Chapter 25

Question 1

What is data parallelism

Answer 1

One operation performs on multiple partitions simultaneously.

Question 2

What is query coordinator

Answer 2

It coordinates the results of independent processor

Question 3

Should query coordinator processing time should less than query server processing time

Answer 3

Yes

Question 4

What is temporal parallelism (pipe lining)

Answer 4

One complex task divided into smaller parts. The smaller tasks runs in parallel in a way that one sub task output goes into the input of another sub task. Its example is car manufacturing. One car manufactured parts by parts in a factory on an assembly line. And during that manufacturing, another cars manufacturing process starts. The same process goes with complex queries in pipe lining.

Question 5

What are limitations(issues) of temporal parallelism (pipe lining)

Answer 5

• The relational operator (complex query) can not be divided more than 10 parts. It means the speed-up can not be greater than 10.
• If there is some sub task that needs all inputs to process than it means it is not pipe lining. Because in pipe lining, a sub task takes some inputs and its output is input for another sub task. If one sub task take all input than it is not pipe lining. Aggregation and sorting are its example.

Question 6

What is full table scan query

Answer 6

All table scans in it.

Question 7

What is point query

Answer 7

Find a specific value

Question 8

What is range query

Answer 8

Range is specified in query

Question 9

What is round robin partitioning technique

Answer 9

It distributes all things equally to processors. There is no bottle neck in it and every processor do its job.

Question 10

What is hash partitioning technique

Answer 10

In uniform hashing every process has same load. It is best for point query.

Question 11

What is range partitioning technique

Answer 11

Partitioning on the base of range

Question 12

What is hot spot or skew

Answer 12

Uneven distribution

Question 13

What are 2 types of parallelism

Answer 13

• Data parallelism

- Temporal parallelism