____                _        
|  _ \ _ __ ___  ___| |_ ___   _________________________________________________
| |_) | '__/ _ \/ __| __/ _ \ 
|  __/| | | (_) \__ \ || (_) | Functions matter! No map-reduce. No join-groupby.
|_|   |_|  \___/|___/\__\___/  _________________________________________________

• DOCUMENTATION

• Why Prosto? • Quick start • How to use • References •

Prosto is a Python data processing toolkit to programmatically author and execute complex data processing workflows. Conceptually, it is an alternative to purely set-oriented approaches to data processing like map-reduce, relational algebra, SQL or data-frame-based tools like pandas.

Prosto radically changes the way data is processed by relying on a novel data processing paradigm: concept-oriented model of data [2]. It treats columns (modelled via mathematical functions) as first-class elements of the data processing pipeline having the same rights as tables. If a traditional data processing graph consists of only set operations than the Prosto workflow consists of two types of operations:

• Table operations produce (populate) new tables from existing tables. A table is an implementation of a mathematical set which is a collection of tuples.

• Column operations produce (evaluate) new columns from existing columns. A column is an implementation of a mathematical function which maps tuples from one set to another set.

An example of such a Prosto workflow consisting of 3 column operations is shown below. The main difference from traditional approaches is that this Prosto workflow will not modify any table - it changes only columns. Formally, if traditional approaches apply set operations by generating new sets from already inferred sets, then Prosto derives new functions from existing functions. In many cases, using functions (column operations) is much simpler and more natural.

Prosto operations are demonstrated in notebooks which can be found in the "notebooks" folder in the main repo. Do your own experiments by tweaking them and playing around with the code: https://github.com/asavinov/prosto/tree/master/notebooks

More detailed information can be found in the documentation: http://prosto.readthedocs.io

Motivation: Why Prosto?

Why functions and column-orientation?

In traditional approaches to data processing we frequently need to produce a new table even though we need to define a new attribute. For example, in SQL, a new relation has to be produced even if we want to define a new calculated attribute. We also need to produce a new relation (using join) if we want to process data from another table. Data aggregation by means of groupy operation produces a new relation too although the goal is to compute a new attribute.

Thus processing data using only set operations is in many quite important cases counter-intuitive. In particular, this is why map-reduce, join-groupby (including SQL) and similar approaches require high expertise and are error-prone. The main unique novel feature of Prosto is that it adds mathematical functions (implemented as columns) to its model by significantly simplifying data processing and analysis. Now, if we want to define a new attribute then we can do it directly without defining new unnecessary table, collection or relation.

More info: Why functions and column-orientation?

Benefits of Prosto

Prosto provides the following unique features and benefits:

• Easily processing data in multiple tables. New derived columns are added directly to tables without creating multiple intermediate tables

• Getting rid of join and group-by. Column definitions such as link columns and aggregate columns are used instead of join and groupby set operations

• Flexibility and modularization via user-defined functions. UDFs describe what needs to be done with the data only in this operation using arbitrary Python code. If UDF for an operation changes then it is not necessary to update other operations

• Parameterization of operations by a model object. A model can be as simple as one value and as complex as a trained deep neural network. This feature leads to a novel view of how data analysis should be organized by combining feature engineering and machine learning so that both model training and model use (predictions or transformations) are part of one data processing workflow. Currently models are supported only as static parameters but in future there will be a possibility to train model within the same workflow

• Future directions. Incremental evaluation and data dictionary

More info: Benefits of Prosto

Quick start

Importing Prosto

Prosto is a toolkit and it is intended to be used from another (Python) application. Before its data processing functions can be used, the module has to be imported:

import prosto as pr

Defining a workflow

A workflow contains definitions of data elements (tables and columns) as well as operations for data generation. Before data processing operations can be defined, a Prosto workflow has to be created:

prosto = pr.Prosto("My Prosto Workflow")

More info: Workflow and operations

Defining a table

Below we define a table with three attributes which will be populated by the specified user-defined function:

sales_data = {
    "product_name": ["beer", "chips", "chips", "beer", "chips"],
    "quantity": [1, 2, 3, 2, 1],
    "price": [10.0, 5.0, 6.0, 15.0, 4.0]
}

sales = prosto.populate(
    # Table definition consists of a name and list of attributes
    table_name="Sales", attributes=["product_name", "quantity", "price"],

    # Table operation is an UDF, list of input tables and model (parameters for UDF)
    func=lambda **m: pd.DataFrame(sales_data), tables=[], model={}
)

The user-defined function in this example returns a pandas data frame with in-memory sales data. In a more realistic case, the data could be loaded from a CSV file or database. This data frame has to contain all attributes declared for this table.

