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Analyze Big Data in MATLAB Using Tall Arrays

This example shows how to 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™.

This example operates on a small subset of data on a single computer, and then it then scales up to analyze all of the data set. However, this analysis technique can scale up even further to work on data sets that are so large they cannot be read into memory, or to work on systems like Apache Spark™.

Introduction to Tall Arrays

Tall arrays and tall tables are used to work with out-of-memory data that has any number of rows. Instead of writing specialized code that takes into account the huge size of the data, tall arrays and tables let you work with large data sets in a manner similar to in-memory MATLAB® arrays. The difference is that tall arrays typically remain unevaluated until you request that the calculations be performed.

This deferred evaluation enables MATLAB to combine the queued calculations where possible and take the minimum number of passes through the data. Since the number of passes through the data greatly affects execution time, it is recommended that you request output only when necessary.

Create datastore for Collection of Files

Creating a datastore enables you to access a collection of data. A datastore can process arbitrarily large amounts of data, and the data can even be spread across multiple files in multiple folders. You can create a datastore for a collection of tabular text files (demonstrated here), spreadsheets, images, a SQL database (Database Toolbox™ required) or Hadoop® sequence files.

Create a datastore for a .csv file containing airline data. Treat 'NA' values as missing so that datastore replaces them with NaN values. Select the variables of interest, and specify a categorical data type for the Origin and Dest variables. Preview the contents.

ds = datastore('airlinesmall.csv');
ds.TreatAsMissing = 'NA';
ds.SelectedVariableNames = {'Year','Month','ArrDelay','DepDelay','Origin','Dest'};
ds.SelectedFormats(5:6) = {'%C','%C'};
pre = preview(ds)
pre=8×6 table
    Year    Month    ArrDelay    DepDelay    Origin    Dest
    ____    _____    ________    ________    ______    ____

    1987     10          8          12        LAX      SJC 
    1987     10          8           1        SJC      BUR 
    1987     10         21          20        SAN      SMF 
    1987     10         13          12        BUR      SJC 
    1987     10          4          -1        SMF      LAX 
    1987     10         59          63        LAX      SJC 
    1987     10          3          -2        SAN      SFO 
    1987     10         11          -1        SEA      LAX 

Create Tall Array

Tall arrays are similar to in-memory MATLAB arrays, except that they can have any number of rows. Tall arrays can contain data that is numeric, logical, datetime, duration, calendarDuration, categorical, or strings. Also, you can convert any in-memory array to a tall array. (The in-memory array A must be one of the supported data types.)

The underlying class of a tall array is based on the type of datastore that backs it. For example, if the datastore ds contains tabular data, then tall(ds) returns a tall table containing the data.

tt = tall(ds)
tt =

  Mx6 tall table

    Year    Month    ArrDelay    DepDelay    Origin    Dest
    ____    _____    ________    ________    ______    ____

     ?        ?         ?           ?          ?        ?  
     ?        ?         ?           ?          ?        ?  
     ?        ?         ?           ?          ?        ?  
     :        :         :           :          :        :
     :        :         :           :          :        :

The display indicates the underlying data type and includes the first several rows of data. The size of the table displays as "Mx6" to indicate that MATLAB does not yet know how many rows of data there are.

Perform Calculations on Tall Arrays

You can work with tall arrays and tall tables in a similar manner in which you work with in-memory MATLAB arrays and tables.

One important aspect of tall arrays is that as you work with them, MATLAB does not perform most operations immediately. These operations appear to execute quickly, because the actual computation is deferred until you specifically request output. This deferred evaluation is important because even a simple command like size(X) executed on a tall array with a billion rows is not a quick calculation.

As you work with tall arrays, MATLAB keeps track of all of the operations to be carried out and optimizes the number of passes through the data. Thus, it is normal to work with unevaluated tall arrays and request output only when you require it. MATLAB does not know the contents or size of unevaluated tall arrays until you request that the array be evaluated and displayed.

Calculate the mean departure delay.

mDep = mean(tt.DepDelay,'omitnan')
mDep =

  tall double

    ?

Gather Results into Workspace

The benefit of deferred evaluation is that when the time comes for MATLAB to perform the calculations, it is often possible to combine the operations in such a way that the number of passes through the data is minimized. So, even if you perform many operations, MATLAB only makes extra passes through the data when absolutely necessary.

