Introduction
PostgreSQL's EXPLAIN command reveals query execution plans but its raw output can be difficult to interpret. Several open-source tools help visualize these plans, making performance bottlenecks easier to identify.
This article examines the top PostgreSQL EXPLAIN tools in 2025, from built-in extensions to visual interfaces. These tools help database administrators, developers, and analysts optimize query performance across their database operations.
Built-in PostgreSQL Tools
Before exploring external tools, it's important to understand the powerful built-in capabilities that PostgreSQL offers for query analysis.
auto_explain
The auto_explain module provides a way to automatically log execution plans for slow queries, making it invaluable for identifying problematic queries in production environments without manual intervention.
Key Features
- Automatically logs execution plans for slow-running queries
- Configurable log level and minimum execution time thresholds
- Can capture nested statement execution plans
- Supports logging query parameters for complete context
Setup and Configuration
To enable auto_explain, you need to load it in your postgresql.conf file:
# Load the extension
shared_preload_libraries = 'auto_explain'
# Basic configuration
auto_explain.log_min_duration = '3s' # Log queries taking over 3 seconds
auto_explain.log_analyze = on # Include actual runtime statistics
auto_explain.log_buffers = on # Include buffer usage statistics
auto_explain.log_timing = on # Include timing information
auto_explain.log_verbose = on # Use EXPLAIN VERBOSE format
auto_explain.log_nested_statements = on # Include nested statementsAfter changing these settings, a server restart is required. Once enabled, slow queries will automatically appear in your PostgreSQL logs with their execution plans.
Use Cases
Auto_explain excels in production environments where manual EXPLAIN execution isn't practical, allowing teams to identify slow queries without modifying application code. It enables gathering performance data over time to spot trends and provides valuable insights when debugging intermittent performance issues that might be difficult to reproduce manually.
pg_stat_statements
While not directly an EXPLAIN tool, pg_stat_statements complements query analysis by tracking execution statistics across all SQL statements executed by the server.
Key Features
pg_stat_statements aggregates comprehensive execution statistics including time, rows processed, and I/O operations while normalizing similar queries to reduce tracking cardinality. It provides both cumulative and per-call statistics for performance trend analysis and separately tracks planning and execution time, offering deeper insight into query optimization opportunities.
Setup and Configuration
Enable pg_stat_statements in your postgresql.conf:
# Load the extension
shared_preload_libraries = 'pg_stat_statements'
# Configure
pg_stat_statements.max = 10000 # Maximum number of statements to track
pg_stat_statements.track = all # Track all statementsAfter a server restart, create the extension in your database:
CREATE EXTENSION pg_stat_statements;Query the statistics view to see execution metrics:
SELECT query, calls, total_exec_time, rows, mean_exec_time
FROM pg_stat_statements
ORDER BY total_exec_time DESC
LIMIT 10;Use Cases
- Identifying frequently executed queries for optimization
- Finding queries with high cumulative execution time
- Analyzing query patterns across your application
- Complementing EXPLAIN analysis with real-world execution statistics
Online Visualizers
While the raw EXPLAIN output provides detailed information, visual representations can make patterns and bottlenecks much easier to identify.
Postgres Explain Visualizer 2 (pev2)
Postgres Explain Visualizer 2 (pev2) is the modern successor to the original pev tool, offering an interactive, browser-based visualization of PostgreSQL execution plans.
Pev2 provides interactive, collapsible nodes for exploring complex plans with color-coded operation costs and timings. It distinguishes between operation types (scans, joins, etc.), offers filters and highlighting, and supports multiple visualization formats. While powerful as a standalone tool, pev2 reaches its full potential when integrated into comprehensive database management platforms like Bytebase.
Pev2 can be used directly in your browser:
- Visit the pev2 demo page
- Run
EXPLAIN (ANALYZE, BUFFERS, FORMAT JSON)for your query - Copy the JSON output and paste it into pev2
- Explore the visualized execution plan
Use Cases
- Analyzing complex query plans with many operations
- Educational tool for understanding query execution
- Comparing different execution strategies visually
GUI Tools with EXPLAIN
Several database management tools integrate explain visualization capabilities, providing a seamless experience for query development and optimization.
pgAdmin
pgAdmin is the most popular open-source administration and development platform for PostgreSQL, offering comprehensive management capabilities including a powerful query tool with integrated EXPLAIN visualization.
pgAdmin integrates pev2 visualization in its query tool, offering EXPLAIN execution from the SQL editor. The tool provides visualization formats including graphical, text, and statistics views for individual query analysis.
How to Use
To analyze query performance with pgAdmin, connect to your database, open the Query Tool, write your SQL query, then click "Explain" from the toolbar. The execution plan appears in the Explain panel for basic analysis.
Use Cases
- Basic PostgreSQL database administration
- Individual query optimization
- General database management tasks
Bytebase: the Collaborative Database DevSecOps Solution
Bytebase offers a comprehensive database DevSecOps platform that seamlessly integrates EXPLAIN visualization with team collaboration features, version control, and change management capabilities.
Bytebase offers embedded pev2 visualization for query analysis while enabling team collaboration through sharing and discussing query plans. It provides version control for SQL queries and their execution plans, integrates with database change management workflows, and supports cross-environment query performance comparison to optimize databases across development stages.
How to Use
Using Bytebase for query analysis is straightforward: connect your PostgreSQL database to the platform, open Bytebase SQL Editor, and write your query. Once ready, right-click the code area and select Explain Query.
Simply click the Visualize Explain button at bottom right to generate and view the visualized execution plan. The collaborative nature of Bytebase allows you to share these plans with team members or save them for future reference, making it ideal for team-based database optimization.
Use Cases
- Collaborative Query Optimization: Review and improve query performance as a team
- Schema Change Analysis: Track how database changes impact query execution plans
- Environment Consistency: Ensure queries perform similarly across dev, test, and prod
- Performance Auditing: Document query execution plans for compliance requirements
- Knowledge Base Building: Create shared repo of optimized queries and executions
Comparison
| Tool | Best For | Setup Complexity | Visual Richness | Change Management | Version Control |
|---|---|---|---|---|---|
| auto_explain | Production monitoring | Medium | Low (text-based) | ❌ | ❌ |
| pg_stat_statements | Statistical analysis | Medium | Low (tabular) | ❌ | ❌ |
| pev2 | Detailed visual analysis | Low | High | ❌ | ❌ |
| pgAdmin | Individual development | Low | Medium | ❌ | ❌ |
| Bytebase | Team collaboration | Medium | High | ✅ | ✅ |
Conclusion
PostgreSQL's EXPLAIN functionality truly shines when paired with proper visualization tools. While built-in extensions provide basic monitoring capabilities and standalone tools offer detailed analysis, modern database operations require more comprehensive solutions.
As databases grow larger and teams become more distributed, collaborative tools like Bytebase become essential. By combining powerful visualization with team collaboration, version control, and change management features, Bytebase bridges the gap between individual query optimization and team-based database governance, making it the preferred choice for organizations serious about PostgreSQL performance at scale.
