Databricks Optimization Playbook: Photon Engine, Predictive I/O & DFP

[!NOTE] Databricks compute costs are driven by DBU consumption. This playbook covers hardware and software acceleration technologies that deliver 3x to 5x speedups while cutting total cloud infrastructure spend.

1. Photon Engine vs Standard Spark JVM

• Standard Spark: Interpreted JVM bytecode execution. Performance is bounded by Java garbage collection, memory indirection, and CPU cache misses.
• Photon: Native C++ vectorized execution engine rewritten from scratch.
  • Processes data in vectorized CPU SIMD register batches.
  • Delivers massive speedups on aggregations, joins, string operations, and Delta MERGE statements.

Staff Architecture: Photon Execution, Predictive I/O, Data Skipping & Serverless Tuning

• How Photon Accelerates Lakehouse Workloads: Photon is Databricks’ vectorized C++ query engine running directly on native memory buffers (eliminating JVM GC pauses on big joins, window functions, and JSON parsing). Note: Python/Scala UDFs break vectorization—replace udf() calls with native Spark SQL expressions, Arrow UDFs, or built-in higher-order functions (transform, filter) so stages stay 100% inside Photon!
• Data Skipping & Statistics Hygiene: Delta automatically collects min/max/nullCount stats on the first 32 columns by default (delta.dataSkippingNumIndexedCols). Place heavy filter/join keys (tenant_id, event_ts, order_id) inside the first 32 columns (or increase dataSkippingNumIndexedCols) so Photon + Predictive I/O prune 99% of irrelevant Parquet files cleanly.
• Avoid Over-Sharding Small Tables & Over-Tuning: Let AQE + Databricks Disk Cache (NVMe SSD cache) + Photon handle coalesce/skew before adding rigid manual .repartition() hints that defeat dynamic file sizing (OptimizeWrite).