Why is my PostgreSQL connection hanging?

You probably hit the connection limit. PostgreSQL defaults to 100 max connections, and each one eats 2-4MB of RAM. Check with: ```sql SELECT count(*) FROM pg_stat_activity WHERE state = 'active'; ``` If you're near 100, you need connection pooling. Install [PgBouncer](https://pgbouncer.github.io/) or your application will randomly hang when it can't get new connections. Don't just increase `max_connections` without adding more RAM - you'll run out of memory and crash the whole server.

Why is this simple query taking 30 seconds?

Run `EXPLAIN ANALYZE` on your query. You're probably missing an index or PostgreSQL is doing a sequential scan on a million-row table. If you see "Seq Scan" in the output, you fucked up your indexing. Create the index you need: ```sql CREATE INDEX CONCURRENTLY idx_whatever ON your_table(column_name); ``` Use `CONCURRENTLY` or PostgreSQL will lock the table for hours on large datasets. Also run `ANALYZE your_table` after creating indexes - PostgreSQL's statistics might be stale.

How do I fix "FATAL: too many connections for role"?

This happens when you hit connection limits for a specific user. Check current connections: ```sql SELECT usename, count(*) FROM pg_stat_activity GROUP BY usename; ``` Either increase the limit for that user or fix your connection pooling. User-level limits exist for a reason - don't just raise them to 1000 and call it fixed.

My JSON queries are slow as shit, what's wrong?

You probably don't have a GIN index on your JSONB column. PostgreSQL can't efficiently query JSON without proper indexes: ```sql CREATE INDEX CONCURRENTLY idx_json_data ON your_table USING GIN (json_column); ``` GIN indexes are huge and slow down writes, but they're mandatory for JSONB query performance. Also use `@>` operator instead of `->` when possible - it's index-friendly.

PostgreSQL crashed with "out of memory" - now what?

You probably set `shared_buffers` too high or don't have enough RAM for your connection count. Each connection uses 2-4MB, so 500 connections = 2GB just for connections before you even store data. Check your settings: ```sql SHOW shared_buffers; SHOW max_connections; ``` Reduce `shared_buffers` to 15-20% of total RAM (not the bullshit 25% from documentation) and implement connection pooling immediately.

Why did my migration from MySQL take 3 days?

MySQL and PostgreSQL have different data types and SQL dialects. `UNSIGNED INTEGER` becomes `BIGINT`, `AUTO_INCREMENT` becomes `SERIAL`, and don't get me started on date formatting differences. Use [pgloader](https://github.com/dimitri/pgloader) for data migration and budget 3x longer than you think for application code changes. Test everything - MySQL's silent data truncation means you might have corrupted data you didn't know about.

My VACUUM is taking forever and locking everything

Don't use `VACUUM FULL` on production - it locks the entire table. Use regular `VACUUM` or `VACUUM ANALYZE` instead. If your table is heavily updated, you need to tune `autovacuum` settings or run manual VACUUMs more frequently. Check vacuum progress: ```sql SELECT pid, now() - pg_stat_get_backend_start_time(pid) as duration, query FROM pg_stat_activity WHERE query LIKE 'VACUUM%'; ```

Can I run PostgreSQL in Docker for production?

Probably not unless you really know what you're doing. PostgreSQL needs specific kernel parameters for shared memory, and Docker's default storage drivers aren't optimized for database workloads. If you must use containers, use dedicated persistent volumes, tune PostgreSQL for containerized environments, and monitor disk I/O carefully. Most production setups just run PostgreSQL directly on the host.

How do I scale PostgreSQL beyond one server?

You can't easily. PostgreSQL doesn't have built-in sharding like MongoDB. Your options: 1. **Read replicas** - Good for read-heavy workloads 2. **Vertical scaling** - Add more RAM/CPU (works until 64-128GB RAM) 3. **Application-level sharding** - Hard as fuck to implement correctly 4. **Third-party solutions** - Citus, Postgres-XL, but they add complexity Most companies that "scale PostgreSQL" are just running bigger servers with read replicas.

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PostgreSQL: Production Implementation Guide

Configuration Requirements

Critical Settings (Production-Ready)

shared_buffers: 15-20% of total RAM (NOT 25% as documented)
effective_cache_size: 75% of total RAM
max_connections: Each connection consumes 2-4MB RAM
Connection limit calculation: 500 connections = 2GB RAM overhead
Memory threshold: Above 500 connections requires connection pooling (mandatory)

Installation Defaults That Will Fail

Default PostgreSQL configuration optimized for 1990s desktop environments
Ubuntu apt install postgresql postgresql-contrib requires immediate tuning
Default max_connections=100 insufficient for most production workloads
Default shared_buffers setting causes performance degradation

Resource Requirements

Memory Planning

Connection overhead: 2-4MB per connection
Minimum production RAM: 16GB for moderate workloads
Memory allocation failure point: >500 connections without pooling
GIN index overhead: Significantly increases memory usage (50MB+ for PostGIS)

Time Investments

Migration from MySQL: Budget 3x estimated time for application code changes
Major version upgrades: 3 hours downtime for 500GB database
Index creation on large tables: 2-hour lock times without CONCURRENT option
PostgreSQL 15+ upgrade: Required for JSON performance improvements

