ARA-C01 Question 335
Single answerJavaA data engineering team has implemented a Java stored procedure in Snowflake to orchestrate several SQL steps for a nightly load. The procedure iterates through a result set of staging records and, for each row, calls Session.sql() to execute an INSERT into a target table. During peak volume, execution time has become unacceptable and warehouse costs have increased. The architect wants to improve performance while keeping the logic inside Snowflake. Which approach is the BEST recommendation?
- A
Refactor the Java stored procedure to build a single set-based SQL operation (for example, INSERT ... SELECT or MERGE) and execute it once instead of issuing one SQL statement per row.
- B
Increase the warehouse size and keep the row-by-row Session.sql() pattern, because Java stored procedures are optimized for iterative DML workloads inside Snowflake.
- C
Replace the Java stored procedure with a Java UDF, because Java UDFs are designed to perform transactional multi-statement DML more efficiently than stored procedures.
- D
Move the per-row INSERT logic into client-side JDBC code outside Snowflake, because Java stored procedures cannot execute SQL statements that modify tables.
Show answer and explanation
Correct answer: A
Explanation
The best recommendation is to redesign the workload around set-based SQL and use the Java stored procedure only for orchestration where necessary. Snowflake architecture is optimized for operating on sets of data rather than processing rows one at a time. In Java stored procedures, repeated Session.sql() calls inside loops can create avoidable overhead and lead to poor performance and higher warehouse consumption. Best practice is to express the transformation as a single SQL statement such as INSERT ... SELECT or MERGE whenever possible.
This aligns with Snowflake guidance for stored procedures and Snowpark: stored procedures are appropriate for control flow and orchestration, but heavy row-by-row procedural DML is generally an anti-pattern in Snowflake. UDFs are also not a substitute here because they are not designed to execute multi-statement DML workflows. Relevant Snowflake documentation includes Snowpark for Java stored procedures, SQL command execution from procedures, and general performance best practices favoring set-based processing over iterative row handling.
- A. Correct.
Correct. In Snowflake, set-based SQL is generally the preferred pattern for performance and scalability. A Java stored procedure can orchestrate logic, but repeatedly calling Session.sql() for individual row-level DML introduces significant overhead and underuses Snowflake's strengths as a massively parallel analytic engine. Refactoring to a single INSERT ... SELECT, MERGE, or similar set-oriented statement typically reduces execution time and cost substantially.
- B. Incorrect.
Incorrect. Increasing warehouse size may mask the symptom temporarily, but it does not address the architectural inefficiency of row-by-row SQL execution. Snowflake performs best with set-based operations, and Java stored procedures are not intended to make iterative per-row DML the preferred pattern. This option reflects the common misconception that compute scaling alone is the best fix for procedural anti-patterns.
- C. Incorrect.
Incorrect. Java UDFs are used to compute and return values, not to orchestrate multi-statement transactional workflows that perform DML against tables. Stored procedures, not UDFs, are the correct construct for executing SQL statements such as INSERT, UPDATE, DELETE, and MERGE. This option confuses the roles of UDFs and stored procedures.
- D. Incorrect.
Incorrect. Java stored procedures in Snowflake can execute SQL, including statements that modify tables, by using the Snowpark API session object. Moving logic to external JDBC code would increase data movement and operational complexity and is not required merely because DML is involved. The core issue is the row-by-row pattern, not an inability of Java stored procedures to execute DML.