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Explore the main themes, entities and connections around MapReduce. Start with the topic map, then use the sections below for research and deeper semantic analysis.
Explore this topic
Start with a few of the strongest sections from the source topic. These are research directions, not a list of keywords you must use.
Criticism
30 related topics
Overview
31 related topics
Dataflow
7 related topics
Uses
6 related topics
Key facts & relationships
High-confidence facts extracted from structured source data. Use them as anchors for further research.
Topics to explore
A structured outline of related entities, concepts and subtopics. Open any item to build a new map centered on it.Browse the full topic structure. Each item opens a new analysis centered on that subject.
Overview
- Programming model
- Big data
- Parallelizable Parallel computing
- Distributed Distributed computing
- Cluster Cluster (computing)
- Map Map (parallel pattern)
- Procedure Procedure (computing)
- Reduce Reduce (parallel pattern)
- Redundancy Redundancy (engineering)
- Fault tolerance
- Map Map (higher-order function)
- Reduce Reduce (higher-order function)
- Functional programming
- Message Passing Interface
- Single-threaded
- Multi-threaded
- Libraries Library (software)
- Open-source Open-source software
- Apache Hadoop
- Generic trademark
- Apache Mahout
- Cluster Computer cluster
- Grid Grid Computing
- Filesystem
- Database
- Associative Associative property
- "commodity" server Commodity computing
- Server farm
- Petabyte
Logical view
- Data domain
- Necessary but not sufficient Necessity and sufficiency
- Distributed file system
- SQL
Dataflow
- Software framework architecture Software framework
- Open-closed principle
- Extensible Extensibility
- Sharding
- Hash Hash function
- Modulo Modulo operation
- Load-balancing Load balancing (computing)
Theoretical background
- Monoid
- Catamorphism
- Kleene star
- Moments Moment (mathematics)
Distribution and reliability
- Google File System
- Atomic Atomicity (programming)
- Side-effects Side-effect (computer science)
- Hadoop
- Single point of failure
Uses
Criticism
- David DeWitt
- Michael Stonebraker
- Parallel databases Parallel database
- Shared-nothing architectures Shared-nothing architecture
- Paradigm shift
- Teradata
- Prior art
- CODASYL
- Low-level language Low-level programming language
- Schema Logical schema
- B-trees B-tree
- Hash partitioning Partition (database)
- Pig (or PigLatin) Pig (programming language)
- Sawzall Sawzall (programming language)
- Apache Hive
- HBase
- Bigtable
- RDBMS
- Danny Hillis
- Connection Machine
- StarLisp
- Common Lisp
- Tree-like Fold (higher-order function)
- Hypercube architecture Hypercube internetwork topology
- CouchDB
- Ars Technica
- Graph Graph (abstract data type)
- Working set
- Disk Hard disk drive
- Latency Latency (engineering)
Implementations of MapReduce
Advanced semantic analysis
Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.
Map overview Semantic statistics
Number of nodes, edges, triples, density and central hubs. Use it to gauge the size and connectivity of the map.MapReduce
Nodes99
Edges98
Triples81
Avg. degree1.98
Density0.020202
Components1
How this topic connects Entity context
Quick relationship hints grouped by predicate. Useful for spotting recurring semantic connections around the current entity.See the strongest relationship patterns around the current topic before diving into the raw triples.
MapReduce
Top relations
related to Lack of novelty · 17
MapReduce → Apache Hive, B-trees, Bigtable, CODASYL, David DeWitt, DeWitt, Greg Jorgensen, HBase, Jorgensen, MapReduce's, Michael Stonebraker, Pig, PigLatin, Sawzall, Stonebraker's, Teradata, They
related to Uses · 10
MapReduce → At Google, Development, FlumeJava, Google, Google's, It, MillWheel, Moreover, Percolator, World Wide Web
related to Distribution and reliability · 8
MapReduce → Because, Each, Google File System, If, Individual, The, This, When
related to Performance considerations · 8
MapReduce → Additional, Combiner, Communication, In, Map, Reduce, The, When
related to background · 5
MapReduce → Algebird, In, Map/Reduce, Properties, Scala
related to Dataflow · 4
MapReduce → Map, Reduce, Software, The
related to Examples · 3
MapReduce → Here, The, Thus
related to Logical view · 3
MapReduce → Map, Reduce, The Map
related to Partition function · 3
MapReduce → Each Map, It, The
is a · 1
MapReduce → programming model and an associated implementation for processing and generating big data sets with a parallel and distributed algorithm on a cluster.A MapReduce program is comp…
Important terminology Word statistics
Frequent words and multi-word phrases across the lead, headings, infobox and body. Useful for terminology coverage.Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.
Important terminology
reduce map data key distributed function framework operation output input one system processing parallel value would use google programming values
Entity relationships Subject–Predicate–Object triples
Extracted RDF-like relationships with confidence and source. The table includes structured facts and lower-confidence contextual relations.| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| MapReduce | is a | programming model and an associated implementation for processing and generating big data sets with a parallel and distributed algorithm on a cluster.A MapReduce program is comp… | 0.90 | text |
| the Combiner function can help to reduce the amount of data written to disk | instance of | Additional modules | 0.80 | text |
| and transmitted over the network | instance of | Additional modules | 0.80 | text |
| Percolator | instance of | Development at Google has since moved on to technologies | 0.80 | text |
| FlumeJava | instance of | Development at Google has since moved on to technologies | 0.80 | text |
| MillWheel that offer streaming operation | instance of | Development at Google has since moved on to technologies | 0.80 | text |
| updates instead of batch processing | instance of | Development at Google has since moved on to technologies | 0.80 | text |
| to allow integrating | instance of | Development at Google has since moved on to technologies | 0.80 | text |
| B-trees | instance of | MapReduce's use of input files and lack of schema support prevents the performance improvements enabled by common database system features | 0.80 | text |
| hash partitioning | instance of | MapReduce's use of input files and lack of schema support prevents the performance improvements enabled by common database system features | 0.80 | text |
| though projects such as Pig | instance of | MapReduce's use of input files and lack of schema support prevents the performance improvements enabled by common database system features | 0.80 | text |
| Hadoop | instance of | may cover use of MapReduce by open source software | 0.80 | text |
No triples extracted yet.
Related concept clusters Concept neighborhoods
Clusters of nearby vocabulary surrounding the topic. Scan them for adjacent concepts and language you may have missed.These clusters group vocabulary that occurs around closely connected concepts in the source material.
Connections between topic areas Semantic bridges
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