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Tall Arrays

Arrays with more rows than fit in memory

Tall arrays provide a way to work with data backed by a datastore that can have millions or billions of rows. You can create tall numeric arrays, cell arrays, categoricals, strings, datetimes, durations, or calendar durations, and you can use any of these tall types as variables in a tall table or tall timetable. Many operations and functions work the same way with tall arrays as they do with in-memory MATLAB^® arrays, but most results are evaluated only when you request them explicitly using gather. MATLAB automatically optimizes the queued calculations by minimizing the number of passes through the data. For more information, see Tall Arrays for Out-of-Memory Data .

For more information about integrating with big data systems or compiling tall array algorithms, see Extend Tall Arrays with Other Products.

Functions

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Creation and Evaluation

`tall`	Create tall array
`datastore`	Create datastore for large collections of data
`gather`	Collect tall array into memory after executing queued operations
`write`	Write tall array to local and remote locations for checkpointing
`mapreducer`	Define execution environment for mapreduce or tall arrays
`tallrng`	Control random number generation for tall arrays

Determine Type

`istall`	Determine if input is tall array
`classUnderlying`	Class of underlying data in tall array
`isaUnderlying`	Determine if tall array data is of specified class

Develop Custom Algorithms

`matlab.tall.transform`	Transform array by applying function handle to blocks of data
`matlab.tall.reduce`	Reduce arrays by applying reduction algorithm to blocks of data
`matlab.tall.movingWindow`	Apply moving window function to blocks of data (Since R2019a)
`matlab.tall.blockMovingWindow`	Apply moving window function and block reduction to padded blocks of data (Since R2019a)

Topics

Tall Arrays for Out-of-Memory Data
Learn about tall arrays and perform an example calculation.
Deferred Evaluation of Tall Arrays
How to leverage deferred execution of tall arrays to optimize performance of calculations.
Index and View Tall Array Elements
Extract, assign, and view portions of a tall array.
Grouped Statistics Calculations with Tall Arrays
This example shows how to calculate grouped statistics of a tall timetable containing power outage data.
Visualization of Tall Arrays
Visualization techniques for tall arrays.
Extend Tall Arrays with Other Products
List of products that enhance capabilities of tall arrays.
Develop Custom Tall Array Algorithms
Development guide for authoring custom algorithms to operate on tall arrays.

Related Information

Using Tall Arrays for Big Data

Featured Examples

Analyze Big Data in MATLAB Using Tall Arrays

Use tall arrays to work with big data in MATLAB®. You can use tall arrays to perform a variety of calculations on different types of data that does not fit in memory. These include basic calculations, as well as machine learning algorithms within Statistics and Machine Learning Toolbox™.

Open Live Script

Histograms of Tall Arrays

Use histogram and histogram2 to analyze and visualize data contained in a tall array.

Open Live Script

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.