LFC Photon Engine Optimization 3 — Questions and Answers
Question 1: Which file size scenario best utilizes Photon's performance capabilities?
- Many small files of 1-2 MB each
- Optimally-sized files of 128 MB to 1 GB (Correct answer)
- One extremely large file of 100 GB
- Compressed files smaller than 10 KB
Correct answer: Optimally-sized files of 128 MB to 1 GB
Photon performs best with optimally-sized files that minimize the small-files problem overhead while providing sufficient parallelism and data for vectorized processing.
Question 2: When using Photon with Delta Lake's OPTIMIZE command, which index structure improves data skipping effectiveness?
- B-tree indexes on all columns
- Z-order clustering on high-cardinality filter columns (Correct answer)
- Full-table hash indexes
- Bitmap indexes on low-cardinality columns
Correct answer: Z-order clustering on high-cardinality filter columns
Z-order clustering co-locates related data by multiple columns, making Delta Lake's file-level statistics more selective and enabling Photon to skip more files.
Question 3: Which SQL operation type is NOT natively accelerated by Photon?
- Hash joins between large tables
- Sorted aggregations with ORDER BY
- Arbitrary Python UDFs wrapped in pandas_udf (Correct answer)
- Window functions with PARTITION BY
Correct answer: Arbitrary Python UDFs wrapped in pandas_udf
Python UDFs, including pandas UDFs, run in the Python interpreter and cannot be executed natively by Photon's C++ engine.
Question 4: In a Photon-enabled cluster, what is the effect of increasing the number of shuffle partitions on a large aggregation query?
- More shuffle partitions always improve Photon performance linearly
- Too many partitions create small shuffle files that reduce Photon's vectorized batch efficiency (Correct answer)
- Photon ignores shuffle partition count and manages it internally
- Photon converts extra partitions into in-memory sort buffers
Correct answer: Too many partitions create small shuffle files that reduce Photon's vectorized batch efficiency
Excessively many shuffle partitions produce very small data batches per partition, reducing the effectiveness of Photon's vectorized processing which benefits from larger batches.
Question 5: Which runtime statistic in the Spark UI is most useful for identifying whether Photon is providing CPU efficiency gains?
- Number of executor threads created
- CPU time vs. wall-clock time ratio per stage (Correct answer)
- Total bytes written to object storage
- Number of task retries
Correct answer: CPU time vs. wall-clock time ratio per stage
A high CPU utilization relative to wall-clock time indicates Photon is efficiently using CPU resources rather than waiting on I/O or coordination overhead.
Question 6: How does Photon's memory management differ from standard Spark's JVM heap usage?
- Photon uses JVM heap memory with compressed object pointers
- Photon allocates off-heap native memory, reducing JVM garbage collection pressure (Correct answer)
- Photon uses disk-backed memory-mapped files for all operations
- Photon shares memory directly with the driver node
Correct answer: Photon allocates off-heap native memory, reducing JVM garbage collection pressure
Photon manages its own native off-heap memory, avoiding JVM garbage collection pauses that can degrade performance in standard Spark.
Question 7: Which Delta Lake table feature works synergistically with Photon to minimize data scanned during queries?
- Delta table versioning for time travel
- Column-level statistics stored in the Delta transaction log (Correct answer)
- CLONE operation for table snapshots
- CDC (Change Data Capture) feed files
Correct answer: Column-level statistics stored in the Delta transaction log
Column-level min/max statistics in Delta's transaction log enable Photon to skip entire files during scan, drastically reducing I/O.
LFC Photon Engine Optimization 3
Which file size scenario best utilizes Photon's performance capabilities?