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To learn and grow as a Postgres hacker, I want to hear directly from you—PostgreSQL users—about the problems you face, big or small. These could be operational challenges, performance bottlenecks, confusing behaviors, missing features, or friction points in your workflows. ... That and the updates being a royal pain. DumBthInker007 13 days ago ... EDIT: Missing primary keys in-between also bothers me, however, never found a decent solution for this at scale.

While 57% reported that the onboarding experience as either “fairly easy” or “extremely easy,” many cited a few ways to make the onboarding experience even better: ... We asked about the biggest pain points people experience when using Postgres, and many responses included the following: - Documentation could be improved (cleaned up, add more tutorials) - Replication, sharding, partitioning, vacuuming - High availability - Options for scaling up and out - Schema development Perhaps you identify with some of the points listed above (no solution is perfect!), but we *also* saw a ton of positive feedback for Postgres, its ecosystem, and the community.

## You don’t need 20 tools. ... ### One boring old SQL database might be the best backend in 2025. medium.com But then reality shows up usually in the form of corporate infrastructure. You get dropped into a production environment that’s basically a dungeon crawler for database queries: - Twelve network hops before your request even touches the DB - Firewalls that block you like a bouncer at the world’s most boring nightclub - Antivirus software that somehow slows down SQL execution - Latency numbers that would make your Redis cry And the worst part? You’re not the database admin. That means: - No installing PGVector for AI search - No PG Cron for internal scheduling - No PG Crypto for secure operations - Sometimes, you don’t even *know* which region your database is in Suddenly, your “one tool for everything” dream feels like playing Elden Ring… with a potato for a GPU. … ## 4. Row-level security: when theory trolls reality On paper, **Row Level Security (RLS)** sounds brilliant. You set rules so each user only sees the rows they’re allowed to. No more messing around with manual filters in your app layer the database enforces it for you. Then you turn it on in production… and suddenly, every query plan looks like it just rolled a critical fail. What used to be a fast `SELECT` now feels like it’s doing a 14-table join for fun. Indexes? Still there, but apparently on vacation. … But here’s what actually happens: - Debugging becomes a questline from hell no proper stack traces, no modern logging tools, just squinting at SQL like it’s an ancient scroll. - Version control turns into duct tape your stored procedure updates live in random `.sql` files or, worse, only exist in production and nobody remembers who wrote them. - Your CI/CD pipeline? Doesn’t even know this logic exists, so testing is… let’s say “optional.” One developer summed it up perfectly: > *“If you maintain jobs and business logic inside Postgres, you’re giving up git for guesswork.”* It’s not that stored procedures are evil they’re great for certain performance-critical cases but making them the *default* place for all your application logic is asking for future pain. When that pain hits, it doesn’t matter how elegant your SQL was you’ll be the one spelunking through functions at 2 AM while prod is down. … ## You don’t need 20 tools. ... You get dropped into a production environment that’s basically a dungeon crawler for database queries: - Twelve network hops before your request even touches the DB - Firewalls that block you like a bouncer at the world’s most boring nightclub - Antivirus software that somehow slows down SQL execution - Latency numbers that would make your Redis cry And the worst part? You’re not the database admin. That means: - No installing PGVector for AI search - No PG Cron for internal scheduling - No PG Crypto for secure operations - Sometimes, you don’t even *know* which region your database is in Suddenly, your “one tool for everything” dream feels like playing Elden Ring… with a potato for a GPU. … ## 4. Row-level security: when theory trolls reality ... You set rules so each user only sees the rows they’re allowed to. No more messing around with manual filters in your app layer the database enforces it for you. Then you turn it on in production… and suddenly, every query plan looks like it just rolled a critical fail. What used to be a fast `SELECT` now feels like it’s doing a 14-table join for fun. Indexes? Still there, but apparently on vacation. ... But here’s what actually happens: - Debugging becomes a questline from hell no proper stack traces, no modern logging tools, just squinting at SQL like it’s an ancient scroll. - Version control turns into duct tape your stored procedure updates live in random `.sql` files or, worse, only exist in production and nobody remembers who wrote them. - Your CI/CD pipeline? Doesn’t even know this logic exists, so testing is… let’s say “optional.” One developer summed it up perfectly: > *“If you maintain jobs and business logic inside Postgres, you’re giving up git for guesswork.”* It’s not that stored procedures are evil they’re great for certain performance-critical cases but making them the *default* place for all your application logic is asking for future pain. When that pain hits, it doesn’t matter how elegant your SQL was you’ll be the one spelunking through functions at 2 AM while prod is down.

