PostgreSQL Performance Tuning Checklist: 2026 Complete Guide

PostgreSQL is one of the most powerful open-source relational databases available today. It handles everything from small application backends to multi-terabyte analytical warehouses. But raw capability means nothing if the database is not tuned for your workload. A default PostgreSQL installation is configured conservatively — designed to run on modest hardware without consuming too many resources. That is sensible for a fresh install, but it leaves enormous performance on the table for production systems.

This checklist is a comprehensive, DBA-focused guide to PostgreSQL performance tuning in 2026. It covers hardware, OS-level settings, core configuration parameters, indexing strategies, query optimization, connection pooling, vacuum management, partitioning, monitoring, caching, replication, and performance testing. Whether you are tuning a new deployment or troubleshooting a slow production database, work through each section systematically.

Why Performance Tuning Matters

Performance tuning is not optional for production PostgreSQL. Here is why it deserves dedicated engineering time:

• Cost reduction — a well-tuned database on a smaller instance often outperforms an untuned database on hardware twice the size. Cloud compute bills drop dramatically when you eliminate wasteful queries and configure memory correctly.
• User experience — slow queries translate directly to slow page loads, timeouts, and frustrated users. A 200ms query that becomes 20ms after indexing changes the entire feel of an application.
• Scalability headroom — tuning buys you time. Instead of sharding or migrating to a new architecture at 10,000 users, proper tuning can carry you to 100,000 users on the same hardware.
• Reliability — untuned databases are more prone to lock contention, connection exhaustion, and disk I/O saturation. These issues cause cascading failures that are expensive to recover from.
• Developer productivity — when the database is fast, developers spend less time waiting for queries and more time shipping features. Slow databases create a drag on the entire engineering organization.

The checklist below is ordered roughly by impact. Start at the top and work your way down. Each section includes concrete settings, SQL examples, and explanations of why each change matters.

Hardware and OS Tuning

Before touching postgresql.conf, make sure your hardware and operating system are not the bottleneck. No amount of configuration tuning can compensate for insufficient RAM or a misconfigured filesystem.

Memory

PostgreSQL is a memory-hungry system. The general rule is: give it as much RAM as your budget allows. For a dedicated database server, at least 25% of total RAM should be available for PostgreSQL's shared buffers, with additional memory for the OS page cache, sort operations, and connection overhead.

Storage

Use SSDs (NVMe preferred) for all production PostgreSQL deployments. Spinning disks are acceptable only for archival or cold storage. The random I/O pattern of database workloads makes SSD performance transformative — not incremental but orders-of-magnitude faster for index lookups and random page reads.

• Use ext4 or XFS on Linux. XFS generally performs better for large databases due to its extent-based allocation.
• Mount the data directory with noatime to eliminate unnecessary filesystem metadata writes.
• Set the I/O scheduler to none or noop for NVMe devices, or deadline/mq-deadline for SATA SSDs.
• Separate WAL (write-ahead log) onto a different physical disk if possible. WAL writes are sequential and benefit from dedicated I/O bandwidth.

Kernel Parameters

On Linux, adjust these sysctl parameters for database workloads:

# Increase shared memory limits
kernel.shmmax = 17179869184    # 16 GB — must be >= shared_buffers
kernel.shmall = 4194304        # shmmax / PAGE_SIZE

# Reduce swappiness — PostgreSQL manages its own memory
vm.swappiness = 1

# Increase dirty page limits for write-heavy workloads
vm.dirty_ratio = 40
vm.dirty_background_ratio = 10

# Increase open file limits
fs.file-max = 1000000

# Network tuning for high connection counts
net.core.somaxconn = 4096
net.ipv4.tcp_max_syn_backlog = 4096

Also set transparent_hugepages to madvise rather than always. PostgreSQL uses huge_pages natively in its configuration, and letting the kernel aggressively use transparent huge pages can cause latency spikes from compaction.

