RAID 0 (striping) distributes data evenly across all disks in the array without parity or redundancy. The theoretical performance increases linearly with each additional disk, but real-world performance depends on multiple factors:

# Example Linux mdadm RAID 0 creation command:
mdadm --create /dev/md0 --level=0 --raid-devices=8 /dev/sd[b-i]

Testing with fio shows different scaling patterns:

# Sequential read test (4KB blocks)
fio --name=seqread --ioengine=libaio --rw=read --bs=4k --direct=1 --size=1G --numjobs=1 --runtime=60 --time_based

# Typical scaling results:
1 disk:  200 MB/s
2 disks: 380 MB/s (+90%)
4 disks: 720 MB/s (+89%)
8 disks: 1.3 GB/s (+80%)
16 disks: 2.1 GB/s (+62%)

1. Controller limitations: HBA/SAS controller bandwidth (6Gbps/12Gbps per port)
2. PCIe lane saturation: x4 vs x8 vs x16 slots
3. Queue depth: More disks increase total queue depth capability
4. File system overhead: XFS generally handles large arrays better than ext4

For database workloads requiring high IOPS:

CREATE TABLE test_data (
  id SERIAL PRIMARY KEY,
  payload BYTEA
) TABLESPACE fast_raid;

For video editing scratch space:

# macOS diskutil RAID creation:
diskutil appleRAID create stripe MyRAID0 JHFS+ disk2 disk3 disk4 disk5

Diminishing returns typically appear when:
- The workload becomes CPU-bound (common with encryption)
- The storage controller reaches bandwidth limits
- The application can't issue enough parallel I/O requests

RAID 0's performance theoretically scales linearly with each additional drive due to striping. The formula for maximum throughput is:

max_throughput = single_drive_speed × number_of_drives

However, in practice, you'll encounter diminishing returns due to:

• Controller bottlenecks (especially with consumer-grade RAID cards)
• OS filesystem overhead
• Queue depth limitations
• Interconnect bandwidth (SATA/SAS/NVMe)

Testing with CrystalDiskMark on identical SSDs shows:

2-drive RAID 0: 1,050 MB/s seq. read
4-drive RAID 0: 1,950 MB/s seq. read  
8-drive RAID 0: 2,300 MB/s seq. read

The performance delta between 4-drive and 8-drive configurations is only ~18% despite doubling the drive count.

When working with multi-drive RAID 0 arrays:

# Linux: Optimal stripe size for development workloads
mdadm --create /dev/md0 --level=0 --raid-devices=8 \
--chunk=128 /dev/sd[b-i]

Key factors affecting performance:

• Chunk size (128KB-256KB ideal for most dev workloads)
• Filesystem choice (XFS generally outperforms EXT4 for RAID)
• Alignment (critical for NVMe RAID)

The point of diminishing returns typically occurs when:

1. Your RAID controller's PCIe lane bandwidth is saturated
2. Drive latency becomes the dominant factor (common with HDDs)
3. Workload becomes CPU-bound rather than storage-bound

For Python developers working with large datasets:

# Test actual throughput with Python
import time
start = time.time()
with open('/mnt/raid0/large_file.dat', 'rb') as f:
    while f.read(1024*1024): pass
print(f"Throughput: {os.path.getsize('/mnt/raid0/large_file.dat')/(time.time()-start)/1e6} MB/s")

For true linear scaling beyond 4 drives, consider:

• NVMe-oF setups
• Parallel filesystems (Lustre, CephFS)
• Object storage for certain workloads