I have always been a fan of RANGE partitioning using a date/time value in PostgreSQL. This isn't always possible, however, and I recently came across a scenario where a table had grown large enough that it had to be partitioned, and the only reasonable key to use was a UUID styled identifier.The goal of this post is to highlight when and why hashing your data across partitions in PostgreSQL might be a better approach.Range vs. Hash Partitioning in PostgreSQLRange Partitioning (A Quick Recap)Range partitioning in PostgreSQL uses boundary values that define slices of the data, often by date or numeric ranges. If you have a transactions table, you might create monthly partitions based on a transaction_date column. This is intuitive for time-series data because each partition holds rows from a specific date range.Advantages of Range Partitioning:Easy pruning for date-based queries. Straightforward approach to archiving old data: drop an entire partition for a past month, rather than issuing a massive DELETE. Pairs nicely with time-based ingestion pipelines, where every day or month gets its own partition. But as convenient as that is, there are cases where range partitioning runs into problems.Why Range Partitioning Can Fall ShortData Skew: If a huge portion of data lands in a single time interval—say, because of a traffic spike in the current month—that monthly partition might end up significantly larger than the others. Complex Backfills: Not everyone ingests data in an orderly, daily manner. Sometimes you need to re-ingest or correct data that spans multiple periods. Merging or splitting range partitions can get cumbersome. Non-Date Dimensions: Some tables aren’t naturally tied to a sequential numeric or date dimension. If your queries center on user IDs or device IDs, dividing by date might not solve your performance issues.

Autovacuum is one of PostgreSQL's most powerful features, designed to maintain database health and optimize performance by automating routine maintenance tasks. However, improper configuration can lead to performance bottlenecks, excessive costs due to resource inefficiency, or uncontrolled table bloat. This blog explores what autovacuum is, its role in performance optimization and cost reduction, and best practices for configuring its parameters.What is Autovacuum? Autovacuum is a background process in PostgreSQL responsible for maintaining table health by performing two critical tasks:1. Vacuuming - Removes dead tuples (rows that have been updated or deleted but are no longer visible). - Frees up space for reuse to prevent table bloat and reduce storage costs.2. Analyzing - Updates table statistics used by the query planner to optimize execution plans, improving query performance.Without autovacuum, dead tuples can accumulate, leading to: - Table Bloat: Increased disk usage drives up storage costs and slows query performance. - Transaction ID Wraparound: A situation that forces the system to go into ‘safe mode’, blocking non-superuser transactions to protect data integrity. This can render the database unusable if not addressed, causing downtime and increased operational costs.By automating these tasks, autovacuum ensures consistent database performance and minimizes unnecessary costs.

Cloud costs can quickly spiral out of control if resources are not optimized. One of the most significant contributors to these costs is CPU core allocation, which forms the basis of the instance size with every major cloud provider. Many organizations over-provision cores for their PostgreSQL databases, paying for unused capacity, or under-provision them, leading to poor performance and missed SLAs.This blog will explore strategies to allocate CPU cores effectively for PostgreSQL databases, ensuring optimal performance while keeping cloud expenses in check.The Cost-Performance Tradeoff in the CloudCloud providers charge based on resource usage, and CPU cores are among the most expensive components. Allocating too many cores leads to wasted costs, while too few can cause performance bottlenecks.PostgreSQL databases are particularly sensitive to CPU allocation, as different workloads—OLTP (Online Transaction Processing) vs. OLAP (Online Analytical Processing)—place varying demands on processing power. Finding the right balance is essential to achieving both cost-efficiency and performance reliability.How CPU Core Allocation Impacts PostgreSQLPostgreSQL can leverage multi-core systems effectively, but how you allocate cores depends on your workload:- OLTP Workloads: High concurrency workloads benefit from multiple cores, allowing PostgreSQL to process many small transactions simultaneously. - OLAP Workloads: Analytical queries often rely on parallel execution, utilizing a few powerful cores to handle complex operations like aggregations and joins.Additionally, PostgreSQL supports parallel query execution, which can distribute certain operations across multiple cores. However, parallelism primarily benefits large analytical queries and can sometimes degrade performance for small or simple queries due to overhead. It is critical to assess your workload before over-allocating resources.

Yup, you read it right. Idle transactions can cause massive table bloat that the vacuum process may not be able to address. Bloat causes degradation in performance and can keep encroaching disk space with dead tuples. This blog delves into how idle transactions cause table bloat, why this is problematic, and practical strategies to avoid it.What Is Table Bloat? Table bloat in PostgreSQL occurs when unused or outdated data, known as dead tuples, accumulates in tables and indexes. PostgreSQL uses a Multi-Version Concurrency Control (MVCC) mechanism to maintain data consistency. Each update or delete creates a new version of a row, leaving the old version behind until it is cleaned up by the autovacuum process or manual vacuuming. Bloat becomes problematic when these dead tuples pile up and are not removed, increasing the size of tables and indexes. The larger the table, the slower the queries, leading to degraded database performance and higher storage costs.How Idle Transactions Cause Table Bloat Idle transactions in PostgreSQL are sessions that are connected to the database but not actively issuing queries. There are two primary states of idle transactions:Idle: The connection is open, but no transaction is running. Idle in Transaction: A transaction has been opened (e.g., via BEGIN) but has neither been committed nor rolled back.

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 insteadWhat 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.