Explain Columnar vs Row Store.
A columnar store saves data by columns for fast analytics, while a row store saves data by rows for efficient transactions.
When to Use
Row stores shine in OLTP workloads like banking systems, e-commerce orders, or user sessions where fast inserts/updates matter.
Columnar stores excel in OLAP workloads such as dashboards, reporting, or machine learning pipelines, where queries scan billions of rows.
Example
If you want the average age of all users, a column store reads only the "Age" column, while a row store must read full rows—even if you only need one field.
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Why Is It Important
Choosing the wrong model can waste resources. Columnar storage improves compression and aggregate performance; row storage speeds up high-volume transactions.
Interview Tips
Frame your answer around OLTP vs. OLAP. Explain the storage layout difference, give one quick example, and mention trade-offs. Interviewers value clarity plus practical context.
Trade-offs
Column stores give blazing-fast reads and compression but struggle with frequent row inserts.
Row stores handle inserts/updates well but slow down with large analytical queries.
Pitfalls
A common mistake is treating one as a universal solution.
Running analytics on a row store or write-heavy workloads on a column store leads to major inefficiencies.
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