DAX (DynamoDB Accelerator) caching - Step-by-Step Execution

Concept Flow - DAX (DynamoDB Accelerator) caching

Client Request

↓

Check DAX Cache

↓

Return Data

↓

Update DAX Cache

The client sends a request which first checks the DAX cache. If data is found (cache hit), it returns immediately. If not (cache miss), it fetches from DynamoDB, returns data, and updates the cache.

Execution Sample

DynamoDB

1. Client requests item with key K
2. DAX checks cache for K
3. If found, return cached item
4. Else, query DynamoDB
5. Return item and update cache

This sequence shows how DAX caching intercepts a request to speed up data retrieval by returning cached data or fetching from DynamoDB if not cached.

Execution Table

Step	Action	Cache State	DynamoDB Query	Result Returned
1	Client requests item with key K	Empty	No	No
2	DAX checks cache for key K	Empty	No	No
3	Cache miss - item not found	Empty	No	No
4	Query DynamoDB for key K	Empty	Yes	No
5	DynamoDB returns item	Empty	Yes	No
6	DAX updates cache with item for key K	Contains item K	No	No
7	Return item to client	Contains item K	No	Yes
8	Client requests item with key K again	Contains item K	No	No
9	DAX checks cache for key K	Contains item K	No	No
10	Cache hit - item found	Contains item K	No	No
11	Return cached item to client	Contains item K	No	Yes

💡 Execution stops after returning cached item on second request, demonstrating cache hit.

Variable Tracker

Variable	Start	After Step 6	After Step 11
Cache State	Empty	Contains item K	Contains item K
DynamoDB Query	No	Yes	No
Result Returned	No	No	Yes

Key Moments - 2 Insights

Why does the first request query DynamoDB but the second does not?

Because the cache is empty at first (see step 3 and 4), so DAX must fetch from DynamoDB. After step 6, the cache contains the item, so the second request finds it immediately (step 10) and skips DynamoDB.

What happens if the cache contains stale data?

DAX may return outdated data until the cache is refreshed or invalidated. This is why cache update (step 6) is important after fetching fresh data from DynamoDB.

Visual Quiz - 3 Questions

Look at the execution table, at which step does DAX update its cache?

A Step 9

B Step 4

C Step 6

D Step 11

💡 Hint

Check the 'Cache State' column to see when it changes from 'Empty' to 'Contains item K'.

At which step does the client receive the item for the first time?

A Step 7

B Step 6

C Step 5

D Step 10

💡 Hint

Look at the 'Result Returned' column to find when it changes to 'Yes' for the first time.

If the cache was never updated, what would happen on the second client request?

A DAX would return cached data immediately

B DAX would query DynamoDB again

C Client would get no data

D DAX would crash

💡 Hint

Refer to the cache state in variable_tracker and how it affects DynamoDB queries.

Concept Snapshot

DAX caching intercepts client requests to DynamoDB.
If data is in cache (cache hit), it returns immediately.
If not (cache miss), it queries DynamoDB, returns data, and updates cache.
This reduces latency and DynamoDB read load.
Cache must be kept fresh to avoid stale data.

Full Transcript

DAX (DynamoDB Accelerator) caching works by intercepting client requests to DynamoDB. When a client asks for data, DAX first checks its in-memory cache. If the data is found (cache hit), it returns the data immediately without querying DynamoDB, speeding up the response. If the data is not in cache (cache miss), DAX queries DynamoDB, returns the data to the client, and updates its cache with this fresh data. This process reduces latency and the number of direct reads to DynamoDB. The cache state changes from empty to containing the requested item after the first fetch. On subsequent requests for the same item, DAX returns the cached data directly. It is important to keep the cache updated to avoid returning stale data. The execution table shows each step from client request to cache check, DynamoDB query if needed, cache update, and returning data to the client.