adplus-dvertising
frame-decoration

Question

What kind of processing do column stores typically rely on?

a.

Tuple-wise processing

b.

Random access processing

c.

Bulk and vector-wised processing

d.

Sequential access processing

Posted under Big Data Computing

Answer: (c).Bulk and vector-wised processing Explanation:Column stores typically rely on bulk and vector-wise processing.

Engage with the Community - Add Your Comment

Confused About the Answer? Ask for Details Here.

Know the Explanation? Add it Here.

Q. What kind of processing do column stores typically rely on?

Similar Questions

Discover Related MCQs

Q. What does the term "cracking" in the context of database cracking reflect?

Q. In the context of database cracking, what does the system do with the base column after the first query on a column?

Q. What data structure is used to maintain partitioning information in cracking?

Q. What is the benefit of continuously reorganizing columns through cracking in response to queries?

Q. Why does cracking stop cracking a column for pieces smaller than L1 cache?

Q. In column-store systems, what allows for efficient query processing when requesting multiple columns of the same table?

Q. When a column is cracked in a column-store system, what happens to the alignment of columns within the same table?

Q. How does sideways cracking address the problem of misalignment in column-store systems when working with multiple columns of the same table?

Q. How does sideways cracking ensure alignment of column-pairs with the same head attribute?

Q. What is the benefit of sideways cracking in column-store systems when multiple columns of the same table are used in a query?

Q. What is the purpose of partial cracking in column-store systems?

Q. How does partial cracking manage storage space for cracking columns?

Q. How does cracking handle updates to the data?

Q. What is the purpose of the auxiliary delete and insert columns in cracking?

Q. How does cracking manage merging pending updates into cracking columns?

Q. What is the primary motivation for the introduction of adaptive merging as a complementary technique to cracking?

Q. How are data handled in adaptive merging after they are sorted in memory with a quicksort action?

Q. What happens if a query in adaptive merging is fully covered by the results column?

Q. How does adaptive merging improve over plain cracking in terms of convergence speed?

Q. What is the main advantage of the crack-sort hybrid compared to other hybrid algorithms?