Photon vs. Spark: A Technical Deep Dive into Databricks’ Next-Generation Engine

Databricks’ Photon engine represents a fundamental shift in big data processing architecture. This technical deep dive explores how Photon’s native execution model delivers substantial performance improvements over traditional Spark, with practical code examples and real-world implications.

Core Architectural Innovations and Outcomes

Native Execution vs. JVM

Photon replaces Spark’s JVM-based execution with native C++ implementation, eliminating garbage collection overhead and enabling direct hardware optimization.

Outcome: 40–60% reduction in processing latency and elimination of GC pauses.

# Traditional Spark Execution
from pyspark.sql import SparkSession

spark = SparkSession.builder \
    .appName("Traditional") \
    .config("spark.memory.fraction", "0.8") \
    .getOrCreate()

# Incurs JVM overhead and GC pauses
df = spark.read.parquet("data.parquet") \
    .filter("revenue > 1000") \
    .groupBy("category") \
    .agg({"revenue": "sum"})

# Photon's Native Execution
class PhotonExecutor:
    def process_query(self, data_path: str):
        # Direct CPU instruction execution
        # Zero JVM overhead
        with self.native_reader(data_path) as reader…