Why it matters
Under-provisioned clusters produce slow jobs, queued applications, and constant user complaints. Over-provisioned clusters waste money that could go into other engineering priorities. Getting capacity planning right is a real financial lever, potentially six figures a year for medium clusters and eight figures for large ones.
Capacity planning also drives hiring and operational focus. A cluster running at 60 percent utilization needs different care than one at 90 percent.
The architecture
Storage capacity planning starts with raw disk, applies the replication factor overhead (3x for default triple replication, 1.5x for erasure coded), and subtracts the reserved-for-overhead percentage (typically 20 percent for balancer headroom and non-HDFS use). Growth rate comes from historical data — measure petabytes per month for the last year and project forward.
Compute capacity is measured in vcores. Multiply cluster vcore count by a target utilization ceiling (typically 75 percent to leave headroom for bursts). This gives you the sustainable compute budget for queue allocations.
How it works end to end
Memory planning ties to container sizing. Each NodeManager reserves memory for OS and services (typically 15 to 20 percent of physical), and the remainder is available for YARN containers. Sum across all NodeManagers to get cluster-wide container memory budget. Divide by typical container size to get concurrent container count.
Network capacity is the trickiest to plan because it varies by workload. Shuffle-heavy MapReduce or Spark jobs generate large cross-rack transfers. Estimate shuffle bytes per hour of peak workload, divide by aggregated cross-rack bandwidth, and compare to available capacity. If the ratio exceeds 60 percent, network is likely to be a bottleneck.
Alerting thresholds should trigger action well before the wall. Alert at 70 percent on any dimension, plan expansion at 80 percent, and treat 90 percent as an incident. Waiting until 100 percent means you are already having outages.