… One is scary plans and statistics out of it which is common like stands are not in place and things are not working. Then vacuum process no post conference is complete without vacuum {ts:65} being talked about. The third is connections then multi- transactions. This is a special feature. Then how memory utilization log contentions are taken there and then we'll also have a quick look on other challenges which might be very and cases … So symptoms are like {ts:187} slow running queries and then you will sometimes you will observe that there are more sequential scans happening or stale data. So these are common symptoms what we see but uh you know most common symptom is what we will say is uh whenever you try to debug the query {ts:206} first of all you need to find it out what exactly or which query is taking time. … Right? That is the {ts:268} important thing because most frequently we will see that if stats are out outdated then query will start giving you uh I mean optimizer will generate a bad plans and because of that you will see that query is getting affected right or you need to see uh there is a right {ts:287} index and which was you know uh the data was less in that table and there is a right index which was uh like getting picked by uh the queries till yesterday but today the data has been grow and now whatever the index right uh it is there on the table it is not actually working

In this code talk, we dive deep into practical tuning techniques to avoid common pitfalls that silently degrade performance by improving an underperforming PostgreSQL database workload. Learn how excessive indexes hurt write throughput, why HOT updates fail, and how vacuum behavior can stall your system. We’ll demonstrate how to use Query Plan Management (QPM) and pg_hint_plan for plan stability and decode wait events to uncover hidden bottlenecks. ... … Otherwise, you'll see high storage and IOPs utilization. {ts:201} And if you have more indexes which are unused or duplicate, every modification to the database will lead to update those indexes unnecessarily, which is where you see storage and I obstacleization. And Postress SQL uses work memory to control the query operations such as

10 ● Cost effective scalability: Scale systems efficiently while minimizing infrastructure costs ● Developer Productivity: Streamline developer workflows to handle rapid iteration with lean teams. ● Operational Costs: Control costs while scaling infrastructure, avoiding over-provisioning. “Had a bunch of issues hitting a scale where the instance size wasnʼt good enough. Downtime was a problem.ˮ “We needed faster iteration cycles with limited infrastructure. The team couldnʼt afford to spend extra time on database maintenance.ˮ 11 ● Schema Migrations: Ensure schema migrations are done without downtime. ● Backup and Recovery: Robust backup and disaster recovery systems as their data grows. ● Operational Costs: Balance performance and cost as they scale data systems. “Schema migrations have always been a challenge, especially without downtime. We donʼt have the luxury of waiting for off-hours.ˮ “We handle over a terabyte of transactional data every day, and backups are critical. Ensuring recovery plans are solid is non-negotiable.ˮ 12 ● High Availability: Zero downtime and fault tolerance across globally distributed systems. ● Real-Time Data Replication: Reliable real-time replication systems to manage large-scale, globally distributed data. ● Performance Monitoring: Require tools to optimize performance at scale and ensure reliable uptime. “Real-time replication across multiple regions is our biggest challenge. We handle petabytes of data that need to be constantly synced.ˮ “Vacuuming and scaling problems with Aurora are a constant headache. Would like something horizontally scalable like CockroachDB.ˮ … 16 Some Postgres schema changes are difficult ● Locking issues (e.g. on a busy table, in a fast a migration can wait at an exclusive lock and cause downtime) https://xata.io/blog/migrations-and-exclusive-locks ● Constraints might require data back-filling (if you add a NOT NULL  UNIQUE column, how do you fill the data? ● Backwards incompatible changes require a multiple step process (e.g. renames) Motivation

