Everything You Need to Know to Start Monitoring Postgres

Keeping your Postgres databases healthy is non-negotiable if you care about application performance and reliability. But monitoring Postgres the right way? That’s where things get tricky. Between the sheer volume of metrics and the noise that comes with them, it’s not always obvious what to pay attention to—or when.

This guide breaks things down with a focus on what matters in real-world production setups.

Postgres Monitoring Fundamentals

Postgres monitoring involves tracking the health, performance, and resource usage of PostgreSQL database servers—especially in production environments. This includes everything from query execution metrics and disk usage patterns to connection pool behavior and lock contention.

Effective monitoring plays a key role for DevOps teams in several ways:

Minimizing Downtime

Database outages impact business operations directly. Every minute of downtime can lead to mounting costs and degraded systems. Monitoring helps catch issues before they snowball.

Detecting Gradual Degradation

PostgreSQL performance problems usually don’t happen all at once—they creep in. Regular monitoring makes it easier to spot these slow-burning issues early, before they turn critical.

Supporting Proactive Operations

Monitoring isn’t just about reacting to problems. It enables teams to stay ahead of them. By spotting early signs of trouble, you can fix things before users notice, keeping systems reliable and customers happy.

Enabling Smarter Capacity Planning

Good monitoring gives you historical data that helps with long-term planning. It helps answer questions like: Are we growing too fast for our current setup? Do we need to scale? Or are we overprovisioned and wasting resources?

Without this kind of visibility, teams either overbuild and overspend or risk underprovisioning and creating performance bottlenecks. Monitoring strikes the balance.

Cache Hit Ratio

SELECT 
  sum(heap_blks_read) as heap_read,
  sum(heap_blks_hit) as heap_hit,
  sum(heap_blks_hit) / (sum(heap_blks_hit) + sum(heap_blks_read)) as ratio
FROM pg_statio_user_tables;

What to watch for: Ratios below 0.99 (99%) might indicate you need more memory for your workload.

Replication Lag

SELECT 
  application_name, 
  pg_wal_lsn_diff(pg_current_wal_lsn(), replay_lsn) AS lag_bytes
FROM pg_stat_replication;

What to watch for: Growing lag indicates your replicas can't keep up with the primary, risking data loss during failover.

Implementing a Robust Postgres Monitoring Framework

Implementing comprehensive Postgres monitoring requires a multi-layered approach that captures both system-level and database-specific metrics. This section provides a practical implementation guide for DevOps engineers.

System-Level Monitoring Implementation

PostgreSQL performance is deeply tied to the health of the system it's running on. So before you start tweaking database settings or digging into slow queries, step back and look at the broader picture—your host system.

Here's what to monitor and why it matters:

CPU Monitoring

Track:

• CPU Utilization %: High usage can bottleneck query processing.
• CPU Steal Time: Especially in virtualized environments, this reveals how often the hypervisor is “stealing” CPU cycles from your VM. High steal time? You're fighting for resources.

Memory Monitoring

Focus on:

• PostgreSQL’s Memory Usage: Keep an eye on shared buffers and work memory.
• Overall Memory Pressure: System-wide memory issues will affect Postgres too.
• Swap Usage: If Postgres hits swap, performance tanks—hard. Avoid this at all costs.

Disk I/O Monitoring

Measure:

• Throughput: How much data is being read/written.
• IOPS: Number of read/write operations per second.
• Latency:10ms for writes or >20ms for reads? You’ve likely got a storage problem.
• Disk Saturation: Utilization may be <100%, but once your disk queue backs up, performance nosedives.

Network Monitoring

Watch:

• Bandwidth Usage: Matters a lot in replicated setups or remote access scenarios.
• Packet Loss / Retransmissions: These signal unstable connections or overloaded networks.

Why All This Matters

System-level issues often masquerade as database problems. That slow query at 2 PM? It might not be Postgres—it could be your disk getting hammered or your CPU being stolen by another VM. Always correlate DB slowdowns with system metrics first.

Database-Specific Monitoring Configuration

To capture Postgres internal metrics, several configuration changes and extensions are necessary:

First, enable the essential statistics collector extensions in your postgresql.conf file:

# Add to postgresql.conf
shared_preload_libraries = 'pg_stat_statements'
pg_stat_statements.track = all
pg_stat_statements.max = 10000
track_io_timing = on
track_activity_query_size = 2048

The pg_stat_statements extension tracks query performance data, while track_io_timing enables detailed I/O timing statistics. Increasing track_activity_query_size ensures longer queries are fully captured rather than truncated.

