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DP-750 · 1.1.2 · Set up and configure an Azure Databricks environment

Configure compute performance

Configure compute performance settings, including CPU, node count, autoscaling, termination, node type, cluster size, and pooling

Size and scale compute around workload demand, then stop idle resources to control cost.

See the whole tuning loop

Infographic explaining compute sizing, autoscaling, instance pools, and automatic termination.

Tune the bottleneck you actually observe. Node type changes CPU and memory per worker. Worker count changes parallel capacity. More workers do not solve a task that itself needs more memory.

See the real settings

Microsoft Learn screenshot of the Azure Databricks create compute form with runtime, Photon, worker type, and autoscaling settings.

The compute form puts runtime, Photon, worker type and autoscaling together. Min and Max define the autoscaling range rather than a fixed cluster size.

Startup and idle cost

  • Repeated cluster startup is slowInstance pool
  • Interactive compute sits unusedAutomatic termination
Performance versus cost

Pools reduce startup latency. Automatic termination reduces idle cost. Neither replaces measuring CPU, memory, skew and task behavior.

Key points

  • Node type: Determines per-node CPU and memory capacity
  • Workers: Determine parallel compute capacity
  • Autoscaling: Adjusts workers within configured bounds
  • Pools: Reduce startup latency by reusing ready instances

Exam trap

More workers do not fix a workload bottlenecked by one oversized task or poor partitioning.

Sources · 1 Microsoft Learn page Show ▾

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