In this lesson, you will learn how to identify disk pressure and node imbalance by analyzing internal database storage metrics.
When managing distributed systems—especially those utilizing wide-column stores—storage metrics are your primary diagnostic tool. Simply watching total disk usage isn't enough; you must look at the distribution of data across your cluster to ensure no single node becomes a bottleneck.
Identifying Node Imbalance
Node imbalance occurs when the distribution of data across your partitions is non-uniform. If your partition key design results in "hot" partitions, some nodes will store significantly more data than others. You can detect this by querying your database's system tables (often found in system.size_estimates or similar internal metadata) to compare the mean_partition_size against individual node capacity.
If you see a wide variance—for example, one node at 80% disk capacity while others sit at 20%—your cluster is suffering from a data skew that will lead to uneven query performance and potential node failure.
Diagnosing Disk Pressure
Disk pressure isn’t always about reaching 100% capacity; it is often about the rate of ingestion relative to the storage engine’s ability to clean up. In systems using LSM-trees, you must monitor the ratio of "live" data to "tombstones." If your metrics show that a large percentage of your disk space is occupied by deleted data (tombstones) that has not yet been compacted, your node is under disk pressure even if the user-facing data volume is small.
You can observe this by checking your internal statistics:
-- Conceptual query to check storage health
SELECT node_id,
total_disk_used,
tombstone_ratio,
pending_compaction_tasks
FROM system_metrics.node_storage_health;
When pending_compaction_tasks stays high and total_disk_used climbs, the system is struggling to reclaim space. This is a clear indicator that your compaction strategy needs adjustment before you trigger a disk-full event. By correlating node-level storage metrics with partition key distribution, you can distinguish between a need to re-partition your data and a need to optimize your background cleanup processes.
Key Takeaway: Disk pressure often stems from inefficient compaction or data skew, both of which are identifiable by comparing per-node storage metrics against cluster-wide averages.