Cache invalidation strategies in Rest API - Step-by-Step Execution

Concept Flow - Cache invalidation strategies

Client requests data

↓

Check cache for data

↓

End

This flow shows how a system checks cache before fetching fresh data and how cache invalidation triggers update the cache.

Execution Sample

Rest API

GET /data
if cache.has(data):
  return cache.get(data)
else:
  fresh = fetch_from_server()
  cache.set(data, fresh)
  return fresh

This code checks if data is in cache; if yes, returns it; if no, fetches fresh data, updates cache, then returns it.

Execution Table

Step	Action	Cache State	Data Returned	Cache Invalidation Trigger
1	Client requests /data	Empty	None	None
2	Check cache for data	Empty	None	None
3	Cache miss, fetch fresh data	Empty	None	None
4	Store fresh data in cache	Data stored	None	None
5	Return fresh data to client	Data stored	Fresh data	None
6	Later client requests /data	Data stored	None	None
7	Check cache for data	Data stored	Cached data	None
8	Return cached data to client	Data stored	Cached data	None
9	Cache expiry time reached	Data stored	None	Time-based expiry
10	Invalidate cache	Empty	None	Cache cleared
11	Next client request /data	Empty	None	None
12	Cache miss, fetch fresh data	Empty	None	None
13	Store fresh data in cache	Data stored	None	None
14	Return fresh data to client	Data stored	Fresh data	None

💡 Execution stops after fresh data is returned and cache is updated or invalidated.

Variable Tracker

Variable	Start	After Step 4	After Step 8	After Step 10	After Step 13	Final
cache	Empty	Data stored	Data stored	Empty	Data stored	Data stored
data returned	None	None	Cached data	None	None	Fresh data

Key Moments - 3 Insights

Why does the system fetch fresh data even though cache exists sometimes?

Because the cache can expire or be invalidated (see steps 9 and 10 in execution_table), so the system must fetch fresh data to keep information up to date.

What happens if the cache is empty when a client requests data?

The system fetches fresh data from the server, stores it in cache, then returns it (see steps 3, 4, and 5).

How does time-based expiry affect cache data?

When the expiry time is reached (step 9), the cache is cleared (step 10), forcing fresh data to be fetched on the next request.

Visual Quiz - 3 Questions

Look at the execution_table, what is the cache state after step 4?

A Empty

B Data stored

C Cache cleared

D Unknown

💡 Hint

Refer to the 'Cache State' column at step 4 in execution_table.

At which step does the cache get invalidated due to time expiry?

A Step 8

B Step 9

C Step 10

D Step 12

💡 Hint

Check the 'Cache Invalidation Trigger' column around steps 9 and 10.

If the cache never expires, what would happen at step 11?

A Cache miss, fetch fresh data

B Return cached data

C Invalidate cache

D Store fresh data

💡 Hint

Look at step 7 and 8 where cache is used if present.

Concept Snapshot

Cache Invalidation Strategies:
- Check cache before fetching data
- If cache miss, fetch fresh data and update cache
- Invalidate cache by:
  * Time-based expiry
  * Manual purge
  * Event-based triggers
- Keeps data fresh and reduces server load

Full Transcript

This visual execution shows how cache invalidation strategies work in a REST API context. When a client requests data, the system first checks if the data is in cache. If yes, it returns cached data immediately. If not, it fetches fresh data from the server, stores it in cache, and returns it. Cache invalidation can happen due to time expiry, manual clearing, or events that signal data changes. When cache is invalidated, the next request triggers fresh data fetching again. This process helps keep data fresh while improving performance by reducing unnecessary server calls.