matlab.io.datastore.HadoopLocationBased class

Package: matlab.io.datastore

Add Hadoop support to datastore

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Description

matlab.io.datastore.HadoopLocationBased is an abstract mixin class that adds Hadoop® support for data stored in files, or other non-file-based data sources such as databases.

To use this mixin class, you must inherit from the matlab.io.datastore.HadoopLocationBased class in addition to inheriting from the matlab.io.Datastore base class. Type the following syntax as the first line of your class definition file: 

classdef MyDatastore < matlab.io.Datastore & ...
                             matlab.io.datastore.HadoopLocationBased 
    ...
end

To add Hadoop support along with parallel processing support, use these lines in your class definition file: 

classdef MyDatastore < matlab.io.Datastore & ...
                             matlab.io.datastore.Partitionable & ...
                             matlab.io.datastore.HadoopLocationBased 
    ...
end

To add support for Hadoop to your custom datastore, you must:

For more details and steps to create your custom datastore with support for Hadoop, see Develop Custom Datastore.

Methods

getLocation Location in Hadoop
initializeDatastore Initialize datastore with information from Hadoop
isfullfile Check if datastore reads full files

Attributes

Sealedfalse

For information on class attributes, see Class Attributes.

Examples

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Implement a datastore with parallel processing and Hadoop support and use it to bring your data from the Hadoop server into MATLAB®. Then use the tall and gather functions on this data.

Create a new .m class definition file that contains the code implementing your custom datastore. You must save this file in your working folder or in a folder that is on the MATLAB path. The name of the .m file must be the same as the name of your object constructor function. For example, if you want your constructor function to have the name MyDatastoreHadoop, then the name of the script file must be MyDatastoreHadoop.m. The .m class definition file must contain these steps:

  • Step 1: Inherit from the datastore classes.

  • Step 2: Define the constructor and the required methods.

  • Step 3: Define your custom file reading function.

This code shows the three steps in a sample implementation of a custom datastore that can read binary files from a Hadoop server.

%% STEP 1: INHERIT FROM DATASTORE CLASSES
classdef MyDatastoreHadoop < matlab.io.Datastore & ...
        matlab.io.datastore.Partitionable & ...
        matlab.io.datastore.HadoopLocationBased
    
    properties (Access = private)
        CurrentFileIndex double
        FileSet matlab.io.datastore.DsFileSet
    end

         
%% STEP 2: DEFINE THE CONSTRUCTOR AND THE REQUIRED METHODS
    methods
        % Define your datastore constructor
        function myds = MyDatastoreHadoop(location,altRoots)
            myds.FileSet = matlab.io.datastore.DsFileSet(location,...
                'FileExtensions','.bin', ...
                'FileSplitSize',8*1024);
            myds.CurrentFileIndex = 1;
             
            if nargin == 2
                 myds.AlternateFileSystemRoots = altRoots;
            end
            
            reset(myds);
        end
        
        % Define the hasdata method
        function tf = hasdata(myds)
            % Return true if more data is available
            tf = hasfile(myds.FileSet);
        end
        
        % Define the read method
        function [data,info] = read(myds)
            % Read data and information about the extracted data
            % See also: MyFileReader()
            if ~hasdata(myds)
                error(sprintf(['No more data to read.\nUse the reset ',... 
                     'method to reset the datastore to the start of ' ,...
                     'the data. \nBefore calling the read method, ',...
                     'check if data is available to read ',...
                     'by using the hasdata method.'])) 
            end
            
            fileInfoTbl = nextfile(myds.FileSet);
            data = MyFileReader(fileInfoTbl);
            info.Size = size(data);
            info.FileName = fileInfoTbl.FileName;
            info.Offset = fileInfoTbl.Offset;
            
            % Update CurrentFileIndex for tracking progress
            if fileInfoTbl.Offset + fileInfoTbl.SplitSize >= ...
                    fileInfoTbl.FileSize
                myds.CurrentFileIndex = myds.CurrentFileIndex + 1 ;
            end
        end
        
        % Define the reset method
        function reset(myds)
            % Reset to the start of the data
            reset(myds.FileSet);
            myds.CurrentFileIndex = 1;
        end
        
        
        % Define the partition method
        function subds = partition(myds,n,ii)
            subds = copy(myds);
            subds.FileSet = partition(myds.FileSet,n,ii);
            reset(subds);
        end
    end      

     
    methods (Hidden = true)   

        % Define the progress method
        function frac = progress(myds)
            % Determine percentage of data read from datastore
            if hasdata(myds) 
               frac = (myds.CurrentFileIndex-1)/...
                             myds.FileSet.NumFiles; 
            else 
               frac = 1;  
            end 
        end
 
        % Define the initializeDatastore method
        function initializeDatastore(myds,hadoopInfo)
            import matlab.io.datastore.DsFileSet;
            myds.FileSet = DsFileSet(hadoopInfo,...
                'FileSplitSize',myds.FileSet.FileSplitSize,...
                'IncludeSubfolders',true, ...
                'FileExtensions','.bin');
            reset(myds);
        end
        
        % Define the getLocation method
        function loc = getLocation(myds)
            loc = myds.FileSet;
        end
        
        % Define the isfullfile method
        function tf = isfullfile(~)
            tf = isequal(myds.FileSet.FileSplitSize,'file'); 
        end

    end
        
    methods (Access = protected)
        % If you use the  FileSet property in the datastore,
        % then you must define the copyElement method. The
        % copyElement method allows methods such as readall
        % and preview to remain stateless 
        function dscopy = copyElement(ds)
            dscopy = copyElement@matlab.mixin.Copyable(ds);
            dscopy.FileSet = copy(ds.FileSet);
        end
        
        % Define the maxpartitions method
        function n = maxpartitions(myds)
            n = maxpartitions(myds.FileSet);
        end
    end
end

%% STEP 3: IMPLEMENT YOUR CUSTOM FILE READING FUNCTION
function data = MyFileReader(fileInfoTbl)
% create a reader object using FileName
reader = matlab.io.datastore.DsFileReader(fileInfoTbl.FileName);

% seek to the offset
seek(reader,fileInfoTbl.Offset,'Origin','start-of-file');

% read fileInfoTbl.SplitSize amount of data
data = read(reader,fileInfoTbl.SplitSize);
end

This step completes the implementation of your custom datastore.

Next, create a datastore object using your custom datastore constructor. If your data is located at hdfs:///path_to_files, then you can use this code. 

setenv('HADOOP_HOME','/path/to/hadoop/install');
ds = MyDatastoreHadoop('hdfs:///path_to_files');

To use tall arrays and the gather function on Apache Spark™ with parallel cluster configuration, set the mapreducer and attach MyDatastoreHadoop.m to the cluster.

mr = mapreducer(cluster);
mr.Cluster.AttachedFiles = 'MyDatastoreHadoop.m';

Create tall array from datastore.

t = tall(ds);

Gather the head of the tall array. 

 hd = gather(head(t));

See Also

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Topics

Introduced in R2019a

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