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

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

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

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

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

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

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

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Q. How does cracking handle updates to the data?

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

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

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

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

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

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

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

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Q. How does the performance of cracking depend on the arrival order of queries?

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Q. What problem does stochastic cracking aim to solve?

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Q. How does stochastic cracking work to reduce the chances of leaving back big uncracked pieces?

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Q. Why is concurrent access to data challenging in the context of database cracking?

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Q. How does database cracking achieve concurrency control for adaptive indexing during read queries?

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