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. 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
gather. MATLAB automatically optimizes
the queued calculations by minimizing the number of passes through
the data. For more information, see Tall Arrays .
For more information about integrating with big data systems or compiling tall array algorithms, see Extend Tall Arrays with Other Products.
||Create tall array|
||Create datastore for large collections of data|
||Define execution environment for mapreduce or tall arrays|
||Collect tall array into memory after executing queued operations|
||Get top rows of tall array|
||Get bottom rows of tall array|
||Top rows in sorted order|
||Determine if input is tall array|
||Class of underlying data in tall array|
||Determine if tall array data is of specified class|
||Write tall array to disk for checkpointing|
Learn about tall arrays and perform an example calculation.
An alphabetical list of MATLAB functions that support tall arrays.
How to leverage deferred execution of tall arrays to optimize performance of calculations.
Extract, assign, and view portions of a tall array.
This example shows how to use the
splitapply functions to calculate grouped statistics of a tall table containing power outage data.
Visualization techniques that are specifically for tall arrays.
List of products that enhance capabilities of tall arrays.