Unlocking the Power of Databricks Photon for Cost Optimization

February 12, 2025

Databricks Photon is an advanced query engine designed to supercharge performance while optimizing costs. Introduced in 2021, Photon enhances SQL workloads by leveraging a vectorized execution model and hardware optimizations. Unlike Apache Spark’s traditional execution model, Photon is written in C++, allowing for better memory management and CPU efficiency. For an in-depth look at its architecture and optimizations, refer to the Photon whitepaper.

What is Photon?

Photon was built to address performance bottlenecks in SQL query execution within Databricks. By utilizing SIMD (Single Instruction, Multiple Data) operations and cache locality, it can process large datasets up to 8x faster than Apache Spark’s traditional execution engine (see Databricks Photon Overview).

As mentioned in Databricks documentation, Photon is particularly effective for interactive and batch queries, significantly reducing execution times for SELECT, FILTER, and JOIN operations. However, it does not accelerate all workloads equally and is primarily optimized for SQL-heavy tasks rather than machine learning pipelines.

How to Enable Photon (Step-by-Step Guide)

Enabling Photon is straightforward in Databricks Clusters. Follow these steps:

Step 1: Navigate to the Cluster Configuration

  • Open your Databricks workspace.
  • Go to the Compute tab and select an existing cluster or create a new one.

Step 2: Enable Photon Acceleration

  • Under the Advanced Options, locate the Photon Acceleration toggle.
  • Enable it by clicking the checkbox.

Step 3: Save and Restart the Cluster

  • Click Save & Restart to apply changes.

Enabling Photon via SQL Warehouse

  • Navigate to SQL Warehouses in your Databricks UI.
  • Select the warehouse where Photon should be enabled.
  • Check the Use Photon option.

For additional details, refer to the Databricks Photon documentation.

Limitations of Photon

While Photon delivers significant performance gains, it is important to understand its limitations:

  • Not all workloads benefit equally: Users have reported that while large SQL queries see noticeable performance gains, smaller workloads or those involving complex transformations may not experience the same improvements.
  • Limited support for UDFs (User-Defined Functions): Since Photon optimizes vectorized execution, Python UDFs that rely on Apache Spark’s execution model may run on the standard Spark engine instead.
  • Feature Parity with Apache Spark: While Photon accelerates SQL queries, some Spark-native operations might still execute on Spark’s traditional runtime.
  • Dependency on Cluster Type: Photon is only available on DBR (Databricks Runtime) 9.1+ and not all instances support it.

 

When Should You Enable Photon?

Photon is not a one-size-fits-all solution but excels in specific scenarios:

✅ Time-Sensitive Queries:

  • If you need low-latency queries for dashboards and BI tools, Photon is a great choice.
  • Example: Running SQL analytics queries on large datasets where response times are critical.

✅ Heavy SQL Workloads:

  • Photon’s columnar processing is designed for data warehousing workloads.
  • If your queries involve frequent aggregations, joins, or scans, enabling Photon can lead to performance gains.

✅ Cost Efficiency for Large-Scale Queries:

  • In scenarios where long-running queries drive up DBU costs, Photon’s faster execution can offset its additional price.

As mentioned in Miles Cole’s analysis, Photon provides the greatest value for workloads that require interactive query speeds. However, if workloads already perform well under Spark’s default execution model, the additional cost may not be justified.

❌ When Not to Use Photon:

  • If your workloads primarily rely on machine learning pipelines. Photon is optimized for SQL and does not accelerate ML-specific operations. According to Databricks ML Runtime documentation, standard Spark execution is still recommended for machine learning use cases.
  • If your queries are small or already performing well. Some users have noted that Photon provides diminishing returns on smaller datasets.
  • If cost considerations outweigh performance needs. Before enabling Photon, benchmark your workload and compare performance improvements vs. additional DBU costs.

Final Thoughts: Is Photon Worth It?

For organizations using Databricks, Photon can be a game-changer for cost-performance optimization. However, the decision to enable it should be driven by workload characteristics and pricing considerations.

🔹 If your primary use case involves SQL-heavy, time-sensitive queries, enabling Photon is an easy win.

🔹 If you primarily run machine learning pipelines, you may not see substantial benefits.

🔹 Always benchmark performance and compare DBU costs to make an informed decision.

By understanding Photon’s strengths, limitations, and cost implications, you can maximize efficiency and reduce costs while leveraging Databricks at scale.

Interested in optimizing your Databricks costs further? Contact us to explore tailored solutions!

 

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