### Distributed Transactions and Write Bottlenecks - Many existing PostgreSQL scaling solutions are described as "half-distributed" because they can distribute **read operations**across a cluster but **rely on a single write node**. - This creates a **significant write bottleneck**, especially problematic for real-time systems processing distributed transactions like payments or account balance updates. A constant influx of new transactions can overwhelm this single write point, leading to **performance degradation**. … ### Managing High-Traffic and Performance Degradation - PostgreSQL does not possess an inherent capability to **automatically scale to meet fluctuating demand**; this responsibility **rests entirely with the user**. - Efficient scaling requires **intricate tuning of the database itself**, beyond merely adding more CPU and memory. **Read-heavy workloads**(e.g., reporting) can experience severe degradation without proper read replicas and caching layers. Conversely, **write-heavy workloads**(e.g., financial transactions) demand meticulous indexing and partitioning strategies to prevent slow inserts and locking issues. … ### Challenges with Large Data Volumes and Real-time Analytics - PostgreSQL may **not be the optimal choice for applications requiring real-time or near real-time analytics**, where refresh rates measured in hours or days can be unacceptable. - For **massive single datasets**(billions of rows, hundreds of gigabytes), especially with frequent joins, PostgreSQL performance can be **extremely slow, with queries potentially taking hours**. While techniques like partitioning can help, they **introduce additional layers of complexity**. - PostgreSQL **does not natively support columnar storage**, a crucial feature for efficient analytical workloads, often necessitating **extensions that are not inherent to the core design**. - This suggests enterprises with specific Online Analytical Processing (OLAP) or big data requirements might need a **hybrid database strategy**, increasing architectural complexity and data synchronization challenges. **High Availability, Resilience, and Data Consistency Concerns** Ensuring continuous operation and maintaining data integrity are paramount, but achieving these with PostgreSQL **demands substantial effort and introduces specific risks**. … ### Complexity of Replication, Failover, and Disaster Recovery Setups - PostgreSQL **does not offer native multi-region replication capabilities**; organizations must rely on **logical replication and third-party tools**like pglogical or BDR. - Horizontal scaling further complicates monitoring, backup, and failover management, necessitating **robust tooling and specialized expertise**. - This reliance on external tools increases **vendor dependency and internal expertise requirements**, shifting the burden of integration and maintenance onto the enterprise and leading to potential vendor lock-in, increased operational overhead, and higher risk of misconfiguration. … ### Demands of Manual Configuration and Performance Tuning - PostgreSQL offers a multitude of configuration "levers and knobs" requiring **substantial effort to learn and tune**, especially for self-hosted instances at scale. This includes mastering backup/restore and connection pooling procedures. - Efficient scaling requires **meticulous database tuning**to match specific workloads. - This extensive manual tuning implies a **high and continuous dependency on specialized DBA expertise**, translating into significant personnel costs and creating a potential **single point of failure**if knowledge is not shared. ### Challenges of Major Version Upgrades and Application Compatibility - PostgreSQL **does not support in-place major version upgrades**. Upgrades typically necessitate either **dumping and restoring the entire dataset or setting up logical replication**. **Application compatibility must be rigorously tested**for existing queries, indexes, and extensions. - Delaying upgrades increases complexity and risk, as outdated versions miss critical security patches, performance improvements, and new features, eventually leading to unsupported systems. This transforms routine maintenance into a **complex, high-risk migration project**impacting business continuity and development velocity. … **instance rather than restoring to an existing one.** *new* - High write activity generates large transaction logs, consuming significant disk space. - These granular limitations mean enterprises cannot rely solely on basic features, necessitating **complex, multi-faceted strategies**that combine backups, PITR, and exports, potentially with third-party tools. **Security Vulnerabilities and Compliance Risks** While PostgreSQL has inherent security features, ensuring a secure and compliant enterprise deployment requires **diligent configuration and ongoing vigilance**. ### Common Weaknesses - Many vulnerabilities stem from **misconfiguration and operational oversight**, not software flaws. **Weak Authentication:**Default installations can allow passwordless logins ("Trust" method) if not managed, and lack robust password policies. Broad IP access increases attack surface. **Unencrypted Connections:**Default installations often **do not enable SSL/TLS encryption**, leaving data vulnerable. **Excess User Privileges:**Granting superuser privileges for routine tasks creates unnecessary risks.

