frappe-core-cache

Frappe Cache & Locking

Quick Reference

Action	Method	Notes
Set value	frappe.cache.set_value(key, val)	With optional TTL
Get value	frappe.cache.get_value(key)	Returns None if missing
Get or generate	frappe.cache.get_value(key, generator=fn)	Calls fn() on cache miss
Delete value	frappe.cache.delete_value(key)	Single key or list of keys
Delete by pattern	frappe.cache.delete_keys(pattern)	Wildcard * matching
Hash set	frappe.cache.hset(name, key, val)	Redis hash field
Hash get	frappe.cache.hget(name, key)	Single hash field
Hash get all	frappe.cache.hgetall(name)	Full hash as dict
Hash delete	frappe.cache.hdel(name, key)	Remove hash field
Hash exists	frappe.cache.hexists(name, key)	Returns bool
Cached document	frappe.get_cached_doc(dt, dn)	Full doc from cache
Clear doc cache	frappe.clear_document_cache(dt, dn)	Invalidate cached doc
Decorator cache	@redis_cache	Auto-cache function result
Request cache	frappe.local.cache	Per-request dict (not Redis)

---

Decision Tree

What caching pattern do you need?
│
├─ Cache a function result automatically?
│  ├─ Pure function (same args → same result) → @redis_cache
│  └─ Need custom key/TTL → manual get_value/set_value
│
├─ Cache a document?
│  ├─ Read-only access → frappe.get_cached_doc()
│  └─ Need to invalidate → frappe.clear_document_cache()
│
├─ Cache structured data (multiple fields)?
│  └─ Redis hash → hset/hget/hgetall
│
├─ Per-request cache (avoid repeated DB calls in one request)?
│  └─ frappe.local.cache dict
│
├─ Prevent concurrent execution?
│  └─ Distributed lock → frappe.lock("resource_name")
│
└─ Invalidate cache?
   ├─ Single key → delete_value(key)
   ├─ Pattern → delete_keys("prefix*")
   └─ All site cache → frappe.clear_cache()

---

String Operations

Set and Get

# Set a value (persists until evicted or deleted)
frappe.cache.set_value("exchange_rate_USD", 1.08)

# Set with TTL (expires after N seconds)
frappe.cache.set_value("exchange_rate_USD", 1.08, expires_in_sec=3600)

# Get value (returns None if missing)
rate = frappe.cache.get_value("exchange_rate_USD")

# Get with generator (calls function on cache miss, stores result)
rate = frappe.cache.get_value(
    "exchange_rate_USD",
    generator=lambda: fetch_exchange_rate("USD"),
)

User-Scoped Values

# Store per-user preference
frappe.cache.set_value("dashboard_layout", "compact", user="[email protected]")

# Retrieve for specific user
layout = frappe.cache.get_value("dashboard_layout", user="[email protected]")

Delete

# Single key
frappe.cache.delete_value("exchange_rate_USD")

# Multiple keys
frappe.cache.delete_value(["exchange_rate_USD", "exchange_rate_EUR"])

# Pattern-based deletion (wildcard)
frappe.cache.delete_keys("exchange_rate*")

---

Hash Operations

Use hashes to group related fields under a single key.

# Set hash fields
frappe.cache.hset("config|notifications", "email_enabled", True)
frappe.cache.hset("config|notifications", "sms_enabled", False)
frappe.cache.hset("config|notifications", "max_retries", 3)

# Get single field
email_on = frappe.cache.hget("config|notifications", "email_enabled")

# Get all fields as dict
config = frappe.cache.hgetall("config|notifications")
# {"email_enabled": True, "sms_enabled": False, "max_retries": 3}

# Delete field
frappe.cache.hdel("config|notifications", "sms_enabled")

# Check existence
exists = frappe.cache.hexists("config|notifications", "email_enabled")

Hash with Generator

# hget with generator — calls function on miss
value = frappe.cache.hget(
    "user|permissions",
    "[email protected]",
    generator=lambda: compute_permissions("[email protected]"),
)

---

@redis_cache Decorator

Automatically cache function return values based on arguments.

from frappe.utils.caching import redis_cache

@redis_cache
def get_item_price(item_code, price_list):
    """Expensive query — cached automatically."""
    return frappe.db.get_value("Item Price",
        {"item_code": item_code, "price_list": price_list},
        "price_list_rate",
    )

# First call — hits database, stores in Redis
price = get_item_price("ITEM-001", "Standard Selling")

# Second call — returns from cache
price = get_item_price("ITEM-001", "Standard Selling")

# Clear all cached results for this function
get_item_price.clear_cache()

With TTL

@redis_cache(ttl=300)  # expires after 5 minutes
def get_exchange_rate(from_currency, to_currency):
    return fetch_rate_from_api(from_currency, to_currency)

Rules for @redis_cache:

• ALWAYS ensure arguments are hashable (strings, numbers, tuples). NEVER pass dicts or lists as arguments.
• ALWAYS call .clear_cache() when underlying data changes.
• NEVER use on functions with side effects — the function will NOT execute on cache hits.

