RAID 5's notorious write performance stems from its parity calculation mechanism. Every write operation requires:
- Reading existing data block
- Reading existing parity block
- Computing new parity (XOR operations)
- Writing new data block
- Writing new parity block
This results in 4 I/O operations for every single write request - known as the "write penalty". For database workloads with frequent small writes (e.g., OLTP systems), this becomes particularly problematic.
Here's a simple fio test comparing random write performance (4KB blocks):
# RAID 5 configuration
fio --name=raid5_test --ioengine=libaio --rw=randwrite --bs=4k \
--numjobs=16 --size=1G --runtime=60 --time_based --group_reporting \
--filename=/dev/md0
# Typical results:
# IOPS: ~1,200 | Latency: 13.3ms avg
# RAID 10 configuration
fio --name=raid10_test --ioengine=libaio --rw=randwrite --bs=4k \
--numjobs=16 --size=1G --runtime=60 --time_based --group_reporting \
--filename=/dev/md1
# Typical results:
# IOPS: ~8,700 | Latency: 1.8ms avg
Different database engines have varying I/O patterns:
- MySQL InnoDB: Doublewrite buffer exacerbates RAID 5 penalties
- PostgreSQL: WAL writes suffer from parity computation overhead
- MongoDB: Journaling becomes bottlenecked
Example PostgreSQL configuration adjustment for RAID 5 (not recommended):
# postgresql.conf adjustments
wal_buffers = 16MB # Up from default 8MB
checkpoint_segments = 32 # Up from default 3
random_page_cost = 2.0 # Lower than default 4.0
For databases requiring both redundancy and performance:
1. RAID 10 (1+0)
Mirroring + striping provides:
- N/2 write performance (where N = number of drives)
- Single drive failure tolerance per mirrored pair
- No parity calculation overhead
2. ZFS with SSD SLOG
Combines redundancy with write acceleration:
# ZFS pool creation example
zpool create dbpool mirror nvme0n1 nvme1n1 \
mirror nvme2n1 nvme3n1 \
log mirror ssd0 ssd1 \
cache ssd2 ssd3
# Database-specific tuning
zfs set recordsize=8K dbpool
zfs set primarycache=metadata dbpool
zfs set atime=off dbpool
3. Hardware RAID with Cache Battery
High-end controllers (e.g., MegaRAID) with:
- 1GB+ write-back cache
- Battery-backed cache protection
- FastPath technology for small writes
Consider RAID 5 only for:
- Read-heavy analytical databases (OLAP)
- Secondary/replica nodes
- Backup storage pools
- With high-quality SSDs (reduces penalty impact)
Transitioning from RAID 5 to better alternatives:
# LVM-based migration example (Linux)
pvcreate /dev/new_disk1 /dev/new_disk2
vgcreate new_vg /dev/new_disk1 /dev/new_disk2
lvcreate -n new_db -L 1T new_vg
mkfs.ext4 /dev/new_vg/new_db
rsync -avx /var/lib/mysql/ /mnt/new_db/
# Update fstab and remount
When configuring storage for database servers, RAID 5 presents a classic engineering trade-off. While offering single-disk redundancy through parity calculations, its write performance characteristics make many DBAs hesitate. Let's examine why:
// Theoretical RAID 5 write operation sequence:
1. Read existing data block
2. Read existing parity block
3. Compute new parity (XOR operations)
4. Write new data block
5. Write new parity block
In MySQL benchmarking on a 4-disk RAID 5 array with 15K RPM SAS drives, we observed:
- Sequential writes: ~120 MB/s
- Random 4K writes: ~350 IOPS
- Write latency spikes during parity recalculations
For transactional databases like PostgreSQL or MongoDB, consider these alternatives:
# Linux mdadm configuration example for RAID 10:
mdadm --create /dev/md0 --level=10 --raid-devices=4 /dev/sd[b-e]
Hybrid Approach: Some enterprises deploy RAID 10 for transaction logs while using RAID 5 for colder data storage.
Hardware RAID controllers with battery-backed cache (BBWC) can mitigate some write penalties:
| Solution | Random Write IOPS | Fault Tolerance |
|---|---|---|
| RAID 5 (HW) | 1,200 | 1 disk |
| RAID 10 (HW) | 8,000+ | 1 disk per mirror |
When you must use RAID 5, these optimizations help:
# MySQL InnoDB configuration tweaks:
innodb_flush_method = O_DIRECT
innodb_io_capacity = 2000
innodb_io_capacity_max = 4000