The mapreduce example
files that ship with MATLAB® illustrate different programming
techniques. You can use these examples as a starting point to quickly
prototype similar mapreduce calculations.
Note
The associated files for these examples are all in the toolbox/matlab/demos/ folder.
| Example Link | Primary File | Description | Notable Programming Techniques |
|---|---|---|---|
| Find Maximum Value with MapReduce | MaxMapReduceExample.m | Find maximum arrival delay | One intermediate key and minimal computation. |
| Compute Mean Value with MapReduce | MeanMapReduceExample.m | Find mean arrival delay | One intermediate key with intermediate state (accumulating intermediate sum and count). |
| Create Histograms Using MapReduce | VisualizationMapReduceExample.m | Visualize data using histograms | Low-volume summaries of data, sufficient to generate a graphic and gain preliminary insights. |
| Compute Mean by Group Using MapReduce | MeanByGroupMapReduceExample.m | Compute mean arrival delay for each day of the week | Perform simple computations on subgroups of input data using several intermediate keys. |
| Compute Maximum Average HSV of Images with MapReduce | HueSaturationValueExample.m | Determine average maximum hue, saturation, and brightness in an image collection | Analyzes an image datastore using three intermediate keys. The outputs are filenames, which can be used to view the images. |
| Simple Data Subsetting Using MapReduce | SubsettingMapReduceExample.m | Create single table from subset of large data set | Extraction of subset of large data set to look for patterns. The procedure is generalized using a parameterized map function to pass in the subsetting criteria. |
| Using MapReduce to Compute Covariance and Related Quantities | CovarianceMapReduceExample.m | Compute covariance and related quantities | Calculate several intermediate values and store them with the same key. Use covariance to obtain a correlation matrix and regression coefficients, and to perform principal components analysis. |
| Compute Summary Statistics by Group Using MapReduce | StatisticsByGroupMapReduceExample.m | Compute summary statistics organized by group | Use an anonymous function to pass an extra grouping parameter to a parameterized map function. This parameterization allows you to quickly recalculate statistics using different grouping variables. |
| Using MapReduce to Fit a Logistic Regression Model | LogitMapReduceExample.m | Fit simple logistic regression model | Chain multiple |
| Tall Skinny QR (TSQR) Matrix Factorization Using MapReduce | TSQRMapReduceExample.m | Tall skinny QR decomposition | Chain multiple |