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6
.gitignore vendored
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build
*.pyc
*.csv
*.json
*.swp
*.egg-info

48
README.md
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# Introduction
This project implements an abstraction of objects that can have access to a variety of data stores, implementing read/write with a simple and expressive interface. This abstraction works with **NoSQL**, **SQL** and **Cloud** data stores and leverages **pandas**.
This project implements an abstraction of objects that can have access to a variety of data stores, implementing read/write functions associated and specific to the data-sources. The classes implement functionalities against :
# Why Use Data-Transport ?
- Rabbitmq-server
- Couchdb-server
- Http Session : {csv,tab,pipe,sql}
- Disk{Reader|Writer} : csv, tab, pipe, sql on disk
Mostly data scientists that don't really care about the underlying database and would like a simple and consistent way to read/write and move data are well served. Additionally we implemented lightweight Extract Transform Loading API and command line (CLI) tool. Finally it is possible to add pre/post processing pipeline functions to read/write
1. Familiarity with **pandas data-frames**
2. Connectivity **drivers** are included
3. Reading/Writing data from various sources
4. Useful for data migrations or **ETL**
### Usage
The basic usage revolves around a factory class (to be a singleton)
## Installation
Within the virtual environment perform the following :
pip install git+https://github.com/lnyemba/data-transport.git
## Features
- read/write from over a dozen databases
- run ETL jobs seamlessly
- scales and integrates into shared environments like apache zeppelin; jupyterhub; SageMaker; ...
## What's new
Unlike older versions 2.0 and under, we focus on collaborative environments like jupyter-x servers; apache zeppelin:
1. Simpler syntax to create reader or writer
2. auth-file registry that can be referenced using a label
3. duckdb support
## Learn More
We have available notebooks with sample code to read/write against mongodb, couchdb, Netezza, PostgreSQL, Google Bigquery, Databricks, Microsoft SQL Server, MySQL ... Visit [data-transport homepage](https://healthcareio.the-phi.com/data-transport)
import transport
p = {"uri":"https://your-server:5984","dbname":"mydatabase","doc":"doc_id"}
couchdb = transport.Factory.instance(type='CouchdbReader',args=p)
#
# let's execute a view
#
result = couchdb.view('view_name/function',key=value)
info = couchdb.read()

151
bin/transport
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#!/usr/bin/env python
__doc__ = """
(c) 2018 - 2021 data-transport
steve@the-phi.com, The Phi Technology LLC
https://dev.the-phi.com/git/steve/data-transport.git
This program performs ETL between 9 supported data sources : Couchdb, Mongodb, Mysql, Mariadb, PostgreSQL, Netezza,Redshift, Sqlite, File
LICENSE (MIT)
Copyright 2016-2020, The Phi Technology LLC
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
"""
import pandas as pd
import numpy as np
import json
import sys
import transport
import time
from multiprocessing import Process
import os
import transport
from transport import etl
# from transport import providers
import typer
from typing_extensions import Annotated
from typing import Optional
import time
from termcolor import colored
app = typer.Typer()
REGISTRY_PATH=os.sep.join([os.environ['HOME'],'.data-transport'])
REGISTRY_FILE= 'transport-registry.json'
CHECK_MARK = ' '.join(['[',colored(u'\u2713', 'green'),']'])
TIMES_MARK= ' '.join(['[',colored(u'\u2717','red'),']'])
# @app.command()
def help() :
print (__doc__)
def wait(jobs):
while jobs :
jobs = [thread for thread in jobs if thread.is_alive()]
time.sleep(1)
@app.command(name="apply")
def apply (path:Annotated[str,typer.Argument(help="path of the configuration file")],
index:int = typer.Option(default= None, help="index of the item of interest, otherwise everything in the file will be processed")):
"""
This function applies data transport ETL feature to read data from one source to write it one or several others
"""
# _proxy = lambda _object: _object.write(_object.read())
if os.path.exists(path):
file = open(path)
_config = json.loads (file.read() )
file.close()
if index :
_config = [_config[ int(index)]]
jobs = []
for _args in _config :
pthread = etl.instance(**_args) #-- automatically starts the process
jobs.append(pthread)
#
# @TODO: Log the number of processes started and estimated time
while jobs :
jobs = [pthread for pthread in jobs if pthread.is_alive()]
time.sleep(1)
#
# @TODO: Log the job termination here ...
@app.command(name="providers")
def supported (format:Annotated[str,typer.Argument(help="format of the output, supported formats are (list,table,json)")]="table") :
"""
This function will print supported providers/vendors and their associated classifications
"""
_df = (transport.supported())
if format in ['list','json'] :
print (json.dumps(_df.to_dict(orient="list")))
else:
print (_df)
print ()
@app.command()
def version():
"""
This function will display version and license information
"""
print (transport.__app_name__,'version ',transport.__version__)
print (transport.__license__)
@app.command()
def generate (path:Annotated[str,typer.Argument(help="path of the ETL configuration file template (name included)")]):
"""
This function will generate a configuration template to give a sense of how to create one
"""
_config = [
{
"source":{"provider":"http","url":"https://raw.githubusercontent.com/codeforamerica/ohana-api/master/data/sample-csv/addresses.csv"},
"target":
[{"provider":"files","path":"addresses.csv","delimiter":","},{"provider":"sqlite","database":"sample.db3","table":"addresses"}]
}
]
file = open(path,'w')
file.write(json.dumps(_config))
file.close()
print (f"""{CHECK_MARK} Successfully generated a template ETL file at {path}""" )
print ("""NOTE: Each line (source or target) is the content of an auth-file""")
@app.command(name="init")
def initregistry (email:Annotated[str,typer.Argument(help="email")],
path:str=typer.Option(default=REGISTRY_PATH,help="path or location of the configuration file"),
override:bool=typer.Option(default=False,help="override existing configuration or not")):
"""
This functiion will initialize the registry and have both application and calling code loading the database parameters by a label
"""
try:
transport.registry.init(email=email, path=path, override=override)
_msg = f"""{CHECK_MARK} Successfully wrote configuration to {path} from {email}"""
except Exception as e:
_msg = f"{TIMES_MARK} {e}"
print (_msg)
print ()
@app.command(name="register")
def register (label:Annotated[str,typer.Argument(help="unique label that will be used to load the parameters of the database")],
auth_file:Annotated[str,typer.Argument(help="path of the auth_file")],
default:bool=typer.Option(default=False,help="set the auth_file as default"),
path:str=typer.Option(default=REGISTRY_PATH,help="path of the data-transport registry file")):
"""
This function will register an auth-file i.e database connection and assign it a label,
Learn more about auth-file at https://healthcareio.the-phi.com/data-transport
"""
try:
if transport.registry.exists(path) :
transport.registry.set(label=label,auth_file=auth_file, default=default, path=path)
_msg = f"""{CHECK_MARK} Successfully added label "{label}" to data-transport registry"""
else:
_msg = f"""{TIMES_MARK} Registry is not initialized, please initialize the registry (check help)"""
except Exception as e:
_msg = f"""{TIMES_MARK} {e}"""
print (_msg)
pass
if __name__ == '__main__' :
app()

21
info/__init__.py
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__app_name__ = 'data-transport'
__author__ = 'The Phi Technology'
__version__= '2.2.6'
__email__ = "info@the-phi.com"
__license__=f"""
Copyright 2010 - 2024, Steve L. Nyemba
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
"""
__whatsnew__=f"""version {__version__}, focuses on collaborative environments like jupyter-base servers (apache zeppelin; jupyter notebook, jupyterlab, jupyterhub)
1. simpler syntax to create readers/writers
2. auth-file registry that can be referenced using a label
3. duckdb support
"""

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notebooks/bigquery.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Writing to Google Bigquery\n",
"\n",
"1. Insure you have a Google Bigquery service account key on disk\n",
"2. The service key location is set as an environment variable **BQ_KEY**\n",
"3. The dataset will be automatically created within the project associated with the service key\n",
"\n",
"The cell below creates a dataframe that will be stored within Google Bigquery"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 1/1 [00:00<00:00, 10106.76it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"['data transport version ', '2.0.4']\n"
]
}
],
"source": [
"#\n",
"# Writing to Google Bigquery database\n",
"#\n",
"import transport\n",
"from transport import providers\n",
"import pandas as pd\n",
"import os\n",
"\n",
"PRIVATE_KEY = os.environ['BQ_KEY'] #-- location of the service key\n",
"DATASET = 'demo'\n",
"_data = pd.DataFrame({\"name\":['James Bond','Steve Rogers','Steve Nyemba'],'age':[55,150,44]})\n",
"bqw = transport.get.writer(provider=providers.BIGQUERY,dataset=DATASET,table='friends',private_key=PRIVATE_KEY)\n",
"bqw.write(_data,if_exists='replace') #-- default is append\n",
"print (['data transport version ', transport.__version__])\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Reading from Google Bigquery\n",
"\n",
"The cell below reads the data that has been written by the cell above and computes the average age within a Google Bigquery (simple query). \n",
"\n",
"- Basic read of the designated table (friends) created above\n",
"- Execute an aggregate SQL against the table\n",
"\n",
"**NOTE**\n",
"\n",
"By design **read** object are separated from **write** objects in order to avoid accidental writes to the database.\n",
"Read objects are created with **transport.get.reader** whereas write objects are created with **transport.get.writer**"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Downloading: 100%|\u001b[32m██████████\u001b[0m|\n",
"Downloading: 100%|\u001b[32m██████████\u001b[0m|\n",
" name age\n",
"0 James Bond 55\n",
"1 Steve Rogers 150\n",
"2 Steve Nyemba 44\n",
"--------- STATISTICS ------------\n",
" _counts f0_\n",
"0 3 83.0\n"
]
}
],
"source": [
"\n",
"import transport\n",
"from transport import providers\n",
"import os\n",
"PRIVATE_KEY=os.environ['BQ_KEY']\n",
"pgr = transport.get.reader(provider=providers.BIGQUERY,dataset='demo',table='friends',private_key=PRIVATE_KEY)\n",
"_df = pgr.read()\n",
"_query = 'SELECT COUNT(*) _counts, AVG(age) from demo.friends'\n",
"_sdf = pgr.read(sql=_query)\n",
"print (_df)\n",
"print ('--------- STATISTICS ------------')\n",
"print (_sdf)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"An **auth-file** is a file that contains database parameters used to access the database. \n",
"For code in shared environments, we recommend \n",
"\n",
"1. Having the **auth-file** stored on disk \n",
"2. and the location of the file is set to an environment variable.\n",
"\n",
"To generate a template of the **auth-file** open the **file generator wizard** found at visit https://healthcareio.the-phi.com/data-transport"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

