Long-running PostgreSQL queries can significantly impact the performance of a PostgreSQL database. Here's an in-depth look at how this happens and some practical tips for troubleshooting:

How Long-Running Queries Impact PostgreSQL Performance:

1. Resource Consumption: Long-running queries consume significant CPU and memory resources. If these resources are exhausted, it can slow down the execution of other queries and overall database performance.
2. Lock Contention: These queries might hold locks for extended periods, blocking other transactions and leading to increased waiting times for other operations.
3. Impact on Query Execution Plans:
   • Plan Caching Issues: PostgreSQL uses a query planner to determine the most efficient way to execute a query. Long-running queries can influence the planner's decisions, especially if the underlying data distribution changes over time.
   • Inaccurate Statistics: PostgreSQL relies on statistics about the data distribution to create execution plans. Long-running queries might operate on stale statistics, leading to suboptimal plan choices.
4. Bloating: Frequent updates and deletions can lead to table bloat, where the physical size of the database grows disproportionately to its actual data content. This can slow down query execution.
5. Impact on Replication: In a replicated environment, long-running queries can delay replication, leading to lags and potentially impacting disaster recovery and high availability setups.

Practical Examples:

1. Example of Lock Contention: A long-running UPDATE query on a heavily used table can hold a write lock for an extended period, preventing other transactions from accessing that table.
2. Example of Plan Inefficiency: Suppose a query initially runs efficiently but over time, due to changes in data distribution, its execution plan becomes less efficient, leading to longer run times.

Tips and Tricks for Troubleshooting:

1. Query Monitoring: Regularly monitor and identify long-running queries using tools like pg_stat_activity.
2. Explain Analyze: Use the EXPLAIN ANALYZE command to understand the execution plan of a query and identify bottlenecks.
3. Statistics Maintenance: Regularly update statistics with ANALYZE to ensure the query planner has accurate data distribution information.
4. Indexing: Review and optimize indexes. Sometimes, adding or modifying indexes can significantly reduce query times.
5. Query Refactoring: Break down complex queries into simpler ones, or rewrite them for efficiency.
6. Partitioning: For very large tables, consider table partitioning to improve query performance.
7. Resource Tuning: Adjust configuration parameters like work_mem and maintenance_work_mem to allocate more resources to query processing.
8. Bloat Management: Regularly vacuum tables to prevent bloat and maintain efficient data storage.
9. Avoid Lock Contention: Identify transactions that hold locks for too long and optimize them to reduce their duration.
10. Monitoring Tools: Use performance monitoring tools like PgAdmin, Grafana, or custom scripts to track and analyze performance metrics over time.

Remember, each PostgreSQL setup is unique, and what works in one scenario might not be applicable in another. A thorough understanding of your specific database environment, workload characteristics, and regular monitoring are key to effectively managing and troubleshooting performance issues.