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Optimize PostgreSQL for Zabbix 7.0 with TimescaleDB compression: guide to enable extensions, tune performance, implement retention policies, and scale time-series storage effectively. Table of Contents
A practical guide to optimizing Linux filesystems for databases and virtualization, covering ext4, XFS, mount options, performance tuning, and real-world CLI examples.
Modern databases and virtualized workloads place intense, often competing demands on storage. Low latency, high throughput, predictable performance, and strong data integrity are all non-negotiable—yet they’re influenced heavily by how your Linux filesystem is chosen, formatted, and mounted. This guide walks through practical, production-tested strategies to optimize Linux filesystems for databases and virtualization, with a focus on performance, reliability, and maintainability.
The article is structured for search visibility and skimmability, while remaining inclusive and concise. Whether you’re running PostgreSQL on bare metal, MySQL inside VMs, or mixed workloads on a hypervisor, these principles apply.
Storage is often the hidden bottleneck. Even fast NVMe drives can underperform if filesystem defaults clash with workload patterns. Databases generate many small, synchronous writes; virtualization platforms favor large, sequential I/O with bursty access. A filesystem tuned for one may harm the other.
Optimizing at the filesystem layer helps you:
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Filesystem tuning complements—rather than replaces—database and hypervisor tuning.
Before changing settings, identify how your system uses storage. Two questions matter most:
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🟢 Typical Patterns |
| Workload | I/O Pattern | Sync Writes | File Count |
|---|---|---|---|
| OLTP databases | Small, random | High | Medium |
| Analytics databases | Large, sequential | Medium | Low |
| VM disk images | Mixed, bursty | Medium | Low |
| Containers | Small, layered | Low–Medium | High |
This context informs filesystem choice and mount options.
Linux offers several mature filesystems. For databases and virtualization, four dominate.
🟢 Filesystem Comparison |
| Filesystem | Strengths | Best Use Cases | Notes |
|---|---|---|---|
| ext4 | Stable, predictable, low overhead | Databases, general purpose | Conservative but reliable |
| XFS | High parallelism, large files | VM images, large DBs | Needs proper sizing |
| Btrfs | Snapshots, checksums | Dev/test, snapshots | Tuning required for DBs |
| ZFS | Data integrity, caching | Enterprise storage stacks | Higher memory usage |
🔹Key takeaway: |
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Defaults are safe, not optimal. Formatting with workload-aware options sets the foundation.
🖥️ ext4 Example (Database Volume) |
mkfs.ext4 -E stride=128,stripe-width=128 -O metadata_csum,64bit /dev/nvme0n1p1
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🖥️ XFS Example (Virtualization Volume) |
mkfs.xfs -f -d agcount=32,su=1m,sw=8 /dev/nvme1n1
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Formatting is disruptive—do it once, do it right.
Mount options control runtime behavior. Small changes here often yield measurable gains.
▶️ Common Performance-Oriented Options |
| Option | Filesystem | Benefit | Caution |
|---|---|---|---|
noatime | ext4, XFS | Eliminates read metadata writes | Minimal downside |
nodiratime | ext4 | Reduces directory updates | Often implied by noatime |
barrier=0 | ext4 | Faster writes | Unsafe without battery-backed cache |
nobarrier | XFS | Lower latency | Same risk as above |
discard | SSDs | Enables TRIM | Prefer scheduled TRIM |
🖥️ Example Mount |
mount -o noatime,nodiratime /dev/nvme0n1p1 /var/lib/postgresql
For databases, prioritize consistency unless you fully understand your storage controller’s guarantees.
Journaling protects against corruption but adds overhead. Tune—not disable—it.
🟢 ext4 Journal Modes |
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For most databases, ordered is ideal. Databases already manage consistency internally; journaling ensures filesystem metadata integrity.
Linux I/O schedulers shape how requests reach the disk. On modern SSDs and NVMe, simpler is better.
🟢 Recommended Schedulers |
| Storage Type | Scheduler |
|---|---|
| NVMe | none |
| SSD | mq-deadline |
| HDD | deadline |
🟢 Check and Set Scheduler |
cat /sys/block/nvme0n1/queue/scheduler
echo none > /sys/block/nvme0n1/queue/scheduler
Schedulers matter more under contention—common on virtualization hosts.
Virtualization adds an abstraction layer. Filesystem choices affect every guest.
▶️ VM Disk Formats |
| Format | Performance | Flexibility |
|---|---|---|
raw | Highest | Low |
qcow2 | Lower | High (snapshots, compression) |
For performance-critical VMs, place raw disks on XFS. Reserve qcow2 for environments where snapshots matter more than latency.
🖥️ Host Mount Example |
mount -o noatime,logbufs=8,logbsize=256k /dev/nvme1n1 /var/lib/libvirt/images
XFS logging options significantly improve VM write performance.
Databases are sensitive to fsync behavior, latency spikes, and metadata overhead.
▶️ PostgreSQL |
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mount -o noatime /dev/nvme0n1p1 /var/lib/postgresql
▶️ MySQL / MariaDB |
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innodb_flush_method=O_DIRECT
This avoids competition between the database buffer pool and the filesystem cache.
Always validate changes with benchmarks that resemble real workloads.
🟢 fio Example (Random Writes) |
fio --name=dbtest --rw=randwrite --bs=4k --iodepth=32 \
--numjobs=4 --size=4G --direct=1
Compare latency percentiles, not just average throughput. Databases feel tail latency first.
Even experienced administrators stumble here.
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Optimization is holistic. Filesystem tweaks can’t fix poor architecture.
Performance must never compromise recoverability.
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xfs_growfs /var/lib/libvirt/images
Plan capacity early to avoid emergency migrations.
Optimize PostgreSQL for Zabbix 7.0 with TimescaleDB compression: guide to enable extensions, tune performance, implement retention policies, and scale time-series storage effectively. Table of Contents
A practical guide to performance tuning XFS file systems on Enterprise Linux, covering mount options, hardware alignment, workload-specific tuning, and monitoring best practices. Table of
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