188
notebooks/etl.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Extract Transform Load (ETL) from Code\n",
"\n",
"The example below reads data from an http source (github) and will copy the data to a csv file and to a database. This example illustrates the one-to-many ETL features.\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>location_id</th>\n",
" <th>address_1</th>\n",
" <th>address_2</th>\n",
" <th>city</th>\n",
" <th>state_province</th>\n",
" <th>postal_code</th>\n",
" <th>country</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2600 Middlefield Road</td>\n",
" <td>NaN</td>\n",
" <td>Redwood City</td>\n",
" <td>CA</td>\n",
" <td>94063</td>\n",
" <td>US</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>24 Second Avenue</td>\n",
" <td>NaN</td>\n",
" <td>San Mateo</td>\n",
" <td>CA</td>\n",
" <td>94401</td>\n",
" <td>US</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>24 Second Avenue</td>\n",
" <td>NaN</td>\n",
" <td>San Mateo</td>\n",
" <td>CA</td>\n",
" <td>94403</td>\n",
" <td>US</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>24 Second Avenue</td>\n",
" <td>NaN</td>\n",
" <td>San Mateo</td>\n",
" <td>CA</td>\n",
" <td>94401</td>\n",
" <td>US</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>24 Second Avenue</td>\n",
" <td>NaN</td>\n",
" <td>San Mateo</td>\n",
" <td>CA</td>\n",
" <td>94401</td>\n",
" <td>US</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id location_id address_1 address_2 city \\\n",
"0 1 1 2600 Middlefield Road NaN Redwood City \n",
"1 2 2 24 Second Avenue NaN San Mateo \n",
"2 3 3 24 Second Avenue NaN San Mateo \n",
"3 4 4 24 Second Avenue NaN San Mateo \n",
"4 5 5 24 Second Avenue NaN San Mateo \n",
"\n",
" state_province postal_code country \n",
"0 CA 94063 US \n",
"1 CA 94401 US \n",
"2 CA 94403 US \n",
"3 CA 94401 US \n",
"4 CA 94401 US "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#\n",
"# Writing to Google Bigquery database\n",
"#\n",
"import transport\n",
"from transport import providers\n",
"import pandas as pd\n",
"import os\n",
"\n",
"#\n",
"#\n",
"source = {\"provider\": \"http\", \"url\": \"https://raw.githubusercontent.com/codeforamerica/ohana-api/master/data/sample-csv/addresses.csv\"}\n",
"target = [{\"provider\": \"files\", \"path\": \"addresses.csv\", \"delimiter\": \",\"}, {\"provider\": \"sqlite\", \"database\": \"sample.db3\", \"table\": \"addresses\"}]\n",
"\n",
"_handler = transport.get.etl (source=source,target=target)\n",
"_data = _handler.read() #-- all etl begins with data being read\n",
"_data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Extract Transform Load (ETL) from CLI\n",
"\n",
"The documentation for this is available at https://healthcareio.the-phi.com/data-transport \"Docs\" -> \"Terminal CLI\"\n",
"\n",
"The entire process is documented including how to generate an ETL configuration file."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

128
notebooks/mongodb.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Writing to mongodb\n",
"\n",
"Insure mongodb is actually installed on the system, The cell below creates a dataframe that will be stored within mongodb"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2.0.4\n"
]
}
],
"source": [
"#\n",
"# Writing to mongodb database\n",
"#\n",
"import transport\n",
"from transport import providers\n",
"import pandas as pd\n",
"_data = pd.DataFrame({\"name\":['James Bond','Steve Rogers','Steve Nyemba'],'age':[55,150,44]})\n",
"mgw = transport.get.writer(provider=providers.MONGODB,db='demo',collection='friends')\n",
"mgw.write(_data)\n",
"print (transport.__version__)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Reading from mongodb\n",
"\n",
"The cell below reads the data that has been written by the cell above and computes the average age within a mongodb pipeline. The code in the background executes an aggregation using **db.runCommand**\n",
"\n",
"- Basic read of the designated collection **find=\\<collection>**\n",
"- Executing an aggregate pipeline against a collection **aggreate=\\<collection>**\n",
"\n",
"**NOTE**\n",
"\n",
"By design **read** object are separated from **write** objects in order to avoid accidental writes to the database.\n",
"Read objects are created with **transport.get.reader** whereas write objects are created with **transport.get.writer**"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" name age\n",
"0 James Bond 55\n",
"1 Steve Rogers 150\n",
"--------- STATISTICS ------------\n",
" _id _counts _mean\n",
"0 0 2 102.5\n"
]
}
],
"source": [
"\n",
"import transport\n",
"from transport import providers\n",
"mgr = transport.get.reader(provider=providers.MONGODB,db='foo',collection='friends')\n",
"_df = mgr.read()\n",
"PIPELINE = [{\"$group\":{\"_id\":0,\"_counts\":{\"$sum\":1}, \"_mean\":{\"$avg\":\"$age\"}}}]\n",
"_sdf = mgr.read(aggregate='friends',pipeline=PIPELINE)\n",
"print (_df)\n",
"print ('--------- STATISTICS ------------')\n",
"print (_sdf)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"An **auth-file** is a file that contains database parameters used to access the database. \n",
"For code in shared environments, we recommend \n",
"\n",
"1. Having the **auth-file** stored on disk \n",
"2. and the location of the file is set to an environment variable.\n",
"\n",
"To generate a template of the **auth-file** open the **file generator wizard** found at visit https://healthcareio.the-phi.com/data-transport"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