The gather function forces evaluation of all queued operations and brings the resulting output back into memory. Since gather returns the entire result in MATLAB, you should make sure that the result will fit in memory. For example, use gather on tall arrays that are the result of a function that reduces the size of the tall array, such as sum, min, mean, and so on.

Use gather to calculate the mean departure delay and bring the answer into memory. This calculation requires a single pass through the data, but other calculations might require several passes through the data. MATLAB determines the optimal number of passes for the calculation and displays this information at the command line.

mDep = gather(mDep)
Evaluating tall expression using the Local MATLAB Session:
- Pass 1 of 2: Completed in 1.4 sec
- Pass 2 of 2: Completed in 1.7 sec
Evaluation completed in 4 sec
mDep = 8.1860

Select Subset of Tall Array

You can extract values from a tall array by subscripting or indexing. You can index the array starting from the top or bottom, or by using a logical index. The functions head and tail are useful alternatives to indexing, enabling you to explore the first and last portions of a tall array. Gather both variables at the same time to avoid extra passes through the data.

h = head(tt);
tl = tail(tt);
[h,tl] = gather(h,tl)
Evaluating tall expression using the Local MATLAB Session:
- Pass 1 of 1: Completed in 1.7 sec
Evaluation completed in 2.2 sec
h=8×6 table
    Year    Month    ArrDelay    DepDelay    Origin    Dest
    ____    _____    ________    ________    ______    ____

    1987     10          8          12        LAX      SJC 
    1987     10          8           1        SJC      BUR 
    1987     10         21          20        SAN      SMF 
    1987     10         13          12        BUR      SJC 
    1987     10          4          -1        SMF      LAX 
    1987     10         59          63        LAX      SJC 
    1987     10          3          -2        SAN      SFO 
    1987     10         11          -1        SEA      LAX 

tl=8×6 table
    Year    Month    ArrDelay    DepDelay    Origin    Dest
    ____    _____    ________    ________    ______    ____

    2008     12         14           1        DAB      ATL 
    2008     12         -8          -1        ATL      TPA 
    2008     12          1           9        ATL      CLT 
    2008     12         -8          -4        ATL      CLT 
    2008     12         15          -2        BOS      LGA 
    2008     12        -15          -1        SFO      ATL 
    2008     12        -12           1        DAB      ATL 
    2008     12         -1          11        ATL      IAD 

Use head to select a subset of 10,000 rows from the data for prototyping code before scaling to the full data set.

ttSubset = head(tt,10000);

Select Data by Condition

You can use typical logical operations on tall arrays, which are useful for selecting relevant data or removing outliers with logical indexing. The logical expression creates a tall logical vector, which then is used to subscript, identifying the rows where the condition is true.

Select only the flights out of Boston by comparing the elements of the categorical variable Origin to the value 'BOS'.

idx = (ttSubset.Origin == 'BOS');
bosflights = ttSubset(idx,:)
bosflights =

  207x6 tall table

    Year    Month    ArrDelay    DepDelay    Origin    Dest
    ____    _____    ________    ________    ______    ____

    1987     10         -8           0        BOS      LGA 
    1987     10        -13          -1        BOS      LGA 
    1987     10         12          11        BOS      BWI 
    1987     10         -3           0        BOS      EWR 
    1987     10         -5           0        BOS      ORD 
    1987     10         31          19        BOS      PHL 
    1987     10         -3           0        BOS      CLE 
    1987     11          5           5        BOS      STL 
     :        :         :           :          :        :
     :        :         :           :          :        :

You can use the same indexing technique to remove rows with missing data or NaN values from the tall array.

idx = any(ismissing(ttSubset),2); 
ttSubset(idx,:) = [];

Determine Largest Delays

Due to the nature of big data, sorting all of the data using traditional methods like sort or sortrows is inefficient. However, the topkrows function for tall arrays returns the top k rows in sorted order.

