MapReduce¶
Dean, J., & Ghemawat, S. (2008). MapReduce: Simplified Data Processing on Large Clusters. Communications of the ACM, 51(1), 107-113.
Cited by¶
5 citations across 5 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Batch Processing
- Computing and data engineering — scheduled jobs and data pipelines, grouped parallel work under one instruction, and request coalescing, all amortising a per-batch setup.
This sourceCanonical batch data-processing model: grouped parallel work amortizing per-job setup over large item sets.
Supported in partVerified against the publisher's abstract
The abstract backs grouped parallel work over large datasets on a cluster, but never mentions scheduled jobs, data pipelines, or request coalescing.
“Users specify the computation in terms of a map and a reduce function, and the underlying runtime system automatically parallelizes the computation across large-scale clusters of machines”
- Computing and data engineering — scheduled jobs and data pipelines, grouped parallel work under one instruction, and request coalescing, all amortising a per-batch setup.
- Bottleneck
This sourceBiblio reference (distributed-computing cluster, never cited in body).
- Concurrency
This sourceOSDI '04). Parallel map/shuffle/reduce across worker clusters. DOI verified.
- Division of Labor
- MapReduce (Dean and Ghemawat, 2008) is the canonical computational instance: a large data-processing task is partitioned into independent map sub-tasks, assigned to differentiated worker nodes, executed in parallel, and re-integrated through the reduce phase — the five-role pattern realized in pure computational substrate with no human performer in sight.
Supported in partVerified against the publisher's abstract
The abstract backs the map/reduce partitioning of a large dataset and its automatic parallel execution across clusters, but supplies no differentiated worker roles and no division-of-labor or five-role framing.
“Users specify the computation in terms of a map and a reduce function, and the underlying runtime system automatically parallelizes the computation across large-scale clusters of machines, handles machine failures, and schedules inter-machine communication to make efficient use of the network and disks.”
- MapReduce (Dean and Ghemawat, 2008) is the canonical computational instance: a large data-processing task is partitioned into independent map sub-tasks, assigned to differentiated worker nodes, executed in parallel, and re-integrated through the reduce phase — the five-role pattern realized in pure computational substrate with no human performer in sight.
- Scalability
This sourceDescribes Google's MapReduce framework: a large data-processing task is partitioned into independent map sub-tasks executed in parallel across thousands of worker nodes and re-integrated through a shuffle-and-reduce phase. Canonical computational instance of partition-assign-execute-reintegrate division of labor in a purely silicon substrate.
Verification¶
Does it exist? Confirmed. This work's DOI resolves to a registered record, which fixes its identity. That is all it fixes.
Does it back the claim? Read against the text for 2 of 5 citations: 2 supported in part. Each verdict is shown under its citation below, with what in the work backs the sentence.
Support is checked per citation rather than per work — the same source can be cited soundly in one article and wrongly in another. Per-citation recording began recently, so a citation with no recorded check is a gap in the record rather than evidence it went unchecked.
See how references were verified.
Registry ID ref:bf03321ea85c · see in the full table