PostgreSQL Configuration Parameters

The following parameters in postgresql.conf have the most impact on performance. The values below assume a dedicated database server with 64 GB of RAM. Scale proportionally for your environment.

shared_buffers

This is PostgreSQL's main in-memory cache for table and index data pages. The default of 128 MB is far too low for any production system.

# Recommended: 25% of total system RAM
# For 64 GB server:
shared_buffers = 16GB

Setting this higher than 25% rarely helps because the OS page cache also caches PostgreSQL data files. Going above 40% can actually hurt performance by reducing the memory available for the page cache and sort operations.

effective_cache_size

This does not allocate memory — it tells the query planner how much memory is available for disk caching (shared_buffers + OS page cache). A correct value helps the planner choose index scans over sequential scans when data is likely cached.

# Recommended: 50-75% of total system RAM
effective_cache_size = 48GB

work_mem

Memory allocated per sort or hash operation, per query. This is not a global pool — a complex query with five sort nodes and two hash joins could allocate 7x work_mem. Set it carefully.

# Start conservative, increase for analytical workloads
# For OLTP: 16-64 MB
# For analytics: 256 MB - 1 GB
work_mem = 64MB

If you see Sort Method: external merge Disk in your EXPLAIN ANALYZE output, work_mem is too low for that query. You can also set it per-session for heavy analytical queries:

SET work_mem = '512MB';
-- Run your analytical query
RESET work_mem;

maintenance_work_mem

Memory for maintenance operations: VACUUM, CREATE INDEX, ALTER TABLE ADD FOREIGN KEY. These run less frequently but benefit greatly from more memory.

# Recommended: 1-2 GB for large databases
maintenance_work_mem = 2GB

wal_buffers

Buffer for write-ahead log entries before they are flushed to disk. The default auto-tuning (1/32 of shared_buffers, capped at 64 MB) is usually fine, but explicitly setting it avoids surprises.

# -1 means auto-tune from shared_buffers
# Explicit setting for predictability:
wal_buffers = 64MB

Additional Important Parameters

# Checkpoint tuning — spread I/O over longer intervals
checkpoint_completion_target = 0.9
max_wal_size = 4GB
min_wal_size = 1GB

# Planner cost settings for SSD storage
random_page_cost = 1.1        # Default 4.0 assumes spinning disk
effective_io_concurrency = 200 # Default 1 — raise for SSDs

# Parallelism — use multiple CPU cores for queries
max_parallel_workers_per_gather = 4
max_parallel_workers = 8
max_parallel_maintenance_workers = 4

# Connection limits
max_connections = 200          # Lower than you think — use pooling

After changing parameters, reload the configuration. Most settings take effect with SELECT pg_reload_conf(); — but shared_buffers, max_connections, and huge_pages require a full restart.

Indexing Strategies

Correct indexing is the single highest-impact optimization you can make. A missing index can make the difference between a 5ms query and a 50-second full table scan. PostgreSQL supports several index types, each suited for different access patterns.

B-tree Indexes

B-tree is the default and most commonly used index type. It supports equality and range queries on sortable data types. Every primary key and unique constraint automatically creates a B-tree index.

-- Standard B-tree index on a frequently filtered column
CREATE INDEX idx_orders_customer_id ON orders (customer_id);

-- Composite index for queries that filter on both columns
CREATE INDEX idx_orders_customer_date ON orders (customer_id, created_at DESC);

-- The column order matters! Put the most selective column first
-- for equality filters, and range columns last.

Covering Indexes (INCLUDE)

Covering indexes add non-key columns to the index leaf pages. This enables index-only scans — PostgreSQL can answer the query entirely from the index without touching the heap table.

-- Covering index: the query can be satisfied without reading the table
CREATE INDEX idx_orders_covering ON orders (customer_id)
  INCLUDE (order_total, status);

-- This query becomes an index-only scan:
SELECT order_total, status
FROM orders
WHERE customer_id = 42;

Partial Indexes

Partial indexes only include rows matching a WHERE clause. They are smaller, faster, and perfect for columns with skewed distributions.

