pg_partman is an extension for PostgreSQL that provides automated partition management. While it significantly eases the process of managing partitioned tables, there can be some negative impacts on performance under certain circumstances:

1. Increased Planning Time: pg_partman can increase the time it takes for PostgreSQL to plan queries, especially when there are a large number of partitions. Each partition adds to the complexity of the query planner's task, potentially leading to longer planning times.
2. Maintenance Overhead: Automated partition maintenance tasks, like creating new partitions or dropping old ones, can be resource-intensive. These operations, if not carefully managed, can consume significant system resources (CPU, I/O), impacting the overall performance.
3. Trigger-Based Partitioning: For versions of PostgreSQL prior to 11, pg_partman uses trigger-based partitioning, which can be less efficient than native declarative partitioning available in later versions. Triggers introduce additional processing for every row inserted, updated, or deleted.
4. Index and Constraint Management: pg_partman requires careful management of indexes and constraints across partitions. Mismanagement can lead to inefficient query performance due to poor index usage or increased constraint validation time.
5. Data Skew: In cases of uneven data distribution across partitions (data skew), some partitions may become significantly larger than others. This can result in unbalanced I/O and memory usage, affecting query performance.
6. Lock Contention: Partition maintenance operations may lead to lock contention, especially in high-concurrency environments. This can impact the performance of both DDL and DML operations.
7. Background Worker Usage: pg_partman uses background workers for some of its tasks. Overuse or improper configuration of these workers can affect the overall performance of the PostgreSQL instance, as they consume memory and processing resources.

To mitigate these potential issues, it's important to:

• Regularly monitor and analyze the performance of partitioned tables.
• Tune the partitioning strategy according to data access patterns.
• Ensure that the database hardware and configuration are suitable for the partitioning strategy employed.
• Upgrade to the latest PostgreSQL version to benefit from improved native partitioning features.