New Feature
6 days ago

Resource Efficiency & Config Recommendations for Apache Spark on Kubernetes

We’ve added a new optimization suite in ILUM that converts runtime telemetry into actionable Spark configuration, useful for data engineering teams tuning Spark on Kubernetes for performance and cost.

What’s new
  • Resource Efficiency Score (0–100)
    Radar across Memory / Executor / I/O / Tasks with per-dimension panels (e.g., memory utilization %, executor active %, I/O data-processing ratio, task balance).
  • Live cost signals
    • Accumulated Cost (runtime × cluster rate) with wall-clock duration.
    • Cost Efficiency score with waste breakdown by Memory / CPU / Executors to spot over-provisioning.
  • Configuration Recommendations (modes: Balanced, Performance, Cost)
    Each recommendation includes a rationale, expected impact (e.g., -15% execution time), and confidence. One-click copy of the exact Spark config.
Example recommendations (auto-derived from workload patterns)
# Adaptive Query Execution
spark.sql.adaptive.enabled = true

# Adaptive partition coalescing
spark.sql.adaptive.coalescePartitions.enabled = true

# Executor memory sizing (value depends on observed usage)
spark.executor.memory = 5g
  • Why it’s suggested: We correlate stage metrics, skew, shuffle sizes, and executor idleness to pick safe defaults. AQE reduces expensive shuffles; coalescing prevents tiny partitions; memory sizing balances spill risk vs. idle RAM.
Where to find it
Jobs → Optimization tab: left side shows Resource/Cost scores; right side lists recommendations with rationale and impact estimates.

Why this helps (practical outcomes)
  • Faster iteration on Spark tuning without trawling logs.
  • Concrete levers for cost optimization (reduce idle executors, right-size memory, coalesce partitions).
  • Transparent reasoning you can review and commit via your normal workflow.