Hadoop MapReduce v1 JobTracker
E707878
Hadoop MapReduce v1 JobTracker was the central master service in early Hadoop versions responsible for scheduling, coordinating, and monitoring MapReduce jobs across a cluster.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Hadoop MapReduce v1 JobTracker canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T7985453 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hadoop MapReduce v1 JobTracker Context triple: [YARN, replaces, Hadoop MapReduce v1 JobTracker]
-
A.
MapReduce
MapReduce is a programming model and processing framework for distributed computation of large data sets across clusters of computers.
-
B.
Hadoop
Hadoop is an open-source framework that enables distributed storage and parallel processing of large data sets across clusters of commodity hardware.
-
C.
Apache Tez
Apache Tez is a distributed data processing framework designed for building high-performance batch and interactive data workflows on Hadoop.
-
D.
Google File System
Google File System is a distributed file system developed by Google to reliably store and process massive amounts of data across clusters of commodity hardware.
-
E.
ApplicationMaster
ApplicationMaster is the per-application coordinator in Hadoop YARN responsible for managing an application's lifecycle, resource requests, and task execution.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Hadoop MapReduce v1 JobTracker Target entity description: Hadoop MapReduce v1 JobTracker was the central master service in early Hadoop versions responsible for scheduling, coordinating, and monitoring MapReduce jobs across a cluster.
-
A.
MapReduce
MapReduce is a programming model and processing framework for distributed computation of large data sets across clusters of computers.
-
B.
Hadoop
Hadoop is an open-source framework that enables distributed storage and parallel processing of large data sets across clusters of commodity hardware.
-
C.
Apache Tez
Apache Tez is a distributed data processing framework designed for building high-performance batch and interactive data workflows on Hadoop.
-
D.
Google File System
Google File System is a distributed file system developed by Google to reliably store and process massive amounts of data across clusters of commodity hardware.
-
E.
ApplicationMaster
ApplicationMaster is the per-application coordinator in Hadoop YARN responsible for managing an application's lifecycle, resource requests, and task execution.
- F. None of above. chosen
Statements (49)
| Predicate | Object |
|---|---|
| instanceOf |
Hadoop component
ⓘ
MapReduce master service ⓘ cluster resource manager ⓘ |
| centralizes |
job monitoring
ⓘ
job scheduling ⓘ task assignment ⓘ |
| communicatesVia | RPC NERFINISHED ⓘ |
| communicatesWith | TaskTracker daemons ⓘ |
| coordinatesWith | TaskTracker NERFINISHED ⓘ |
| deprecatedIn | Hadoop 2.x NERFINISHED ⓘ |
| detects | TaskTracker failures ⓘ |
| exposes | web UI for job status ⓘ |
| hasComponent |
job history management
ⓘ
job scheduler ⓘ task tracker heartbeat manager ⓘ |
| hasSinglePointOfFailure | true ⓘ |
| implements |
MapReduce job coordination
ⓘ
MapReduce job monitoring ⓘ MapReduce job scheduling ⓘ |
| language | Java ⓘ |
| license | Apache License 2.0 ⓘ |
| manages |
Map tasks
ⓘ
Reduce tasks ⓘ |
| monitors | TaskTracker heartbeats ⓘ |
| partOf |
Apache Hadoop
NERFINISHED
ⓘ
Apache Hadoop MapReduce v1 NERFINISHED ⓘ |
| replacedBy |
YARN ApplicationMaster for MapReduce
NERFINISHED
ⓘ
YARN ResourceManager NERFINISHED ⓘ |
| responsibleFor |
accepting MapReduce job submissions
ⓘ
assigning tasks to TaskTrackers ⓘ handling task failures ⓘ job status reporting ⓘ maintaining cluster status ⓘ maintaining job metadata ⓘ maintaining task metadata ⓘ scheduling tasks close to data ⓘ splitting input data into map tasks ⓘ tracking task progress ⓘ |
| roleInSystem | central master service for MapReduce v1 ⓘ |
| runsOn | dedicated master node ⓘ |
| scalesPoorlyBeyond | several thousand nodes ⓘ |
| schedules |
map tasks
ⓘ
reduce tasks ⓘ |
| storesMetadataIn |
filesystem
ⓘ
memory ⓘ |
| usedIn |
Hadoop 0.x
NERFINISHED
ⓘ
Hadoop 1.x ⓘ |
| usesConcept |
data locality
ⓘ
rack awareness ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: Hadoop MapReduce v1 JobTracker Description of subject: Hadoop MapReduce v1 JobTracker was the central master service in early Hadoop versions responsible for scheduling, coordinating, and monitoring MapReduce jobs across a cluster.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.