150
notebooks/mssqlserver.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Writing to Microsoft SQLServer\n",
"\n",
"1. Insure the Microsoft SQL Server is installed and you have access i.e account information\n",
"2. The target database must be created before hand.\n",
"3. We created an authentication file that will contain user account and location of the database\n",
"\n",
"The cell below creates a dataframe that will be stored in a Microsoft SQL Server database.\n",
"\n",
"**NOTE** This was not tested with a cloud instance"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#\n",
"# Writing to Google Bigquery database\n",
"#\n",
"import transport\n",
"from transport import providers\n",
"import pandas as pd\n",
"import os\n",
"\n",
"AUTH_FOLDER = os.environ['DT_AUTH_FOLDER'] #-- location of the service key\n",
"MSSQL_AUTH_FILE= os.sep.join([AUTH_FOLDER,'mssql.json'])\n",
"\n",
"_data = pd.DataFrame({\"name\":['James Bond','Steve Rogers','Steve Nyemba'],'age':[55,150,44]})\n",
"msw = transport.get.writer(provider=providers.MSSQL,table='friends',auth_file=MSSQL_AUTH_FILE)\n",
"msw.write(_data,if_exists='replace') #-- default is append\n",
"print (['data transport version ', transport.__version__])\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Reading from Microsoft SQL Server database\n",
"\n",
"The cell below reads the data that has been written by the cell above and computes the average age within an MS SQL Server (simple query). \n",
"\n",
"- Basic read of the designated table (friends) created above\n",
"- Execute an aggregate SQL against the table\n",
"\n",
"**NOTE**\n",
"\n",
"By design **read** object are separated from **write** objects in order to avoid accidental writes to the database.\n",
"Read objects are created with **transport.get.reader** whereas write objects are created with **transport.get.writer**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n",
"import transport\n",
"from transport import providers\n",
"import os\n",
"AUTH_FOLDER = os.environ['DT_AUTH_FOLDER'] #-- location of the service key\n",
"MSSQL_AUTH_FILE= os.sep.join([AUTH_FOLDER,'mssql.json'])\n",
"\n",
"msr = transport.get.reader(provider=providers.MSSQL,table='friends',auth_file=MSSQL_AUTH_FILE)\n",
"_df = msr.read()\n",
"_query = 'SELECT COUNT(*) _counts, AVG(age) from friends'\n",
"_sdf = msr.read(sql=_query)\n",
"print (_df)\n",
"print ('\\n--------- STATISTICS ------------\\n')\n",
"print (_sdf)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"An **auth-file** is a file that contains database parameters used to access the database. \n",
"For code in shared environments, we recommend \n",
"\n",
"1. Having the **auth-file** stored on disk \n",
"2. and the location of the file is set to an environment variable.\n",
"\n",
"To generate a template of the **auth-file** open the **file generator wizard** found at visit https://healthcareio.the-phi.com/data-transport"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'provider': 'sqlserver',\n",
" 'dataset': 'demo',\n",
" 'table': 'friends',\n",
" 'username': '<username>',\n",
" 'password': '<password>'}"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\n",
"{\n",
" \"provider\":\"sqlserver\",\n",
" \"dataset\":\"demo\",\"table\":\"friends\",\"username\":\"<username>\",\"password\":\"<password>\"\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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@ -1,161 +0,0 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Writing to MySQL\n",
"\n",
"1. Insure MySQL is actually installed on the system, \n",
"2. There is a database called demo created on the said system\n",
"\n",
"The cell below creates a dataframe that will be stored within postgreSQL"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2.0.4\n"
]
}
],
"source": [
"#\n",
"# Writing to PostgreSQL database\n",
"#\n",
"import transport\n",
"from transport import providers\n",
"import pandas as pd\n",
"_data = pd.DataFrame({\"name\":['James Bond','Steve Rogers','Steve Nyemba'],'age':[55,150,44]})\n",
"myw = transport.get.writer(provider=providers.MYSQL,database='demo',table='friends',auth_file=\"/home/steve/auth-mysql.json\")\n",
"myw.write(_data,if_exists='replace') #-- default is append\n",
"print (transport.__version__)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Reading from MySQL\n",
"\n",
"The cell below reads the data that has been written by the cell above and computes the average age within a MySQL (simple query). \n",
"\n",
"- Basic read of the designated table (friends) created above\n",
"- Execute an aggregate SQL against the table\n",
"\n",
"**NOTE**\n",
"\n",
"By design **read** object are separated from **write** objects in order to avoid accidental writes to the database.\n",
"Read objects are created with **transport.get.reader** whereas write objects are created with **transport.get.writer**"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" name age\n",
"0 James Bond 55\n",
"1 Steve Rogers 150\n",
"2 Steve Nyemba 44\n",
"--------- STATISTICS ------------\n",
" _counts AVG(age)\n",
"0 3 83.0\n"
]
}
],
"source": [
"\n",
"import transport\n",
"from transport import providers\n",
"myr = transport.get.reader(provider=providers.MYSQL,database='demo',table='friends',auth_file='/home/steve/auth-mysql.json')\n",
"_df = myr.read()\n",
"_query = 'SELECT COUNT(*) _counts, AVG(age) from friends'\n",
"_sdf = myr.read(sql=_query)\n",
"print (_df)\n",
"print ('--------- STATISTICS ------------')\n",
"print (_sdf)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"An **auth-file** is a file that contains database parameters used to access the database. \n",
"For code in shared environments, we recommend \n",
"\n",
"1. Having the **auth-file** stored on disk \n",
"2. and the location of the file is set to an environment variable.\n",
"\n",
"To generate a template of the **auth-file** open the **file generator wizard** found at visit https://healthcareio.the-phi.com/data-transport"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'host': 'klingon.io',\n",
" 'port': 3306,\n",
" 'username': 'me',\n",
" 'password': 'foobar',\n",
" 'provider': 'mysql',\n",
" 'database': 'demo',\n",
" 'table': 'friends'}"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"{\n",
" \"host\":\"klingon.io\",\"port\":3306,\"username\":\"me\",\"password\":\"foobar\", \"provider\":\"mysql\",\n",
" \"database\":\"demo\",\"table\":\"friends\"\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Writing data-transport plugins\n",
"\n",
"The data-transport plugins are designed to automate pre/post processing i.e\n",
"\n",
" - Read -> Post processing\n",
" - Write-> Pre processing\n",
" \n",
"In this example we will assume, data and write both pre/post processing to any supported infrastructure. We will equally show how to specify the plugins within a configuration file"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"#\n",
"# Writing to Google Bigquery database\n",
"#\n",
"import transport\n",
"from transport import providers\n",
"import pandas as pd\n",
"import os\n",
"import shutil\n",
"#\n",
"#\n",
"\n",
"DATABASE = '/home/steve/tmp/demo.db3'\n",
"if os.path.exists(DATABASE) :\n",
" os.remove(DATABASE)\n",
"#\n",
"# \n",
"_data = pd.DataFrame({\"name\":['James Bond','Steve Rogers','Steve Nyemba'],'age':[55,150,44]})\n",
"litew = transport.get.writer(provider=providers.SQLITE,database=DATABASE)\n",
"litew.write(_data,table='friends')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Reading from SQLite\n",
"\n",
"The cell below reads the data that has been written by the cell above and computes the average age from a plugin function we will write. \n",
"\n",
"- Basic read of the designated table (friends) created above\n",
"- Read with pipeline functions defined in code\n",
"\n",
"**NOTE**\n",
"\n",
"It is possible to use **transport.factory.instance** or **transport.instance** or **transport.get.<[reader|writer]>** they are the same. It allows the maintainers to know that we used a factory design pattern."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" name age\n",
"0 James Bond 55\n",
"1 Steve Rogers 150\n",
"2 Steve Nyemba 44\n",
"\n",
"\n",
" name age autoinc\n",
"0 James Bond 5.5 0\n",
"1 Steve Rogers 15.0 1\n",
"2 Steve Nyemba 4.4 2\n"
]
}
],
"source": [
"\n",
"import transport\n",
"from transport import providers\n",
"import os\n",
"import numpy as np\n",
"def _autoincrement (_data,**kwargs) :\n",
" \"\"\"\n",
" This function will add an autoincrement field to the table\n",
" \"\"\"\n",
" _data['autoinc'] = np.arange(_data.shape[0])\n",
" \n",
" return _data\n",
"def reduce(_data,**_args) :\n",
" \"\"\"\n",
" This function will reduce the age of the data frame\n",
" \"\"\"\n",
" _data.age /= 10\n",
" return _data\n",
"reader = transport.get.reader(provider=providers.SQLITE,database=DATABASE,table='friends')\n",
"#\n",
"# basic read of the data created in the first cell\n",
"_df = reader.read()\n",
"print (_df)\n",
"print ()\n",
"print()\n",
"#\n",
"# read of the data with pipeline function provided to alter the database\n",
"print (reader.read(pipeline=[_autoincrement,reduce]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The parameters for instianciating a transport object (reader or writer) can be found at [data-transport home](https://healthcareio.the-phi.com/data-transport)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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@ -1,162 +0,0 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Writing to PostgreSQL\n",
"\n",
"1. Insure PostgreSQL is actually installed on the system, \n",
"2. There is a database called demo created on the said system\n",
"\n",
"The cell below creates a dataframe that will be stored within postgreSQL"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2.0.4\n"
]
}
],
"source": [
"#\n",
"# Writing to PostgreSQL database\n",
"#\n",
"import transport\n",
"from transport import providers\n",
"import pandas as pd\n",
"_data = pd.DataFrame({\"name\":['James Bond','Steve Rogers','Steve Nyemba'],'age':[55,150,44]})\n",
"pgw = transport.get.writer(provider=providers.POSTGRESQL,database='demo',table='friends')\n",
"pgw.write(_data,if_exists='replace') #-- default is append\n",
"print (transport.__version__)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Reading from PostgreSQL\n",
"\n",
"The cell below reads the data that has been written by the cell above and computes the average age within a PostreSQL (simple query). \n",
"\n",
"- Basic read of the designated table (friends) created above\n",
"- Execute an aggregate SQL against the table\n",
"\n",
"\n",
"**NOTE**\n",
"\n",
"By design **read** object are separated from **write** objects in order to avoid accidental writes to the database.\n",
"Read objects are created with **transport.get.reader** whereas write objects are created with **transport.get.writer**"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" name age\n",
"0 James Bond 55\n",
"1 Steve Rogers 150\n",
"2 Steve Nyemba 44\n",
"--------- STATISTICS ------------\n",
" _counts avg\n",
"0 3 83.0\n"
]
}
],
"source": [
"\n",
"import transport\n",
"from transport import providers\n",
"pgr = transport.get.reader(provider=providers.POSTGRESQL,database='demo',table='friends')\n",
"_df = pgr.read()\n",
"_query = 'SELECT COUNT(*) _counts, AVG(age) from friends'\n",
"_sdf = pgr.read(sql=_query)\n",
"print (_df)\n",
"print ('--------- STATISTICS ------------')\n",
"print (_sdf)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"An **auth-file** is a file that contains database parameters used to access the database. \n",
"For code in shared environments, we recommend \n",
"\n",
"1. Having the **auth-file** stored on disk \n",
"2. and the location of the file is set to an environment variable.\n",
"\n",
"To generate a template of the **auth-file** open the **file generator wizard** found at visit https://healthcareio.the-phi.com/data-transport"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'host': 'klingon.io',\n",
" 'port': 5432,\n",
" 'username': 'me',\n",
" 'password': 'foobar',\n",
" 'provider': 'postgresql',\n",
" 'database': 'demo',\n",
" 'table': 'friends'}"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"{\n",
" \"host\":\"klingon.io\",\"port\":5432,\"username\":\"me\",\"password\":\"foobar\", \"provider\":\"postgresql\",\n",
" \"database\":\"demo\",\"table\":\"friends\"\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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notebooks/s3.ipynb
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@ -1,131 +0,0 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Writing to AWS S3\n",
"\n",
"We have setup our demo environment with the label **aws** passed to reference our s3 access_key and secret_key and file (called friends.csv). In the cell below we will write the data to our aws s3 bucket named **com.phi.demo**"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2.2.1\n"
]
}
],
"source": [
"#\n",
"# Writing to mongodb database\n",
"#\n",
"import transport\n",
"from transport import providers\n",
"import pandas as pd\n",
"_data = pd.DataFrame({\"name\":['James Bond','Steve Rogers','Steve Nyemba'],'age':[55,150,44]})\n",
"mgw = transport.get.writer(label='aws')\n",
"mgw.write(_data)\n",
"print (transport.__version__)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Reading from AWS S3\n",
"\n",
"The cell below reads the data that has been written by the cell above and computes the average age within a mongodb pipeline. The code in the background executes an aggregation using\n",
"\n",
"- Basic read of the designated file **friends.csv**\n",
"- Compute average age using standard pandas functions\n",
"\n",
"**NOTE**\n",
"\n",
"By design **read** object are separated from **write** objects in order to avoid accidental writes to the database.\n",
"Read objects are created with **transport.get.reader** whereas write objects are created with **transport.get.writer**"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" bname age\n",
"0 James Bond 55\n",
"1 Steve Rogers 150\n",
"2 Steve Nyemba 44\n",
"--------- STATISTICS ------------\n",
"83.0\n"
]
}
],
"source": [
"\n",
"import transport\n",
"from transport import providers\n",
"import pandas as pd\n",
"\n",
"def cast(stream) :\n",
" print (stream)\n",
" return pd.DataFrame(str(stream))\n",
"mgr = transport.get.reader(label='aws')\n",
"_df = mgr.read()\n",
"print (_df)\n",
"print ('--------- STATISTICS ------------')\n",
"print (_df.age.mean())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"An **auth-file** is a file that contains database parameters used to access the database. \n",
"For code in shared environments, we recommend \n",
"\n",
"1. Having the **auth-file** stored on disk \n",
"2. and the location of the file is set to an environment variable.\n",
"\n",
"To generate a template of the **auth-file** open the **file generator wizard** found at visit https://healthcareio.the-phi.com/data-transport"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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notebooks/sqlite.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Writing to SQLite3+\n",
"\n",
"The requirements to get started are minimal (actually none). The cell below creates a dataframe that will be stored within SQLite 3+"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2.0.4\n"
]
}
],
"source": [
"#\n",
"# Writing to PostgreSQL database\n",
"#\n",
"import transport\n",
"from transport import providers\n",
"import pandas as pd\n",
"_data = pd.DataFrame({\"name\":['James Bond','Steve Rogers','Steve Nyemba'],'age':[55,150,44]})\n",
"sqw = transport.get.writer(provider=providers.SQLITE,database='/home/steve/demo.db3',table='friends')\n",
"sqw.write(_data,if_exists='replace') #-- default is append\n",
"print (transport.__version__)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Reading from SQLite3+\n",
"\n",
"The cell below reads the data that has been written by the cell above and computes the average age within a PostreSQL (simple query). \n",
"\n",
"- Basic read of the designated table (friends) created above\n",
"- Execute an aggregate SQL against the table\n",
"\n",
"\n",
"**NOTE**\n",
"\n",
"By design **read** object are separated from **write** objects in order to avoid accidental writes to the database.\n",
"Read objects are created with **transport.get.reader** whereas write objects are created with **transport.get.writer**"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" name age\n",
"0 James Bond 55\n",
"1 Steve Rogers 150\n",
"2 Steve Nyemba 44\n",
"--------- STATISTICS ------------\n",
" _counts AVG(age)\n",
"0 3 83.0\n"
]
}
],
"source": [
"\n",
"import transport\n",
"from transport import providers\n",
"sqr = transport.get.reader(provider=providers.SQLITE,database='/home/steve/demo.db3',table='friends')\n",
"_df = sqr.read()\n",
"_query = 'SELECT COUNT(*) _counts, AVG(age) from friends'\n",
"_sdf = sqr.read(sql=_query)\n",
"print (_df)\n",
"print ('--------- STATISTICS ------------')\n",
"print (_sdf)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"An **auth-file** is a file that contains database parameters used to access the database. \n",
"For code in shared environments, we recommend \n",
"\n",
"1. Having the **auth-file** stored on disk \n",
"2. and the location of the file is set to an environment variable.\n",
"\n",
"To generate a template of the **auth-file** open the **file generator wizard** found at visit https://healthcareio.the-phi.com/data-transport"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"\n",
"{\n",
" \"provider\":\"sqlite\",\n",
" \"database\":\"/home/steve/demo.db3\",\"table\":\"friends\"\n",
"}\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