Other table operations like project, product and filter allow for processing table data from already existing input tables which in turn could be populated using other operations.

More info: Table operations

Defining a column

A column is formally interpreted as a mathematical function which maps tuples (defined by table attributes) of this table to tuples in another table.

There exist many different ways to compute a mapping form one table to another table. One of the simplest column operations is a calculate column which computes output values of the mapping using the values of the specified input columns of the same table:

calc_column = prosto.calculate(
    # Column definition consists of a name and table it belongs to
    name="amount", table=sales.id,

    # Column operation is UDF, list of input columns and model (parameters for UDF)
    func=lambda x: x["quantity"] * x["price"], columns=["quantity", "price"], model=None
)

This new column will store the amount computed for each record as a product of quantity and price. Note that the input columns could be also derived columns computed from some other data in this or other tables.

Other column operations like link, aggregate or roll allow for producing link columns referencing records in other tables and aggregate data.

More info: Column operations

Executing a workflow

When a workflow is defined it is not executed - it stores only operation definitions. In order to really process data, the workflow has to be executed:

prosto.run()

Prosto translates a workflow into a graph of operations (topology) taking into account their dependencies and then executes each operation: table operations will populate tables and column operations will evaluate columns.

Now we can explore the result by reading data form the table along with the calculate column:

df = table.get_df()
print(df)

   product_name quantity price amount
0  beer         1        10.0  10.0
1  chips        2        5.0   10.0
2  chips        3        6.0   18.0
3  beer         2        15.0  30.0
4  chips        1        4.0   4.0

Although it looks like a normal table, the last column was derived from the data in other columns. If we change input data, then we can again run this workflow and the derived column will contain updated results.

The full power of Prosto comes from the ability to process data in multiple tables by definining derived links (instead of joins) and then aggregating data based on these links (without groupby). Note that both linking and aggregation do not require and will not produce new tables: only columns are defined and evaluated. For example, we might use column paths like my_derived_link::my_column in operations in order to access data in other tables.

How to use

Install from source code

Check out the source code and execute this command in the project directory (where setup.py is located):

$ pip install .

Or alternatively:

$ python setup.py install

Install from PYPI

This command will install the latest release of Prosto from PYPI:

$ pip install prosto

How to test

Run tests from the project root:

$ python -m pytest

or

$ python setup.py test

References

[1]: A.Savinov. On the importance of functions in data modeling, Eprint: arXiv:2012.15570 [cs.DB], 2019. https://www.researchgate.net/publication/348079767_On_the_importance_of_functions_in_data_modeling

[2]: A.Savinov. Concept-oriented model: Modeling and processing data using functions, Eprint: arXiv:1606.02237 [cs.DB], 2019. https://www.researchgate.net/publication/337336089_Concept-oriented_model_Modeling_and_processing_data_using_functions

[3]: A.Savinov. From Group-By to Accumulation: Data Aggregation Revisited, Proc. IoTBDS 2017, 370-379. https://www.researchgate.net/publication/316551218_From_Group-by_to_Accumulation_Data_Aggregation_Revisited

[4]: A.Savinov. Concept-oriented model: the Functional View, Eprint: arXiv:1606.02237 [cs.DB], 2016. https://www.researchgate.net/publication/303840097_Concept-Oriented_Model_the_Functional_View

[5]: A.Savinov. Joins vs. Links or Relational Join Considered Harmful, Proc. IoTBD 2016, 362-368. https://www.researchgate.net/publication/301764816_Joins_vs_Links_or_Relational_Join_Considered_Harmful