-- Only index active orders — skip the 95% that are completed
CREATE INDEX idx_orders_active ON orders (created_at)
  WHERE status = 'active';

-- Only index non-null values
CREATE INDEX idx_users_email_verified ON users (email)
  WHERE email_verified_at IS NOT NULL;

GIN Indexes

Generalized Inverted Indexes are essential for full-text search, JSONB queries, and array operations.

-- Full-text search index
CREATE INDEX idx_articles_search ON articles
  USING gin(to_tsvector('english', title || ' ' || body));

-- JSONB containment queries
CREATE INDEX idx_events_data ON events USING gin(metadata jsonb_path_ops);

-- Array overlap/containment
CREATE INDEX idx_products_tags ON products USING gin(tags);

GiST Indexes

Generalized Search Tree indexes support geometric data, range types, and nearest-neighbor queries. They are commonly used with PostGIS for geospatial data.

-- Range type index for scheduling/booking systems
CREATE INDEX idx_reservations_period ON reservations
  USING gist(tstzrange(start_time, end_time));

-- PostGIS spatial index
CREATE INDEX idx_locations_geom ON locations
  USING gist(geom);

Finding Missing Indexes

Use pg_stat_user_tables to identify tables with excessive sequential scans:

SELECT
    schemaname,
    relname AS table_name,
    seq_scan,
    seq_tup_read,
    idx_scan,
    n_live_tup,
    ROUND(seq_scan::numeric / GREATEST(seq_scan + idx_scan, 1) * 100, 1) AS seq_scan_pct
FROM pg_stat_user_tables
WHERE n_live_tup > 10000
ORDER BY seq_tup_read DESC
LIMIT 20;

Tables with a high seq_scan_pct and large row counts are prime candidates for new indexes. Also review SQL formatting best practices to ensure your queries are readable before optimizing them.

Query Optimization with EXPLAIN ANALYZE

EXPLAIN ANALYZE is your primary tool for understanding query performance. It runs the query, measures actual execution times, and shows the planner's chosen strategy.

EXPLAIN (ANALYZE, BUFFERS, FORMAT TEXT)
SELECT o.id, o.order_total, c.name
FROM orders o
JOIN customers c ON c.id = o.customer_id
WHERE o.created_at >= '2026-01-01'
  AND o.status = 'shipped'
ORDER BY o.created_at DESC
LIMIT 50;

Key things to look for in the output:

• Seq Scan on large tables — usually means a missing index. If the table has more than a few thousand rows, a sequential scan is almost always suboptimal for filtered queries.
• Nested Loop with high row counts — nested loops are efficient when the inner side is indexed and returns few rows. If the inner loop processes thousands of rows per outer row, consider a hash or merge join (the planner may need better statistics or more work_mem).
• Sort Method: external merge Disk — the sort spilled to disk because work_mem was too small. Increase it or add an index that provides the required sort order.
• Rows Removed by Filter — a high number means the index is not selective enough. A more specific index or a composite index could eliminate these rows earlier.
• Buffers: shared read — high shared read counts mean data is not cached. This is expected for first executions but should decrease on repeated runs if shared_buffers is sized correctly.

Common Query Anti-Patterns

These patterns frequently cause performance problems:

-- Anti-pattern 1: Functions on indexed columns prevent index use
-- BAD:
SELECT * FROM orders WHERE EXTRACT(YEAR FROM created_at) = 2026;
-- GOOD:
SELECT * FROM orders
WHERE created_at >= '2026-01-01' AND created_at < '2027-01-01';

-- Anti-pattern 2: Implicit type casting
-- BAD (if user_id is integer):
SELECT * FROM orders WHERE user_id = '42';
-- GOOD:
SELECT * FROM orders WHERE user_id = 42;

