Databricks Data Engineer Professional Question 30
Select 4A data engineering team is working on optimizing query performance on a Delta Lake table containing billions of e-commerce transaction records. The table has the following characteristics:
- Queries frequently filter data by 'region' and 'transaction_date'.
- The size of each transaction record is small (around a few KB).
- Queries often require accessing recent transactions or specific regions.
Which optimization techniques should the team apply to ensure the best query performance?
- A
Partition the table by 'region' and 'transaction_date'.
- B
Apply Z-Ordering on the 'transaction_date' and 'region' columns.
- C
Use Bloom filters on the 'region' column.
- D
Reduce the file sizes by merging smaller files into larger ones.
- E
Partition the table by 'transaction_amount' to improve performance for filtering on transaction amounts.
Show answer and explanation
Correct answers: A, B, C, D
Explanation
To optimize query performance on a Delta Lake table with billions of records, the team should focus on techniques that align with the query patterns and data characteristics. Partitioning ensures that only relevant partitions are scanned, Z-Ordering improves data locality within partitions, Bloom filters reduce unnecessary file scans, and optimizing file sizes improves I/O efficiency. Partitioning by unrelated or high-cardinality columns like 'transaction_amount' would lead to suboptimal results.
- A. Correct.
Partitioning the table by 'region' and 'transaction_date' is a good optimization approach because it aligns with the filtering patterns in queries, allowing Spark to skip unnecessary partitions during query execution.
- B. Correct.
Applying Z-Ordering on 'transaction_date' and 'region' enhances data locality for queries that filter on these columns, reducing the number of files scanned.
- C. Correct.
Using Bloom filters on the 'region' column improves query performance by allowing quick exclusion of files that do not contain values matching the filter condition.
- D. Correct.
Reducing the file sizes by merging smaller files into larger ones optimizes file access, reduces I/O overhead, and improves query execution efficiency, especially for small record sizes.
- E. Incorrect.
Partitioning by 'transaction_amount' is not a suitable optimization in this case since the queries do not frequently filter on this column. Additionally, 'transaction_amount' is likely to have high cardinality, leading to inefficient partitioning.