID 5, 8TB drives, one drive failed// Must read 7 surviving drives completely = 7 × 8TB = 56TB // Convert to bits: 56 TB = 56 × 10^12 × 8 = 448 × 10^12 bits // Probability of at least one URE:// P(URE) = 1 - (1 - 1/10^14)^(448 × 10^12)// P(URE) ≈ 1 - e^(-448 × 10^12 / 10^14)// P(URE) ≈ 1 - e^(-4.48)// P(URE) ≈ 1 - 0.0113// P(URE) ≈ 98.87% // CRITICAL: With consumer drives, there's a 99% chance of hitting// an unrecoverable read error during rebuild of an 8×8TB RAID 5! // With enterprise drives (1 in 10^15):// P(URE) ≈ 1 - e^(-0.448)// P(URE) ≈ 36% // Still a 36% chance of rebuild failure with enterprise drives! // This is why:// 1. RAID 6 is essential for large drives// 2. Enterprise drives are not optional for production// 3. Checksum verification (ZFS, btrfs) catches silent errorsA RAID 5 array with 8TB+ consumer drives has a higher probability of rebuild failure than rebuild success. The combination of extended rebuild time and URE probability makes RAID 5 effectively unreliable for modern drive sizes. This is not theoretical—production systems experience rebuild failures regularly.
Correlated Failures:
MTTDL calculations assume failures are independent. In practice, failures correlate:
Batch Effects: Drives from the same manufacturing batch share defects, failing around the same age.
Environmental Stress: Power events, temperature excursions, and vibration affect all drives simultaneously.
Rebuild Stress: The intense I/O of rebuild pushes aging drives over the edge. It's common for a second drive to fail during rebuild.
Operational Errors: Human mistakes (reformatting wrong drive, pulling wrong disk) correlate with other operational activities.
With the background on failure models established, let's compare RAID levels on reliability characteristics comprehensively.
RAID 0: Zero Fault Tolerance
RAID 0 provides no redundancy whatsoever. Any single drive failure results in complete, unrecoverable data loss. MTTDL is simply single-drive MTBF divided by the number of drives.
The only 'protection' RAID 0 provides is that data is distributed—a failed drive loses only its stripe units, not necessarily entire files. But partial file loss is often worse than complete loss for databases.
| Property | RAID 0 | RAID 1 | RAID 5 | RAID 6 | RAID 10 |
|---|---|---|---|---|---|
| Fault Tolerance (drives) | 0 | N-1 | 1 | 2 | 1-N/2* |
| Survives 1 failure | No | Yes | Yes | Yes | Yes |
| Survives 2 failures | No | Yes** | No | Yes | Maybe*** |
| Survives 3 failures | No | Yes** | No | No | Maybe*** |
| Rebuild risk | N/A | Low | High (URE) | Moderate | Very low |
| Degraded vulnerability | N/A | Low | Critical | Elevated | Low |
| Silent corruption protection | None | None | None | None | None |
Table Notes:
RAID 10 vs RAID 6 Reliability Debate:
Which is more reliable for an 8-drive array?
RAID 6 (8 drives):
RAID 10 (4 pairs):
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// Probability of surviving 2 failures: RAID 6 vs RAID 10 // RAID 6 (8 drives):// Survives ANY 2 failures// P(survive 2 failures) = 100% // RAID 10 (8 drives, 4 pairs):// Survives 2 failures unless both are in same pair// After first failure: 1 drive per pair is critical// Second failure safe if in different pair // Given: 2 random, independent failures// P(second failure in same pair as first) = 1/7 (one critical drive of 7 remaining)// P(survive 2 failures) = 6/7 ≈ 85.7% // But consider: Which 2-failure scenario is more likely? // RAID 6: Prolonged rebuild exposes all drives to stress// RAID 10: Quick rebuild, brief vulnerability window // Real-world factors:// - RAID 6 rebuild for 18TB drives: 48+ hours of all drives under load// - RAID 10 rebuild: 8 hours affecting one pair only// - Correlated failures more likely during long rebuilds // Calculation with rebuild-induced failure:// P(second failure during 48hr RAID 6 rebuild) >> P(second failure during 8hr RAID 10 rebuild) // Key insight: Theoretical P(survive) favors RAID 6// Real-world P(survive) often favors RAID 10 due to rebuild dynamicsFor write-intensive OLTP databases, RAID 10's combination of fast rebuilds, low degraded impact, and reduced URE exposure often makes it more reliable in practice than RAID 6. For read-heavy workloads where capacity matters, RAID 6's guaranteed 2-drive tolerance is valuable. Both are vastly superior to RAID 5 with modern drive sizes.
