Row key design strategies in Hadoop - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a row key by concatenating user ID and timestamp.

Hadoop

row_key = user_id + [1] + timestamp

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

A_

B:

C-

D+

Attempts:

3 left

💡 Hint

Common Mistakes

Using '+' can cause confusion with arithmetic operations.

Using '-' might be confused with negative numbers.

Using an underscore '_' is a common way to separate parts in a row key for readability and uniqueness.

2fill in blank

medium

Complete the code to reverse the timestamp string for better row key distribution.

Hadoop

reversed_ts = timestamp[::[1]1]

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

A{

B[

C(

D-

Attempts:

3 left

💡 Hint

Common Mistakes

Using brackets without proper slice causes syntax error.

Using parentheses or braces is invalid for slicing.

The minus sign '-' is used as the step value in the slice [::-1] to reverse the string.

3fill in blank

hard

Fix the error in the code to create a salted row key for load balancing.

Hadoop

salt = str(hash(user_id) [1] 10)
salted_key = salt + '_' + user_id + '_' + timestamp

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

A+

B%

C**

D//

Attempts:

3 left

💡 Hint

Common Mistakes

Using '//' causes integer division but not remainder.

Using '+' or '**' causes type errors.

The modulus operator '%' is used to limit the salt value within a range.

4fill in blank

hard

Fill both blanks to create a set comprehension that combines user IDs with reversed timestamps to form row keys.

Hadoop

row_keys = {user_id[1] '_' + timestamp[::[2]1] for user_id, timestamp in data.items()}

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

A+

B.

C-

D*

Attempts:

3 left

💡 Hint

Common Mistakes

Using '-' or '*' for string concatenation causes errors.

Omitting the step in slicing causes incorrect reversal.

Use '+' to concatenate strings and '-' as the step in slicing [::-1] for reversing the timestamp.

5fill in blank

hard

Fill all three blanks to create a filtered set of row keys where timestamp is greater than a threshold.

Hadoop

filtered_keys = {user_id[1] timestamp for user_id, timestamp in data.items() if timestamp [2] threshold and user_id[3] 'active'}

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

A+

B>

C==

D-

Attempts:

3 left

💡 Hint

Common Mistakes

Using '-' for string concatenation causes errors.

Using '=' instead of '==' in condition causes syntax errors.

Use '+' to concatenate strings, '>' to compare timestamp with threshold, and '==' to check if user_id equals 'active'.

Practice

1. What is the main purpose of designing a good row key in Hadoop tables?

easy

A. To speed up data retrieval by organizing data efficiently

B. To increase the size of the data stored

C. To make data encryption easier

D. To reduce the number of columns in the table

Solution

1. Step 1: Understand the role of row keys

   Row keys determine how data is stored and accessed in Hadoop tables, affecting retrieval speed.

2. Step 2: Identify the main goal of row key design

   The goal is to organize data so that searches and reads are fast and efficient.

3. Final Answer:

   To speed up data retrieval by organizing data efficiently -> Option A

4. Quick Check:

   Row key design = speed up retrieval [OK]

Hint: Good row keys help find data faster [OK]

Common Mistakes:

• Thinking row keys reduce data size
• Confusing row keys with encryption keys
• Assuming row keys reduce columns

2. Which of the following is a correct way to create a composite row key in Hadoop?

easy

A. row_key = user_id + '_' + timestamp

B. row_key = user_id * timestamp

C. row_key = timestamp - user_id

D. row_key = user_id / timestamp

Solution

1. Step 1: Understand composite row keys

   Composite keys combine meaningful parts as strings to create unique keys.

2. Step 2: Check the syntax for combining parts

   Concatenating with an underscore is a common and correct way to combine parts as strings.

3. Final Answer:

   row_key = user_id + '_' + timestamp -> Option A

4. Quick Check:

   Composite key = string concat with separator [OK]

Hint: Use string concat with separator for composite keys [OK]

Common Mistakes:

• Using arithmetic operations instead of string concat
• Not adding a separator between parts
• Mixing data types without conversion

3. Given the following Python code to create row keys, what will be the output list?

user_ids = ['u1', 'u2']
timestamps = [100, 200]
row_keys = [f'{uid}_{ts}' for uid in user_ids for ts in timestamps]
print(row_keys)

medium

A. ['u1_100', 'u2_200', 'u1_200', 'u2_100']

B. ['u1_100', 'u2_100', 'u1_200', 'u2_200']

C. ['u2_100', 'u2_200', 'u1_100', 'u1_200']

D. ['u1_100', 'u1_200', 'u2_100', 'u2_200']

Solution

1. Step 1: Understand the nested loop order in list comprehension

   The outer loop is over user_ids, inner loop over timestamps, so for each user_id, all timestamps are combined.

2. Step 2: Generate the list step-by-step

   For 'u1': 'u1_100', 'u1_200'; for 'u2': 'u2_100', 'u2_200'.

3. Final Answer:

   ['u1_100', 'u1_200', 'u2_100', 'u2_200'] -> Option D

4. Quick Check:

   Outer loop user_ids, inner timestamps = ['u1_100', 'u1_200', 'u2_100', 'u2_200'] [OK]

Hint: Outer loop first, inner loop second in list comprehensions [OK]

Common Mistakes:

• Mixing order of loops
• Assuming timestamps loop first
• Confusing list comprehension syntax

4. Identify the error in this row key design code snippet:

def create_row_key(user_id, timestamp):
    return user_id + timestamp

key = create_row_key('user1', 1234567890)
print(key)

medium

A. SyntaxError due to missing colon

B. TypeError because user_id is string and timestamp is int

C. NameError because function is not defined

D. No error, code runs fine

Solution

1. Step 1: Analyze the function parameters and operation

   user_id is a string, timestamp is an integer; adding them directly causes a type error.

2. Step 2: Understand Python type rules for + operator

   Python cannot add string and int directly without conversion.

3. Final Answer:

   TypeError because user_id is string and timestamp is int -> Option B

4. Quick Check:

   String + int without conversion = TypeError [OK]

Hint: Cannot add string and int directly in Python [OK]

Common Mistakes:

• Ignoring data types in addition
• Assuming implicit conversion
• Thinking syntax or name error

5. You want to design a row key for a large Hadoop table storing user activity logs. The goal is to avoid hot-spotting and allow fast range scans by time. Which strategy is best?

hard

A. Use only user ID as the row key

B. Use timestamp as the row key without modification

C. Use a reversed timestamp prefix followed by user ID, like row_key = reversed_timestamp + '_' + user_id

D. Use a random UUID as the row key

Solution

1. Step 1: Understand hot-spotting and range scan needs

   Hot-spotting happens when many writes go to the same key range; reversed timestamp spreads writes.

2. Step 2: Evaluate each option for balancing and scan speed

   Reversed timestamp prefix balances data and allows scanning by time; user ID alone or random UUIDs do not support range scans well.

3. Final Answer:

   Use a reversed timestamp prefix followed by user ID, like row_key = reversed_timestamp + '_' + user_id -> Option C

4. Quick Check:

   Reverse timestamp + user ID = balanced, fast scans [OK]

Hint: Reverse timestamp to avoid hot-spotting [OK]

Common Mistakes:

• Using only user ID causes hot-spotting
• Using raw timestamp causes write concentration
• Random UUIDs prevent range scans