Photon, Velox, and Native Engines Shaping the Future of Query Performance

Introduction

As data volumes continue to explode and analytics become more real-time, query performance has moved from being a technical concern to a business-critical priority. Organizations no longer tolerate slow dashboards, lagging reports, or inefficient pipelines. To meet these demands, a new generation of execution engines, Photon, Velox, and others, native query engines, are redefining how data is processed at scale.

These technologies are not just incremental improvements over traditional systems; they represent a fundamental shift in how query execution is designed, optimized, and delivered.

The Evolution of Query Engines

Historically, query engines were built around interpreted execution models. Systems like Apache Spark brought scalability but often sacrificed raw performance due to JVM overhead and generalized execution paths.

Over time, the need for faster and more efficient processing led to innovations such as:

• Vectorized execution
• Columnar storage formats
• Cost-based optimization

However, even with these improvements, there remained a performance gap—especially for compute-intensive workloads. This is where native execution engines come into play.

Understanding Native Query Engines

Native query engines are built using low-level programming languages like C++ instead of JVM-based languages. This allows them to:

• Achieve better memory management
• Utilize CPU caches more efficiently
• Reduce serialization overhead
• Leverage SIMD (Single Instruction Multiple Data) operations

The result is significantly faster query execution, especially for analytical workloads.

Photon for High-Performance Execution in the Lakehouse

Photon is a native query engine developed by Databricks, designed to accelerate workloads on the lakehouse architecture. Built in C++, Photon integrates seamlessly with Apache Spark while replacing its execution layer with a highly optimized engine.

Key features of Photon include:

1. Vectorized Processing – Photon processes data in batches rather than row by row, improving CPU efficiency and reducing overhead.
2. Native Code Execution – By bypassing the JVM, Photon achieves faster execution times and better resource utilization.
3. Automatic Acceleration – Users do not need to rewrite queries—Photon automatically optimizes workloads behind the scenes.
4. Tight Integration with Delta Lake – Photon is optimized for Delta Lake, enabling faster reads and writes for transactional data.

For organizations already using Spark, Photon offers a seamless way to boost performance without major architectural changes.

Velox for A Unified Execution Engine

Velox, developed by Meta (Facebook), is another major innovation in the query engine space. Unlike Photon, which is tightly integrated with Databricks, Velox is designed as a reusable execution engine that can be embedded into multiple systems.

Velox aims to unify query execution across different platforms, reducing duplication of effort and improving consistency.

Key highlights of Velox include:

1. Reusable Engine Architecture – Velox can be integrated with engines like Presto, Spark, and others, providing a shared execution layer.
2. Advanced Vectorization – It uses highly optimized vectorized processing for efficient computation.
3. Memory Efficiency – Velox includes sophisticated memory management techniques to handle large-scale workloads.
4. Open Source Flexibility – As an open-source project, Velox encourages community contributions and innovation.

Velox represents a shift toward modular data systems, where execution engines are decoupled from query planners and storage layers.

The Rise of Native Execution in Modern Data Systems

Both Photon and Velox are part of a broader trend: moving away from generic execution layers toward specialized, high-performance engines.

This trend is driven by several factors:

1. Hardware Awareness – Modern CPUs offer advanced capabilities such as vector instructions and multi-level caching. Native engines are designed to fully exploit these features.
2. Columnar Data Formats – Formats like Parquet and ORC store data in columns, enabling efficient scanning and compression. Native engines are optimized for these formats.
3. Real-Time Analytics – Businesses increasingly require real-time insights, which demand low-latency query execution.
4. Cost Efficiency – Faster queries mean less compute time, which translates to lower infrastructure costs.

Benefits of Native Engines

Adopting native query engines can deliver significant advantages:

1. Faster Query Execution – Reduced latency leads to better user experience and quicker insights.
2. Improved Resource Utilization – Efficient CPU and memory usage lowers operational costs.
3. Scalability – Native engines handle large datasets more effectively.
4. Simplified Optimization – Many optimizations are handled automatically, reducing manual tuning.

Challenges and Considerations

Despite their benefits, native engines are not without challenges:

1. Ecosystem Lock-In – Some solutions, like Photon, are tied to specific platforms.
2. Complexity – Integrating engines like Velox requires engineering effort.
3. Debugging and Observability – Low-level execution can make debugging more complex.
4. Skill Requirements – Teams may need expertise in performance tuning and system internals.

Conclusion

Whether you are working within a managed platform like Databricks or building custom data infrastructure, understanding these technologies is essential. As data continues to grow in scale and importance, the ability to process it quickly and efficiently will define the success of modern data-driven organizations.

Drop a query if you have any questions regarding query performance and we will get back to you quickly.