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.
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Understanding Split-Brain Scenarios in Highly Available PostgreSQL Clusters

High Availability (HA) refers to a system design approach that ensures a service remains accessible even in the event of hardware or software failures. In PostgreSQL, HA is typically implemented through replication, failover mechanisms, and clustering solutions to minimize downtime and ensure data consistency. Hence, HA is very important for your mission-critical applications.  In this blog post, we will try to explore a critical failure condition known as a split-brain scenario that can occur in PostgreSQL HA clusters. We will first see what split-brain means, and then how it can impact PostgreSQL clusters, and finally discuss how to prevent it through architectural choices and tools available in the PostgreSQL ecosystem
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How to Upgrade Major PostgreSQL Versions: A Practical Production Guide

PostgreSQL versions follow a well-defined five-year support lifecycle. Each major release receives bug fixes, security patches, and minor updates for five years from its initial release date. After that point, the version reaches end-of-life (EOL) and no longer receives official updates. Staying on an EOL version exposes your systems to security risks, potential compatibility issues, and missing performance improvements introduced in later releases. You can always check the current support status of PostgreSQL versions on the official PostgreSQL Versioning Policy page. Upgrading to the latest version ensures long-term stability, access to new features, and better support. Recently, I worked on upgrading a critical production PostgreSQL environment from version 11 to 15. Version 15 was chosen because the client’s application had only been tested up to that release. The system supported large batch workloads and live applications, so we had to be meticulous. While this article draws from that specific project, the steps are broadly applicable to anyone planning a major PostgreSQL upgrade, especially when crossing several versions. This guide outlines a generalized, production-ready approach for performing major version upgrades using the pg_dump/pg_restore method.
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Best Practices for TimescaleDB Massive Delete Operations

Welcome to the second part of our TimescaleDB best practices series! In the first part, we explored how to perform massive backfill operations efficiently, sharing techniques to optimize performance and avoid common pitfalls. If you haven’t had a chance to read the first part yet, you can check it out using this link In today's blog, we will discuss another crucial aspect of time-series data management: massive delete operations. As your data grows over time, older records often lose their relevance but continue to occupy valuable disk space, potentially increasing storage costs and might degrade the performance if not managed well.  Let's walk through some strategies to clean up or downsample aged data in TimescaleDB, helping you maintain a lean, efficient, and cost-effective database.
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How to Safely Perform Backfill Operations in TimescaleDB

Backfilling data into a TimescaleDB hypertable in production can be very tricky, especially when automated processes like compression policies are involved. From past experience, we have seen that if backfill operations aren’t handled properly, they can interfere with these automated tasks, sometimes causing them to stop working altogether.  This blog covers a safer and more reliable approach to backfilling hypertables, along with best practices to prevent disruptions to compression and other background processes. What is a Backfill Operation? Backfilling means adding old or missing data into the database table after some time has already passed.  Imagine you are collecting temperature readings every hour, but your system was down for a day and didn’t save any data. Later, you get that missing data from the local storage of the device or cloud storage, and want to put it back in the right hypertable, which is called backfilling.  In TimescaleDB, this is common with time-series data, but it needs to be done carefully. That’s because TimescaleDB might already be doing things in the background, like compressing old data to save space. If we are not careful, backfilling can mess up these automatic tasks.
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Step by Step Guide on Setting Up Physical Streaming Replication in PostgreSQL

Physical streaming replication in PostgreSQL allows you to maintain a live copy of your database on a standby server, which continuously receives updates from the primary server’s WAL (Write-Ahead Log). This standby (or hot standby) can handle read-only queries and be quickly promoted to primary in case of failover, providing high availability and disaster recovery. In this guide, I will walk through provisioning a primary PostgreSQL 16 server and a standby server on Linux, configuring them for streaming replication, and verifying that everything works. I assume you are an experienced engineer familiar with Linux, but new to PostgreSQL replication, so I will keep it friendly and straightforward. Figure: Real-time data streaming from a primary PostgreSQL server (left) to a standby server (right). The standby constantly applies WAL records received from the primary over a network connection, keeping an up-to-date copy of the database ready for failover. Step 1: Prepare Two Linux Servers and Install PostgreSQL 16 Before diving into PostgreSQL settings, set up two Linux servers (virtual or physical). One will act as the primary database server, and the other as the standby (read replica). For a smooth replication setup, both servers should be as similar as possible in OS, hardware, and PostgreSQL version. In particular, ensure the following prerequisites: PostgreSQL 16 is installed on both servers via the official PostgreSQL repositories. Both servers must run the same major PostgreSQL version and architecture (mixing different versions won’t work for physical replication). If you haven’t installed PostgreSQL yet, do so now (e.g., on Ubuntu: sudo apt install postgresql-16, or on RHEL/CentOS: use the PostgreSQL Yum repository). Make sure the PostgreSQL service is running on the primary server. Network connectivity: The standby must be able to reach the primary on the PostgreSQL port (default 5432). If the servers are in a cloud environment like AWS EC2, configure the security group or firewall to allow the standby’s IP to connect to the primary on port 5432. For example, in AWS you’d add an inbound rule permitting the standby’s private IP address (or subnet) access to port 5432 on the primary. It is best to use private network interfaces for replication to reduce latency and avoid exposing the database publicly. System settings: Ensure your servers have the necessary OS user and permissions for PostgreSQL. The installation usually creates a postgres UNIX user that owns the data directories. You will run many commands as this postgres user. Also, verify that important prerequisites like consistent time sync (NTP) are in place, as it is generally good practice for database servers (though not specific to replication). With the infrastructure ready, let’s proceed to configure the primary PostgreSQL server to accept replication connections.
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Improving PostgreSQL Performance with Partitioning

