In a modern Hadoop deployment used by a regional transit agency, how does MapReduce relate to YARN?
Select an answer to reveal the explanation.
Short Explanation
YARN is the traffic controller handing out lanes; MapReduce is one kind of convoy using those lanes. In modern Hadoop, MR applications request containers through YARN. MR does not swallow HDFS into the ResourceManager's memory.
Full Explanation
In a modern Hadoop deployment for a regional transit agency, MapReduce runs as an application framework whose resources are scheduled by YARN—compute frameworks sit on YARN while HDFS remains the storage layer. MapReduce does not permanently replace HDFS or store all blocks inside the ResourceManager heap. YARN is not only a SQL dialect; it schedules compute frameworks including MapReduce. MapReduce jobs should not bypass YARN to pin themselves only to unmanaged bare-metal PIDs in a governed municipal cluster. Exam caveat: YARN schedules MapReduce (and other) applications; HDFS stores data—keep the layering straight. Operational check: submit a MapReduce job, confirm it appears as a YARN application with allocated containers, and verify it reads/writes HDFS paths rather than ResourceManager memory. In municipal platform operations, record the rationale in the architecture decision record so future on-call staff know which pattern was chosen and which failure modes the rejected options would create for city services. Re-validate after the next maintenance window that monitoring, access paths, and recovery steps still match the documented design for agencies that depend on this platform.