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.
Speed Up and Deploy MapReduce Using Other Products
Capabilities of other products to speed up and share mapreduce algorithms.

Troubleshooting

Debug MapReduce Algorithms

This example shows how to debug mapreduce algorithms in MATLAB^®. 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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Find Maximum Value with MapReduce

Find the maximum value of a single variable in a data set using mapreduce. It demonstrates the simplest use of mapreduce since there is only one key and minimal computation.

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Compute Mean Value with MapReduce

Compute the mean of a single variable in a data set using mapreduce. It demonstrates a simple use of mapreduce with one key, minimal computation, and an intermediate state (accumulating intermediate sum and count).

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Create Histograms Using MapReduce

Visualize patterns in a large data set without having to load all of the observations into memory simultaneously. It demonstrates how to compute lower volume summaries of the data that are sufficient to generate a graphic.

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Compute Mean by Group Using MapReduce

Compute the mean by group in a data set using mapreduce. It demonstrates how to do computations on subgroups of data.

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Simple Data Subsetting Using MapReduce

Extract a subset of a large data set.

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Using MapReduce to Compute Covariance and Related Quantities

Compute the mean and covariance for several variables in a large data set using mapreduce. It then uses the covariance to perform several follow-up calculations that do not require another iteration over the entire data set.

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Compute Summary Statistics by Group Using MapReduce

Compute summary statistics organized by group using mapreduce. It demonstrates the use of an anonymous function to pass an extra grouping parameter to a parameterized map function. This parameterization allows you to quickly recalculate the statistics using a different grouping variable.

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Using MapReduce to Fit a Logistic Regression Model

Use mapreduce to carry out simple logistic regression using a single predictor. It demonstrates chaining multiple mapreduce calls to carry out an iterative algorithm. Since each iteration requires a separate pass through the data, an anonymous function passes information from one iteration to the next to supply information directly to the mapper.

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Tall Skinny QR (TSQR) Matrix Factorization Using MapReduce

Compute a tall skinny QR (TSQR) factorization using mapreduce. It demonstrates how to chain mapreduce calls to perform multiple iterations of factorizations, and uses the info argument of the map function to compute numeric keys.

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Compute Maximum Average HSV of Images with MapReduce

Use ImageDatastore and mapreduce to find images with maximum hue, saturation and brightness values in an image collection.

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