---

frappe.local.cache: Request-Scoped Cache

frappe.local.cache is a plain Python dict that lives for the duration of a single HTTP request. It is NOT stored in Redis.

def get_user_settings():
    """Avoid repeated DB calls within a single request."""
    if "user_settings" not in frappe.local.cache:
        frappe.local.cache["user_settings"] = frappe.get_doc(
            "User Settings", frappe.session.user
        )
    return frappe.local.cache["user_settings"]

Use frappe.local.cache when:

• The same data is needed multiple times in one request
• The data does NOT need to persist across requests
• You want zero Redis overhead

---

Document Caching

# Get cached document (read-only, no permission check)
settings = frappe.get_cached_doc("System Settings")
item = frappe.get_cached_doc("Item", "ITEM-001")

# Invalidate when document changes
frappe.clear_document_cache("Item", "ITEM-001")

# Cached single value
val = frappe.db.get_value("Item", "ITEM-001", "item_name", cache=True)

NEVER modify a document returned by frappe.get_cached_doc() — it returns a shared reference. Modifications corrupt the cache for all subsequent reads.

---

Distributed Locking

Prevent concurrent execution of critical sections using Redis-based locks.

# Context manager (recommended)
with frappe.lock("process_payroll"):
    # Only one worker executes this block at a time
    process_all_salary_slips()
    # Lock auto-released on exit

# Manual lock/unlock
frappe.lock("inventory_sync")
try:
    sync_inventory()
finally:
    frappe.unlock("inventory_sync")  # ALWAYS unlock in finally

Rules:

• ALWAYS use with frappe.lock() (context manager) to guarantee release.
• NEVER hold locks for more than a few seconds — long locks cause worker starvation.
• ALWAYS use descriptive lock names to avoid collisions.

---

Cache Invalidation Patterns

Pattern 1: TTL-Based (Time-to-Live)

frappe.cache.set_value("dashboard_stats", compute_stats(), expires_in_sec=300)

Best for: Data that can be slightly stale (exchange rates, dashboard aggregates).

Pattern 2: Event-Based Invalidation

# In hooks.py
doc_events = {
    "Item Price": {
        "on_update": "my_app.cache.invalidate_price_cache",
        "on_trash": "my_app.cache.invalidate_price_cache",
    }
}

# In my_app/cache.py
def invalidate_price_cache(doc, method):
    frappe.cache.delete_keys("item_price*")
    # Or clear specific function cache:
    # get_item_price.clear_cache()

Best for: Data that MUST be fresh immediately after changes.

Pattern 3: Hybrid (TTL + Event)

@redis_cache(ttl=600)
def get_pricing_rules():
    return frappe.get_all("Pricing Rule", fields=["*"])

# Event hook clears cache immediately on change
def on_pricing_rule_update(doc, method):
    get_pricing_rules.clear_cache()

Best for: Frequently read data with occasional updates.

---

Common Cache Keys (Internal)

Key Pattern	Content
doctype::meta::{dt}	DocType metadata
user_permissions::{user}	User permission cache
bootinfo::{user}	User boot info
notifications::{user}	Notification counts
document_cache::{dt}::{dn}	Cached document

NEVER write to internal cache keys directly. ALWAYS use the documented API methods (get_cached_doc, clear_document_cache, etc.).

---

Performance Guidelines

1. ALWAYS set TTL on cached values that derive from external data — without TTL, stale data persists until manual invalidation or Redis eviction.
2. NEVER cache large objects (>1 MB) — Redis uses pickle serialization, and large values increase serialization overhead and memory usage.
3. ALWAYS use frappe.local.cache for data needed multiple times within a single request — it avoids Redis round-trips entirely.
4. NEVER use frappe.clear_cache() as a routine invalidation strategy — it clears ALL cache keys for the site, causing a cold-cache performance hit.
5. ALWAYS prefix custom cache keys with your app name (e.g., myapp|exchange_rate) to avoid collisions with Frappe internals.

---

Redis Configuration

Default config: {bench}/config/redis_cache.conf

Setting	Default	Description
Port	13000	Redis cache port
Bind	127.0.0.1	Listen address
maxmemory-policy	allkeys-lru	Eviction policy
maxmemory	256mb	Max memory (adjustable)

---

Key Namespacing

All cache keys are automatically prefixed by Frappe with the site name:

# You write:
frappe.cache.set_value("my_key", "value")

# Redis stores:
# "mysite.localhost|my_key"

frappe.cache.make_key(key, user, shared) handles prefixing. The shared=True parameter removes the site prefix for cross-site keys (rare use case).

---

Version Differences

Feature	v14	v15	v16
frappe.cache.set_value	Available	Available	Available
@redis_cache	Not available	Available	Available
@redis_cache(ttl=)	Not available	Available	Available
frappe.lock context mgr	Available	Available	Available
frappe.local.cache	Available	Available	Available
hget with generator	Available	Available	Available

---

See Also

• references/examples.md — Cache implementation patterns
• references/anti-patterns.md — Common cache mistakes
• references/api-reference.md — Complete API signatures
• frappe-core-database — Database queries that benefit from caching
• frappe-core-permissions — User permission caching