Photon
Warehouse on Photon, same 40-minute dashboard
Symptoms
- UDF for currency conversion
- Tiny files in gold
Photon interview questions
Interactive Databricks interview questions on Photon. Same topic as /learn/databricks/photon. Photon is Databricks' native vectorized engine. Eligible operators run off-JVM; UDFs often fall back to Spark.
Question 1 of 3
beginner
PhotonWhat is Photon?
Answer it out loud, then reveal. Play steps through like the simulators.
Production scenario
Case 1 of 1 · symptoms
All questions on this page
Indexed as FAQ. Open any item if you prefer a list to Play.
beginnerWhat is Photon?
Photon · tap to open the answer
Short: Databricks' native vectorized engine for eligible SQL/DataFrame operators.
Detailed: It runs in C++ on the data plane, not a new storage format. If the plan is eligible, scans/joins/aggs get faster. UDFs and some expressions fall back to the JVM.
Common mistake: Photon as a magic switch that always 10×s every notebook.
Follow-up: Where do you see whether Photon ran?
intermediateA Python UDF in the query — what happens to Photon?
Photon · tap to open the answer
Short: That operator (and often the pipeline around it) falls back.
Detailed: SQL UI / explain: Photon vs JVM. Rewrite with built-ins. Pandas UDFs are still not Photon-native.
Common mistake: Turning Photon on and keeping the UDF.
Follow-up: Does Photon run on all-purpose, jobs, and warehouses?
seniorPhoton on, runtime unchanged. How do you investigate?
Photon · tap to open the answer
Short: The bottleneck isn't a Photon-eligible operator — tiny files, skew, or a UDF/scan of the whole lake.
Detailed: Check Photon usage % in the SQL profile. If it's high and you're still slow, it's I/O or shuffle skew. If it's ~0%, find the fallback expression. Don't buy bigger Photon SKUs first.
Senior: Photon accelerates a good plan. It does not fix a bad model, tiny files, or collect().
Common mistake: Upgrading DBU SKU without a profile.
Follow-up: What's the interview closer?