1) Identify which type of database/data processing system you would choose (Key-Value store, Column-oriented store, Document-oriented store, Graph database, Relational database, Streaming engine) in each scenario below. a) Highly structured multi-table data that requires enforcing data constraints. b) Stock market data ticker with decisions that must be made in real time. c) LinkedIn type data with interconnected nodes where much of the information resides in the links between nodes. d) An image storage system that allows lookup images by file name. e) A collection of JSON objects (e.g., tweets). f) Data that is stored in large sparse tables that are continuously growing (new rows/columns).
1) Identify which type of database/data processing system you would choose (Key-Value store, Column-oriented store, Document-oriented store, Graph database, Relational database, Streaming engine) in each scenario below. a) Highly structured multi-table data that requires enforcing data constraints. b) Stock market data ticker with decisions that must be made in real time. c) LinkedIn type data with interconnected nodes where much of the information resides in the links between nodes. d) An image storage system that allows lookup images by file name. e) A collection of JSON objects (e.g., tweets). f) Data that is stored in large sparse tables that are continuously growing (new rows/columns).
Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
Problem 1PE
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Transcribed Image Text:1) Identify which type of database/data processing system you would choose (Key-Value store,
Column-oriented store, Document-oriented store, Graph database, Relational database,
Streaming engine) in each scenario below.
2)
a) Highly structured multi-table data that requires enforcing data constraints.
b) Stock market data ticker with decisions that must be made in real time.
c) LinkedIn type data with interconnected nodes where much of the information resides in
the links between nodes.
d)
e)
f)
An image storage system that allows lookup images by file name.
A collection of JSON objects (e.g., tweets).
Data that is stored in large sparse tables that are continuously growing (new
rows/columns).
a) Consider the following graph
B
Compute the page rank for the nodes in this graph. If you are multiplying matrices manually,
you may stop computing after 6 steps. If you use a tool (e.g., Matlab, python) for matrix
multiplication, you should get your answer to converge.
b) Now consider a graph with dead-end nodes Q and Z:
What is the page rank of Q?
What is the page rank of Z?
X
Z
Z
![c) Exercise 5.1.6 from Mining of Massive Datasets
...
Figure 5.9: A chain of dead ends
Exercise 5.1.6: Suppose we recursively eliminate dead ends from the graph,
solve the remaining graph, and estimate the PageRank for the dead-end pages
as described in Section 5.1.4. Suppose the graph is a chain of dead ends, headed
by a node with a self-loop, as suggested in Fig. 5.9. What would be the Page-
Rank assigned to each of the nodes?
3) Given the input data [(1pm, $8), (2pm, $16), (3pm, $17), (4pm, $26), (5pm, $12), (6pm,
$22), (7pm, $23), (8pm, $22), (9pm, $24), (10pm, $28), (11pm, $26), (12am, $30)].
a) What will the Hive query "compute average price" return? (yes, this question is as
obvious as it seems, it is asked for comparison with part-b and part-c)
b) What will a Storm streaming query "compute average price per each 3 hour window"
return? (tumbling, i.e., non-overlapping window of tuples). For example, the first window
would 1pm-4pm. Second window would be 4pm-7pm. If you are wondering about
overlap, I recommend defaulting to [1pm-4pm) [4pm-7pm). (i.e., including first but not
last part of the time range).
c) What will a Storm query "compute average price per each 3 hour window" return?
(sliding, i.e. overlapping window of tuples, moving the window forward 2 hours each
time). First window is 1pm-4pm, second window is 3pm-6pm and so on.
NOTE: when Storm does not have a full window, you cannot output anything until the
window fills with data.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fcf0f0343-449e-4bb7-8afb-dee2cbd67ba4%2Fba013641-4959-4669-a2d5-2e38dd311ed5%2Flqy5wxp_processed.png&w=3840&q=75)
Transcribed Image Text:c) Exercise 5.1.6 from Mining of Massive Datasets
...
Figure 5.9: A chain of dead ends
Exercise 5.1.6: Suppose we recursively eliminate dead ends from the graph,
solve the remaining graph, and estimate the PageRank for the dead-end pages
as described in Section 5.1.4. Suppose the graph is a chain of dead ends, headed
by a node with a self-loop, as suggested in Fig. 5.9. What would be the Page-
Rank assigned to each of the nodes?
3) Given the input data [(1pm, $8), (2pm, $16), (3pm, $17), (4pm, $26), (5pm, $12), (6pm,
$22), (7pm, $23), (8pm, $22), (9pm, $24), (10pm, $28), (11pm, $26), (12am, $30)].
a) What will the Hive query "compute average price" return? (yes, this question is as
obvious as it seems, it is asked for comparison with part-b and part-c)
b) What will a Storm streaming query "compute average price per each 3 hour window"
return? (tumbling, i.e., non-overlapping window of tuples). For example, the first window
would 1pm-4pm. Second window would be 4pm-7pm. If you are wondering about
overlap, I recommend defaulting to [1pm-4pm) [4pm-7pm). (i.e., including first but not
last part of the time range).
c) What will a Storm query "compute average price per each 3 hour window" return?
(sliding, i.e. overlapping window of tuples, moving the window forward 2 hours each
time). First window is 1pm-4pm, second window is 3pm-6pm and so on.
NOTE: when Storm does not have a full window, you cannot output anything until the
window fills with data.
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