This guide explains all the elements needed to successfully develop and plug in a new MADlib module.

The files for the examples can be found in the hello world folder of the source code.

Installation

Follow steps in Installation Guide for MADlib.

Module Anatomy

MADlib source code is organized such that the core logic of a machine learning or statistical module is located in a common location and the database-port specific code is located in a ports folder. Since all currently supported databases are based on Postgres, the postgres port contains all the port-specific files, with greenplum and hawq inheriting from it. Before proceeding with this guide, it is recommended that you familiarize yourself with the MADlib architecture.

Adding A New Module

Let us add a new module called hello_world. Inside this module we implement a User-Defined SQL Aggregate (UDA), called avg_var which computes the mean and variance for a given numerical column of a table. We'll implement a distributed version of Welford's online algorithm for computing the mean and variance.

Unlike an ordinary UDA in PostgreSQL, avg_var will also work on a distributed database and take advantage of the underlying distributed network for parallel computations. The usage of avg_var is very simple: users simply run the following command in psql:

sql select avg_var(bath) from houses

which will print three numbers on the screen: mean, variance and number of rows in column bath of table houses.

Below are the main steps we will go through in this guide:

Register the module.
Define the SQL functions.
Implement the functions in C++.
Register the C++ header files.

1. Register the module

Add the following line to the file called Modules.yml under ./src/config/ yaml

- name: hello_world

and create two folders: ./src/ports/postgres/modules/hello_world and ./src/modules/hello_world. The names of the folders need to match the name of the module specified in Modules.yml.

2. Define the SQL functions

Create file avg_var.sql_in under folder ./src/ports/postgres/modules/hello_world. Inside this file we define the aggregate function and other helper functions for computing mean and variance. The actual implementations of those functions will be in separate C++ files which we will describe in the next section.

At the beginning of file avg_var.sql_in the command m4_include(SQLCommon.m4')` is necessary to run the m4 macro processor. M4 is used to add platform-specific commands in the SQL definitions and is run while deploying MADlib to the database.

We define the aggregate function avg_var using built-in PostgreSQL command CREATE AGGREGATE.

DROP AGGREGATE IF EXISTS MADLIB_SCHEMA.avg_var(DOUBLE PRECISION);
CREATE AGGREGATE MADLIB_SCHEMA.avg_var(DOUBLE PRECISION) (
    SFUNC=MADLIB_SCHEMA.avg_var_transition,
    STYPE=double precision[],
    FINALFUNC=MADLIB_SCHEMA.avg_var_final,
    m4_ifdef(`__POSTGRESQL__', `', `PREFUNC=MADLIB_SCHEMA.avg_var_merge_states,')
    INITCOND='{0, 0, 0}'
);

We also define parameters passed to CREATE AGGREGATE:

SFUNC
- the name of the state transition function to be called for each input row. The state transition function, avg_var_transition in this example, is defined in the same file avg_var.sql_in and implemented later in C++.
FINALFUNC
- the name of the final function called to compute the aggregate's result after all input rows have been traversed. The final function, avg_var_final in this example, is defined in the same file avg_var.sql_in and implemented later in C++.
PREFUNC
- the name of the merge function called to combine the aggregate's state values after each segment, or partition, of data has been traversed. The merge function is needed for distributed datasets on Greenplum and HAWQ. For PostgreSQL, the data is not distributed, and the merge function is not necessary. For completeness we implement a merge function called avg_var_merge_states in this guide.
INITCOND
- the initial condition for the state value. In this example it is an all-zero double array corresponding to the values of mean, variance, and the number of rows, respectively.

The transition, merge, and final functions are defined in the same file avg_var.sql_in as the aggregate function. More details about those functions can be found in the PostgreSQL documentation.

3. Implement the functions in C++

Create the header and the source files, avg_var.hpp and avg_var.cpp, under the folder ./src/modules/hello_world. In the header file we declare the transition, merge and final functions using the macro DECLARE_UDF(MODULE, NAME). For example, the transition function avg_var_transition is declared as DECLARE_UDF(hello_world, avg_var_transition). The macro DECLARE_UDF is defined in the file dbconnector.hpp under ./src/ports/postgres/dbconnector.

Under the hood, each of the three UDFs is declared as a subclass of dbconnector::postgres::UDF. The behavior of those UDFs is solely determined by its member function

AnyType run(AnyType &args);

In other words, we only need to implement the following methods in the avg_var.cpp file:

AnyType avg_var_transition::run(AnyType& args);
AnyType avg_var_merge_states::run(AnyType& args);
AnyType avg_var_final::run(AnyType& args);

Here the AnyType class works for both passing data from the DBMS to the C++ function, as well as returning values back from C++. Refer to TypeTraits_impl.hpp for more details.