Discussion: There are objective reasons for that disconnect: 1. There are very few database courses in colleges, 2. Database changes can't be instantaneously implemented, and they are irreversible for the most part 3. Lack of development tools How can we improve the situation?

like we work with those objections and help to implement, right? So yeah, but we, yeah, for everything we had episode. There are episodes for everything. So this was number 1, heavy load contention. And I chose the most popular reasons. Of course there are other reasons. But in my view, DDL and queue-like

- ## Slower performance: There are various performance issues and backup recovery challenges that people face with Postgres. A lot of times you have a query which is running slow and you suddenly see there is performance degradation in your database environment. When finding a query, Postgres due to its relational database structure has to begin with the first row and then read through the entire table to find the relevant data. Therefore, it performs slower especially when there is a large number of data stored in the rows and columns of a table containing many fields of additional information to compare.

## Introduction PostgreSQL is a powerful open-source relational database system with over 30 years of active development. While it offers robust features and reliability, newcomers often encounter challenges that can lead to performance issues, security vulnerabilities, or unexpected behavior. This guide identifies the most common PostgreSQL pitfalls and provides practical solutions to help you avoid them. ## Connection Management Issues ### Connection Pooling Neglect One of the most common mistakes in PostgreSQL deployments is failing to implement connection pooling. #### The Problem Each PostgreSQL connection consumes server resources (approximately 10MB of RAM). Applications that create new connections for each database operation can quickly exhaust server resources. `// Bad practice: Creating new connections for each operation` const { Pool, Client } = require('pg') // In a web application handling requests app.get('/data', async (req, res) => { … ## Query Performance Issues ### Missing Indexes Failing to create proper indexes is one of the most common causes of poor PostgreSQL performance. #### The Problem Without appropriate indexes, PostgreSQL must perform sequential scans on entire tables, which becomes increasingly slow as data grows. `-- A query that will be slow without proper indexing` SELECT * FROM orders WHERE customer_id = 12345; … ## Data Integrity Issues ### Improper Constraint Usage Not utilizing PostgreSQL's constraint features can lead to data integrity problems. #### The Problem Without proper constraints, invalid data can enter your database: `-- Table without proper constraints` CREATE TABLE users ( id SERIAL, email TEXT, age INTEGER ); -- This allows duplicate emails and negative ages INSERT INTO users (email, age) VALUES ('[email protected]', -10); INSERT INTO users (email, age) VALUES ('[email protected]', 25); … ### Inconsistent Data Types Using inconsistent data types across tables can lead to unexpected behavior. #### The Problem `CREATE TABLE orders (` id SERIAL PRIMARY KEY, customer_id INTEGER, total NUMERIC(10, 2) ); CREATE TABLE customers ( id BIGINT PRIMARY KEY, name TEXT ); -- This foreign key relationship will have issues because of different integer types ALTER TABLE orders ADD CONSTRAINT fk_customer FOREIGN KEY (customer_id) REFERENCES customers(id); … ### Overly Permissive Privileges Giving database users more privileges than they need is a common security mistake. #### The Problem Using a single database user with full privileges for all application operations: `-- Giving too many privileges` GRANT ALL PRIVILEGES ON DATABASE myapp TO webuser; … ## Configuration Pitfalls ### Default Configuration Settings PostgreSQL's default configuration settings are conservative and not optimized for performance. #### The Problem Using default settings can lead to suboptimal performance, especially for larger databases. #### The Solution Tune important configuration parameters for your specific workload: `-- Example configuration adjustments in postgresql.conf` … ## Monitoring and Maintenance Pitfalls ### Lack of Regular VACUUM Failing to run VACUUM regularly can lead to bloated tables and degraded performance. #### The Problem Without VACUUM, PostgreSQL can't reclaim space from deleted rows, leading to table bloat. … ### Overuse of JOINs Designing schemas that require too many JOINs can lead to performance issues. … **Connection Management**: Implement connection pooling and ensure connections are properly closed. **Query Performance**: Create appropriate indexes, avoid N+1 queries, and use query optimization techniques. **Data Integrity**: Use constraints effectively and maintain consistent data types. **Security**: Prevent SQL injection with parameterized queries and implement the principle of least privilege. **Transaction Management**: Keep transactions short and ensure proper commit/rollback handling. **Configuration**: Tune PostgreSQL settings for your specific workload. **Maintenance**: Regular VACUUM and statistics updates are essential. **Schema Design**: Avoid anti-patterns like EAV and excessive JOINs. By addressing these common pitfalls, you'll build more robust, efficient, and maintainable PostgreSQL-based applications.

