Many developers assume that total IOPS simply scales linearly with disk count (IOPS_total = N × IOPS_single_disk). While this holds true for ideal scenarios, RAID implementations introduce complex bottlenecks that break this simplistic model.

RAID 0 (Striping):
Reads benefit from parallelization, but writes require coordination across all disks in the stripe. Example sequential read pattern:

# Theoretical RAID 0 read throughput calculation
disks = 4
single_disk_iops = 150
read_iops = disks * single_disk_iops  # 600 IOPS (best case)

RAID 1/10 (Mirroring):
Reads can be load-balanced across mirrors, but writes must commit to all copies. Sample write penalty calculation:

# RAID 10 write penalty
effective_write_iops = (disks / 2) * single_disk_iops * 0.8  # ~240 IOPS

Consider these Linux examples measuring actual performance:

# RAID 5 sequential read benchmark (4 disks)
fio --name=ra5test --ioengine=libaio --rw=read \
--bs=4k --numjobs=4 --size=1G --runtime=60 \
--directory=/mnt/raid5 --iodepth=32 --group_reporting

Key observations from production systems:

• RAID 5 shows 60-70% of theoretical read performance during random workloads
• RAID 10 write throughput degrades by 15-20% under heavy contention
• SSD-based RAID arrays exhibit different patterns due to absence of seek times

For read-heavy workloads:
- Prefer wider RAID 10 configurations over RAID 5/6
- Implement read caching at application layer (Redis/Memcached)

For write-heavy workloads:
- Consider RAID 1 over RAID 5 for small write operations
- Align filesystem block size with RAID stripe size

Many developers assume that IOPS (Input/Output Operations Per Second) scales linearly with disk count, but this oversimplification ignores crucial RAID-level considerations. While it's true that adding disks generally increases IOPS capacity, the relationship isn't as straightforward as a simple multiplication.

Mirrored RAID configurations (RAID 1 and RAID 10) provide superior read performance because:

• Read requests can be distributed across mirrored pairs
• No parity calculations are required for reads
• Multiple concurrent reads of the same data are possible

// Benchmarking RAID 10 read performance
storage.benchmark({
  raid_level: 10,
  operation: 'read',
  threads: 8,
  block_size: '4k',
  duration: '60s'
});

Striped configurations face different performance characteristics:

• RAID 0 offers excellent sequential performance but no redundancy
• RAID 5/6 introduce write penalties due to parity calculations
• Random I/O patterns may cause spindle contention

The write penalty varies significantly by RAID level:

When designing storage systems, consider these factors:

// Calculating effective IOPS considering RAID overhead
function calculateEffectiveIOPS(baseIOPS, raidLevel, diskCount) {
  const penalties = {
    '0': 1,
    '1': 2,
    '5': 4,
    '6': 6,
    '10': 2
  };
  
  return (diskCount * baseIOPS) / penalties[raidLevel];
}

Choose RAID levels based on your access patterns:

• Database workloads: RAID 10 for mixed read/write
• Backup storage: RAID 6 for capacity and redundancy
• Temporary data: RAID 0 for maximum throughput

Modern systems employ several optimizations:

• Write-back caching to mitigate RAID 5/6 penalties
• Stripe size tuning to match workload patterns
• Controller battery backup for cache consistency