Parallel scan in DynamoDB - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to start a parallel scan with 4 segments.

DynamoDB

response = table.scan(Segment=[1], TotalSegments=4)

Drag options to blanks, or click blank then click option'

A1

B4

C5

D0

Attempts:

3 left

💡 Hint

Common Mistakes

Using Segment=4 which is out of range.

Starting Segment count at 1 instead of 0.

The Segment parameter starts from 0 up to TotalSegments - 1. So the first segment is 0.

2fill in blank

medium

Complete the code to specify the total number of segments for a parallel scan.

DynamoDB

response = table.scan(Segment=2, TotalSegments=[1])

Drag options to blanks, or click blank then click option'

A4

B2

C1

D3

Attempts:

3 left

💡 Hint

Common Mistakes

Setting TotalSegments less than or equal to Segment.

Using TotalSegments=1 for parallel scan which defeats the purpose.

TotalSegments defines how many segments the table is divided into for parallel scanning. It must be at least 1.

3fill in blank

hard

Fix the error in the parallel scan code by completing the missing parameter.

DynamoDB

response = table.scan(Segment=1, TotalSegments=3, [1]=True)

Drag options to blanks, or click blank then click option'

AParallelScan

BConsistentRead

CExclusiveStartKey

DReturnConsumedCapacity

Attempts:

3 left

💡 Hint

Common Mistakes

Using ParallelScan as a parameter which does not exist.

Confusing ExclusiveStartKey with consistency setting.

ConsistentRead=True ensures strongly consistent reads during the scan.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps segment numbers to their scan results.

DynamoDB

results = {segment: table.scan(Segment=[1], TotalSegments=[2]) for segment in range(4)}

Drag options to blanks, or click blank then click option'

Asegment

B4

C3

Dsegment + 1

Attempts:

3 left

💡 Hint

Common Mistakes

Using segment + 1 for Segment which goes out of range.

Setting TotalSegments less than the range count.

The segment variable is used for the Segment parameter, and TotalSegments is 4 to match the range.

5fill in blank

hard

Fill all three blanks to filter scan results for items with attribute 'status' equal to 'active' in each segment.

DynamoDB

filtered = {seg: [item for item in table.scan(Segment=[1], TotalSegments=[2])['Items'] if item.get('[3]') == 'active'] for seg in range(3)}

Drag options to blanks, or click blank then click option'

Aseg

B3

Cstatus

Dsegment

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'segment' instead of 'seg' causing undefined variable error.

Filtering by wrong attribute name.

Use 'seg' for Segment, 3 for TotalSegments, and 'status' as the attribute to filter by.

Practice

1. What is the main purpose of using Parallel Scan in DynamoDB?

easy

A. To update multiple items in a table simultaneously

B. To backup data in parallel to another region

C. To create multiple tables for faster access

D. To split a table scan into multiple parts that run at the same time

Solution

1. Step 1: Understand the concept of Parallel Scan

   Parallel Scan divides the scan operation into segments that run concurrently to speed up reading the entire table.

2. Step 2: Identify the main purpose

   The main goal is to speed up scanning by running parts in parallel, not updating or backing up data.

3. Final Answer:

   To split a table scan into multiple parts that run at the same time -> Option D

4. Quick Check:

   Parallel Scan = split scan parts [OK]

Hint: Parallel scan means splitting scan into parts running together [OK]

Common Mistakes:

• Confusing scan with update operations
• Thinking parallel scan creates multiple tables
• Assuming parallel scan is for backup

2. Which two parameters are required to perform a parallel scan in DynamoDB?

easy

A. Segment and TotalSegments

B. PartitionKey and SortKey

C. Limit and FilterExpression

D. IndexName and ProjectionExpression

Solution

1. Step 1: Recall parameters for parallel scan

   Parallel scan requires specifying which segment to scan and how many total segments exist.

2. Step 2: Match parameters to options

   Segment and TotalSegments control the parts of the scan; other options relate to different operations.

3. Final Answer:

   Segment and TotalSegments -> Option A

4. Quick Check:

   Parallel scan params = Segment + TotalSegments [OK]

Hint: Remember: Segment and TotalSegments split the scan [OK]

Common Mistakes:

• Using PartitionKey and SortKey which are for queries
• Confusing Limit with segment control
• Mixing index parameters with scan parameters

3. Given a table with 1000 items and a parallel scan with TotalSegments=5, what does setting Segment=2 do?

medium

A. Scans the first part of the table items

B. Scans all items in the table

C. Scans the third part of the table items

D. Scans only 2 items from the table

Solution

1. Step 1: Understand segment numbering

   Segments are zero-based, so Segment=2 means the third segment out of 5.

2. Step 2: Identify what scanning Segment=2 means

   It scans only the third part of the table, not the whole table or just two items.

3. Final Answer:

   Scans the third part of the table items -> Option C

4. Quick Check:

   Segment=2 means third part scanned [OK]

Hint: Segments start at 0; Segment=2 is third part [OK]

Common Mistakes:

• Thinking Segment=2 scans whole table
• Assuming segments start at 1
• Confusing segment number with item count

4. You wrote this code for parallel scan but it returns incomplete data:

for segment in range(3):
    response = table.scan(Segment=segment, TotalSegments=3)
    print(response['Items'])

What is the likely problem?

medium

A. TotalSegments should be 1 for parallel scan

B. You must combine results from all segments to get full data

C. Segment numbers should start from 1, not 0

D. You cannot use scan with Segment parameter

Solution

1. Step 1: Analyze the code behavior

   The code scans each segment separately but prints results immediately without combining.

2. Step 2: Understand why data is incomplete

   Each segment returns part of data; to get full data, results must be combined from all segments.

3. Final Answer:

   You must combine results from all segments to get full data -> Option B

4. Quick Check:

   Combine all segment results for full scan [OK]

Hint: Combine all segment results to get full table data [OK]

Common Mistakes:

• Starting segments at 1 instead of 0
• Setting TotalSegments to 1 disables parallelism
• Believing scan can't use Segment parameter

5. You want to speed up scanning a large DynamoDB table with 10 million items. You set TotalSegments=10 and run scans in parallel. Which approach ensures you get all items without missing or duplicating data?

hard

A. Run scans for all segments (0 to 9) and combine all results

B. Run scan only on Segment=0 with TotalSegments=10

C. Run scans on segments 1 to 10 (1-based) and combine results

D. Run a single scan without segments to avoid duplicates

Solution

1. Step 1: Understand segment indexing and coverage

   Segments are zero-based, so with TotalSegments=10, segments are 0 through 9.

2. Step 2: Ensure full coverage without overlap

   Running all segments from 0 to 9 and combining results covers entire table exactly once.

3. Step 3: Identify incorrect options

   Running only Segment=0 misses data; segments 1 to 10 are off by one; single scan is slower and not parallel.

4. Final Answer:

   Run scans for all segments (0 to 9) and combine all results -> Option A

5. Quick Check:

   All segments 0-9 combined = full scan [OK]

Hint: Run all zero-based segments and combine results [OK]

Common Mistakes:

• Using 1-based segment numbers instead of 0-based
• Running only one segment expecting full data
• Avoiding parallel scan due to fear of duplicates