28
setup.py
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"""
This is a build file for the
"""
from setuptools import setup, find_packages
import os
import sys
# from version import __version__,__author__
from info import __version__, __author__,__app_name__,__license__
def read(fname):
return open(os.path.join(os.path.dirname(__file__), fname)).read()
args = {
"name":__app_name__,
"version":__version__,
"author":__author__,"author_email":"info@the-phi.com",
"license":__license__,
# "packages":["transport","info","transport/sql"]},
"packages": find_packages(include=['info','transport', 'transport.*'])}
args["keywords"]=['mongodb','duckdb','couchdb','rabbitmq','file','read','write','s3','sqlite']
args["install_requires"] = ['pyncclient','duckdb-engine','pymongo','sqlalchemy','pandas','typer','pandas-gbq','numpy','cloudant','pika','nzpy','termcolor','boto3','boto','pyarrow','google-cloud-bigquery','google-cloud-bigquery-storage','flask-session','smart_open','botocore','psycopg2-binary','mysql-connector-python','numpy','pymssql']
args["url"] = "https://healthcareio.the-phi.com/git/code/transport.git"
args['scripts'] = ['bin/transport']
# if sys.version_info[0] == 2 :
# args['use_2to3'] = True
# args['use_2to3_exclude_fixers']=['lib2to3.fixes.fix_import']
setup(**args)

