In the MapReduce programming model, how does fault tolerance typically work when a worker node fails during a job?

Introduction / Context:
MapReduce is designed to process very large data sets across many commodity machines that can fail at any time. Fault tolerance is therefore a core feature of the MapReduce model and of implementations such as Hadoop. Interviewers frequently ask how MapReduce handles node failures because it reveals your understanding of distributed computing principles and how large scale data processing frameworks maintain reliability and correctness.

Given Data / Assumptions:

- We are using the MapReduce programming model, possibly with a framework such as Hadoop.

- The data resides in a distributed file system that stores blocks redundantly across multiple nodes.

- There is a master or coordinator component that tracks worker nodes and task assignments.

- Worker nodes can fail during the execution of map or reduce tasks.

Concept / Approach:
In MapReduce, each job is split into many map tasks and reduce tasks. A master node monitors worker nodes that execute these tasks. If a worker node fails or becomes unresponsive, the master detects this through heartbeats or timeouts. Because the underlying distributed file system stores multiple replicas of data blocks, the master can schedule the same map or reduce tasks on other healthy nodes that have copies of the input data. This rescheduling mechanism, combined with data replication, allows the job to complete successfully even if some nodes fail midway through execution.

Step-by-Step Solution:
Step 1: Recognize that MapReduce uses a master or job tracker to coordinate tasks across worker nodes. Step 2: Recall that the distributed file system, such as HDFS, replicates data blocks across several machines. Step 3: When a worker node fails, the master stops receiving heartbeats from that node and marks it as dead. Step 4: The master identifies map or reduce tasks that were running on the failed node and marks them as pending again. Step 5: The master reschedules those tasks on other healthy nodes that hold copies of the required data blocks. Step 6: This process continues until all tasks successfully complete, delivering a correct job result despite failures.

Verification / Alternative check:
Consider a scenario where a map task halfway through processing a large block suddenly fails due to node hardware issues. The master notices the missing heartbeat and reassigns that map task to another node. Because the input data is replicated, the new node reads the same block and recomputes the map output. Reduce tasks that depended on that output will then fetch data from the new location. If the entire job were cancelled or partial results returned silently, the framework would not be considered fault tolerant. Thus, the rescheduling strategy described in option A matches the behavior expected from a robust MapReduce implementation.

Why Other Options Are Wrong:
Option B is incorrect because cancelling the entire job on each failure would make MapReduce impractical at scale, where node failures are common. Option C is wrong because silently ignoring failed tasks would produce incorrect and incomplete results, undermining the framework. Option D is unrealistic since MapReduce is designed to use many workers, not rely on a single powerful backup server; this would also introduce a single point of failure and defeat the purpose of distribution.

Common Pitfalls:
A common pitfall is to overlook the role of data replication in the distributed file system. Without replicated blocks, rescheduling failed tasks on other nodes would not be possible. Another mistake is to think that all tasks are restarted from scratch when one node fails; in reality, only the tasks that were running on the failed node are rescheduled. In interviews, highlight the interaction between the master node, worker nodes, heartbeats, and the distributed file system, and emphasize that MapReduce achieves fault tolerance by rescheduling failed tasks on other nodes using replicated data.

Final Answer:
MapReduce handles node failures by having the master detect failed workers and reschedule their map or reduce tasks on other healthy nodes that hold replicated copies of the input data, allowing the job to finish correctly.