Hadoop - Performance Tuning
You have a large dataset with many columns but only a few are needed for analysis. Which data serialization format is best to optimize query speed and storage, and why?
You have a large dataset with many columns but only a few are needed for analysis. Which data serialization format is best to optimize query speed and storage, and why?
hard📝 Application Q15 of 15
Step-by-Step Solution
Solution:
Step 1: Understand the dataset and query needs
Many columns exist but only a few are needed, so reading only those columns is important.Step 2: Compare serialization formats for columnar access
Parquet stores data column-wise, enabling fast queries on selected columns and saving storage.Step 3: Eliminate other options
Avro is row-wise, so it reads all columns; CSV and JSON lack efficient columnar storage and schema.Final Answer:
Parquet, because it stores data column-wise allowing fast column queries -> Option CQuick Check:
Column-wise storage = Parquet best for selective queries [OK]
Quick Trick: Column-wise format like Parquet speeds up column queries [OK]
Common Mistakes:
- Choosing Avro for column queries
- Picking CSV or JSON for big data efficiency
- Ignoring storage and query speed trade-offs
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