758
transport.py
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"""
This file implements data transport stuctures in order to allow data to be moved to and from anywhere
We can thus read data from disk and write to the cloud,queue, or couchdb or SQL
"""
from flask import request, session
import os
import pika
import json
import numpy as np
from couchdbkit import Server
import re
from csv import reader
from datetime import datetime
import boto
import botocore
from smart_open import smart_open
"""
@TODO: Write a process by which the class automatically handles reading and creating a preliminary sample and discovers the meta data
"""
class Reader:
def __init__(self):
self.nrows = 0
self.xchar = None
def row_count(self):
content = self.read()
return np.sum([1 for row in content])
"""
This function determines the most common delimiter from a subset of possible delimiters. It uses a statistical approach to guage the distribution of columns for a given delimiter
"""
def delimiter(self,sample):
m = {',':[],'\t':[],'|':[],'\x3A':[]}
delim = m.keys()
for row in sample:
for xchar in delim:
if row.split(xchar) > 1:
m[xchar].append(len(row.split(xchar)))
else:
m[xchar].append(0)
#
# The delimiter with the smallest variance, provided the mean is greater than 1
# This would be troublesome if there many broken records sampled
#
m = {id: np.var(m[id]) for id in m.keys() if m[id] != [] and int(np.mean(m[id]))>1}
index = m.values().index( min(m.values()))
xchar = m.keys()[index]
return xchar
"""
This function determines the number of columns of a given sample
@pre self.xchar is not None
"""
def col_count(self,sample):
m = {}
i = 0
for row in sample:
row = self.format(row)
id = str(len(row))
#id = str(len(row.split(self.xchar)))
if id not in m:
m[id] = 0
m[id] = m[id] + 1
index = m.values().index( max(m.values()) )
ncols = int(m.keys()[index])
return ncols;
"""
This function will clean records of a given row by removing non-ascii characters
@pre self.xchar is not None
"""
def format (self,row):
if isinstance(row,list) == False:
#
# We've observed sometimes fields contain delimiter as a legitimate character, we need to be able to account for this and not tamper with the field values (unless necessary)
cols = self.split(row)
#cols = row.split(self.xchar)
else:
cols = row ;
return [ re.sub('[^\x00-\x7F,\n,\r,\v,\b,]',' ',col.strip()).strip().replace('"','') for col in cols]
#if isinstance(row,list) == False:
# return (self.xchar.join(r)).format('utf-8')
#else:
# return r
"""
This function performs a split of a record and tries to attempt to preserve the integrity of the data within i.e accounting for the double quotes.
@pre : self.xchar is not None
"""
def split (self,row):
pattern = "".join(["(?:^|",self.xchar,")(\"(?:[^\"]+|\"\")*\"|[^",self.xchar,"]*)"])
return re.findall(pattern,row.replace('\n',''))
class Writer:
def format(self,row,xchar):
if xchar is not None and isinstance(row,list):
return xchar.join(row)+'\n'
elif xchar is None and isinstance(row,dict):
row = json.dumps(row)
return row
"""
It is important to be able to archive data so as to insure that growth is controlled
Nothing in nature grows indefinitely neither should data being handled.
"""
def archive(self):
pass
def flush(self):
pass
"""
This class is designed to read data from an Http request file handler provided to us by flask
The file will be heald in memory and processed accordingly
NOTE: This is inefficient and can crash a micro-instance (becareful)
"""
class HttpRequestReader(Reader):
def __init__(self,**params):
self.file_length = 0
try:
#self.file = params['file']
#self.file.seek(0, os.SEEK_END)
#self.file_length = self.file.tell()
#print 'size of file ',self.file_length
self.content = params['file'].readlines()
self.file_length = len(self.content)
except Exception, e:
print "Error ... ",e
pass
def isready(self):
return self.file_length > 0
def read(self,size =-1):
i = 1
for row in self.content:
i += 1
if size == i:
break
yield row
"""
This class is designed to write data to a session/cookie
"""
class HttpSessionWriter(Writer):
"""
@param key required session key
"""
def __init__(self,**params):
self.session = params['queue']
self.session['sql'] = []
self.session['csv'] = []
self.tablename = re.sub('..+$','',params['filename'])
self.session['uid'] = params['uid']
#self.xchar = params['xchar']
def format_sql(self,row):
values = "','".join([col.replace('"','').replace("'",'') for col in row])
return "".join(["INSERT INTO :table VALUES('",values,"');\n"]).replace(':table',self.tablename)
def isready(self):
return True
def write(self,**params):
label = params['label']
row = params ['row']
if label == 'usable':
self.session['csv'].append(self.format(row,','))
self.session['sql'].append(self.format_sql(row))
"""
This class is designed to read data from disk (location on hard drive)
@pre : isready() == True
"""
class DiskReader(Reader) :
"""
@param path absolute path of the file to be read
"""
def __init__(self,**params):
Reader.__init__(self)
self.path = params['path'] ;
def isready(self):
return os.path.exists(self.path)
"""
This function reads the rows from a designated location on disk
@param size number of rows to be read, -1 suggests all rows
"""
def read(self,size=-1):
f = open(self.path,'rU')
i = 1
for row in f:
i += 1
if size == i:
break
yield row
f.close()
"""
This function writes output to disk in a designated location
"""
class DiskWriter(Writer):
def __init__(self,**params):
if 'path' in params:
self.path = params['path']
else:
self.path = None
if 'name' in params:
self.name = params['name'];
else:
self.name = None
if os.path.exists(self.path) == False:
os.mkdir(self.path)
"""
This function determines if the class is ready for execution or not
i.e it determines if the preconditions of met prior execution
"""
def isready(self):
p = self.path is not None and os.path.exists(self.path)
q = self.name is not None
return p and q
"""
This function writes a record to a designated file
@param label <passed|broken|fixed|stats>
@param row row to be written
"""
def write(self,**params):
label = params['label']
row = params['row']
xchar = None
if 'xchar' is not None:
xchar = params['xchar']
path = ''.join([self.path,os.sep,label])
if os.path.exists(path) == False:
os.mkdir(path) ;
path = ''.join([path,os.sep,self.name])
f = open(path,'a')
row = self.format(row,xchar);
f.write(row)
f.close()
"""
This class hierarchy is designed to handle interactions with a queue server using pika framework (our tests are based on rabbitmq)
"""
class MessageQueue:
def __init__(self,**params):
self.host= params['host']
self.uid = params['uid']
self.qid = params['qid']
def isready(self):
#self.init()
resp = self.connection is not None and self.connection.is_open
self.close()
return resp
def close(self):
if self.connection.is_closed == False :
self.channel.close()
self.connection.close()
"""
This class is designed to publish content to an AMQP (Rabbitmq)
The class will rely on pika to implement this functionality
We will publish information to a given queue for a given exchange
"""
class QueueWriter(MessageQueue,Writer):
def __init__(self,**params):
#self.host= params['host']
#self.uid = params['uid']
#self.qid = params['queue']
MessageQueue.__init__(self,**params);
def init(self,label=None):
properties = pika.ConnectionParameters(host=self.host)
self.connection = pika.BlockingConnection(properties)
self.channel = self.connection.channel()
self.info = self.channel.exchange_declare(exchange=self.uid,type='direct',durable=True)
if label is None:
self.qhandler = self.channel.queue_declare(queue=self.qid,durable=True)
else:
self.qhandler = self.channel.queue_declare(queue=label,durable=True)
self.channel.queue_bind(exchange=self.uid,queue=self.qhandler.method.queue)
"""
This function writes a stream of data to the a given queue
@param object object to be written (will be converted to JSON)
@TODO: make this less chatty
"""
def write(self,**params):
xchar = None
if 'xchar' in params:
xchar = params['xchar']
object = self.format(params['row'],xchar)
label = params['label']
self.init(label)
_mode = 2
if isinstance(object,str):
stream = object
_type = 'text/plain'
else:
stream = json.dumps(object)
if 'type' in params :
_type = params['type']
else:
_type = 'application/json'
self.channel.basic_publish(
exchange=self.uid,
routing_key=label,
body=stream,
properties=pika.BasicProperties(content_type=_type,delivery_mode=_mode)
);
self.close()
def flush(self,label):
self.init(label)
_mode = 1 #-- Non persistent
self.channel.queue_delete( queue=label);
self.close()
"""
This class will read from a queue provided an exchange, queue and host
@TODO: Account for security and virtualhosts
"""
class QueueReader(MessageQueue,Reader):
"""
@param host host
@param uid exchange identifier
@param qid queue identifier
"""
def __init__(self,**params):
#self.host= params['host']
#self.uid = params['uid']
#self.qid = params['qid']
MessageQueue.__init__(self,**params);
if 'durable' in params :
self.durable = True
else:
self.durable = False
self.size = -1
self.data = {}
def init(self,qid):
properties = pika.ConnectionParameters(host=self.host)
self.connection = pika.BlockingConnection(properties)
self.channel = self.connection.channel()
self.channel.exchange_declare(exchange=self.uid,type='direct',durable=True)
self.info = self.channel.queue_declare(queue=qid,durable=True)
"""
This is the callback function designed to process the data stream from the queue
"""
def callback(self,channel,method,header,stream):
r = []
if re.match("^\{|\[",stream) is not None:
r = json.loads(stream)
else:
r = stream
qid = self.info.method.queue
if qid not in self.data :
self.data[qid] = []
self.data[qid].append(r)
#
# We stop reading when the all the messages of the queue are staked
#
if self.size == len(self.data[qid]) or len(self.data[qid]) == self.info.method.message_count:
self.close()
"""
This function will read, the first message from a queue
@TODO:
Implement channel.basic_get in order to retrieve a single message at a time
Have the number of messages retrieved be specified by size (parameter)
"""
def read(self,size=-1):
r = {}
self.size = size
#
# We enabled the reader to be able to read from several queues (sequentially for now)
# The qid parameter will be an array of queues the reader will be reading from
#
if isinstance(self.qid,basestring) :
self.qid = [self.qid]
for qid in self.qid:
self.init(qid)
# r[qid] = []
if self.info.method.message_count > 0:
self.channel.basic_consume(self.callback,queue=qid,no_ack=False);
self.channel.start_consuming()
else:
pass
#self.close()
# r[qid].append( self.data)
return self.data
class QueueListener(QueueReader):
def init(self,qid):
properties = pika.ConnectionParameters(host=self.host)
self.connection = pika.BlockingConnection(properties)
self.channel = self.connection.channel()
self.channel.exchange_declare(exchange=self.uid,type='direct',durable=True )
self.info = self.channel.queue_declare(passive=True,exclusive=True,queue=qid)
self.channel.queue_bind(exchange=self.uid,queue=self.info.method.queue,routing_key=qid)
#self.callback = callback
def read(self):
self.init(self.qid)
self.channel.basic_consume(self.callback,queue=self.qid,no_ack=True);
self.channel.start_consuming()
"""
This class is designed to write output as sql insert statements
The class will inherit from DiskWriter with minor adjustments
@TODO: Include script to create the table if need be using the upper bound of a learner
"""
class SQLDiskWriter(DiskWriter):
def __init__(self,**args):
DiskWriter.__init__(self,**args)
self.tablename = re.sub('\..+$','',self.name).replace(' ','_')
"""
@param label
@param row
@param xchar
"""
def write(self,**args):
label = args['label']
row = args['row']
if label == 'usable':
values = "','".join([col.replace('"','').replace("'",'') for col in row])
row = "".join(["INSERT INTO :table VALUES('",values,"');\n"]).replace(':table',self.tablename)
args['row'] = row
DiskWriter.write(self,**args)
class Couchdb:
"""
@param uri host & port reference
@param uid user id involved
@param dbname database name (target)
"""
def __init__(self,**args):
uri = args['uri']
self.uid = args['uid']
dbname = args['dbname']
self.server = Server(uri=uri)
self.dbase = self.server.get_db(dbname)
if self.dbase.doc_exist(self.uid) == False:
self.dbase.save_doc({"_id":self.uid})
"""
Insuring the preconditions are met for processing
"""
def isready(self):
p = self.server.info() != {}
if p == False or self.dbase.dbname not in self.server.all_dbs():
return False
#
# At this point we are sure that the server is connected
# We are also sure that the database actually exists
#
q = self.dbase.doc_exist(self.uid)
if q == False:
return False
return True
def view(self,id,**args):
r =self.dbase.view(id,**args)
r = r.all()
return r[0]['value'] if len(r) > 0 else []
"""
This function will read an attachment from couchdb and return it to calling code. The attachment must have been placed before hand (otherwise oops)
@T: Account for security & access control
"""
class CouchdbReader(Couchdb,Reader):
"""
@param filename filename (attachment)
"""
def __init__(self,**args):
#
# setting the basic parameters for
Couchdb.__init__(self,**args)
if 'filename' in args :
self.filename = args['filename']
else:
self.filename = None
def isready(self):
#
# Is the basic information about the database valid
#
p = Couchdb.isready(self)
if p == False:
return False
#
# The database name is set and correct at this point
# We insure the document of the given user has the requested attachment.
#
doc = self.dbase.get(self.uid)
if '_attachments' in doc:
r = self.filename in doc['_attachments'].keys()
else:
r = False
return r
def stream(self):
content = self.dbase.fetch_attachment(self.uid,self.filename).split('\n') ;
i = 1
for row in content:
yield row
if size > 0 and i == size:
break
i = i + 1
def read(self,size=-1):
if self.filename is not None:
self.stream()
else:
return self.basic_read()
def basic_read(self):
document = self.dbase.get(self.uid)
del document['_id'], document['_rev']
return document
"""
This class will write on a couchdb document provided a scope
The scope is the attribute that will be on the couchdb document
"""
class CouchdbWriter(Couchdb,Writer):
"""
@param uri host & port reference
@param uid user id involved
@param filename filename (attachment)
@param dbname database name (target)
"""
def __init__(self,**args):
Couchdb.__init__(self,**args)
uri = args['uri']
self.uid = args['uid']
if 'filename' in args:
self.filename = args['filename']
else:
self.filename = None
dbname = args['dbname']
self.server = Server(uri=uri)
self.dbase = self.server.get_db(dbname)
#
# If the document doesn't exist then we should create it
#
"""
write a given attribute to a document database
@param label scope of the row repair|broken|fixed|stats
@param row row to be written
"""
def write(self,**params):
document = self.dbase.get(self.uid)
label = params['label']
row = params['row']
if label not in document :
document[label] = []
document[label].append(row)
self.dbase.save_doc(document)
def flush(self,**params) :
size = params['size'] if 'size' in params else 0
has_changed = False
document = self.dbase.get(self.uid)
for key in document:
if key not in ['_id','_rev','_attachments'] :
content = document[key]
else:
continue
if isinstance(content,list) and size > 0:
index = len(content) - size
content = content[index:]
document[key] = content
else:
document[key] = {}
has_changed = True
self.dbase.save_doc(document)
def archive(self,params=None):
document = self.dbase.get(self.uid)
content = {}
_doc = {}
for id in document:
if id in ['_id','_rev','_attachments'] :
_doc[id] = document[id]
else:
content[id] = document[id]
content = json.dumps(content)
document= _doc
now = str(datetime.today())
name = '-'.join([document['_id'] , now,'.json'])
self.dbase.save_doc(document)
self.dbase.put_attachment(document,content,name,'application/json')
class s3 :
"""
@TODO: Implement a search function for a file given a bucket??
"""
def __init__(self,args) :
"""
This function will extract a file or set of files from s3 bucket provided
@param access_key
@param secret_key
@param path location of the file
@param filter filename or filtering elements
"""
try:
self.s3 = boto.connect_s3(args['access_key'],args['secret_key'])
self.bucket = self.s3.get_bucket(args['bucket'].strip(),validate=False) if 'bucket' in args else None
# self.path = args['path']
self.filter = args['filter'] if 'filter' in args else None
self.filename = args['file'] if 'file' in args else None
except Exception as e :
self.s3 = None
self.bucket = None
print e
def buckets(self):
"""
This function is a wrapper around the bucket list of buckets for s3
"""
return self.s3.get_all_buckets()
class s3Reader(s3,Reader) :
"""
Because s3 contains buckets and files, reading becomes a tricky proposition :
- list files if file is None
- stream content if file is Not None
@TODO: support read from all buckets, think about it
"""
def __init__(self,args) :
s3.__init__(self,args)
def files(self):
r = []
try:
return [item.name for item in self.bucket if item.size > 0]
except Exception as e:
pass
return r
def stream(self,limit=-1):
"""
At this point we should stream a file from a given bucket
"""
key = self.bucket.get_key(self.filename.strip())
if key is None :
yield None
else:
count = 0
with smart_open(key) as remote_file:
for line in remote_file:
if count == limit and limit > 0 :
break
yield line
count += 1
def read(self,limit=-1) :
if self.filename is None :
#
# returning the list of files because no one file was specified.
return self.files()
else:
return self.stream(10)
"""
This class acts as a factory to be able to generate an instance of a Reader/Writer
Against a Queue,Disk,Cloud,Couchdb
The class doesn't enforce parameter validation, thus any error with the parameters sent will result in a null Object
"""
class Factory:
def instance(self,**args):
source = args['type']
params = args['args']
anObject = None
if source in ['HttpRequestReader','HttpSessionWriter']:
#
# @TODO: Make sure objects are serializable, be smart about them !!
#
aClassName = ''.join([source,'(**params)'])
else:
stream = json.dumps(params)
aClassName = ''.join([source,'(**',stream,')'])
try:
anObject = eval( aClassName)
#setattr(anObject,'name',source)
except Exception,e:
print ['Error ',e]
return anObject
class s3Writer(s3,Writer) :
def __init__(self,args) :
s3.__init__(self,args)
"""
This class implements a data-source handler that is intended to be used within the context of data processing, it allows to read/write anywhere transparently.
The class is a facade to a heterogeneous class hierarchy and thus simplifies how the calling code interacts with the class hierarchy
"""
class DataSource:
def __init__(self,sourceType='Disk',outputType='Disk',params={}):
self.Input = DataSourceFactory.instance(type=sourceType,args=params)
self.Output= DataSourceFactory.instance(type=outputType,args=params)
def read(self,size=-1):
return self.Input.read(size)
def write(self,**args):
self.Output.write(**args)
conf = json.loads(open('config.json').read())
#x = s3Reader( dict(conf,**{'bucket':'com.phi.sample.data','file':'Sample-Spreadsheet-5000-rows.csv'}))
x = s3Reader(conf)
print conf
print x.bucket.get_all_keys()
# r = x.read()
# for item in r :
# print item
#print buckets[1].get_key('Sample-Spreadsheet-5000-rows.csv')