`Transition function`

AnyType avgvartransition::run(AnyType& args) {// get current state value AvgVarTransitionState<MutableArrayHandle<double> > state = args[0]; // get current row value double x = args[1].getAs<double>(); double d = (x - state.avg);

// online update mean
    state.avg += d / static_cast<double>(state.numRows + 1);
    double new_d = (x - state.avg);
    double a = static_cast<double>(state.numRows) / static_cast<double>(state.numRows + 1);

    // online update variance
    state.var = state.var * a + d * new_d / static_cast<double>(state.numRows + 1);
    state.numRows ++;
    return state;

}

AnyType 
avgvartransition::run(AnyType& args) {
 
	// get current state value 
	AvgVarTransitionState<MutableArrayHandle<double> > state = args[0]; 
 
	// get current row value 
	double x = args[1].getAs<double>(); 
	double d = (x - state.avg);

	// online update mean
    state.avg += d / static_cast<double>(state.numRows + 1);
    double new_d = (x - state.avg);
    double a = static_cast<double>(state.numRows) / static_cast<double>(state.numRows + 1);

    // online update variance
    state.var = state.var * a + d * new_d / static_cast<double>(state.numRows + 1);
    state.numRows ++;
    return state;
}

There are two arguments for avg_var_transition, as specified in avg_var.sql_in. The first one is an array of SQL double type, corresponding to the current mean, variance, and number of rows traversed and the second one is a double representing the current tuple value.

We will describe classAvgVarTransitionState later. Basically it takes args[0], a SQL double array, passes the data to the appropriate C++ types and stores them in the state instance.

Both the mean and the variance are updated in an online manner to avoid accumulating large intermediate sum.

`Merge function`

AnyType
avg_var_merge_states::run(AnyType& args) {
    AvgVarTransitionState<MutableArrayHandle<double> > stateLeft = args[0];
    AvgVarTransitionState<ArrayHandle<double> > stateRight = args[1];

    // Merge states together and return
    stateLeft += stateRight;
    return stateLeft;
}

Again: the arguments contained in AnyType& args are defined in avg_var.sql_in.
The details are hidden in method of class AvgVarTransitionState which overloads the operator +=

`Final function`

AnyType
avg_var_final::run(AnyType& args) {
    AvgVarTransitionState<MutableArrayHandle<double> > state = args[0];

    // If we haven't seen any data, just return Null. This is the standard
    // behavior of aggregate function on empty data sets (compare, e.g.,
    // how PostgreSQL handles sum or avg on empty inputs)
    if (state.numRows == 0)
        return Null();

    return state;
}

Class AvgVarTransitionState overloads the AnyType() operator such that we can directly return state, an instance of AvgVarTransitionState, while the function is expected to return a AnyType.

Bridging class

Below is the method that overloads the operator += for the bridging class AvgVarTransitionState:

/**
* @brief Merge with another State object
* 
* We update mean and variance in a online fashion 
* to avoid intermediate large sum. 
*/ 
template <class OtherHandle> 
AvgVarTransitionState &operator+=( 
	const AvgVarTransitionState<OtherHandle> &inOtherState) {

	if (mStorage.size() != inOtherState.mStorage.size())
        throw std::logic_error("Internal error: Incompatible transition "
                               "states");
    double avg_ = inOtherState.avg;
    double var_ = inOtherState.var;
    uint16_t numRows_ = static_cast<uint16_t>(inOtherState.numRows);
    double totalNumRows = static_cast<double>(numRows + numRows_);
    double p = static_cast<double>(numRows) / totalNumRows;
    double p_ = static_cast<double>(numRows_) / totalNumRows;
    double totalAvg = avg * p + avg_ * p_;
    double a = avg - totalAvg;
    double a_ = avg_ - totalAvg;

    numRows += numRows_;
    var = p * var + p_ * var_ + p * a * a + p_ * a_ * a_;
    avg = totalAvg;
    return *this;
}

Given the mean, variance and the size of two data sets, Welford’s method computes the mean and variance of the two data sets combined.

4. Register the C++ header files

The SQL functions defined in avg_var.sql_in need to be able to locate the actual implementations from the C++ files. This is done by simply adding the following line to the file declarations.hpp under ./src/modules/

#include "hello_world/avg_var.hpp"

5. Running the new module

To use the new module, for example, we can launch psql terminal and apply it to the patients dataset, described here. The result below shows that half of the 20 patients have had second heart attacks within 1 year (yes = 1).

SELECT madlib.avg_var(second_attack) FROM patients;

    -- ************ --
    --    Result    --
    -- ************ --
    +-------------------+
    | avg_var           |
    |-------------------|
    | [0.5, 0.25, 20.0] |
    +-------------------+
-- (average, variance, count) --

The files for above exercise can be found in the examples folder of the source code.

Adding An Iterative UDF

In this session we demonstrate a slightly more complicated example which requires invoking a UDA iteratively. Such cases can often be found in many machine learning modules where the underlying optimization algorithm takes iterative steps towards the optimum of the objective. In this example we implement a simple logistic regression solver as an iterative UDF. In particular, the user will be able to type the following command in psql to train a logistic regression classifier:

SELECT madlib.logregr_simple_train('patients','logreg_mdl', 'second_attack', 'ARRAY[1, treatment, trait_anxiety]');

and to see the results:

SELECT * FROM logreg_mdl;

Here the data is stored in a SQL TABLE called patients. The target for logistic regression is the column second_attach and the features are columns treatment and trait_anxiety. The 1 entry in the ARRAY denotes an additional bias term in the model.

