frappe-ops-performance（冰沙操作性能）

Performance Tuning

Frappe/ERPNext performance depends on four layers: database (MariaDB), cache (Redis), application server (Gunicorn), and background workers (RQ). ALWAYS tune all four layers together — optimizing one while ignoring others creates new bottlenecks.

Quick Reference

# Check system health
bench doctor

# Show pending background jobs
bench --site mysite.com show-pending-jobs

# Clear all caches
bench --site mysite.com clear-cache
bench --site mysite.com clear-website-cache

# Purge stuck background jobs
bench purge-jobs

# Enable MariaDB slow query log
# In /etc/mysql/mariadb.conf.d/50-server.cnf:
# slow_query_log = 1
# slow_query_log_file = /var/log/mysql/slow.log
# long_query_time = 1

# Check Gunicorn worker count
# In Procfile or supervisor config: -w [workers]
# Formula: workers = (2 * CPU_CORES) + 1

Performance Decision Tree

What is slow?
|
+-- Page loads are slow?
|   +-- Check Gunicorn workers (are they saturated?)
|   +-- Check MariaDB slow query log
|   +-- Check Redis memory (is cache evicting?)
|   +-- Enable CDN for static assets
|
+-- Background jobs are delayed?
|   +-- bench doctor (check worker count and pending jobs)
|   +-- Increase RQ worker count
|   +-- Check for long-running jobs blocking queues
|
+-- Database queries are slow?
|   +-- Enable slow query log
|   +-- Run EXPLAIN on slow queries
|   +-- Add indexes on frequently filtered columns
|   +-- Use get_cached_value instead of get_value
|
+-- Server runs out of memory?
|   +-- Reduce Gunicorn workers
|   +-- Set Redis maxmemory
|   +-- Check MariaDB innodb_buffer_pool_size
|   +-- Look for memory leaks in custom code
|
+-- High CPU usage?
|   +-- Profile Python code (cProfile)
|   +-- Check for N+1 query patterns
|   +-- Review custom scheduled jobs

MariaDB Tuning

Critical Settings

# /etc/mysql/mariadb.conf.d/50-server.cnf
[mysqld]
# InnoDB buffer pool — MOST important setting
# Set to 50-70% of available RAM on dedicated DB server
# Set to 25-40% of RAM on shared server
innodb_buffer_pool_size = 2G

# Buffer pool instances (1 per GB of buffer pool)
innodb_buffer_pool_instances = 2

# Log file size (larger = better write performance, slower recovery)
innodb_log_file_size = 256M

# Flush method — use O_DIRECT to avoid double buffering
innodb_flush_method = O_DIRECT

# Character set (ALWAYS use utf8mb4 for Frappe)
character-set-server = utf8mb4
collation-server = utf8mb4_unicode_ci

# Key buffer for MyISAM (Frappe uses InnoDB, keep small)
key_buffer_size = 32M

# Query cache (DISABLE for MariaDB 10.4+ / MySQL 8.0+)
query_cache_type = 0
query_cache_size = 0

# Connection limits
max_connections = 200
wait_timeout = 600
interactive_timeout = 600

# Temp tables
tmp_table_size = 64M
max_heap_table_size = 64M

# Slow query log
slow_query_log = 1
slow_query_log_file = /var/log/mysql/slow.log
long_query_time = 1

Slow Query Analysis

# Enable slow query log (runtime, no restart needed)
SET GLOBAL slow_query_log = 1;
SET GLOBAL long_query_time = 1;

# Analyze slow queries with mysqldumpslow
mysqldumpslow -t 10 -s c /var/log/mysql/slow.log
# -t 10: top 10 queries
# -s c: sort by count (use -s t for total time)

# Use EXPLAIN to analyze specific queries
EXPLAIN SELECT * FROM `tabSales Invoice` WHERE customer = 'ABC';
# Look for: type=ALL (full table scan), rows > 10000, Using filesort

Index Optimization

-- Check for missing indexes on frequently filtered columns
SHOW INDEX FROM `tabSales Invoice`;

-- Add index for common filter patterns
ALTER TABLE `tabSales Invoice` ADD INDEX idx_customer_date (customer, posting_date);

-- Frappe way: add index via DocType definition
-- In doctype JSON: set "in_list_view" or "search_index" on fields
-- OR use hooks.py:
-- after_migrate = ["myapp.patches.add_custom_indexes"]

Redis Configuration

Memory Management

# /etc/redis/redis.conf (or bench config/redis_cache.conf)

# Set maximum memory — NEVER let Redis use all available RAM
maxmemory 512mb

# Eviction policy — allkeys-lru is best for cache use
maxmemory-policy allkeys-lru

# Disable persistence for cache Redis (performance boost)
save ""
appendonly no

Frappe Redis Architecture

Frappe uses THREE Redis instances:

ALWAYS set maxmemory on redis-cache. Without it, Redis grows unbounded and can trigger OOM killer.

