Platform cache

#SalesforceApexHourswww.ApexHours.com
Salesforce Apex Hours
Farmington Hill Salesforce Developer Group
Boost your App with Platform Cache
#SalesforceApexHours
#SalesforceApexHours
Speaker
Date
Venue/Link
Daniel Stange
Saturday, January 04, 2020 10:00 AM EST ( 8:30 PM IST )
https://zoom.us/j/476658815
www.apexhours.com

Who I am
Amit Chaudhary (Salesforce MVP)
• Active on Salesforce Developer Community
• Blogging at http://amitsalesforce.blogspot.in/
• Co-Organizer of – FarmingtonHillsSFDCDug
• Founder of www.ApexHours.com
• Follow us @Amit_SFDC or @ApexHours

Upcoming Session
1. OAuth Concepts on JAN 11, 2020 10:00 AM EST (8:30 PM IST) with Susannah Kate St-Germain.
2. Become an Order of Execution Hero on JAN 18, 2020 10:00 AM EST (8:30 PM IST) with Daniel Stange
and Marc B. Kirsch
3. Salesforce integration Pattern & Best Practices on FEB 1, 2019 10:00 AM EST (8:30 PM IST) with Jitendra
Zaa
4. Microservices in Salesforce on FEB 22, 2020 10:00 AM EST (8:30 PM IST) with Jigar Shah
All Upcoming session : http://www.apexhours.com/sessions-in-2020/

Our Speaker
Daniel Stange
Technical Architect
DIA die.interaktiven
20x certified
System & Application Architect
Frankfurt User Group Leader
@stangomat
linked.in/daniel-stange
github.com/dstdia

Agenda
• Platform Cache - But why?
• Key Concepts
• Getting Started with Platform Cache (Demo / Code!)
• Platform Cache as a key/value storage
• Platform Cache as a readthrough cache
• When to Use (and when not to use) Platform Cache
• Q & A

Key Takeaways
• Boost your app‘s performance significantly
• Understand which kind of data to put in the cache
• Know the obstacles when you use to the platform cache and
prepare for cache misses and unexpected results

Platform Cache?
But why?

Impact for read access to data
(EE Scratch Org on CS83 / EE Dev Sandbox on CS86)
Avg. to fetch 10000 records
as a Map through SOQL
182-370ms
1.8 to 5.7x
Faster Access
65-97ms
Avg. to fetch the same
map from the Org cache

DB vs. Cache Retrieve / per Item
Impact of bulkification (100 retrieved values)
DB: Initial 367ms => 3,67ms per item access
Cache: Initial 117ms => 1,17ms per item access
DB: avg. 11,52ms
Cache: avg. 3.67ms

Other Gains
• Handle Limits better when accessing frequently used
data
• Speed up data access (2x to 3x faster than Database access)
• Reduce SOQL calls (access is „free“)
• Reduce memory footprint
• Trade-Offs:
• Heap size limit (when working with maps in variables)
• Cache access time (slower than accessing variables)
• Less control over SOQL calls (if implemented for cache misses)

Big News for ISVs

Key Concepts

Platform Cache
• Simple & temporary Key-Value-Storage (Map<String, Object>)
• Quite Easy to implement ANY cache strategy
=> but fairly easy to design a GOOD, sustainable strategy
• By default VISIBLE and MUTABLE
=> but can be RESTRICTED and set to IMMUTABLE

Data to Cache
• PREPARABLE - REUSABLE
• Static: Not changing frequently
• Frequently needed in operations
• Expensive to get (or keep) (in terms of system limits)
• e.g. Taxonomies, Schedules, Mappings, Conversion Rates, etc.

Cache Types
ORG
one cache for all
users and contexts
variable TTL
SESSION
scoped per user
Session
TTL = Session
length
TTL: Time to Live

ORG: 24 hrs
DEFAULT TTL
Time-to-Live (TTL)
ORG: 5 min
MINIMUM TTL
SESSION: 5 min
ORG: 48 hrs
MAXIMUM TTL
SESSION: 8 hrs

Cache Eviction Strategy
1
4
3
7
5
6
ORDER OF LAST ACCESS
2
CACHE CAPACITY
LRU: Least Recently Used Eviction Strategy

Considerations
How (and when) to build / maintain your cache
• Explicit Caching
• Button: “Rebuild the Cache now“
• Scheduled Apex: „Rebuild the Cache every night at 1 a.m.“
• Implicit Caching
• Eagerly: „Upon a Cache Miss, cache all items again“
• Lazy: „Upon a Cache Miss, cache the missing item again“

