# MapReduce

`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

## 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.

Create a map function for use in a `mapreduce`

algorithm.

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

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.