Expertise Requirements

Basic operation: Standard database administration skills
Scaling beyond single server: Requires serious operational expertise
Extension management: Steep learning curves (PostGIS, TimescaleDB)
Performance tuning: Understanding of query planner and indexing strategies

Critical Failure Modes

Connection Management Failures

Symptom: Application randomly hangs on new connections
Root cause: Hitting max_connections limit (default 100)
Impact: Complete application unavailability
Solution: Implement PgBouncer connection pooling immediately

Memory Exhaustion Patterns

Trigger: shared_buffers set too high OR excessive connections
Warning signs: "out of memory" crashes
Recovery: Reduce shared_buffers to 15-20% RAM, implement connection pooling
Prevention: Monitor connection count and memory usage

Query Performance Degradation

Primary cause: Missing indexes on large tables
Detection: EXPLAIN ANALYZE shows "Seq Scan" operations
Impact: 30+ second query times on simple operations
Solution: CREATE INDEX CONCURRENTLY (prevents table locks)

Technology Comparisons

PostgreSQL vs MySQL

Complex queries: PostgreSQL handles 5+ table joins; MySQL fails
JSON handling: PostgreSQL JSONB vs MySQL's inferior JSON columns
Data consistency: PostgreSQL truly ACID compliant vs MySQL's conditional ACID
Migration difficulty: 3+ days for application code changes from MySQL

PostgreSQL vs MongoDB

JSON performance: PostgreSQL JSONB 60% faster with proper indexing
Data consistency: PostgreSQL ACID vs MongoDB "eventual consistency"
Scaling complexity: PostgreSQL vertical scaling vs MongoDB horizontal scaling
Real-world migrations: 3 documented cases showing PostgreSQL superiority

PostgreSQL vs Cloud Alternatives

Aurora PostgreSQL: NOT real PostgreSQL; MySQL-compatible layer with compatibility issues
Supabase: Good until scale; pricing becomes expensive fast
Neon: 200ms+ latency spikes during auto-scaling events
RDS: Reliable but vendor lock-in with Amazon's ecosystem

Production Scale Thresholds

Vertical Scaling Limits

Effective range: Up to 64-128GB RAM
Performance threshold: 10-100TB depending on query patterns
Coverage: Handles 99% of companies without additional complexity

Horizontal Scaling Reality

No built-in sharding: Unlike MongoDB
Read replicas: Only solution for read-heavy workloads
Application-level sharding: Extremely difficult to implement correctly
Third-party solutions: Citus, Postgres-XL add significant complexity

Extension Ecosystem Intelligence

PostGIS (Geospatial)

Performance: Handles millions of proximity queries per day
Learning curve: Steeper than Elasticsearch
Memory impact: 50MB+ extension size
Use case: Only viable option for location services at scale

TimescaleDB (Time Series)

Alternative to: InfluxDB retention policy issues
Architecture: PostgreSQL with better time-based partitioning
Pricing: Open-source version sufficient; cloud pricing expensive
Performance: Adequate for most time-series use cases

pgvector (AI/ML)

Capacity limit: Under 10 million vectors performs adequately
Performance expectation: NOT Pinecone-level performance
Scaling threshold: Performance degrades significantly beyond 10M vectors
Write performance: HNSW indexes kill write performance

Critical Warnings

Version and Compatibility

Minimum version: PostgreSQL 15+ required for JSON performance
Current stable: PostgreSQL 17.6 (August 2025)
Beta warning: PostgreSQL 18 in beta; avoid for production
Extension compatibility: Always test extensions on new versions first

Docker and Containerization

Production viability: Generally not recommended
Required expertise: Kernel parameter tuning and persistent volume management
Storage issues: Docker default storage drivers not optimized for databases
Recommendation: Run PostgreSQL directly on host for production

Connection Pooling Requirements

Mandatory threshold: Above 100 concurrent connections
Tool recommendation: PgBouncer transaction pooling mode
Mode limitations: Session pooling breaks session state; statement pooling breaks prepared statements
Implementation: Not optional above 100 connections

Operational Intelligence

Backup and Recovery

Tool: pg_dump/pg_restore for PostgreSQL-to-PostgreSQL migrations
Performance: Faster and more reliable than third-party tools
Logical replication: Longer process but provides rollback option
Downtime planning: Budget 3 hours for 500GB database upgrades

Monitoring and Debugging

Essential extension: pg_stat_statements for query performance tracking
Configuration tool: PgTune for initial hardware-based configuration
Log analysis: pgbadger for analyzing PostgreSQL logs
Performance dashboard: pghero for real-time database monitoring

Common Production Issues

VACUUM operations: Never use VACUUM FULL in production (locks entire table)
Index creation: Always use CONCURRENTLY option on large tables
JSON queries: Require GIN indexes; massive performance impact without them
Statistics maintenance: Manual ANALYZE required after index creation

Decision Framework

When to Choose PostgreSQL

Complex queries: More than 3-table joins
JSON requirements: Need ACID compliance with JSON data
Data consistency: ACID compliance non-negotiable
Future scaling: Anticipate growth beyond simple CRUD operations

When to Consider Alternatives

Simple applications: SQLite for single-user applications
Horizontal scaling required: MongoDB for distributed architectures
Legacy constraints: MySQL if already deeply integrated
Specific use cases: Specialized databases for time-series, graph, or vector data

Implementation Success Factors

Team expertise: Database administration skills required
Operational capacity: Monitoring and maintenance capabilities
Scale planning: Understand vertical vs horizontal scaling implications
Extension requirements: Evaluate specific feature needs (geospatial, time-series, etc.)

