MapReduce

Programming technique for analyzing data sets that do not fit in memory

mapreduce is a programming technique which is suitable for analyzing large data sets that otherwise cannot fit in your computer’s memory. Using a datastore to process the data in small chunks, the technique is composed of a Map phase, which formats the data or performs a precursory calculation, and a Reduce phase, which aggregates all of the results from the Map phase. For more information, see Getting Started with MapReduce.

For information about using other products with mapreduce, see Speed Up and Deploy MapReduce Using Other Products.

Functions

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General

`mapreduce`	Programming technique for analyzing data sets that do not fit in memory
`datastore`	Create datastore for large collections of data

Auxiliary Object Methods

`add`	Add single key-value pair to KeyValueStore
`addmulti`	Add multiple key-value pairs to KeyValueStore
`hasnext`	Determine if ValueIterator has one or more values available
`getnext`	Get next value from ValueIterator

Execution Environment

`mapreducer`	Define execution environment for mapreduce or tall arrays
`gcmr`	Get current mapreducer configuration

Objects

`KeyValueStore`	Store key-value pairs for use with mapreduce
`ValueIterator`	An iterator over intermediate values for use with mapreduce

Topics

Getting Started with MapReduce
Learn about the MapReduce programming technique and run an example calculation.
Write a Map Function
Create a map function for use in a mapreduce algorithm.
Write a Reduce Function
Create a reduce function for use in a mapreduce algorithm.
Build Effective Algorithms with MapReduce
Summary of mapreduce example files.
Speed Up and Deploy MapReduce Using Other Products
Capabilities of other products to speed up and share mapreduce algorithms.
Find Maximum Value with MapReduce
This example shows how to find the maximum value of a single variable in a data set using mapreduce.
Compute Mean Value with MapReduce
This example shows how to compute the mean of a single variable in a data set using mapreduce.
Create Histograms Using MapReduce
This example shows how to visualize patterns in a large data set without having to load all of the observations into memory simultaneously.
Compute Mean by Group Using MapReduce
This example shows how to compute the mean by group in a data set using mapreduce.
Simple Data Subsetting Using MapReduce
This example shows how to extract a subset of a large data set.
Using MapReduce to Compute Covariance and Related Quantities
This example shows how to compute the mean and covariance for several variables in a large data set using mapreduce.
Compute Summary Statistics by Group Using MapReduce
This example shows how to compute summary statistics organized by group using mapreduce.
Using MapReduce to Fit a Logistic Regression Model
This example shows how to use mapreduce to carry out simple logistic regression using a single predictor.
Tall Skinny QR (TSQR) Matrix Factorization Using MapReduce
This example shows how to compute a tall skinny QR (TSQR) factorization using mapreduce.
Compute Maximum Average HSV of Images with MapReduce
This example shows how to use ImageDatastore and mapreduce to find images with maximum hue, saturation and brightness values in an image collection.

Troubleshooting

Debug MapReduce Algorithms

This example shows how to debug your mapreduce algorithms in MATLAB^® using a simple example file, MaxMapReduceExample.m. Debugging enables you to follow the movement of data between the different phases of mapreduce execution and inspect the state of all intermediate variables.

Featured Examples

Analyze Big Data in MATLAB Using MapReduce

Use the mapreduce function to process a large amount of file-based data. The MapReduce algorithm is a mainstay of many modern "big data" applications. This example operates on a single computer, but the code can scale up to use Hadoop®.

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