Tall Arrays and mapreduce - MATLAB & Simulink (original) (raw)

Analyze big data sets in parallel using MATLAB® tall arrays and datastores or mapreduce on Spark™ and Hadoop® clusters, and parallel pools

You can use Parallel Computing Toolbox™ to evaluate tall-array expressions in parallel using a parallel pool on your desktop. Using tall arrays allows you to run big data applications that do not fit in memory on your machine. You can also use Parallel Computing Toolbox to scale up tall-array processing by connecting to a parallel pool running on a MATLAB Parallel Server™ cluster. Alternatively, you can use a Spark enabled Hadoop cluster running MATLAB Parallel Server. For more information, see Big Data Workflow Using Tall Arrays and Datastores.

Functions

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tall Create tall array
datastore Create datastore for large collections of data
mapreduce Programming technique for analyzing data sets that do not fit in memory
mapreducer Define parallel execution environment for mapreduce and tall arrays
partition Partition a datastore
numpartitions Number of datastore partitions

Classes

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parallel.Pool Parallel pool of workers
parallel.cluster.Hadoop Hadoop cluster for mapreducer, mapreduce and tall arrays
parallel.cluster.Spark Spark cluster for mapreducer, mapreduce and tall arrays (Since R2022b)

Examples and How To

Concepts

Process Big Data in the Cloud

Process Big Data in the Cloud

Access a large data set in the cloud and process it in a cloud cluster using MATLAB® capabilities for big data.

Use Parallel Computing to Optimize Big Data Set for Analysis

Use Parallel Computing to Optimize Big Data Set for Analysis

Optimize data preprocessing for analysis using parallel computing.