# Enterprise-scale challenges: Real-world PostgreSQL issues you'll face What works perfectly in your test environment or small deployment often falls apart under actual enterprise demands. This isn't theory; it's what happens in practice. As your traffic grows, your once-speedy queries begin to crawl. Replication that seemed reliable starts to lag. Keeping everything running takes twice the time and three times the effort you planned for. High availability is essential, and every decision about performance, scaling, and reliability carries real consequences. … #### Handling high-traffic and performance bottlenecks PostgreSQL doesn’t automatically scale to meet demand; that part is up to you. The read vs. write problem hits different workloads - Read-heavy workloads (reporting, analytics, search engines) can crush performance if read replicas and caching layers aren’t in place. - Write-heavy workloads (financial transactions, real-time updates) need indexing and partitioning strategies to avoid slow inserts and locking issues. Query performance degrades silently until it's obvious to everyone - A query that ran in milliseconds last year might take seconds this year as data grows. - Index bloat, inefficient joins, and poorly optimized queries slow everything down over time unless teams continuously monitor execution plans. Scaling too late costs more than you think - If read replicas, connection pooling, or indexing aren’t set up early, PostgreSQL slows down when it matters most—during peak traffic. - Scaling PostgreSQL efficiently isn’t just adding more CPU and memory; it requires tuning the database itself. … Why upgrades aren’t simple - PostgreSQL doesn’t support in-place major version upgrades; you need to dump and restore data or set up logical replication. - Application compatibility must be tested to ensure queries, indexes, and extensions still work. - The longer you wait, the more painful the migration becomes. #### Multi-cloud and hybrid deployments: More work than expected Most enterprises don't run PostgreSQL in just one place. You likely have some databases on-premises, others in AWS or Azure, and perhaps more spread across multiple cloud providers. This diversity creates challenges you might not see coming. Configuration drift creates unexpected problems - A PostgreSQL instance in AWS might be configured differently than one running on-prem, leading to unexpected query performance differences and security gaps. - Schema changes, replication settings, and connection pooling can drift over time, causing failures during failover or recovery. Security and compliance multiply across environments - Every cloud provider has different security standards, and keeping PostgreSQL compliant across environments isn’t automatic. - A misconfigured instance in one region could expose vulnerabilities that IT teams don’t catch until an audit—or worse, a breach. Replication and latency challenges grow exponentially - PostgreSQL does not have native multi-region replication, but it supports logical replication and third-party tools (like pglogical or BDR) for distributed setups. - Data consistency issues arise when replication lags, leading to stale reads or conflicts between primary and secondary databases. … - Data consistency risks: PostgreSQL needs persistent storage to protect your data when pods restart or move between nodes. Unlike stateless applications, database containers can't be recreated without careful planning. If your Kubernetes storage isn't properly configured, you risk data corruption or loss during routine operations. - Failover protection requires extra work: While Kubernetes can restart failed pods, this basic function doesn't provide the PostgreSQL-specific failover capabilities your production systems need. To maintain availability, you must implement tools like Patroni for proper leader election and failover. These add complexity and demand specific expertise. - Operational overhead increases: Running PostgreSQL on Kubernetes means managing Operators, persistent volumes, failover procedures, and container-aware backup solutions. Each requires specialized knowledge across both PostgreSQL and Kubernetes technologies. PostgreSQL can function in Kubernetes environments, but the reality is far more complex than most teams anticipate. Without expertise in both technologies, what seems straightforward quickly becomes a significant commitment.