After making these changes, restart PostgreSQL and create the extension in each database:

CREATE EXTENSION pg_stat_statements;

For deeper insights into WAL (Write-Ahead Log) usage and replication, consider also enabling:

wal_level = logical

This setting provides more detailed WAL information for monitoring purposes, though it may slightly increase WAL volume.

Data Collection and Visualization Architecture

A complete monitoring system requires three components working together:

1. A metrics collection agent that gathers data from PostgreSQL and the host system. Options include:
   • postgres_exporter for Prometheus-based systems
   • Telegraf for InfluxDB-based systems
   • Custom collection scripts using psql and cron
2. A time-series database to store historical metric data with appropriate retention policies:
   • Short-term high-resolution data (1-second intervals for 24-48 hours)
   • Medium-term aggregated data (1-minute intervals for 2-4 weeks)
   • Long-term trend data (15-minute intervals for 1+ years)
3. A visualization platform that provides both real-time dashboards and historical analysis:
   • Pre-built dashboard templates for common monitoring scenarios
   • Custom dashboard capabilities for specific application needs
   • Annotation support for tracking deployments and configuration changes

A particularly effective stack combines:

• PostgreSQL Exporter for metrics collection
• Prometheus for storage and alerting
• Grafana for visualization and dashboard creation

Alert Configuration Strategy

Effective alerting requires careful threshold setting and proper prioritization. Configure these essential alerts for Postgres environments:

High-Priority Alerts (Require Immediate Action):

• Replication lag exceeding 2 minutes or growing steadily
• Connection count exceeding 85% of max_connections
• Transaction rollback rates above 10% sustained for 5+ minutes
• Disk space below 15% free on database volumes
• Cache hit ratio dropping below 98% for production systems
• Lock contention causing blocking sessions lasting 2+ minutes

Medium-Priority Alerts (Require Investigation):

• Query times exceeding 200% of historical baselines
• Temporary file usage growing abnormally
• Table bloat exceeding 30% of table size
• Autovacuum not running for 24+ hours
• Index usage dropping significantly in critical tables

Implement tiered alerting with warning thresholds at 70-80% of critical values to provide advance notice of developing issues.

Configure different notification channels based on alert severity—use chat integrations for warnings and direct messaging/paging for critical alerts.

For each alert, provide context-rich notifications that include:

• The specific metric triggering the alert
• Recent trend data (not just the current value)
• Links to relevant dashboards for further investigation
• Basic troubleshooting steps for common issues

Troubleshooting and Resolving PostgreSQL Performance Problems

When PostgreSQL performance degrades, a systematic troubleshooting approach helps identify root causes quickly.

This section covers common performance issues, their symptoms, diagnostic approaches, and resolution strategies.

Query Performance Degradation

Query slowdowns are among the most common and visible PostgreSQL problems. They directly impact application performance and user experience.

Observable Symptoms: Query performance issues typically manifest as increased response times in application layers, growing query queues in pg_stat_activity, CPU utilization spikes on database servers, and increased wait times in application logs.

Diagnostic Approach:

First, identify the problematic queries using pg_stat_statements:

SELECT 
  substring(query, 1, 80) as query_excerpt,
  total_time/calls as avg_time_ms,
  calls,
  rows/calls as avg_rows,
  100.0 * shared_blks_hit/nullif(shared_blks_hit + shared_blks_read, 0) AS hit_percent
FROM pg_stat_statements 
WHERE calls > 100  -- Focus on frequently executed queries
ORDER BY total_time DESC 
LIMIT 15;

For specific slow queries, examine their execution plans to identify bottlenecks:

EXPLAIN ANALYZE SELECT * FROM large_table WHERE non_indexed_column = 'value';

Look for sequential scans on large tables, nested loops with large outer result sets, and operations like hash joins that spill to disk.

Resolution Strategies:

1. Index optimization: Add missing indexes for frequently filtered columns, but be cautious about over-indexing, which impacts write performance.

CREATE INDEX idx_table_column ON large_table(frequently_filtered_column);

2. Query restructuring: Rewrite problematic queries to use more efficient patterns:
   • Replace correlated subqueries with joins where possible
   • Use CTEs to simplify complex queries
   • Add LIMIT clauses to prevent excessive data retrieval
3. Statistics management: Ensure the optimizer has current statistics:

ANALYZE table_with_stale_stats;

4. For temporary performance relief while implementing long-term fixes, consider query result caching at the application level.

