Service Health Check Archives

PostgreSQL Database SLAs: Why Hidden Issues Often Break Customer Commitments

SLAs feel reassuring when signed—but their substance lies in what happens behind the scenes. Often, the most damaging breaches don’t stem from cloud outages or server failures, but from invisible issues hidden in how PostgreSQL was initially set up and configured. Increasingly sluggish queries, split-brain scenarios, silent backup failures, any of these can suddenly explode into customer-facing crises. 1. Slow Queries: The Sneaky SLA Saboteur The Hidden Cost of Delayed Queries A seemingly minor tuning oversight, like a missing index or outdated statistics, can turn a 200 ms query into a 10-second slog. It might not seem urgent initially, but as concurrency increases, cascading delays build up. A Slow Query Accelerated 1000× In one case study, an engineer faced a painfully slow query that scanned 50 million rows through a sequential scan—even though it was a simple query filtering on two columns (col_1, col_2) and selecting by id. After creating an index using those columns plus an INCLUDE (id) clause, the query performance improved dramatically: what had taken seconds dropped to just milliseconds, representing up to a 1,000× improvement in the worst-case runtime. [Ref: Learnings from a slow query analysis in PostgreSQL] This shows how even a simple query, if not indexed properly, can pose an SLA risk as data volume increases.

When PostgreSQL performance slows down, here is where to look first

PostgreSQL is built to perform. However, as workloads increase and systems evolve, even the most robust setups can begin to show signs of strain. Whether you are scaling a product or supporting enterprise SLAs, performance slowdowns tend to surface when you least want them to. If you are a technology leader overseeing a team of developers who manage PostgreSQL as part of a broader application stack, or you are responsible for uptime and customer satisfaction at scale, knowing where to look first can make all the difference.

Checklist: Is Your PostgreSQL Deployment Production-Grade?

One of the things I admire most about PostgreSQL is its ease of getting started. I have seen many developers and teams pick it up, launch something quickly, and build real value without needing a DBA or complex tooling. That simplicity is part of what makes PostgreSQL so widely adopted. However, over time, as the application grows and traffic increases, new challenges emerge. Queries slow down, disk usage balloons, or a minor issue leads to unexpected downtime. This is a journey I have witnessed unfold across many teams. I don’t think of it as a mistake or an oversight; it is simply the natural progression of a system evolving from development to production scale. The idea behind this blog is to help you assess your current situation and identify steps that can enhance the robustness, security, and scalability of your PostgreSQL deployment.

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.

Enhancing PostgreSQL Performance Monitoring: A Comprehensive Guide to pg_stat_statements

PostgreSQL has a rich set of features designed to handle complex queries efficiently. Much like any database system, however, its performance can degrade over time due to inefficient queries, improper indexing, and various other factors. To tackle these challenges, PostgreSQL provides several tools and extensions, among which `pg_stat_statements` stands out as a critical component for performance monitoring and tuning. Introduction to pg_stat_statements `pg_stat_statements` is an extension for PostgreSQL that tracks execution statistics of SQL statements. It is designed to provide insight into the performance characteristics of database queries by collecting data on various metrics such as execution time, number of calls, and I/O operations. This extension is immensely useful for database administrators and developers looking to optimize their SQL queries and improve overall database performance.