My recommended methodology for performance improvement of PostgreSQL starts with query optimization. The second step is architectural improvements, part of which is the partitioning of large tables. Partitioning in PostgreSQL is one of those advanced features that can be a powerful performance booster. If your PostgreSQL tables are becoming very large and sluggish, partitioning might be the cure. The Big Table Problem Large tables tend to grow uncontrollably, especially in OLTP or time-series workloads. As millions or billions of rows accumulate, you begin to notice: Slow queries due to full table scans or massive indexes. Heavy I/O usage, especially when indexes cannot fit in memory. Bloated memory during operations like sorting or joining. Increased maintenance cost, with longer VACUUM, ANALYZE, and REINDEX times. Hard-to-manage retention policies, as purging old rows becomes expensive. These problems are amplified in cloud-hosted databases, where every IOPS, GB, or CPU upgrade increases cost.
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Finding Bottlenecks and Avoiding Over-Optimization via Explain Plans

Performance optimization in a production database is important, but trying to over-optimize can make things more complicated without real improvements. In this post, I’ll share two very basic EXPLAIN ANALYZE outputs from a production system. A user asked us to help optimize these queries. I've changed the table and column names for privacy. We will look at how to spot slow parts of a query, improve performance the right way, and avoid unnecessary tuning. Plan A: Identifying and Resolving a Bottleneck Execution Plan A (Before Optimization) Nested Loop (cost=1000.42..25607.38 rows=1 width=129) (actual time=78.521..90.445 rows=0 loops=1) -> Gather (cost=1000.00..25598.95 rows=1 width=65) (actual time=78.520..90.443 rows=0 loops=1) Workers Planned: 2 Workers Launched: 2 -> Parallel Seq Scan on <table_1> e (cost=0.00..24598.85 rows=1 width=65) (actual time=75.351..75.351 rows=0 loops=3) Filter: ((<column_1>) = '<date_value>'::date) AND ((<column_2>)::text = '<event_type>'::text) Rows Removed by Filter: <number_removed_rows> -> Index Scan using <index_name> on <table_2> a (cost=0.42..8.43 rows=1 width=41) (never executed) Index Cond: ((<column_3>)::text = (<column_4>)::text) Filter: ((<column_5>)::text = '<default_value>'::text) Planning Time: 0.466 ms Execution Time: 90.580 ms
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SELECT FOR UPDATE – Reduce Contention and Avoid Deadlocks to Improve Performance in PostgreSQL

Relational databases are at the heart of countless applications around the world, from high-traffic e-commerce websites to enterprise resource planning (ERP) systems and financial services. Concurrency management—where multiple database transactions operate on the same data simultaneously—is critical to getting good performance and avoiding problems like deadlocks or data inconsistencies. When multiple transactions need to modify the same rows, ensuring data consistency can become tricky. A single wrong approach to locking can lead to suboptimal performance or even bring your application to a standstill as numerous transactions block one another. One tool in PostgreSQL’s arsenal to handle concurrency is SELECT FOR UPDATE. It allows you to lock specific rows before updating them, preventing other transactions from modifying those rows until your transaction completes. In this blog, we will dive deep into SELECT FOR UPDATE in PostgreSQL. We will explore how it helps in reducing contention, avoiding deadlocks, and ultimately boosting performance when dealing with highly concurrent applications.
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When HASH partitioning works better than RANGE

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 PostgreSQL Range 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 Short Data 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.
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