Connection Management Issues

Connection problems can cause application errors and waste server resources with idle connections.

Observable Symptoms: Watch for "too many connections" errors in logs, application timeouts during connection attempts, high counts of idle connections, or slow connection establishment times.

Diagnostic Approach:

Examine the current connection state and identify patterns:

SELECT 
  datname, 
  usename, 
  application_name,
  state, 
  count(*),
  max(extract(epoch from now() - state_change)) as max_state_duration_sec
FROM pg_stat_activity 
WHERE state IS NOT NULL
GROUP BY 1, 2, 3, 4
ORDER BY count(*) DESC;

Also, check for long-running transactions that might be holding connections:

SELECT 
  pid, 
  usename, 
  application_name,
  state,
  age(now(), xact_start) as xact_age,
  wait_event_type,
  wait_event,
  query
FROM pg_stat_activity 
WHERE xact_start IS NOT NULL
ORDER BY xact_start ASC
LIMIT 10;

Resolution Strategies:

1. Implement connection pooling with pgBouncer, which significantly reduces connection overhead:

# pgbouncer.ini configuration
[databases]
mydb = host=127.0.0.1 port=5432 dbname=mydb

[pgbouncer]
listen_port = 6432
listen_addr = *
auth_type = md5
auth_file = /etc/pgbouncer/userlist.txt
pool_mode = transaction
max_client_conn = 1000
default_pool_size = 20

2. Adjust application connection pool settings to prevent connection spikes:
   • Set appropriate minimum and maximum pool sizes
   • Configure connection timeouts and validation queries
   • Implement backoff strategies for connection failures
3. Configure PostgreSQL timeout settings to release abandoned connections:

# Add to postgresql.conf
idle_in_transaction_session_timeout = '5min'
statement_timeout = '30s'  # Use cautiously

4. For applications making short, frequent database connections, switch to persistent connection patterns or implement a service layer that maintains connections.

Memory Utilization Problems

Memory issues in PostgreSQL can severely impact performance as operations shift from memory to disk.

Observable Symptoms: Look for increased disk I/O activity, declining cache hit ratios, slower overall query performance across many queries, and swapping activity on the database server.

Diagnostic Approach:

Check the buffer cache efficiency:

SELECT 
  sum(heap_blks_read) as heap_read,
  sum(heap_blks_hit) as heap_hit,
  (sum(heap_blks_hit) * 100.0 / nullif(sum(heap_blks_hit) + sum(heap_blks_read), 0)) as hit_percent
FROM pg_statio_user_tables;

Examine which tables might benefit most from increased caching:

SELECT 
  relname, 
  heap_blks_read, 
  heap_blks_hit,
  (heap_blks_hit * 100.0 / nullif(heap_blks_hit + heap_blks_read, 0)) as hit_percent
FROM pg_statio_user_tables
WHERE heap_blks_read > 0
ORDER BY heap_blks_read DESC
LIMIT 10;

Resolution Strategies:

1. Adjust PostgreSQL memory parameters in postgresql.conf based on server resources:

# For a dedicated database server with 32GB RAM
shared_buffers = '8GB'     # 25% of RAM
work_mem = '64MB'          # Depends on max_connections and query complexity
maintenance_work_mem = '512MB'  # For vacuum operations
effective_cache_size = '24GB'   # 75% of RAM - helps query planner

2. For servers with limited memory, identify and optimize memory-intensive queries:
   • Reduce work_mem for specific user roles that run large sorts/joins
   • Break complex queries into smaller steps with temporary tables
3. If memory pressure persists despite optimization, consider vertical scaling (adding RAM) or horizontal scaling (sharding data across multiple servers).

Database Bloat and Vacuum Inefficiency

Table and index bloat occurs when UPDATE and DELETE operations leave behind dead tuples that aren't promptly removed by vacuum processes.

Observable Symptoms: Database size grows disproportionately to actual data volume, queries gradually slow down, and vacuum processes run longer or more frequently.