174
transport/__init__.py
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@ -1,174 +0,0 @@
"""
Data Transport, The Phi Technology LLC
Steve L. Nyemba, steve@the-phi.com
This library is designed to serve as a wrapper to a set of supported data stores :
- couchdb
- mongodb
- Files (character delimited)
- Queues (RabbmitMq)
- Session (Flask)
- s3
- sqlite
The supported operations are read/write and providing meta data to the calling code
We separated reads from writes to mitigate accidents associated with writes.
Source Code is available under MIT License:
https://healthcareio.the-phi.com/data-transport
https://hiplab.mc.vanderbilt.edu/git/hiplab/data-transport
"""
import numpy as np
from transport import sql, nosql, cloud, other
import pandas as pd
import json
import os
from info import __version__,__author__,__email__,__license__,__app_name__,__whatsnew__
from transport.iowrapper import IWriter, IReader, IETL
from transport.plugins import PluginLoader
from transport import providers
import copy
from transport import registry
PROVIDERS = {}
def init():
global PROVIDERS
for _module in [cloud,sql,nosql,other] :
for _provider_name in dir(_module) :
if _provider_name.startswith('__') or _provider_name == 'common':
continue
PROVIDERS[_provider_name] = {'module':getattr(_module,_provider_name),'type':_module.__name__}
def _getauthfile (path) :
f = open(path)
_object = json.loads(f.read())
f.close()
return _object
def instance (**_args):
"""
This function returns an object of to read or write from a supported database provider/vendor
@provider provider
@context read/write (default is read)
@auth_file: Optional if the database information provided is in a file. Useful for not sharing passwords
kwargs These are arguments that are provider/vendor specific
"""
global PROVIDERS
# if not registry.isloaded () :
# if ('path' in _args and registry.exists(_args['path'] )) or registry.exists():
# registry.load() if 'path' not in _args else registry.load(_args['path'])
# print ([' GOT IT'])
# if 'label' in _args and registry.isloaded():
# _info = registry.get(_args['label'])
# if _info :
# #
# _args = dict(_args,**_info)
if 'auth_file' in _args:
if os.path.exists(_args['auth_file']) :
#
# @TODO: add encryption module and decryption to enable this to be secure
#
f = open(_args['auth_file'])
#_args = dict (_args,** json.loads(f.read()) )
#
# we overrite file parameters with arguments passed
_args = dict (json.loads(f.read()),**_args )
f.close()
else:
filename = _args['auth_file']
raise Exception(f" {filename} was not found or is invalid")
if 'provider' not in _args and 'auth_file' not in _args :
if not registry.isloaded () :
if ('path' in _args and registry.exists(_args['path'] )) or registry.exists():
registry.load() if 'path' not in _args else registry.load(_args['path'])
_info = {}
if 'label' in _args and registry.isloaded():
_info = registry.get(_args['label'])
else:
_info = registry.get()
if _info :
#
# _args = dict(_args,**_info)
_args = dict(_info,**_args) #-- we can override the registry parameters with our own arguments
if 'provider' in _args and _args['provider'] in PROVIDERS :
_info = PROVIDERS[_args['provider']]
_module = _info['module']
if 'context' in _args :
_context = _args['context']
else:
_context = 'read'
_pointer = getattr(_module,'Reader') if _context == 'read' else getattr(_module,'Writer')
_agent = _pointer (**_args)
#
loader = None
#
# @TODO:
# define a logger object here that will used by the wrapper
# this would allow us to know what the data-transport is doing and where/how it fails
#
# if 'plugins' in _args :
# _params = _args['plugins']
# if 'path' in _params and 'names' in _params :
# loader = PluginLoader(**_params)
# elif type(_params) == list:
# loader = PluginLoader()
# for _delegate in _params :
# loader.set(_delegate)
loader = None if 'plugins' not in _args else _args['plugins']
return IReader(_agent,loader) if _context == 'read' else IWriter(_agent,loader)
else:
#
# We can handle the case for an ETL object
#
raise Exception ("Missing or Unknown provider")
pass
class get :
"""
This class is just a wrapper to make the interface (API) more conversational and easy to understand
"""
@staticmethod
def reader (**_args):
if not _args or ('provider' not in _args and 'label' not in _args):
_args['label'] = 'default'
_args['context'] = 'read'
return instance(**_args)
@staticmethod
def writer(**_args):
"""
This function is a wrapper that will return a writer to a database. It disambiguates the interface
"""
if not _args or ('provider' not in _args and 'label' not in _args):
_args['label'] = 'default'
_args['context'] = 'write'
return instance(**_args)
@staticmethod
def etl (**_args):
if 'source' in _args and 'target' in _args :
return IETL(**_args)
else:
raise Exception ("Malformed input found, object must have both 'source' and 'target' attributes")
def supported ():
_info = {}
for _provider in PROVIDERS :
_item = PROVIDERS[_provider]
if _item['type'] not in _info :
_info[_item['type']] = []
_info[_item['type']].append(_provider)
_df = pd.DataFrame()
for _id in _info :
if not _df.shape[0] :
_df = pd.DataFrame(_info[_id],columns=[_id.replace('transport.','')])
else:
_df = pd.DataFrame(_info[_id],columns=[_id.replace('transport.','')]).join(_df, how='outer')
return _df.fillna('')
class factory :
pass
factory.instance = instance
init()

6
transport/cloud/__init__.py
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"""
Steve L. Nyemba, nyemba@gmail.com
This namespace implements support for cloud databases databricks,bigquery ...
"""
from . import bigquery, databricks, nextcloud, s3

