Bucketing for sampling in Hadoop - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding Bucketing Purpose in Hadoop Sampling

Why is bucketing used in Hadoop sampling when working with large datasets?

A To encrypt data buckets for secure sampling

B To compress data files to save storage space during sampling

C To divide data into fixed-size files for parallel processing and enable efficient sampling

D To sort data alphabetically before sampling

💡 Hint

Think about how dividing data helps in processing and sampling.

Bucketing splits data into fixed buckets based on a hash of a column, which helps parallelize processing and allows sampling by selecting specific buckets efficiently.

❓ data_output

intermediate

Output of Sampling Using Buckets in Hive

Given a Hive table bucketed by user_id into 4 buckets, what is the output count when sampling bucket number 2 only?

Table data (user_id): [101, 102, 103, 104, 105, 106, 107, 108]

Hash function: bucket = hash(user_id) % 4

Hadoop

SELECT COUNT(*) FROM table WHERE input__file__name LIKE '%bucket_2%';

A 4

B 0

C 1

D 2

💡 Hint

Calculate hash(user_id) % 4 for each user_id and count how many fall into bucket 2.

Applying hash(user_id) % 4 to each user_id, only two user_ids fall into bucket 2, so the count is 2.

🔧 Debug

advanced

Identify the Error in Bucketing Sampling Query

What error will this Hive query produce?

SELECT * FROM users TABLESAMPLE(BUCKET 3 OUT OF 5) WHERE age > 30;

A SyntaxError: TABLESAMPLE clause must come after WHERE clause

B No error, query runs successfully

C RuntimeError: Sampling on non-bucketed table

D SemanticError: BUCKET number cannot be greater than total buckets

💡 Hint

Check the order of clauses and bucket numbers.

The query is valid if the table is bucketed into 5 buckets. TABLESAMPLE clause can appear before WHERE. No error occurs.

❓ Predict Output

advanced

Result of Sampling Buckets in Hadoop MapReduce Job

What is the output of this pseudocode for sampling bucket 1 from a bucketed dataset with 3 buckets?

for record in dataset:
    if hash(record.key) % 3 == 1:
        print(record.key)

Dataset keys: [10, 11, 12, 13, 14]

A 11, 14

B 10, 13

C None

D 12, 15

💡 Hint

Calculate hash(key) % 3 for each key and select those equal to 1.

Assuming hash(key) = key, keys 10 and 13 satisfy hash(key) % 3 == 1, so they are printed.

🚀 Application

expert

Choosing Buckets for Stratified Sampling in Hadoop

You have a large dataset bucketed by 'region' into 10 buckets. You want to perform stratified sampling to get a representative sample from each region. Which approach is best?

A Sample equal number of records from each bucket by reading all buckets and filtering by region

B Randomly select 3 buckets and use all data from those buckets as sample

C Use TABLESAMPLE(BUCKET 1 OUT OF 10) to get a sample from all regions

D Sample only from the bucket with the largest region to reduce data size

💡 Hint

Think about how to get balanced samples from all regions.

Sampling equal records from each bucket ensures representation from all regions, as buckets correspond to regions. Other options risk bias or incomplete coverage.