Podcast
Questions and Answers
Which phase of the MapReduce process involves collecting all pairs with the same key?
Which phase of the MapReduce process involves collecting all pairs with the same key?
- Reduce
- Map
- Group by Key (correct)
- Partition
In MapReduce, what is the primary function of the Map phase?
In MapReduce, what is the primary function of the Map phase?
- Grouping key-value pairs with the same key.
- Collecting all values belonging to the key and outputting the result.
- Reading input data and producing a set of key-value pairs. (correct)
- Ordering same key-value pairs.
Which statement accurately describes how MapReduce handles data?
Which statement accurately describes how MapReduce handles data?
- All phases are distributed with many tasks doing the work in parallel. (correct)
- The Map phase is executed on a single machine, while the Reduce phase is distributed.
- The Reduce phase is executed on a single machine, while the Map phase is distributed.
- All phases are executed on a single machine to minimize network overhead.
What aspect of program execution does the MapReduce environment primarily handle?
What aspect of program execution does the MapReduce environment primarily handle?
Where are the intermediate results stored during a MapReduce operation?
Where are the intermediate results stored during a MapReduce operation?
What is the role of the master node in MapReduce?
What is the role of the master node in MapReduce?
What happens to in-progress map tasks when a map worker fails in MapReduce?
What happens to in-progress map tasks when a map worker fails in MapReduce?
What is the immediate consequence of a master node failure in MapReduce?
What is the immediate consequence of a master node failure in MapReduce?
According to the 'rule of thumb' for setting up MapReduce jobs, how should the number of map tasks (M) relate to the number of nodes in the cluster?
According to the 'rule of thumb' for setting up MapReduce jobs, how should the number of map tasks (M) relate to the number of nodes in the cluster?
In a MapReduce setup, how does the number of reduce tasks (R) typically compare to the number of map tasks (M)?
In a MapReduce setup, how does the number of reduce tasks (R) typically compare to the number of map tasks (M)?
What step is taken once a map task is completed?
What step is taken once a map task is completed?
What is the benefit of having more map tasks?
What is the benefit of having more map tasks?
What is the purpose of the master periodically pinging the workers?
What is the purpose of the master periodically pinging the workers?
What happens to idle reduce tasks if a reduce worker fails?
What happens to idle reduce tasks if a reduce worker fails?
What happens to map tasks that were completed at a worker that failed?
What happens to map tasks that were completed at a worker that failed?
Where are the input and final output of MapReduce tasks stored?
Where are the input and final output of MapReduce tasks stored?
What does DFS stand for?
What does DFS stand for?
What is the purpose of the Partioning Function?
What is the purpose of the Partioning Function?
What are the different task statuses for the master node?
What are the different task statuses for the master node?
Flashcards
Map-Reduce
Map-Reduce
A programming model and an associated implementation for processing and generating big datasets.
MAP Phase
MAP Phase
Reads input and produces a set of key-value pairs in MapReduce.
Group by Key
Group by Key
Collects all pairs with the same key.
Reduce Phase
Reduce Phase
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Map-Reduce Environment
Map-Reduce Environment
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Distributed File System (DFS)
Distributed File System (DFS)
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Intermediate Results Location
Intermediate Results Location
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Master Node
Master Node
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Master Failure
Master Failure
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M Map Tasks
M Map Tasks
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R Reduce tasks
R Reduce tasks
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Study Notes
- MapReduce reads input and produces a set of key-value pairs.
- It collects all pairs with the same key using Hash merge, Shuffle, Sort, and Partition.
- All values belonging to the key are collected and outputted.
- All phases are distributed with many tasks doing the work
- The Map-Reduce environment partitions the input data, schedules the program's execution across a set of machines, performs the "group by key" step, handles node failures, and manages required inter-machine communication.
- Input and final output are stored on the distributed file system (DFS).
- The scheduler tries to schedule map tasks close to the physical storage location of input data.
- Intermediate results are stored on the local FS of Map and Reduce workers.
- Output is often input to another MapReduce task.
- The master node handles coordination, including task status (idle, in-progress, completed).
- Idle tasks get scheduled as workers become available.
- When a map task completes, it sends the master the location and sizes of its R intermediate files, one for each reducer.
- The master pushes this information to reducers.
- The master periodically pings workers to detect failures.
- In the event of a Map worker failure, map tasks completed or in-progress at the worker are reset to idle.
- Idle tasks are eventually rescheduled on other worker(s).
- In the event of a Reduce worker failure, only in-progress tasks are reset to idle, and idle Reduce tasks are restarted on other worker(s).
- In the event of master failure, the MapReduce task is aborted, and the client is notified.
- With M map tasks and R reduce tasks, M should be much larger than the number of nodes in the cluster.
- One DFS chunk per map is common as it improves dynamic load balancing and speeds up recovery from worker failures.
- R is usually smaller than M, because the output is spread across R files.
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