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®.

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.

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).

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.

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

Extract a subset of a large data set.

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.

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.

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.

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.

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