Diagnostic Approach:

Identify tables with high dead tuple counts:

SELECT 
  schemaname, 
  relname, 
  n_dead_tup, 
  n_live_tup,
  n_dead_tup * 100.0 / nullif(n_dead_tup + n_live_tup, 0) as dead_percentage,
  last_vacuum,
  last_autovacuum
FROM pg_stat_user_tables
ORDER BY n_dead_tup DESC
LIMIT 15;

Check for vacuum process activity and potential blockers:

SELECT datname, usename, pid, state, query 
FROM pg_stat_activity 
WHERE query LIKE '%vacuum%';

Resolution Strategies:

1. Adjust autovacuum settings for more aggressive cleanup:

# Add to postgresql.conf
autovacuum_vacuum_scale_factor = 0.05  # Default is 0.2
autovacuum_analyze_scale_factor = 0.025  # Default is 0.1
autovacuum_vacuum_cost_limit = 1000  # Default is 200

2. For tables with extreme bloat, schedule manual vacuum operations during low-traffic periods:

VACUUM FULL ANALYZE problematic_table;  -- Locks table, use cautiously

3. Identify and fix long-running transactions that prevent vacuum from removing dead tuples:

SELECT pid, age(now(), xact_start), usename, query
FROM pg_stat_activity
WHERE xact_start < (now() - interval '1 hour')
ORDER BY age(now(), xact_start) DESC;

4. Implement table partitioning for very large tables to make vacuum operations more manageable and to enable partition pruning for improved query performance.

PostgreSQL Monitoring Tool Ecosystem

The PostgreSQL monitoring landscape offers various solutions ranging from lightweight open-source tools to comprehensive enterprise platforms.

Specialized PostgreSQL Monitoring Solutions

Last9 stands out as a purpose-built telemetry data platform with robust PostgreSQL monitoring capabilities. We provide high-cardinality observability specifically designed for database environments where traditional monitoring solutions struggle with the volume and dimensionality of metrics.

For DevOps teams managing critical database infrastructure, Last9 offers correlated monitoring across metrics, logs, and traces. This correlation capability helps identify root causes faster during incident response, showing relationships between database performance, application behavior, and underlying infrastructure metrics.

PostgreSQL Exporter provides a lightweight approach for teams already using Prometheus. This open-source exporter exposes PostgreSQL metrics in Prometheus format, enabling integration with existing monitoring infrastructure. While less comprehensive than dedicated solutions, its minimal footprint makes it suitable for resource-constrained environments or development instances.

Implementation requires:

• Installation on each PostgreSQL server
• Configuration of metric collection intervals
• Creation of appropriate Prometheus scraping jobs
• Development of custom Grafana dashboards

pgwatch2 offers a middle-ground solution with more PostgreSQL-specific features than basic exporters. This metrics-focused monitoring platform includes preset metric definitions and dashboards specifically optimized for PostgreSQL workloads. Its flexibility in metric collection intervals allows for tailored monitoring based on database importance and resource constraints.

pg_stat_monitor extends PostgreSQL's built-in statistics collector with more detailed metrics retention. As a PostgreSQL extension rather than an external tool, it provides low-overhead monitoring capabilities directly within the database system. This extension is particularly valuable for query performance tracking with minimal impact on production workloads.

PostgreSQL Monitoring Best Practices

Effective PostgreSQL monitoring extends beyond tool selection to encompass methodologies and workflows that ensure actionable insights. This section outlines proven approaches for maximizing monitoring effectiveness in production environments.

Establishing Performance Baselines

Performance baselining creates the foundation for effective anomaly detection. Without understanding normal behavior patterns, identifying problematic deviations becomes guesswork rather than data-driven analysis.

Comprehensive baselining requires gathering metrics across multiple time frames and workload patterns:

1. Daily patterns: Capture regular business-hour peaks and overnight processing windows
2. Weekly patterns: Document weekend vs. weekday differences
3. Monthly patterns: Identify end-of-month reporting or billing cycle impacts
4. Seasonal patterns: Record holiday traffic or academic semester variations for relevant applications

For each pattern, document:

• Query throughput rates (queries per second)
• Resource utilization levels (CPU, memory, I/O)
• Connection count ranges
• Transaction rates
• Wait event distributions

Gather at least three cycles of each pattern type before establishing threshold values. For example, collect three weeks of data to establish daily and weekly patterns, and three months for monthly patterns.

Store baseline data outside the monitored system to enable comparison during total system failures. Use version control for baseline documentation to track how database workload characteristics evolve.

Implementing Cross-Metric Correlation

PostgreSQL performance issues rarely manifest in isolation. The most insightful monitoring implementations correlate metrics across different subsystems to reveal cause-and-effect relationships.

