Abstract
- A temporary data store layer, much faster to obtain the data compared to obtaining data from Database
- The above cache server is using Read-through Cache Strategy
- Memcached and Redis are popular Cache Server, they have API which can be interacted with programming languages
# The cached data has a key 'myKey', and value 'hi there'
# The cached data will stay in the Cacahe server for 3600s
SECONDS = 1
cache.set('myKey, 'hi there', 3600 * SECONDS)
cache.get('myKey')Better performance
The ability to reduce compute/database workloads, and scale the Cache Server independently. Thus better Scalability (可扩展性).
When to use cache server?
Use Cache Server when data is read frequently but modified infrequently.
Cache server is not ideal for persisting data since cached data is stored in Main Memory.
Expiration policy
It is a good practice to set how long it takes for the cached data to expire and get removed from Cache Server. Or cached data will stay in the Main Memory forever.
Not to make the expiration date too short as this will cause the system to reload data from the Database too frequently. Not to make the expiration date too long as the data can become stale.
Consistency
Basically keep the data in Database and Cache Server in sync. Inconsistency can happen because data-modifying operations on the database and cache server are not in a single transaction.
When scaling across multiple regions, maintaining consistency between the database and cache server is challenging.
Mitigating failures
A single Cache Server is a Single Point of Failure. Multiple cache servers across different data centers are recommended to avoid single point of failure.
It is recommended to over-provision the required memory by certain percentages. This provides a buffer as the memory usage increases