Frappe Caching API

import frappe

# Basic Redis cache
frappe.cache.set_value("my_key", {"data": "value"})
result = frappe.cache.get_value("my_key")

# get_cached_value — cached database lookup (ALWAYS prefer over get_value for reads)
value = frappe.db.get_cached_value("Customer", "CUST-001", "customer_name")
# Equivalent to get_value but caches in Redis — dramatically faster for repeated reads

# Hashed cache (group related values)
frappe.cache.hset("settings", "key1", "value1")
frappe.cache.hget("settings", "key1")

# Clear specific cache
frappe.cache.delete_value("my_key")
frappe.cache.delete_keys("prefix*")

# Clear all cache (use sparingly)
# bench --site mysite.com clear-cache

Gunicorn Workers

Worker Count Formula

workers = (2 * CPU_CORES) + 1

Examples:
  2 CPU cores  →  5 workers
  4 CPU cores  →  9 workers
  8 CPU cores  → 17 workers

Configuration

# Traditional: edit Procfile or supervisor config
# In supervisor.conf:
command=/home/frappe/frappe-bench/env/bin/gunicorn \
    -b 127.0.0.1:8000 \
    -w 9 \                    # Worker count
    --timeout 120 \           # Request timeout (seconds)
    --graceful-timeout 30 \   # Graceful shutdown timeout
    --max-requests 5000 \     # Restart worker after N requests (prevents memory leaks)
    --max-requests-jitter 500 \
    frappe.app:application

# Docker: set via environment variable or command override

Memory Calculation

Each Gunicorn worker consumes 150-300MB RAM. ALWAYS verify total memory fits:

Required RAM = workers * 300MB + MariaDB buffer pool + Redis + OS overhead

Example (4 CPU, 8GB RAM server):
  9 workers * 300MB = 2.7GB (Gunicorn)
  + 2GB (MariaDB innodb_buffer_pool_size)
  + 1GB (Redis total)
  + 1.5GB (OS + other)
  = 7.2GB — fits in 8GB

NEVER set more workers than your RAM allows. Swapping kills performance.

Background Workers (RQ)

Worker Queues

Tuning Worker Count

# Supervisor: duplicate worker sections with unique names
# For high-volume sites, increase short/default workers:

[program:frappe-bench-frappe-worker-short-1]
command=bench worker --queue short
...

[program:frappe-bench-frappe-worker-short-2]
command=bench worker --queue short
...

# Docker: scale via docker compose
docker compose up -d --scale queue-short=3 --scale queue-long=2

Diagnosing Job Backlogs

# Check overall health
bench doctor
# Expected: Workers online: N, no pending jobs

# Check specific site queues
bench --site mysite.com show-pending-jobs

# Clear stuck jobs (use when jobs are permanently stuck)
bench purge-jobs

CDN Setup for Static Assets

# site_config.json
{
    "cdn_url": "https://cdn.example.com"
}
# All /assets/ URLs will be prefixed with the CDN URL

# ALTERNATIVELY: configure at Nginx level
# location /assets {
#     alias /home/frappe/frappe-bench/sites/assets;
#     expires 1y;
#     add_header Cache-Control "public, immutable";
# }

Monitoring

bench doctor

bench doctor
# Output:
# -----Checking scheduler------
# mysite.com: scheduler is running
# Workers online: 3
# -----None Jobs-----

Key Log Locations

Scheduled Job Log (DocType)

Check Setup > Scheduled Job Log in ERPNext UI for:

• Job execution times
• Failed jobs with error details
• Frequency analysis

RQ Dashboard (Optional)

# Install RQ dashboard for web-based job monitoring
pip install rq-dashboard
rq-dashboard --redis-url redis://localhost:11000
# Access at http://localhost:9181

Common Bottleneck Diagnosis

Scaling Patterns

Vertical Scaling (single server):
  1. Add RAM → increase innodb_buffer_pool_size + Redis maxmemory
  2. Add CPU → increase Gunicorn workers + RQ workers
  3. Use SSD → dramatic improvement for database I/O

Horizontal Scaling (multiple servers):
  1. Separate DB server (MariaDB on dedicated host)
  2. Separate Redis server(s)
  3. Multiple app servers behind load balancer
  4. Read replicas for reporting queries
  5. Kubernetes with frappe_docker for auto-scaling

Version Differences

Reference Files

Related Skills

• frappe-ops-deployment — Production deployment setup
• frappe-ops-backup — Backup and disaster recovery
• frappe-ops-bench — Bench CLI reference
• frappe-core-database — Database API and query patterns