Hot spares are idle drives configured to automatically replace failed drives. They dramatically reduce MTTR by eliminating the delay of human detection, procurement, and installation.
Hot Spare Benefits:
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// MTTDL Impact of Hot Spares // Without hot spare:// MTTR includes:// - Detection delay: 1-24 hours (depends on monitoring)// - Procurement: 0 (if spare kept) to 24-48 hours (overnight shipping)// - Physical installation: 0.5-1 hour// - Rebuild time: 24-72 hours (for large drives)// Total MTTR: 26-145 hours // With hot spare:// MTTR includes:// - Detection delay: ~0 (automatic)// - Procurement: ~0 (drive already present)// - Physical installation: ~0 (already installed)// - Rebuild time: 24-72 hours// Total MTTR: 24-72 hours // Example MTTDL improvement for RAID 5:// MTTDL = MTBF² / (N × (N-1) × MTTR) // Without hot spare (MTTR = 80 hours):// MTTDL = 500,000² / (8 × 7 × 80) = 55,803,571 hours // With hot spare (MTTR = 48 hours):// MTTDL = 500,000² / (8 × 7 × 48) = 93,005,952 hours // Improvement: 66% longer MTTDL with hot spare // For RAID 6, improvement is squared (MTTR² in formula)// For RAID 10, proportional improvementRebuild Priority and Speed:
Rebuild operations compete with production I/O. RAID controllers offer priority settings:
High Priority/Fast Rebuild: Rebuild completes quickly, but production performance suffers. Reduces vulnerability window but impacts users.
Low Priority/Slow Rebuild: Production performance maintained, but rebuild takes longer. Extended vulnerability period.
For critical systems, high-priority rebuild is usually correct—the temporary performance impact is preferable to extended data loss risk.
Hot spares must be equal or greater capacity than any drive they might replace. With growing drive sizes and mixed arrays, ensure spares match your largest drives. A 4TB spare cannot rebuild an 8TB failed drive. Maintain spare inventory that covers your largest deployments.
Database systems have specific reliability requirements that go beyond general storage considerations.
Write Ordering and Crash Consistency:
Databases rely on write ordering guarantees: WAL records must be durable before data pages are written. RAID controllers with write-back cache can reorder writes for efficiency, potentially violating these guarantees.
Protections:
RAID Is Not Backup:
This cannot be overstated: RAID protects against hardware failure only. It does not protect against:
DROP TABLE is mirrored instantly to all copiesRAID is an availability mechanism, not a durability mechanism. True durability requires backups stored independently, off-site replication, and point-in-time recovery capability.
Every experienced DBA has a story about an organization that lost data because 'we have RAID' was confused with 'we have backups.' RAID and backups serve different purposes. RAID keeps you running when hardware fails. Backups save you when everything else fails. You need both.
End-to-End Data Integrity:
Traditional RAID has a critical limitation: it assumes data read from disk is correct. In practice, silent data corruption (bit rot) can occur:
Solution: Checksumming Filesystems
ZFS, btrfs, and similar filesystems maintain checksums for every block:
For critical database systems, ZFS or similar provides protection no traditional RAID can match.
We've examined RAID reliability with mathematical and practical rigor. Here are the essential takeaways:
What's Next:
With performance and reliability analysis complete, we'll now examine the practical art of RAID selection. The next page provides a decision framework for choosing RAID levels based on specific database requirements, constraints, and priorities.
You now understand RAID reliability deeply—from failure statistics through MTTDL calculations to real-world failure modes. You can evaluate RAID configurations for reliability, understand why RAID 5 is no longer acceptable for large drives, and appreciate the importance of checksumming filesystems for complete data protection.