159
transport/cloud/bigquery.py
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"""
Implementing support for google's bigquery
- cloud.bigquery.Read
- cloud.bigquery.Write
"""
import json
from google.oauth2 import service_account
from google.cloud import bigquery as bq
from multiprocessing import Lock, RLock
import pandas as pd
import pandas_gbq as pd_gbq
import numpy as np
import time
MAX_CHUNK = 2000000
class BigQuery:
def __init__(self,**_args):
path = _args['service_key'] if 'service_key' in _args else _args['private_key']
self.credentials = service_account.Credentials.from_service_account_file(path)
self.dataset = _args['dataset'] if 'dataset' in _args else None
self.path = path
self.dtypes = _args['dtypes'] if 'dtypes' in _args else None
self.table = _args['table'] if 'table' in _args else None
self.client = bq.Client.from_service_account_json(self.path)
def meta(self,**_args):
"""
This function returns meta data for a given table or query with dataset/table properly formatted
:param table name of the name WITHOUT including dataset
:param sql sql query to be pulled,
"""
table = _args['table'] if 'table' in _args else self.table
try:
if table :
_dataset = self.dataset if 'dataset' not in _args else _args['dataset']
sql = f"""SELECT column_name as name, data_type as type FROM {_dataset}.INFORMATION_SCHEMA.COLUMNS WHERE table_name = '{table}' """
_info = {'credentials':self.credentials,'dialect':'standard'}
return pd_gbq.read_gbq(sql,**_info).to_dict(orient='records')
# return self.read(sql=sql).to_dict(orient='records')
# ref = self.client.dataset(self.dataset).table(table)
# _schema = self.client.get_table(ref).schema
# return [{"name":_item.name,"type":_item.field_type,"description":( "" if not hasattr(_item,"description") else _item.description )} for _item in _schema]
else :
return []
except Exception as e:
return []
def has(self,**_args):
found = False
try:
_has = self.meta(**_args)
found = _has is not None and len(_has) > 0
except Exception as e:
pass
return found
class Reader (BigQuery):
"""
Implementing support for reading from bigquery, This class acts as a wrapper around google's API
"""
def __init__(self,**_args):
super().__init__(**_args)
def apply(self,sql):
return self.read(sql=sql)
def read(self,**_args):
SQL = None
table = self.table if 'table' not in _args else _args['table']
if 'sql' in _args :
SQL = _args['sql']
elif table:
table = "".join(["`",table,"`"]) if '.' in table else "".join(["`:dataset.",table,"`"])
SQL = "SELECT * FROM :table ".replace(":table",table)
if not SQL :
return None
if SQL and 'limit' in _args:
SQL += " LIMIT "+str(_args['limit'])
if (':dataset' in SQL or ':DATASET' in SQL) and self.dataset:
SQL = SQL.replace(':dataset',self.dataset).replace(':DATASET',self.dataset)
_info = {'credentials':self.credentials,'dialect':'standard'}
return pd_gbq.read_gbq(SQL,**_info) if SQL else None
# return self.client.query(SQL).to_dataframe() if SQL else None
class Writer (BigQuery):
"""
This class implements support for writing against bigquery
"""
lock = RLock()
def __init__(self,**_args):
super().__init__(**_args)
self.parallel = False if 'lock' not in _args else _args['lock']
self.table = _args['table'] if 'table' in _args else None
self.mode = {'if_exists':'append','chunksize':900000,'destination_table':self.table,'credentials':self.credentials}
self._chunks = 1 if 'chunks' not in _args else int(_args['chunks'])
self._location = 'US' if 'location' not in _args else _args['location']
def write(self,_data,**_args) :
"""
This function will perform a write to bigquery
:_data data-frame to be written to bigquery
"""
try:
if self.parallel or 'lock' in _args :
Writer.lock.acquire()
_args['table'] = self.table if 'table' not in _args else _args['table']
self._write(_data,**_args)
finally:
if self.parallel:
Writer.lock.release()
def submit(self,_sql):
"""
Write the output of a massive query to a given table, biquery will handle this as a job
This function will return the job identifier
"""
_config = bq.QueryJobConfig()
_config.destination = self.client.dataset(self.dataset).table(self.table)
_config.allow_large_results = True
# _config.write_disposition = bq.bq_consts.WRITE_APPEND
_config.dry_run = False
# _config.priority = 'BATCH'
_resp = self.client.query(_sql,location=self._location,job_config=_config)
return _resp.job_id
def status (self,_id):
return self.client.get_job(_id,location=self._location)
def _write(self,_info,**_args) :
_df = None
if type(_info) in [list,pd.DataFrame] :
if type(_info) == list :
_df = pd.DataFrame(_info)
elif type(_info) == pd.DataFrame :
_df = _info
if '.' not in _args['table'] :
self.mode['destination_table'] = '.'.join([self.dataset,_args['table']])
else:
self.mode['destination_table'] = _args['table'].strip()
if 'schema' in _args :
self.mode['table_schema'] = _args['schema']
#
# Let us insure that the types are somewhat compatible ...
# _map = {'INTEGER':np.int64,'DATETIME':'datetime64[ns]','TIMESTAMP':'datetime64[ns]','FLOAT':np.float64,'DOUBLE':np.float64,'STRING':str}
# _mode = copy.deepcopy(self.mode)
# _mode = self.mode
# _df.to_gbq(**self.mode) #if_exists='append',destination_table=partial,credentials=credentials,chunksize=90000)
#
# Let us adjust the chunking here
if 'if_exists' in _args :
self.mode['if_exists'] = _args['if_exists']
self._chunks = 10 if _df.shape[0] > MAX_CHUNK and self._chunks == 1 else self._chunks
_indexes = np.array_split(np.arange(_df.shape[0]),self._chunks)
for i in _indexes :
# _df.iloc[i].to_gbq(**self.mode)
pd_gbq.to_gbq(_df.iloc[i],**self.mode)
time.sleep(1)
pass

111
transport/cloud/databricks.py
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"""
This file implements databricks handling, This functionality will rely on databricks-sql-connector
LICENSE (MIT)
Copyright 2016-2020, The Phi Technology LLC
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
@TODO:
- Migrate SQLite to SQL hierarchy
- Include Write in Chunks from pandas
"""
import os
import sqlalchemy
# from transport.common import Reader,Writer
import pandas as pd
class Bricks:
"""
:host
:token
:database
:cluster_path
:table
"""
def __init__(self,**_args):
_host = _args['host']
_token= _args['token']
_cluster_path = _args['cluster_path']
self._schema = _args['schema'] if 'schema' in _args else _args['database']
_catalog = _args['catalog']
self._table = _args['table'] if 'table' in _args else None
#
# @TODO:
# Sometimes when the cluster isn't up and running it takes a while, the user should be alerted of this
#
_uri = f'''databricks+connector://token:{_token}@{_host}?http_path={_cluster_path}&catalog={_catalog}&schema={self._schema}'''
self._engine = sqlalchemy.create_engine (_uri)
pass
def meta(self,**_args):
table = _args['table'] if 'table' in _args else self._table
if not table :
return []
else:
if sqlalchemy.__version__.startswith('1.') :
_m = sqlalchemy.MetaData(bind=self._engine)
_m.reflect(only=[table])
else:
_m = sqlalchemy.MetaData()
_m.reflect(bind=self._engine)
#
# Let's retrieve te information associated with a table
#
return [{'name':_attr.name,'type':_attr.type} for _attr in _m.tables[table].columns]
def has(self,**_args):
return self.meta(**_args)
def apply(self,_sql):
try:
if _sql.lower().startswith('select') :
return pd.read_sql(_sql,self._engine)
except Exception as e:
pass
class Reader(Bricks):
"""
This class is designed for reads and will execute reads against a table name or a select SQL statement
"""
def __init__(self,**_args):
super().__init__(**_args)
def read(self,**_args):
limit = None if 'limit' not in _args else str(_args['limit'])
if 'sql' in _args :
sql = _args['sql']
elif 'table' in _args :
table = _args['table']
sql = f'SELECT * FROM {table}'
if limit :
sql = sql + f' LIMIT {limit}'
if 'sql' in _args or 'table' in _args :
return self.apply(sql)
else:
return pd.DataFrame()
pass
class Writer(Bricks):
def __init__(self,**_args):
super().__init__(**_args)
def write(self,_data,**_args):
"""
This data will write data to data-bricks against a given table. If the table is not specified upon initiazation, it can be specified here
_data: data frame to push to databricks
_args: chunks, table, schema
"""
_schema = self._schema if 'schema' not in _args else _args['schema']
_table = self._table if 'table' not in _args else _args['table']
_df = _data if type(_data) == pd.DataFrame else _data
if type(_df) == dict :
_df = [_df]
if type(_df) == list :
_df = pd.DataFrame(_df)
_df.to_sql(
name=_table,schema=_schema,
con=self._engine,if_exists='append',index=False);
pass

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transport/cloud/nextcloud.py
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"""
We are implementing transport to and from nextcloud (just like s3)
"""
import os
import sys
from transport.common import IEncoder
import pandas as pd
from io import StringIO
import json
import nextcloud_client as nextcloud
class Nextcloud :
def __init__(self,**_args):
pass
self._delimiter = None
self._handler = nextcloud.Client(_args['url'])
_uid = _args['uid']
_token = _args['token']
self._uri = _args['folder'] if 'folder' in _args else './'
if self._uri.endswith('/') :
self._uri = self._uri[:-1]
self._file = None if 'file' not in _args else _args['file']
self._handler.login(_uid,_token)
def close(self):
try:
self._handler.logout()
except Exception as e:
pass
class Reader(Nextcloud):
def __init__(self,**_args):
# self._file = [] if 'file' not in _args else _args['file']
super().__init__(**_args)
pass
def read(self,**_args):
_filename = self._file if 'file' not in _args else _args['file']
#
# @TODO: if _filename is none, an exception should be raised
#
_uri = '/'.join([self._uri,_filename])
if self._handler.get_file(_uri) :
#
#
_info = self._handler.file_info(_uri)
_content = self._handler.get_file_contents(_uri).decode('utf8')
if _info.get_content_type() == 'text/csv' :
#
# @TODO: enable handling of csv, xls, parquet, pickles
_file = StringIO(_content)
return pd.read_csv(_file)
else:
#
# if it is neither a structured document like csv, we will return the content as is
return _content
return None
class Writer (Nextcloud):
"""
This class will write data to an instance of nextcloud
"""
def __init__(self,**_args) :
super().__init__(**_args)
self
def write(self,_data,**_args):
"""
This function will upload a file to a given destination
:file has the uri of the location of the file
"""
_filename = self._file if 'file' not in _args else _args['file']
_uri = '/'.join([self._uri,_filename])
if type(_data) == pd.DataFrame :
f = StringIO()
_data.to_csv(f,index=False)
_content = f.getvalue()
elif type(_data) == dict :
_content = json.dumps(_data,cls=IEncoder)
else:
_content = str(_data)
self._handler.put_file_contents(_uri,_content)

