DEA-C01 Autovacuum Practice Question
A company runs a transactional database on Amazon RDS for PostgreSQL with Multi-AZ deployment. The database size is 2 TB and experiences moderate write load. The company recently enabled RDS Performance Insights and noticed a high number of 'TupleLock' wait events during peak hours. The development team reports that a batch update job runs every hour, updating millions of rows in a large table. The job takes longer than expected. The DBA suspects that excessive row-level locking is causing contention. The team wants to minimize lock contention without changing the application code. Which solution should be implemented?
⚠ Common exam trap
Candidates often assume that increasing instance size resolves all performance issues, but lock contention due to dead tuples requires database-level tuning like autovacuum.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Tune the autovacuum settings (e.g., autovacuum_vacuum_scale_factor and autovacuum_vacuum_threshold) to run more frequently and aggressively.
Tuning autovacuum settings (autovacuum_vacuum_scale_factor and autovacuum_vacuum_threshold) reduces lock contention by cleaning up dead tuples more frequently. In PostgreSQL, row-level locks on heavily updated tables can cause 'TupleLock' wait events. Frequent autovacuum prevents accumulation of dead tuples, reducing the need for lock escalation and shortening update times. Option B (increasing instance size) may improve throughput but does not directly address lock contention. Option C (RDS Proxy) manages connections, not locks. Option D (pg_partman partitioning) reduces row contention but requires application code changes (stem prohibits code changes).
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Tune the autovacuum settings (e.g., autovacuum_vacuum_scale_factor and autovacuum_vacuum_threshold) to run more frequently and aggressively.
Why this is correct
Correct. Tuning autovacuum reduces dead tuple accumulation, minimizing row-level lock contention without application changes.
- ✗
Increase the RDS instance size to a larger instance class with more vCPUs and memory.
Why it's wrong here
Incorrect. Increasing instance size provides more resources but does not address the root cause of lock contention from dead tuples.
- ✗
Enable RDS Proxy to manage database connections and reduce connection overhead.
Why it's wrong here
Incorrect. RDS Proxy manages database connections and connection pooling, not lock contention on row-level operations.
- ✗
Implement table partitioning using the pg_partman extension to split the large table into smaller partitions.
Why it's wrong here
Incorrect. Table partitioning with pg_partman can reduce lock contention by distributing updates across partitions, but it requires changing table schemas and application queries, which is not allowed.
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