Effective correlation strategies include:

1. Query-System Correlation: Link specific query execution metrics with system resource metrics during the same time periods. For example, correlate sequential scan counts with disk I/O metrics to identify when inadequate indexing impacts storage subsystems.
2. Application-Database Correlation: Connect application deployment events, code changes, and user activity patterns with database performance metrics. This relationship helps identify when application changes trigger database issues or when database constraints impact application performance.
3. Temporal Correlation: Analyze metrics using consistent time windows across systems. Use annotations in visualization tools to mark significant events like deployments, configuration changes, or maintenance activities.
4. Dependency Correlation: Map relationships between different database objects (tables, views, functions) and track how performance issues propagate through these dependencies.

Implementation typically requires:

• Unified timestamping across monitoring systems
• Consistent metadata tagging for services and components
• Centralized event logging for system changes
• Visualization tools that support the overlay of different metric types

Pattern-Based Query Monitoring Approach

Traditional threshold-based monitoring often fails to detect gradual performance degradation. A more effective approach monitors query execution patterns rather than individual instances.

Implement pattern monitoring by:

1. Grouping similar queries through normalization (replacing literal values with placeholders)
2. Tracking statistical distributions of execution times rather than simple averages
3. Monitoring execution plan stability for critical queries
4. Detecting new query patterns that might indicate application changes

This approach enables the detection of:

• Gradually increasing execution times that might stay below absolute thresholds
• Growing variance in performance that indicates inconsistent execution
• Plan regressions where the optimizer selects increasingly inefficient strategies
• New query patterns that might require optimization or indexing

Pattern-based monitoring requires more sophisticated data collection and analysis but provides earlier warning of developing problems before they impact users.

Tiered Metric Retention Strategy

Different metrics have different values over time. Implementing a tiered retention strategy optimizes storage costs while preserving long-term insights.

This tiered approach preserves detailed data for recent troubleshooting while maintaining sufficient historical context for capacity planning without excessive storage requirements.

Conclusion

The PostgreSQL monitoring landscape continues to evolve with new tools, techniques, and approaches. As your monitoring practice matures, gradually incorporate the advanced techniques:

• Pattern-based anomaly detection
• Cross-system correlation
• Query performance testing in CI/CD pipelines
• Business-context enriched monitoring

FAQs

What monitoring frequency provides the best balance between insight and overhead?

The optimal monitoring frequency varies by metric type and environment criticality:

For production business-critical systems:

• High-impact metrics (connections, replication lag, error rates): 10-15 second collection intervals
• Performance metrics (query execution, resource utilization): 30-60 second intervals
• Growth metrics (database size, index bloat): 5-15 minute intervals

For development/staging environments:

• Reduced collection frequency (1-5 minute intervals) to minimize overhead
• Focus on query performance metrics to catch issues before production deployment
• Periodic rather than continuous collection for non-critical metrics

Implement metric aggregation strategies to maintain long-term visibility without excessive storage requirements. Consider streaming high-resolution metrics to temporary storage for immediate troubleshooting, while aggregating to lower resolutions for long-term trend analysis.

What's the appropriate balance between database-specific and system-level monitoring?

Effective PostgreSQL monitoring requires both perspectives working together:

System-level metrics provide essential context for database behavior:

• CPU saturation explains query performance degradation
• Memory pressure indicates potential shared_buffers configuration issues
• Disk I/O latency impacts transaction throughput
• Network bandwidth constraints affect replication performance

Database-specific metrics reveal issues that system monitoring might miss:

• Query plan changes causing performance degradation without resource spikes
• Lock contention creates bottlenecks without high resource utilization
• Replication conflicts requiring logical resolution
• Autovacuum effectiveness issues cause bloat without obvious symptoms

Begin implementation with system-level monitoring to ensure basic resource adequacy, then layer database-specific monitoring to optimize performance. The most valuable insights often come from correlating both levels—for example, identifying how specific queries impact system resources or how system constraints affect query execution.

What's the most effective approach to high-availability PostgreSQL monitoring?

