The EXPLAIN ANALYZE statement in PostgreSQL provides critical insights into query execution, helping to identify and troubleshoot performance issues. Datadog has enhanced its Database Monitoring service to automate the capture of execution plans from PostgreSQL using the auto_explain extension. Users can set criteria, such as queries that take longer than one second, to automatically gather this data. Once enabled, Datadog links the execution details with application performance management (APM) traces, allowing for a streamlined process to pinpoint performance problems. The article highlights two primary ways to utilize EXPLAIN ANALYZE for troubleshooting. First, comparing estimated versus actual row counts can reveal discrepancies that lead to inefficient query plans. For instance, if a query's estimated rows are significantly lower than the actual rows processed, it may trigger a less efficient nested loop join instead of a preferred hash join. This indicates the need for updated table statistics through the ANALYZE command. Second, the article emphasizes the importance of understanding where reads occur during query execution. By analyzing disk reads versus cache hits, users can determine if performance issues stem from slow storage access or if the query primarily runs from cached data. A high number of disk reads suggests a need to improve caching or reduce I/O. In contrast, low disk reads with high cache hits indicate adequate RAM, prompting a focus on query optimization rather than hardware upgrades. This detailed approach helps users effectively address slow queries within Datadog's platform.