Defining the data model

The data model consists of a set of related classes programmed in one of the supported languages aimed are representing the objects used in the application (e.g. in a wordcount application, the data model would be text).

In order to define that the application objects are going to be stored in the underlying persistent storage backend, the data model must be enriched with the Storage Object Interface (SOI).

The SOI provides a set of functionalities that all objects stored in the persistent storage backend will need. Consequently, the user must inherit the SOI on its data model classes, and give some insights of the class attributes.

The following subsections detail how to enrich the data model in Java and Python applications.

Java

To define that a class objects are going to be stored in the persistent storage backend, the class must extend the StorageObject class (as well as implement the Serializable interface). This class is provided by the persistent storage backend.

import storage.StorageObject;
import java.io.Serializable;

class MyClass extends StorageObject implements Serializable {

    private double[] vector;

    /**
    * Write here your class-specific
    * constructors, attributes and methods.
    */
}

The StorageObject object enriches the class with some methods that allow the user to interact with the persistent storage backend. These methods can be found in Table 19.

Table 19 Available methods from StorageObject

Name	Returns	Comments
makePersistent(String id)	Nothing	Inserts the object in the database with the id. If id is null, a random UUID will be computed instead.
deletePersistent()	Nothing	Removes the object from the storage. It does nothing if it was not already there.
getID()	String	Returns the current object identifier if the object is not persistent (null instead).

These functions can be used from the application in order to persist an object (pushing the object into the persistent storage) with make_persistent, remove it from the persistent storage with delete_persistent or getting the object identifier with getID for the later interaction with the storage backend.

import MyPackage.MyClass;

class Test{
    // ...
    public static void main(String args[]){
        // ...
        MyClass my_obj = new MyClass();
        my_obj.matrix = new double[10];
        my_obj.makePersistent();         // make persistent without parameter
        String obj_id = my_obj.getID();  // get the idenfier provided by the storage framework
        // ...
        my_obj.deletePersistent();
        // ...
        MyClass my_obj2 = new MyClass();
        my_obj2.matrix = new double[20];
        my_obj2.makePersistent("obj2");  // make persistent providing identifier
        // ...
        my_obj2.delete_persistent();
        // ...
    }
}

Python

To define that a class objects are going to be stored in the persistent storage backend, the class must inherit the StorageObject class. This class is provided by the persistent storage backend.

from storage.api import StorageObject

class MyClass(StorageObject):
     ...

In addition, the user has to give details about the class attributes using the class documentation. For example, if the user wants to define a class containing a numpy ndarray as attribute, the user has to specify this attribute starting with @ClassField followed by the attribute name and type:

from storage.api import StorageObject

class MyClass(StorageObject):
    """
    @ClassField matrix numpy.ndarray
    """
    pass

Important

Methods inside the class are not supported by all storage backends. dataClay is currently the only backend that provides support for them (see Enabling COMPSs applications with dataClay).

Then, the user can use the instantiated object normally:

from MyFile import MyClass
import numpy as np

my_obj = MyClass()
my_obj.matrix = np.random.rand(10, 2)
...

The following code snippet gives some examples of several types of attributes:

from storage.api import StorageObject

class MyClass(StorageObject):
    """
    # Elemmental types
    @ClassField field1 int
    @ClassField field2 str
    @ClassField field3 np.ndarray

    # Structured types
    @ClassField field4 list <int>
    @ClassField field5 set <list<float>>

    # Another class instance as attribute
    @ClassField field6 AnotherClassName

    # Complex dictionaries:
    @ClassField field7 dict <<int,str>, dict<<int>, list<str>>>
    @ClassField field8 dict <<int>, AnotherClassName>

    # Dictionary with structured value:
    @ClassField field9 dict <<k1: int, k2: int>, tuple<v1: int, v2: float, v3: text>>
    # Plain definition of the same dictionary:
    @ClassField field10 dict <<int,int>, str>
    """
    pass

Finally, the StorageObject class includes some functions in the class that will be available from the instantiated objects (Table 20).

Table 20 Available methods from StorageObject in Python

Name	Returns	Comments
make_persistent(String id)	Nothing	Inserts the object in the database with the id. If id is null, a random UUID will be computed instead.
delete_persistent()	Nothing	Removes the object from the storage. It does nothing if it was not already there.
getID()	String	Returns the current object identifier if the object is not persistent (None instead).

These functions can be used from the application in order to persist an object (pushing the object into the persistent storage) with make_persistent, remove it from the persistent storage with delete_persistent or getting the object identifier with getID for the later interaction with the storage backend.

import numpy as np

my_obj = MyClass()
my_obj.matrix = np.random.rand(10, 2)
my_obj.make_persistent()  # make persistent without parameter
obj_id = my_obj.getID()   # get the idenfier provided by the storage framework
...
my_obj.delete_persistent()
...
my_obj2 = MyClass()
my_obj2.matrix = np.random.rand(10, 3)
my_obj2.make_persistent('obj2')  # make persistent providing identifier
...
my_obj2.delete_persistent()
...

C/C++

Unsupported

Persistent storage is not supported with C/C++ COMPSs applications.