-- Anti-pattern 3: SELECT * when you only need specific columns
-- BAD:
SELECT * FROM orders WHERE customer_id = 42;
-- GOOD:
SELECT id, order_total, status FROM orders WHERE customer_id = 42;

-- Anti-pattern 4: NOT IN with subqueries (use NOT EXISTS instead)
-- BAD:
SELECT * FROM customers WHERE id NOT IN (SELECT customer_id FROM orders);
-- GOOD:
SELECT c.* FROM customers c
WHERE NOT EXISTS (SELECT 1 FROM orders o WHERE o.customer_id = c.id);

-- Anti-pattern 5: OFFSET for pagination on large result sets
-- BAD:
SELECT * FROM orders ORDER BY id LIMIT 20 OFFSET 100000;
-- GOOD (keyset pagination):
SELECT * FROM orders WHERE id > 100000 ORDER BY id LIMIT 20;

Connection Pooling with PgBouncer

Every PostgreSQL connection consumes approximately 5-10 MB of RAM and carries kernel-level overhead for process management. If your application opens hundreds of connections (common in microservice architectures), the database spends more time managing connections than executing queries.

PgBouncer is the standard solution. It sits between your application and PostgreSQL, multiplexing many client connections onto a small pool of database connections.

# pgbouncer.ini

[databases]
myapp = host=127.0.0.1 port=5432 dbname=myapp

[pgbouncer]
listen_addr = 0.0.0.0
listen_port = 6432

# Pool mode:
# session    — connection assigned for entire client session (safest)
# transaction — connection returned after each transaction (best performance)
# statement  — connection returned after each statement (most restrictive)
pool_mode = transaction

# Pool sizing
default_pool_size = 25
max_client_conn = 1000
min_pool_size = 5
reserve_pool_size = 5
reserve_pool_timeout = 3

# Timeouts
server_idle_timeout = 300
client_idle_timeout = 600
query_timeout = 60

In transaction mode, a pool of 25 server connections can handle 1,000+ client connections efficiently. The tradeoff is that session-level features (SET statements, prepared statements, LISTEN/NOTIFY) do not persist across transactions. Most ORMs and application frameworks work correctly in transaction mode with minimal configuration.

With connection pooling in place, you can often lower PostgreSQL's max_connections to 100 or less, which reduces memory overhead and lock contention.

VACUUM and Autovacuum Tuning

PostgreSQL uses Multi-Version Concurrency Control (MVCC). When a row is updated or deleted, the old version is not immediately removed — it becomes a "dead tuple" that must be cleaned up by VACUUM. If dead tuples accumulate, tables bloat, indexes grow larger, and queries slow down.

How Autovacuum Works

Autovacuum runs automatically in the background. It triggers when the number of dead tuples exceeds a threshold:

threshold = autovacuum_vacuum_threshold + autovacuum_vacuum_scale_factor * n_live_tup

With defaults (threshold = 50, scale_factor = 0.2), a table with 1 million rows will not vacuum until 200,050 dead tuples accumulate. For write-heavy tables, this is too late.

Recommended Autovacuum Settings

# Make autovacuum more aggressive globally
autovacuum_vacuum_threshold = 50
autovacuum_vacuum_scale_factor = 0.05    # Default 0.2 — trigger at 5% dead tuples
autovacuum_analyze_threshold = 50
autovacuum_analyze_scale_factor = 0.02   # Update statistics more frequently

# Allow autovacuum to do more work per cycle
autovacuum_vacuum_cost_delay = 2ms       # Default 20ms
autovacuum_vacuum_cost_limit = 1000      # Default 200

# Allow more autovacuum workers for databases with many tables
autovacuum_max_workers = 5               # Default 3

# Prevent transaction ID wraparound — critical for uptime
autovacuum_freeze_max_age = 200000000

Per-Table Autovacuum Overrides

High-churn tables (session stores, job queues, event logs) should have more aggressive autovacuum settings:

ALTER TABLE sessions SET (
    autovacuum_vacuum_scale_factor = 0.01,
    autovacuum_vacuum_threshold = 100,
    autovacuum_vacuum_cost_delay = 0
);

ALTER TABLE job_queue SET (
    autovacuum_vacuum_scale_factor = 0.02,
    autovacuum_analyze_scale_factor = 0.01
);

Monitoring Vacuum Activity

-- Check dead tuple counts and last vacuum times
SELECT
    schemaname,
    relname,
    n_live_tup,
    n_dead_tup,
    ROUND(n_dead_tup::numeric / GREATEST(n_live_tup, 1) * 100, 1) AS dead_pct,
    last_vacuum,
    last_autovacuum,
    last_analyze,
    last_autoanalyze
FROM pg_stat_user_tables
ORDER BY n_dead_tup DESC
LIMIT 20;

Table Partitioning

Partitioning splits a large table into smaller physical pieces while presenting a single logical table to queries. It improves performance for time-series data, multi-tenant systems, and any table that grows indefinitely.

Range Partitioning (Most Common)

-- Create a partitioned table
CREATE TABLE events (
    id          bigserial,
    event_type  text NOT NULL,
    payload     jsonb,
    created_at  timestamptz NOT NULL DEFAULT now()
) PARTITION BY RANGE (created_at);

-- Create monthly partitions
CREATE TABLE events_2026_01 PARTITION OF events
    FOR VALUES FROM ('2026-01-01') TO ('2026-02-01');

CREATE TABLE events_2026_02 PARTITION OF events
    FOR VALUES FROM ('2026-02-01') TO ('2026-03-01');

CREATE TABLE events_2026_03 PARTITION OF events
    FOR VALUES FROM ('2026-03-01') TO ('2026-04-01');

-- Indexes on partitioned tables are created on each partition
CREATE INDEX ON events (created_at);
CREATE INDEX ON events (event_type, created_at);

Benefits of partitioning:

• Partition pruning — queries that filter on the partition key skip irrelevant partitions entirely. A query for March 2026 data reads only events_2026_03.
• Efficient bulk deletes — instead of DELETE FROM events WHERE created_at < '2025-01-01' (which generates massive WAL and dead tuples), you simply DROP TABLE events_2025_01.
• Parallel maintenance — VACUUM, REINDEX, and ANALYZE can run on individual partitions without locking the entire table.
• Smaller indexes — each partition has its own indexes, which are smaller and fit in memory more easily.

Automate partition creation with tools like pg_partman to avoid missing partitions and causing insert failures.

Monitoring Tools

You cannot tune what you do not measure. PostgreSQL provides powerful built-in statistics, and several external tools make them actionable.

pg_stat_statements

This extension tracks execution statistics for all SQL statements. It is the single most important monitoring tool for PostgreSQL performance.

-- Enable in postgresql.conf
shared_preload_libraries = 'pg_stat_statements'
pg_stat_statements.track = all
pg_stat_statements.max = 10000

-- After restart, create the extension
CREATE EXTENSION IF NOT EXISTS pg_stat_statements;

-- Find the slowest queries by total time
SELECT
    queryid,
    calls,
    ROUND(total_exec_time::numeric, 1) AS total_ms,
    ROUND(mean_exec_time::numeric, 1) AS mean_ms,
    ROUND(stddev_exec_time::numeric, 1) AS stddev_ms,
    rows,
    LEFT(query, 100) AS query_preview
FROM pg_stat_statements
ORDER BY total_exec_time DESC
LIMIT 20;

-- Find queries with the worst average time
SELECT
    queryid,
    calls,
    ROUND(mean_exec_time::numeric, 1) AS mean_ms,
    ROUND((shared_blks_read * 8.0 / 1024)::numeric, 1) AS avg_read_mb,
    LEFT(query, 100) AS query_preview
FROM pg_stat_statements
WHERE calls > 100
ORDER BY mean_exec_time DESC
LIMIT 20;

pgBadger

pgBadger is a log analyzer that generates detailed HTML reports from PostgreSQL log files. It identifies slow queries, lock waits, temporary file usage, checkpoint spikes, and connection patterns over time. Configure PostgreSQL logging first:

# postgresql.conf logging for pgBadger
log_min_duration_statement = 100   # Log queries taking > 100ms
log_checkpoints = on
log_connections = on
log_disconnections = on
log_lock_waits = on
log_temp_files = 0                 # Log all temp file usage
log_line_prefix = '%t [%p]: user=%u,db=%d,app=%a,client=%h '
log_statement = 'none'            # Avoid double-logging with min_duration

Real-Time Monitoring Queries

-- Active queries right now
SELECT
    pid,
    age(clock_timestamp(), query_start) AS duration,
    state,
    wait_event_type,
    wait_event,
    LEFT(query, 80) AS query_preview
FROM pg_stat_activity
WHERE state != 'idle'
  AND pid != pg_backend_pid()
ORDER BY query_start;

-- Table and index bloat estimates
SELECT
    schemaname || '.' || tablename AS table_name,
    pg_size_pretty(pg_total_relation_size(schemaname || '.' || tablename)) AS total_size,
    pg_size_pretty(pg_relation_size(schemaname || '.' || tablename)) AS table_size,
    pg_size_pretty(pg_indexes_size(schemaname || '.' || tablename)) AS index_size
FROM pg_tables
WHERE schemaname = 'public'
ORDER BY pg_total_relation_size(schemaname || '.' || tablename) DESC
LIMIT 20;

Caching Strategies

PostgreSQL has excellent built-in caching through shared_buffers and the OS page cache, but application-level caching can further reduce database load.

Application-Level Caching

• Redis or Memcached — cache frequently read, rarely changed data (user profiles, configuration, product catalogs). Set appropriate TTLs and invalidate on writes.
• Materialized views — precompute expensive aggregations and refresh them periodically.

-- Materialized view for a dashboard
CREATE MATERIALIZED VIEW daily_order_stats AS
SELECT
    date_trunc('day', created_at) AS day,
    COUNT(*) AS order_count,
    SUM(order_total) AS revenue,
    AVG(order_total) AS avg_order_value
FROM orders
WHERE created_at >= now() - interval '90 days'
GROUP BY 1;

-- Create an index on the materialized view
CREATE UNIQUE INDEX ON daily_order_stats (day);

-- Refresh without locking reads
REFRESH MATERIALIZED VIEW CONCURRENTLY daily_order_stats;

Query Result Caching

For read-heavy APIs, implement a cache-aside pattern: check the cache first, query the database on cache miss, and write the result to cache. This is especially effective for endpoints that serve the same data to many users (product pages, search results, leaderboards).

Consider using PostgreSQL's UNLOGGED tables for ephemeral cache-like data that does not need crash recovery. Unlogged tables are significantly faster for writes because they skip WAL.

Replication for Read Scaling

When a single PostgreSQL instance cannot handle your read throughput, streaming replication distributes reads across replicas.

Streaming Replication Setup

# Primary server postgresql.conf
wal_level = replica
max_wal_senders = 5
wal_keep_size = 2GB
hot_standby = on

# Primary server pg_hba.conf — allow replication connections
host replication replicator 10.0.0.0/24 scram-sha-256

# On the replica:
pg_basebackup -h primary-host -D /var/lib/postgresql/16/main \
  -U replicator -Fp -Xs -P -R

The -R flag creates a standby.signal file and configures the replica to connect to the primary automatically. Once running, the replica receives WAL records in near real-time and can serve read queries.

Read Routing

Direct write queries to the primary and read queries to replicas. Most connection poolers and ORMs support read/write splitting. Be aware of replication lag — a query sent to a replica immediately after a write may not see the new data. For consistency-critical reads, always query the primary.

Synchronous replication guarantees zero data loss but adds latency to every write. Use it for critical financial data; use asynchronous replication for everything else.