We add the solver to the hello_world module created above. Here are the main steps to follow:

in ./src/ports/postgres/modules/hello_world
- create file __init__.py_in
- create file simple_logistic.py_in
- create file simple_logistic.sql_in
in ./src/modules/hello_world
- create file simple_logistic.cpp
- create file simple_logistic.hpp
in ./src/modules
- modify file declarations.hpp -- appending a new line #include "hello_world/simple_logistic.hpp" to the end.

Compared to the steps presented in the last session, here we do not need to modify the Modules.yml file because we are not creating new module. Another difference is that we create an additional .py_in python file along with the .sql_in file. And that is where most of the iterative logic will be implemented.

1. Overview

The overall logic is split into three parts. All the UDF and UDA are defined in simple_logistic.sql_in. The transition, merge and final functions are implemented in C++. Those functions together constitute the UDA called __logregr_simple_step which takes one step from the current state to decrease the logistic regression objective. And finally in simple_logistic.py_in the plpy package is used to implement in python a UDF called logregr_simple_train which invokes __logregr_simple_step iteratively until convergence.

Note that The SQL function logregr_simple_train is defined in simple_logistic.sql_in as follows:

CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.logregr_simple_train ( 
	source_table 		VARCHAR, 
	out_table 			VARCHAR, 
	dependent_varname 	VARCHAR, 
	independent_varname VARCHAR, 
	max_iter 			INTEGER, 
	tolerance 			DOUBLE PRECISION, 
	verbose 			BOOLEAN 
) RETURNS VOID AS $$ 
PythonFunction(hello_world, simple_logistic, logregr_simple_train) 
$$ LANGUAGE plpythonu 
m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `');

where PythonFunction(hello_world, simple_logistic, logregr_simple_train) denotes that the actual implementation is provided by a python function logregr_simple_train inside the file simple_logistic in module hello_world, as shown below:

def logregr_simple_train( 
	schema_madlib, source_table, out_table, dependent_varname, 
	independent_varname, max_iter=None, 
	tolerance=None, 
	verbose=None, 
	**kwargs): 
""" 
Train logistic model

	@param schema_madlib Name of the MADlib schema, properly escaped/quoted
    @param source_table Name of relation containing the training data
    @param out_table Name of relation where model will be outputted
    @param dependent_varname Name of dependent column in training data (of type BOOLEAN)
    @param independent_varname Name of independent column in training data (of type
                   DOUBLE PRECISION[])
    @param max_iter The maximum number of iterations that are allowed.
    @param tolerance The precision that the results should have
    @param kwargs We allow the caller to specify additional arguments (all of
           which will be ignored though). The purpose of this is to allow the
           caller to unpack a dictionary whose element set is a superset of
           the required arguments by this function.

    @return A composite value which is __logregr_simple_result defined in simple_logistic.sql_in
    """

    return __logregr_train_compute(
        schema_madlib, source_table, out_table, dependent_varname,
        independent_varname, max_iter, tolerance,
        verbose, **kwargs)

2. Iterative procedures in `plpy`

The iterative logic is implemented using the PL/Python procedural language. In the beginning of `simple_logistic.py_in` we import a Python module called `plpy` which provides several functions to execute database commands. Implementing the iterative logic using `plpy` is simple, as demonstrated below:

update_plan = plpy.prepare(
	""" 
	SELECT 
		{schema_madlib}.simplestep( ({depcol})::boolean, ({indcol})::double precision[], ($1)) FROM {tblsource} """.format( tbloutput=tbloutput, schemamadlib=schemamadlib, depcol=depcol, indcol=indcol, tblsource=tblsource), ["double precision[]"])
state = None for it in range(0, maxiter): restuple = plpy.execute(updateplan, [state]) state = restuple[0].values()[0]

- thelogregrsimplestepis a UDA defined insimplelogistic.sqlinand implemented usingtransition,mergeandfinalfunctions provided in C++ files in./src/modules/hello_world.

-logregrsimplesteptakes three arguments, the target, the features and the previous state.

- the state is initialized asNonewhich is interpreted asnullvalue in SQL byplpy.

- a more sophisticated iterative scheme for logistic regression will also involve optimality verification and convergence guarantee procedures which are neglected on purpose here for simplicity.

- for a production-level implementation of logistic regression, refer to the moduleregress`.

3. Running the new iterative module

The example below demonstrates the usage of madlib.logregr_simple_train on the patients table. The trained classification model is stored in the table called logreg_mdl and can be viewed using standard SQL query.

SELECT madlib.logregr_simple_train( 
    'patients',                                 -- source table
    'logreg_mdl',                               -- output table
    'second_attack',                            -- labels
    'ARRAY[1, treatment, trait_anxiety]');      -- features
SELECT * FROM logreg_mdl;

-- ************ --
--    Result    --
-- ************ --
+--------------------------------------------------+------------------+
| coef                                             |   log_likelihood |
|--------------------------------------------------+------------------|
| [-6.27176619714, -0.84168872422, 0.116267554551] |         -9.42379 |
+--------------------------------------------------+------------------+