Photon: Why Your Databricks SQL is Suddenly 3x Faster

Abiola-David — Sun, 22 Mar 2026 19:48:00 GMT

If you’ve been working with newer clusters in Databricks, chances are you’ve noticed the term Photon appearing in your cluster configuration or query profiles. At first glance, it might look like just another performance feature—but in reality, Photon represents a fundamental shift in how queries are executed.

This isn’t just an incremental improvement. Photon is a completely redesigned execution engine, built from the ground up in C++, and it’s one of the key reasons why many workloads are now running 2x–5x faster without any code changes.

What Exactly is Photon?

Photon is a high-performance vectorized query engine designed to accelerate SQL and DataFrame workloads in Databricks.

Traditionally, Apache Spark (Apache Spark) executes queries using a JVM-based engine. While powerful, it has limitations when it comes to fully utilizing modern CPU capabilities.

Photon changes that by:

Moving execution closer to native hardware (C++)
Leveraging modern CPU optimizations
Reducing overhead from the JVM layer

The result? Faster queries, lower latency, and better resource utilization.

Why Photon Feels So Fast

Let’s break down what’s really happening under the hood.

Vectorized Execution (The Real Game-Changer)

Traditional execution processes data row by row:

Row 1 → Process Row 2 → Process Row 3 → Process

Photon flips this model to columnar batch processing:

Batch of 1000 values → Process together

Why this matters:

Better CPU cache utilization
Fewer function calls
Exploits SIMD (Single Instruction, Multiple Data)

In simple terms: the CPU does more work per cycle

This is where a huge chunk of that 3x performance gain comes from.

Native C++ Engine (Goodbye JVM Bottlenecks)

Photon is written in C++ instead of Java/Scala, which allows it to:

Eliminate JVM overhead
Reduce garbage collection pauses
Execute closer to the hardware

What this means for you:

Faster joins
Faster aggregations
Lower query latency

This is especially noticeable in:

Large aggregations
Complex joins
BI dashboard queries

Seamless Integration with Spark (No Code Changes Required)

One of the most powerful aspects of Photon is:

You don’t need to rewrite anything

It works with:

Spark SQL
DataFrame APIs
Existing pipelines

So your existing code like:

SELECT region, SUM(sales) FROM catalog.schema.sales_table GROUP BY region

…automatically benefits from Photon when enabled.

This makes it:

Developer-friendly
Low-risk to adopt
Instant performance upgrade

Deep Optimization for Delta Lake

Photon is tightly integrated with Delta Lake, which is the backbone of the Lakehouse architecture.

Why this matters:

Photon understands:

Delta file formats
Metadata
Statistics
Data skipping

So it can:

Read less data
Skip unnecessary files
Optimize I/O operations

Result: Blazing-fast Lakehouse queries

topic Photon: Why Your Databricks SQL is Suddenly 3x Faster in MVP Articles

Photon: Why Your Databricks SQL is Suddenly 3x Faster