Performance Testing

Never deploy configuration changes to production without testing. PostgreSQL includes pgbench for synthetic benchmarks, and you should supplement it with realistic workload tests.

pgbench Basics

# Initialize test data (scale factor 100 = ~1.6 GB)
pgbench -i -s 100 mydb

# Run a 60-second benchmark with 20 clients
pgbench -c 20 -j 4 -T 60 mydb

# Run with custom queries
pgbench -c 20 -j 4 -T 60 -f custom_workload.sql mydb

Testing Methodology

• Baseline first — run pgbench with default settings and record TPS (transactions per second), latency, and I/O metrics.
• Change one parameter at a time — if you change shared_buffers and work_mem simultaneously, you cannot attribute improvements to either change.
• Test with realistic data — synthetic benchmarks with uniform distributions do not reflect real workloads. Restore a production backup to your test environment.
• Test under sustained load — a 10-second test cannot reveal autovacuum contention, checkpoint stalls, or memory pressure. Run tests for at least 10 minutes.
• Monitor during tests — watch CPU, memory, disk I/O, and pg_stat_activity during the benchmark. Bottlenecks reveal themselves under load.

For more developer tools that complement your database workflow, including SQL formatters and JSON validators, explore our tools collection.

Checklist Summary

Use this checklist as a quick reference. Work through each item for any new PostgreSQL deployment or when troubleshooting performance issues.

Hardware and OS

• Use NVMe SSDs for data and WAL storage
• Mount with noatime; use XFS or ext4
• Set vm.swappiness = 1
• Configure appropriate I/O scheduler
• Separate WAL onto a dedicated disk if possible

Configuration

• Set shared_buffers to 25% of RAM
• Set effective_cache_size to 50-75% of RAM
• Tune work_mem based on workload type (OLTP vs. analytics)
• Set maintenance_work_mem to 1-2 GB
• Lower random_page_cost to 1.1 for SSDs
• Raise effective_io_concurrency for SSDs
• Enable parallel query workers
• Tune checkpoint settings to reduce I/O spikes

Indexing

• Audit for missing indexes using pg_stat_user_tables
• Use covering indexes (INCLUDE) for index-only scans
• Use partial indexes for skewed data distributions
• Use GIN indexes for JSONB and full-text search
• Remove unused indexes (they slow down writes)

Queries

• Use EXPLAIN (ANALYZE, BUFFERS) on slow queries
• Eliminate functions on indexed columns in WHERE clauses
• Replace NOT IN with NOT EXISTS
• Use keyset pagination instead of OFFSET
• Avoid SELECT *

Connection Management

• Deploy PgBouncer in transaction mode
• Keep PostgreSQL max_connections low (100-200)
• Monitor connection counts and pool utilization

Vacuum

• Lower autovacuum_vacuum_scale_factor to 0.05
• Set per-table overrides for high-churn tables
• Monitor dead tuple counts and vacuum frequency
• Watch for transaction ID wraparound warnings

Partitioning

• Partition time-series and append-only tables by range
• Automate partition creation with pg_partman
• Use DROP TABLE instead of DELETE for partition expiry

Monitoring

• Enable pg_stat_statements and review top queries weekly
• Configure PostgreSQL logging for pgBadger analysis
• Set up alerts for replication lag, connection exhaustion, and disk usage
• Track table and index bloat over time

Caching and Replication

• Implement application-level caching for hot read paths
• Use materialized views for expensive aggregations
• Set up streaming replication for read scaling
• Test with pgbench and realistic workloads before deploying changes

PostgreSQL performance tuning is an ongoing discipline, not a one-time task. Workloads change, data grows, and new queries appear. Schedule quarterly reviews of your pg_stat_statements data, vacuum health, and index usage. The teams that treat performance tuning as a continuous practice consistently outperform those that wait for emergencies. For related database content, check out our SQL formatting best practices guide.

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