Monitoring high-availability PostgreSQL clusters requires additional dimensions beyond single-instance monitoring:

1. Replication Status Monitoring
   • Track replication lag in both bytes and time units
   • Monitor WAL generation rate on primary versus replay rate on replicas
   • Check for replication slot accumulation that could cause disk fill-up
   • Verify consistent configuration across all instances
2. Failover Readiness Verification
   • Confirm replica promotion capability through regular testing
   • Monitor archive_command success rates for point-in-time recovery capability
   • Verify WAL retention policies match recovery point objectives
   • Test automated failover mechanisms regularly and monitor their status
3. Connection Distribution Awareness
   • Track connection counts across primary and read replicas
   • Monitor load balancer configuration and connection routing
   • Verify application connection string failover capabilities
   • Check for inappropriate writes directed to replicas
4. Consistency Verification
   • Implement periodic data consistency checks between primary and replicas
   • Monitor for replication conflicts in logical replication setups
   • Track table checksums if enabled
   • Verify that critical tables maintain consistent row counts across instances

For maximum reliability, implement independent monitoring of each cluster node with a separate monitoring instance outside the database cluster to ensure visibility during cluster-wide issues.

What performance impact should I expect from comprehensive PostgreSQL monitoring?

Properly implemented monitoring creates minimal overhead on production systems:

• Basic monitoring using pg_stat* views and system metrics: <1% CPU impact
• pg_stat_statements with moderate settings: 2-3% CPU impact
• Full monitoring with query capture and detailed metrics: 3-5% CPU impact

Several factors influence monitoring overhead:

1. Collection Frequency: Collecting metrics every 10 seconds versus every minute creates proportionally higher overhead.
2. Query Normalization: Using tools that normalize queries by replacing literals with placeholders reduces collection overhead.
3. Monitoring Query Complexity: Simple count or status queries create minimal impact, while complex monitoring queries with multiple joins may create noticeable overhead.

Extension Configuration: Adjusting pg_stat_statements settings directly impacts performance:

# Lower overhead but less detail
pg_stat_statements.track = top    # Only track top statements by execution time
pg_stat_statements.max = 1000     # Track fewer statements

# Higher overhead but more comprehensive
pg_stat_statements.track = all    # Track all statements
pg_stat_statements.max = 10000    # Track more statements

To minimize impact while maintaining visibility:

• Schedule resource-intensive collection during lower-usage periods
• Implement tiered monitoring based on system criticality
• Use read replicas for running expensive monitoring queries
• Consider sampling approaches for extremely high-throughput systems

Should development and production environments use identical monitoring configurations?

While using consistent monitoring tools across environments provides significant benefits, the configuration should be environment-appropriate:

Shared Elements:

• Core metrics collection methodology
• Dashboard layouts and visualization approaches
• Query performance tracking mechanisms
• Alert condition logic (even if thresholds differ)

Environment-Specific Adjustments:

• Collection frequencies (lower in development)
• Retention periods (shorter in development)
• Alert thresholds (more permissive in development)
• Alert notification channels (different teams/channels)

Development environment monitoring helps catch issues early in the deployment pipeline, but excessive monitoring overhead in development environments can slow development cycles. A balanced approach applies similar monitoring structures with adjusted sensitivity based on environment criticality.

The most effective strategy uses production monitoring data to inform development environment testing. For example, identify the top 20 resource-intensive queries in production and ensure they perform acceptably in development before deployment.

How can I implement non-intrusive PostgreSQL monitoring in resource-constrained environments?

For environments where monitoring overhead must be minimized, several strategies can maintain visibility with reduced impact:

1. Strategic Sampling: Instead of constant collection, sample metrics at varying intervals:
   • Critical metrics: Regular collection (every 1-5 minutes)
   • Secondary metrics: Intermittent collection (every 15-30 minutes)
   • Detailed diagnostics: On-demand collection only when investigating issues
2. Read Replica Offloading: Direct resource-intensive monitoring queries to replicas:
   • Create a dedicated monitoring user with replica-only connection configuration
   • Implement connection routing that ensures monitoring queries use replicas
   • Schedule intensive collection during periods of lower replica usage
3. Lightweight Agent Configuration:
   • Minimize in-memory buffering in collection agents
   • Implement efficient batching of metrics transmission
   • Use delta calculations at the agent level rather than sending raw counters
   • Limit the number of metrics collected to those with clear actionable value
4. Exterior Monitoring: Supplement internal metrics with external observations:
   • Application-side query timing measurements
   • TCP connection success rate monitoring
   • Synthetic transaction testing
   • Black-box query execution timing

pg_stat_statements Optimization:

# Lower-impact configuration
pg_stat_statements.track = top
pg_stat_statements.track_utility = off
pg_stat_statements.max = 500

These approaches trade some monitoring detail for reduced overhead, making them appropriate for systems where performance impact must be minimized while maintaining essential visibility.