What happens to the system performance when the number of users is doubled, the number of processing nodes is doubled, and the amount of data is doubled in an MPPDB database

Question

Options

• APerformance improvement
• BPerformance degradation
• CUnpredictable
• DThe performance remains the same

• A
  10% (4)
• B
  15% (6)
• C
  5% (2)
• D
  71% (29)

Explanation

Massively Parallel Processing Databases (MPPDBs) are architected for linear scalability using a shared-nothing model, meaning each node operates independently with its own CPU, memory, and storage. When users, nodes, and data are all doubled simultaneously, the additional nodes absorb the additional users and data proportionally - each node's individual workload remains unchanged, so overall throughput stays constant.

Why the distractors fail:

• A (Improvement): No gain occurs because the extra resources are fully consumed by the extra load; there is no spare capacity.
• B (Degradation): MPPDB's shared-nothing design eliminates the inter-node contention or bottlenecks that would cause slowdown under proportional scaling.
• C (Unpredictable): Linear scalability is the defining guarantee of MPPDB architecture - the outcome is entirely predictable when scaling is balanced.

Memory tip: Picture a restaurant doubling its tables, staff, and customers all at once - each waiter still serves the same number of tables, so service speed doesn't change. In MPPDB, "proportional scaling = stable performance."

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