Stream vs polling comparison in DynamoDB - Visual Side-by-Side Comparison

Concept Flow - Stream vs polling comparison

Start

↓

New Data in DB?

+--Yes--+↓

Stream

↓

Event

↓

Process

↓

End

This flow shows how data changes trigger processing either instantly via streams or repeatedly via polling.

Execution Sample

DynamoDB

1. Stream: Wait for DB event -> Process event
2. Polling: Repeat every 5s -> Check DB -> Process new data

Shows the two ways to detect and process new data in DynamoDB: event-driven stream vs time-driven polling.

Execution Table

Step	Method	Action	Data Detected	Process Triggered	Next Step
1	Stream	Wait for DB event	No	No	Wait
2	Stream	Wait for DB event	Yes (New item added)	Yes	Process event
3	Stream	Process event	N/A	N/A	Wait for next event
4	Polling	Check DB	No new data	No	Wait 5 seconds
5	Polling	Wait 5 seconds	N/A	N/A	Check DB again
6	Polling	Check DB	New item found	Yes	Process new data
7	Polling	Process new data	N/A	N/A	Wait 5 seconds

💡 Stream waits for events and processes immediately; polling repeatedly checks DB at intervals.

Variable Tracker

Variable	Start	After Step 2	After Step 4	After Step 6	Final
Data Detected	No	Yes	No	Yes	N/A
Process Triggered	No	Yes	No	Yes	N/A
Wait State	Waiting	Processing	Waiting	Processing	Waiting

Key Moments - 3 Insights

Why does the stream method process data immediately after detection?

Because streams trigger processing right when the data change event happens, as shown in execution_table step 2.

Why does polling sometimes wait even if new data exists?

Polling only checks at fixed intervals (e.g., every 5 seconds), so it may delay processing until the next check, as seen in steps 4 and 6.

Is polling more resource-intensive than streams?

Yes, because polling repeatedly queries the database even when no new data exists, unlike streams which react only on events.

Visual Quiz - 3 Questions

Look at the execution_table, at which step does the stream detect new data?

A Step 1

B Step 4

C Step 2

D Step 6

💡 Hint

Check the 'Data Detected' column for the Stream method in execution_table.

According to variable_tracker, what is the 'Process Triggered' state after step 4?

A Yes

B No

C Waiting

D Processing

💡 Hint

Look at the 'Process Triggered' row and the 'After Step 4' column in variable_tracker.

If polling interval is shortened, how would execution_table change?

A More frequent 'Check DB' steps

B Fewer 'Process' steps

C Stream steps would increase

D No change in polling steps

💡 Hint

Polling frequency affects how often 'Check DB' happens, visible in execution_table steps 4 and 6.

Concept Snapshot

Stream vs Polling in DynamoDB:
- Stream: Event-driven, triggers instantly on data change
- Polling: Time-driven, checks DB at intervals
- Stream is efficient, low latency
- Polling can cause delays and higher resource use
- Choose based on app needs and cost considerations

Full Transcript

This visual compares DynamoDB Streams and Polling methods for detecting data changes. Streams wait for database events and process data immediately when changes occur, shown in steps 1-3. Polling repeatedly checks the database at fixed intervals, like every 5 seconds, shown in steps 4-7, which can delay processing. Variables track when data is detected and processing triggered. Key moments clarify why streams react instantly and polling may wait. Quizzes test understanding of detection steps and states. The snapshot summarizes pros and cons of each method for beginners.