VACUUM FULL in PostgreSQL – What you need to be mindful of

If you have worked with PostgreSQL for a while, you have probably come across the command VACUUM FULL. At first glance, it might seem like a silver bullet for reclaiming disk space and optimizing tables. After all, who would not want to tidy things up and make their database more efficient, right? But here is the thing: while VACUUM FULL can be useful in some situations, it is not the hero it might seem. In fact, it can cause more problems than it solves if you are not careful. Let us dive into: - What VACUUM FULL actually does - When you should use it - Why it is not the best solution for most cases - And what to do instead What Does VACUUM FULL Actually Do? PostgreSQL uses something called Multi-Version Concurrency Control (MVCC). Without getting too technical, MVCC keeps multiple versions of rows around to handle updates and deletes efficiently. These older versions of rows - called dead tuples - are cleaned up by a process called vacuuming. A regular VACUUM removes those dead tuples so the space can be reused. VACUUM FULL, however, goes further. It rewrites the entire table to remove dead space completely. It also rebuilds all the indexes on the table. Essentially, it is like dumping all your clothes out of the closet, refolding everything, and putting it back in neatly. Sounds great, right? So, why not use it all the time? When Should You Actually Use VACUUM FULL? There are a few very specific situations where VACUUM FULL makes sense: After Massive Deletions Imagine you delete millions of rows from a table. Regular vacuuming might not reclaim that disk space immediately, and the table could still look bloated. In this case, VACUUM FULL can shrink the table and give you that disk space back. Disk Space Crunch If your database server is running out of disk space and you need to reclaim it fast, VACUUM FULL can help (though it is still not ideal—more on that later). Post-Migration Cleanup If you have migrated a large table or reorganized your data, VACUUM FULL can clean things up during planned downtime. Outside of these scenarios, though, VACUUM FULL is usually not your best option. Why? Let us break it down.
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Understanding Wait Events in PostgreSQL

As databases grow in size and complexity, performance issues inevitably arise. Whether it is slow query execution, lock contention, or disk I/O bottlenecks, identifying the root cause of these issues is often the most challenging aspect of database management. One way to understand performance bottlenecks is to determine what the database is waiting for. Wait events in PostgreSQL provide detailed insights into what a database backend process is waiting for when it is not actively executing queries. Understanding and analyzing these events enables DBAs to resolve bottlenecks with precision. What Are Wait Events in PostgreSQL? Wait events represent the specific resources or conditions that a PostgreSQL backend process is waiting on while it is idle. When a process encounters a delay due to resource contention, input/output (I/O) operations, or other reasons, PostgreSQL logs the wait event to help you understand the source of the problem. Why Wait Events Matter Wait events can help reveal the underlying cause for slow query execution. For example: - When a query waits for a lock held by another transaction, it logs a Lock event. - When a process is waiting for disk reads, it logs an I/O event. - When a replication delay occurs, it logs a Replication event. By analyzing and acting on wait events, DBAs can: - Reduce query execution times. - Optimize hardware utilization. - Improve user experience by minimizing delays. How PostgreSQL Tracks Wait Events PostgreSQL backend processes constantly update their current state, including any associated wait events. These states are exposed through dynamic management views like pg_stat_activity and pg_stat_wait_events. By querying these views, you can see which events are impacting performance in real-time.
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3 Essential PostgreSQL Priorities for 2025

As IT budgets tighten and workloads increase, 2025 is the year to focus on maximizing PostgreSQL efficiency, security, and reliability. Whether you are running fully-managed or self-managed PostgreSQL databases, these three priorities - Reducing cloud costs - Increasing data security, and - Enhancing availability will be key to staying competitive. Here is a deep dive into each priority and actionable steps to make them a reality. 1. Reduce Cloud Costs Without Compromising Performance Cloud costs can escalate quickly when PostgreSQL instances are not optimized for the workload. Here is how to implement cost-saving measures with technical precision: Instance Sizing and Scaling Analyze Workload Patterns: Use tools like pg_stat_activity and pg_stat_user_tables to identify peak usage and idle times. Leverage this data to choose the right instance type and size. Autoscaling with Load Balancers: Deploy PostgreSQL in a cloud environment using managed services that support autoscaling or set up custom scaling policies. Storage and Index Optimization Partitioning: Use table partitioning to manage large datasets efficiently and reduce query processing times. For instance, partition large logs by time, and ensure that queries use partition pruning. Index Tuning: Remove redundant indexes using pg_stat_user_indexes and optimize index types (e.g., switching from B-Tree to GiST or GIN indexes for specific queries). This reduces storage requirements and speeds up query performance. Query Optimization EXPLAIN and ANALYZE: Run slow queries through EXPLAIN to pinpoint inefficiencies. Common culprits include sequential scans on large tables and ineffcient join strategies with large datasets. Caching Frequently Accessed Data: Use tools like pgpool-II to enable query result caching and connection pooling, minimizing redundant query execution. These optimizations not only reduce costs but also improve overall database responsiveness.
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