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EECS 485 Final UPDATED Exam Questions and CORRECT Answers

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EECS 485 Final UPDATED Exam Questions and CORRECT Answers Map Reduce System provides - 1 - Correct Answer- Automatic parallelization & distribution Map Reduce System provides - 2 - Correct Answer- Fault-tolerance Map Reduce System provides - 3 - Correct Answer- Status and monitoring tools Map...

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  • August 24, 2024
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  • EECS 485
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EECS 485 Final UPDATED Exam
Questions and CORRECT Answers

Map Reduce System provides - 1 - Correct Answer- Automatic parallelization & distribution


Map Reduce System provides - 2 - Correct Answer- Fault-tolerance


Map Reduce System provides - 3 - Correct Answer- Status and monitoring tools


Map Reduce System provides - 4 - Correct Answer- Clean abstraction for programmers


Map Reduce is Popular because - 1 - Correct Answer- Easy to write distributed programs


Map Reduce is Popular because - 2 - Correct Answer- Built-in reliability on large clusters


Map Reduce is Popular because - 3 - Correct Answer- Byte streams, not relations


Map Reduce is Popular because - 4 - Correct Answer- Schema-later, or schema-never


Map Reduce is Popular because - 5 - Correct Answer- Your choice of programming
languages


Map Reduce is Popular because - 6 - Correct Answer- Hadoop relatively easy to administer


Mappers can work... - Correct Answer- Mappers can work independently


Reducers can work... - Correct Answer- Reducers can work independently


Grouper does not have to... - Correct Answer- Count

,Map Reduce Optimization 1 - Correct Answer- the mappers don't need write the words down,
just "1" for each word of a certain length


Map Reduce Optimization 2 - Correct Answer- mappers do part of the reducer work: number
of 1-letter words, 2-letter words, etc


Master work - Correct Answer- Dividing the work


Grouper work - Correct Answer- Grouping the values by keys


Work of the mappers and reducers - Correct Answer- differs with problem


MR Computation - Correct Answer- Input key/value pairs


Output different key/value pairs


Map Function - Correct Answer- Takes an input pair and produces a set of intermediate
key/value pairs


MapReduce library - Correct Answer- Groups together all intermediate values associated with
the same intermediate key and passes them to the Reduce function


Reduce function - Correct Answer- Accepts an intermediate key and a list of values for that
key.


Merges together these values to form a possibly smaller set of values.


Map and reduce have related types - Correct Answer- map (k1, v1) → list(k2, v2)


reduce (k2, list(v2)) → list(v2)


MR output is smaller - Correct Answer- computing summary statistics

,MR output is larger - Correct Answer- computing some kind of data structure for downstream
use


Output value produced per reduce invocation - Correct Answer- Zero or one


Execution Pipeline - Correct Answer- Stage 1, partition input data and run map() on many
machines


Then group intermediate data by intermediate key


Stage 2, partition intermediate data by key and run reduce() on many machines


Output is whatever reduce() emits


Shuffle/Sort - Correct Answer- Default: hash(key)%R


- Break input into M chunks


- Process each chunk w/ map process


- Group-by map output keys


- Place key-groups into R chunks


- Process each chunk w/ reduce process


- reduce fn's outputs go to disk


What can be a MapReduce program? - Correct Answer- - URL counting in logs

, - Inverted index construction for search engines, Sorting


- Massive image conversion, others


How do we know if a machine goes down? - Correct Answer- Heartbeat messages tell master
which machines are online


Map Worker Dies - Correct Answer- - Just restart that task on a different box


- You lose the map() work, but no big deal


Reduce Worker Dies - Correct Answer- - Restart the reducer, using output from source
mappers


Relational database management system (RDBMS) machine dies - Correct Answer- Query is
restarted


What about slow, not dead, machines? - Correct Answer- - Speculative execution for
stragglers


- Kill the 2nd-place finisher


What about data placement? - Correct Answer- Use GFS across cluster disks; start tasks
where the target data already lies


Isn't the intermediate data size large? - Correct Answer- - Use a local reducer called a
Combiner at each map


- Compress data between map and reduce


Mapper and Reducer are... - Correct Answer- Stateless - no side effects

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