Useful Links for Further Investigation

Tools That Don't Suck (And Some That Do)

Link	Description
PostgreSQL Documentation	This is the real deal - comprehensive and actually accurate. Not marketing bullshit. Start here when you need to understand how something actually works, not how someone thinks it should work.
PostgreSQL Release Notes	Read these before upgrading. I've saved myself hours of debugging by checking what broke between versions. The PostgreSQL team actually documents their shit.
PgTune	Generates sane PostgreSQL configuration based on your hardware. Much better starting point than the garbage defaults. Still need to tune based on your actual workload, but won't leave you with a database optimized for 1999.
pghero	Web dashboard that shows you what's actually happening in your database. Identifies slow queries, missing indexes, and bloated tables. Install this on day one or spend months guessing why things are slow.
Supabase	Actually good PostgreSQL hosting with real-time features. Pricing is reasonable until you scale, then it gets expensive fast. Better than managing your own database if you're a small team.
Neon	Interesting branching concept for dev environments. Production latency can be inconsistent during auto-scaling events. Good for development workflows, questionable for production.
AWS RDS	Reliable but locks you into Amazon's ecosystem. Aurora PostgreSQL compatibility is weird - it's not real PostgreSQL. RDS proper works fine if you can tolerate vendor lock-in.
PostGIS	The gold standard for geospatial data. Steep learning curve but incredibly powerful. If you need location queries, this is your only real option that doesn't suck.
TimescaleDB	PostgreSQL for time-series data. Actually works, unlike InfluxDB's retention policy nightmare. Open-source version is sufficient for most use cases.
pgvector	AI embeddings in PostgreSQL. Works fine for smaller datasets (under 10M vectors). Don't expect Pinecone performance but saves you from running a separate vector database.
PgBouncer	Connection pooling that actually works. Mandatory if you have more than 100 concurrent connections. Transaction pooling mode works for most applications. Session pooling breaks shit.
pgcli	Command-line PostgreSQL client with autocomplete and syntax highlighting. Much better than the default psql for interactive use. Install this instead of suffering through vanilla psql.
pg_stat_statements	Shows you which queries are slow. Enable this extension immediately or spend months guessing why your application is slow. Essential for production debugging.
pgAdmin	Web-based PostgreSQL admin interface. Heavy, slow, and crashes frequently. Better than phpPgAdmin but that's not saying much. Use pgcli or DataGrip instead.
Adminer	Single PHP file database management tool. Works in a pinch but the interface feels like 2005. Fine for quick one-off tasks, terrible for serious database work.
pgloader	Migrates data from MySQL to PostgreSQL. Actually works and handles data type conversions. Still budget 3x longer than you think for application code changes.
pg_dump/pg_restore	Built-in backup and restore tools. Use these for PostgreSQL-to-PostgreSQL migrations. Faster and more reliable than third-party tools.
pgbadger	Log analysis tool that generates useful reports from PostgreSQL logs. Shows you slow queries, connection issues, and errors. Free alternative to expensive monitoring solutions.
Patroni	High-availability clustering for PostgreSQL. Complex to set up but actually works for automatic failover. Don't attempt unless you have serious ops experience.
PostgreSQL Community Forums	Official community hub with mailing lists, user groups, and IRC channels. More reliable than Reddit and includes regional user groups worldwide.
Stack Overflow PostgreSQL Tag	Good for specific technical questions. Search before asking - most problems have been solved already. The answers are usually better than random blog posts.

PostgreSQL: Production Implementation Guide

Configuration Requirements

Critical Settings (Production-Ready)

Installation Defaults That Will Fail

Resource Requirements

Memory Planning

Time Investments

Expertise Requirements

Critical Failure Modes

Connection Management Failures

Memory Exhaustion Patterns

Query Performance Degradation

Technology Comparisons

PostgreSQL vs MySQL

PostgreSQL vs MongoDB

PostgreSQL vs Cloud Alternatives

Production Scale Thresholds

Vertical Scaling Limits

Horizontal Scaling Reality

Extension Ecosystem Intelligence

PostGIS (Geospatial)

TimescaleDB (Time Series)

pgvector (AI/ML)

Critical Warnings

Version and Compatibility

Docker and Containerization

Connection Pooling Requirements

Operational Intelligence

Backup and Recovery

Monitoring and Debugging

Common Production Issues

Decision Framework

When to Choose PostgreSQL

When to Consider Alternatives

Implementation Success Factors

Useful Links for Further Investigation

Tools That Don't Suck (And Some That Do)

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