Integrating DiskSpaceChart with Prometheus and Grafana

DiskSpaceChart: Visualize Your Server Storage in Real Time

Overview

DiskSpaceChart is a visualization component that displays disk usage over time for one or more servers, helping you spot growth trends, spikes, and potential capacity issues before they impact operations.

Why real-time disk monitoring matters

• Prevent outages: Catch disk-full conditions before services fail.
• Capacity planning: Identify growth rates to schedule storage upgrades.
• Alerting: Trigger alerts on rapid usage increases or threshold breaches.
• Investigation: Correlate disk usage spikes with deployments, logs, or jobs.

Key metrics to display

• Total capacity: The full size of the filesystem or volume.
• Used space: Absolute used bytes and percentage.
• Free space: Remaining bytes and percentage.
• I/O activity (optional): Read/write throughput to correlate heavy I/O with growth.
• Inode usage (optional): Important for many small files.
• Per-mount/partition breakdown: Show each mount point or LVM volume separately.

Design considerations

• Time window: Default to last 1 hour with quick options (15m, 1h, 6h, 24h, 7d).
• Resolution & sampling: Use adaptive sampling (higher resolution for recent data).
• Stacked vs. separate series: Stacked area charts work well for partitions contributing to total; separate lines are clearer for comparisons.
• Percent vs. absolute: Show both—percentage is quick for thresholds; bytes are needed for capacity planning.
• Color & accessibility: Use distinct, colorblind-safe palettes and provide patterns or labels for clarity.
• Annotations: Mark deployments, backups, or maintenance windows to explain sudden changes.

Data collection

• Agents: Use lightweight agents (node_exporter, Telegraf, custom daemon) to poll df/inodes and report metrics.
• Metrics format: Export as timestamped series for total_bytes, used_bytes, free_bytes, used_percent, inodes_used.
• Push vs. pull: Prefer pull (Prometheus) for many servers; push (Pushgateway) for short-lived jobs.
• Retention: Keep high-resolution recent data (e.g., 1–7 days), downsample older data for long-term trends.

Storage and back end

• Time-series DB: Prometheus, InfluxDB, or TimescaleDB are suitable.
• Downsampling/rollups: Store raw recent data, aggregate older data (hourly/daily) to save space.
• Query performance: Index by host and mount; limit series cardinality by normalizing mount names.

Visualization implementation (example stack)

• Data source: Prometheus (node_exporter mounts metrics)
• Visualization library: Grafana, or custom UI with React + D3 or Chart.js
• Frontend features: Live streaming updates (WebSocket/Server-Sent Events), hover tooltips, legend toggle, per-host filtering, alert indications.

Example visualization patterns

• Stacked area (by mount): Shows how partitions contribute to total used.
• Line for used_percent: Easy threshold detection across hosts.
• Bar + sparkline: Bar for current free space, sparkline for trend.
• Heatmap: Hosts vs. time to identify which machines show sustained growth.

Alerting strategy

• Threshold alerts: e.g., used_percent > 85% for 5 minutes.
• Rate-of-change alerts: sudden increase > X GB in Y minutes.
• Inode alerts: inodes_used > 90%.
• Composite alerts: combine high I/O with rising usage.
• Noise reduction: Require sustained breach, suppress during known maintenance windows.

Troubleshooting common issues

• False spikes from backups: Annotate scheduled jobs; use rate-based alerts.
• Monitoring agent gaps: Alert on missing metrics or stale timestamps.
• High cardinality: Normalize mount paths; avoid per-file metrics.
• Clock drift: Use NTP on servers and enforce consistent timestamps.

Example quick implementation (concept)

• Collect df output every 15s with node_exporter.
• Scrape Prometheus, store 15s samples for 24h, 1m samples for 7d, hourly thereafter.
• Grafana dashboard: top panel showing used_percent across hosts, middle panel stacked area by mount for a selected host, bottom panel table of current free bytes with alert status.

Best practices checklist

• Monitor both bytes and inodes.
• Use adaptive retention and downsampling.
• Provide per-host and aggregated views.
• Implement both threshold and rate-of-change alerts.
• Annotate known maintenance and backup windows.
• Use accessible colors and clear labels.

Next steps

• Instrument one critical host and build a minimal dashboard.
• Define alerts and test with simulated growth.
• Roll out agents across clusters and iterate on retention and visuals.