Considerations
Invalidation
• Requires careful consideration
• Platform data vs. off-platform data
• Frequency of change
• Ability to „listen“ to change events
• Strategies:
• Rebuild
• Evict / Replace
• Combinations

Digging Into the Platform Cache

Preparation Work
• Enterprise Editions Sandboxs come with a 10 MB capacity
• Request a trial capacity if you are using a Developer Org or Scratch
Org
• SFDX / Scratch Orgs: "features": ["PlatformCache"]
• Trailhead: “Platform Cache Basics”
bit.ly/cachetrail

Place your screenshot here
Cache
.[Org/Session]
.put(
Namespace
.PartitionName
.Key,
Object
);
Store

Retrieve
Cache
.[Org/Session]
.get(
Namespace
.PartitionName
.Key
);

Demo
Sample Implementation and Benchmark Scripts at
https://github.com/dstdia/PlatformCache

Implementation
Catch
miss
Load
from
source
Check for
key
Store
Key /
Value
Retrieve
Value
Return
Value

CacheBuilder Interface
Catch
miss
Load
from
source
Check for
key
Store
Key /
Value
Retrieve
Value
Return
Value

Two Implementation Paths
• READ-THROUGH IMPLEMENTATION using CACHEBUILDER
interface
• Data sits in sObjects
• Access can be expressed in one SOQL query
• CUSTOM IMPLEMENTATION
• Any off-platform data
• Cross-object data that can‘t be accessed through one specific SOQL query

Sample Sales District
Everything with Country
= DE and PostalCode
LIKE ‘35%’ should be
assigned and have Nils
as an Owner.
https://github.com/
dstdia/PlatformCache

Creating an Account
Postal Code starts with 35
and Country = DE
Should end up in Central
Sample District being
assigned

And there it is!

Read-Through Cache

Custom Implementation
• Design a way to POPULATE the cache
• Design a way to manage changes in the cached data
(INVALIDATE)
• Design the request to the cache
• Catch cache misses
• Handle misses gracefully (load data, put into cache, return)

„Read Through"
• Implement the standard Interface Cache.CacheBuilder
• Implement the doLoad() method:
• Design a Query to retrieve a record
...
sd = (SalesDistrict__c) Cache.Org.get(
Cache_SalesDistrict.class,
salesDistrictKey);
...

Considerations

Nice! Easy! Fast!
Where‘s the catch?
• It‘s still a cache, not a database.
• It is short lived and per default visible and mutable
• Understand the key concepts of the platform cache
(in particular TTL, LRU)
• Expect the cache to fail you.
• What’s your strategy to invalidate cache?

Build your Strategies
• The Cache will go away... Schedule a Cache Rebuild < TTL
• Cached items will be pushed out… Use the CacheBuilder interface
• Cached data goes stale… Rebuild From Triggers
• Cached items must adhere to the 100kB size limits
Reduce memory footprint by using Apex Classes instead of sObject
or reduce the number of queried fields

Apex Classes vs. sObjects
List<CachedProduct>
cachedProducts;
public class CachedProduct
{
String name;
String productCode;
Decimal leadTime;
}
List<Product2> products
= [SELECT Name,
ProductCode,
LeadTime
FROM Product2];

Size matters
No cached item may exceed
100kB

Size matters
100kB Cache storage equals
12.5k objects
same list, but stored in an Apex defined type
9k sObject records
Product2 records with Id, Name, ProductCode
queried

Repeat after me… :-)
• Don‘t use the cache as a fast, limit-free database.
• Don‘t use it as temporary storage for transactional data
• No items larger than 100kB
• Cached Items are not persisted – don‘t rely on them being there.
• Unless flagged as immutable, don’t rely on the integrity of cached
items

Best Practices
• Cache lists or maps of objects rather than single objects.
Tradeoff:
Better performance of fewer, larger operations
vs. 100kB item size limit
• Use a wrapper class to reduce sObject overhead
• Use the fully qualified name of your cache partition
• Consider using the ‘immutable‘ flag and the visibility enum

Further Reading
• Developer Documentation: bit.ly/cachedoc
• Trailhead: bit.ly/cachetrail
• Session Code: github.com/dstdia/PlatformCache/
• Keir Bowden’s blog post: bit.ly/cache-buzzard
• Josh Kaplan‘s blog post: bit.ly/cache-sfblog
• Amit Chaudhary: http://bit.ly/cache-amit

Q&A
Connect with me
• @stangomat
• Daniel.stange@die.interaktiven.de
• github.com/dstdia
Sample Code from the session:
https://github.com/dstdia/PlatformCache

Thank you!
Q & A

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