Calculate the top 10 greatest departure delays.

biggestDelays = topkrows(ttSubset,10,'DepDelay');
biggestDelays = gather(biggestDelays)
Evaluating tall expression using the Local MATLAB Session:
Evaluation completed in 0.34 sec
biggestDelays=10×6 table
    Year    Month    ArrDelay    DepDelay    Origin    Dest
    ____    _____    ________    ________    ______    ____

    1988      3        772         785        ORD      LEX 
    1989      3        453         447        MDT      ORD 
    1988     12        397         425        SJU      BWI 
    1987     12        339         360        DEN      STL 
    1988      3        261         273        PHL      ROC 
    1988      7        261         268        BWI      PBI 
    1988      2        257         253        ORD      BTV 
    1988      3        236         240        EWR      FLL 
    1989      2        263         227        BNA      MOB 
    1989      6        224         225        DFW      JAX 

Visualize Data in Tall Arrays

Plotting every point in a big data set is not feasible. For that reason, visualization of tall arrays involves reducing the number of data points using sampling or binning.

Visualize the number of flights per year with a histogram. The visualization functions pass through the data and immediately evaluate the solution when you call them, so gather is not required.

histogram(ttSubset.Year,'BinMethod','integers')
Evaluating tall expression using the Local MATLAB Session:
Evaluation completed in 0.94 sec
xlabel('Year')
ylabel('Number of Flights')
title('Number of Flights by Year, 1987 - 1989')

Scale to Entire Data Set

Instead of using the smaller data returned from head, you can scale up to perform the calculations on the entire data set by using the results from tall(ds).

tt = tall(ds);
idx = any(ismissing(tt),2); 
tt(idx,:) = [];
mnDelay = mean(tt.DepDelay,'omitnan');
biggestDelays = topkrows(tt,10,'DepDelay'); 
[mnDelay,biggestDelays] = gather(mnDelay,biggestDelays)
Evaluating tall expression using the Local MATLAB Session:
- Pass 1 of 2: Completed in 0.68 sec
- Pass 2 of 2: Completed in 1.5 sec
Evaluation completed in 2.5 sec
mnDelay = 8.1310
biggestDelays=10×6 table
    Year    Month    ArrDelay    DepDelay    Origin    Dest
    ____    _____    ________    ________    ______    ____

    1991      3          -8        1438       MCO      BWI 
    1998     12         -12        1433       CVG      ORF 
    1995     11        1014        1014       HNL      LAX 
    2007      4         914         924       JFK      DTW 
    2001      4         887         884       MCO      DTW 
    2008      7         845         855       CMH      ORD 
    1988      3         772         785       ORD      LEX 
    2008      4         710         713       EWR      RDU 
    1998     10         679         673       MCI      DFW 
    2006      6         603         626       ABQ      PHX 

histogram(tt.Year,'BinMethod','integers')
Evaluating tall expression using the Local MATLAB Session:
- Pass 1 of 2: Completed in 1.7 sec
- Pass 2 of 2: Completed in 1.1 sec
Evaluation completed in 3.4 sec
xlabel('Year')
ylabel('Number of Flights')
title('Number of Flights by Year, 1987 - 2008')

Use histogram2 to further break down the number of flights by month for the whole data set. Since the bins for Month and Year are known ahead of time, specify the bin edges to avoid an extra pass through the data.

year_edges = 1986.5:2008.5;
month_edges = 0.5:12.5;
histogram2(tt.Year,tt.Month,year_edges,month_edges,'DisplayStyle','tile')
Evaluating tall expression using the Local MATLAB Session:
- Pass 1 of 1: Completed in 1.3 sec
Evaluation completed in 1.5 sec
colorbar
xlabel('Year')
ylabel('Month')
title('Airline Flights by Month and Year, 1987 - 2008')

Data Analytics and Machine Learning with Tall Arrays

You can perform more sophisticated statistical analysis on tall arrays, including calculating predictive analytics and performing machine learning, using the functions in Statistics and Machine Learning Toolbox™.

For more information, see Tall Array Support, Usage Notes, and Limitations (Statistics and Machine Learning Toolbox).

Scale to Big Data Systems

A key capability of tall arrays in MATLAB is the connectivity to big data platforms, such as computing clusters and Apache Spark™.

This example only scratches the surface of what is possible with tall arrays for big data. See Extend Tall Arrays with Other Products for more information about using:

  • Statistics and Machine Learning Toolbox™

  • Database Toolbox™

  • Parallel Computing Toolbox™

  • MATLAB® Distributed Computing Server™

  • MATLAB Compiler™

See Also

Related Topics