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transport/cloud/s3.py
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"""
Data Transport - 1.0
Steve L. Nyemba, The Phi Technology LLC
This file is a wrapper around s3 bucket provided by AWS for reading and writing content
TODO:
- Address limitations that will properly read csv if it is stored with content type text/csv
"""
from datetime import datetime
import boto3
# from boto.s3.connection import S3Connection, OrdinaryCallingFormat
import numpy as np
import botocore
from smart_open import smart_open
import sys
import json
from io import StringIO
import pandas as pd
import json
class s3 :
"""
@TODO: Implement a search function for a file given a bucket??
"""
def __init__(self,**args) :
"""
This function will extract a file or set of files from s3 bucket provided
@param access_key
@param secret_key
@param path location of the file
@param filter filename or filtering elements
"""
try:
self._client = boto3.client('s3',aws_access_key_id=args['access_key'],aws_secret_access_key=args['secret_key'],region_name=args['region'])
self._bucket_name = args['bucket']
self._file_name = args['file']
self._region = args['region']
except Exception as e :
print (e)
pass
def has(self,**_args):
_found = None
try:
if 'file' in _args and 'bucket' in _args:
_found = self.meta(**_args)
elif 'bucket' in _args and not 'file' in _args:
_found = self._client.list_objects(Bucket=_args['bucket'])
elif 'file' in _args and not 'bucket' in _args :
_found = self.meta(bucket=self._bucket_name,file = _args['file'])
except Exception as e:
_found = None
pass
return type(_found) == dict
def meta(self,**args):
"""
This function will return information either about the file in a given bucket
:name name of the bucket
"""
_bucket = self._bucket_name if 'bucket' not in args else args['bucket']
_file = self._file_name if 'file' not in args else args['file']
_data = self._client.get_object(Bucket=_bucket,Key=_file)
return _data['ResponseMetadata']
def close(self):
self._client.close()
class Reader(s3) :
"""
Because s3 contains buckets and files, reading becomes a tricky proposition :
- list files if file is None
- stream content if file is Not None
@TODO: support read from all buckets, think about it
"""
def __init__(self,**_args) :
super().__init__(**_args)
def _stream(self,**_args):
"""
At this point we should stream a file from a given bucket
"""
_object = self._client.get_object(Bucket=_args['bucket'],Key=_args['file'])
_stream = None
try:
_stream = _object['Body'].read()
except Exception as e:
pass
if not _stream :
return None
if _object['ContentType'] in ['text/csv'] :
return pd.read_csv(StringIO(str(_stream).replace("\\n","\n").replace("\\r","").replace("\'","")))
else:
return _stream
def read(self,**args) :
_name = self._file_name if 'file' not in args else args['file']
_bucket = args['bucket'] if 'bucket' in args else self._bucket_name
return self._stream(bucket=_bucket,file=_name)
class Writer(s3) :
"""
"""
def __init__(self,**_args) :
super().__init__(**_args)
#
#
if not self.has(bucket=self._bucket_name) :
self.make_bucket(self._bucket_name)
def make_bucket(self,bucket_name):
"""
This function will create a folder in a bucket,It is best that the bucket is organized as a namespace
:name name of the folder
"""
self._client.create_bucket(Bucket=bucket_name,CreateBucketConfiguration={'LocationConstraint': self._region})
def write(self,_data,**_args):
"""
This function will write the data to the s3 bucket, files can be either csv, or json formatted files
"""
content = 'text/plain'
if type(_data) == pd.DataFrame :
_stream = _data.to_csv(index=False)
content = 'text/csv'
elif type(_data) == dict :
_stream = json.dumps(_data)
content = 'application/json'
else:
_stream = _data
file = StringIO(_stream)
bucket = self._bucket_name if 'bucket' not in _args else _args['bucket']
file_name = self._file_name if 'file' not in _args else _args['file']
self._client.put_object(Bucket=bucket, Key = file_name, Body=_stream,ContentType=content)
pass

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transport/common.py
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import json
import numpy as np
from datetime import datetime
class IEncoder (json.JSONEncoder):
def default (self,object):
if type(object) == np.integer :
return int(object)
elif type(object) == np.floating:
return float(object)
elif type(object) == np.ndarray :
return object.tolist()
elif type(object) == datetime :
return object.isoformat()
else:
return super(IEncoder,self).default(object)

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transport/duck.py
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"""
This file will be intended to handle duckdb database
"""
import duckdb
from transport.common import Reader,Writer
class Duck(Reader):
def __init__(self,**_args):
super().__init__(**_args)
self._path = None if 'path' not in _args else _args['path']
self._handler = duckdb.connect() if not self._path else duckdb.connect(self._path)
class DuckReader(Duck) :
def __init__(self,**_args):
super().__init__(**_args)
def read(self,**_args) :
pass

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transport/etl.py
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#!/usr/bin/env python
__doc__ = """
(c) 2018 - 2021 data-transport
steve@the-phi.com, The Phi Technology LLC
https://dev.the-phi.com/git/steve/data-transport.git
This program performs ETL between 9 supported data sources : Couchdb, Mongodb, Mysql, Mariadb, PostgreSQL, Netezza,Redshift, Sqlite, File
LICENSE (MIT)
Copyright 2016-2020, The Phi Technology LLC
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Usage :
transport --config <path-to-file.json> --procs <number-procs>
@TODO: Create tables if they don't exist for relational databases
example of configuration :
1. Move data from a folder to a data-store
transport [--folder <path> ] --config <config.json> #-- assuming the configuration doesn't have folder
transport --folder <path> --provider <postgresql|mongo|sqlite> --<database|db> <name> --table|doc <document_name>
In this case the configuration should look like :
{folder:..., target:{}}
2. Move data from one source to another
transport --config <file.json>
{source:{..},target:{..}} or [{source:{..},target:{..}},{source:{..},target:{..}}]
"""
import pandas as pd
import numpy as np
import json
import sys
import transport
import time
import os
from multiprocessing import Process
# SYS_ARGS = {}
# if len(sys.argv) > 1:
# N = len(sys.argv)
# for i in range(1,N):
# value = None
# if sys.argv[i].startswith('--'):
# key = sys.argv[i][2:] #.replace('-','')
# SYS_ARGS[key] = 1
# if i + 1 < N:
# value = sys.argv[i + 1] = sys.argv[i+1].strip()
# if key and value and not value.startswith('--'):
# SYS_ARGS[key] = value
# i += 2
class Transporter(Process):
"""
The transporter (Jason Stathem) moves data from one persistant store to another
- callback functions
:onFinish callback function when finished
:onError callback function when an error occurs
:source source data specification
:target destination(s) to move the data to
"""
def __init__(self,**_args):
super().__init__()
# self.onfinish = _args['onFinish']
# self._onerror = _args['onError']
self._source = _args['source']
self._target = _args['target']
#
# Let's insure we can support multiple targets
self._target = [self._target] if type(self._target) != list else self._target
pass
def run(self):
_reader = transport.get.etl(source=self._source,target=self._target)
#
if 'cmd' in self._source or 'query' in self._source :
_query = self._source['cmd'] if 'cmd' in self._source else self._source['query']
return _reader.read(**_query)
else:
return _reader.read()
# def _read(self,**_args):
# """
# This function
# """
# _reader = transport.factory.instance(**self._source)
# #
# # If arguments are provided then a query is to be executed (not just a table dump)
# if 'cmd' in self._source or 'query' in self._source :
# _query = self._source['cmd'] if 'cmd' in self._source else self._source['query']
# return _reader.read(**_query)
# else:
# return _reader.read()
# # return _reader.read() if 'query' not in self._source else _reader.read(**self._source['query'])
# def _delegate_write(self,_data,**_args):
# """
# This function will write a data-frame to a designated data-store, The function is built around a delegation design pattern
# :data data-frame or object to be written
# """
# if _data.shape[0] > 0 :
# for _target in self._target :
# if 'write' not in _target :
# _target['context'] = 'write'
# # _target['lock'] = True
# else:
# # _target['write']['lock'] = True
# pass
# _writer = transport.factory.instance(**_target)
# _writer.write(_data,**_args)
# if hasattr(_writer,'close') :
# _writer.close()
# def write(self,_df,**_args):
# """
# """
# SEGMENT_COUNT = 6
# MAX_ROWS = 1000000
# # _df = self.read()
# _segments = np.array_split(np.arange(_df.shape[0]),SEGMENT_COUNT) if _df.shape[0] > MAX_ROWS else np.array( [np.arange(_df.shape[0])])
# # _index = 0
# for _indexes in _segments :
# _fwd_args = {} if not _args else _args
# self._delegate_write(_df.iloc[_indexes],**_fwd_args)
# time.sleep(1)
# #
# # @TODO: Perhaps consider writing up each segment in a thread/process (speeds things up?)
# pass
def instance(**_args):
pthread = Transporter (**_args)
pthread.start()
return pthread
pass
# class Post(Process):
# def __init__(self,**args):
# super().__init__()
# self.store = args['target']
# if 'provider' not in args['target'] :
# pass
# self.PROVIDER = args['target']['type']
# # self.writer = transport.factory.instance(**args['target'])
# else:
# self.PROVIDER = args['target']['provider']
# self.store['context'] = 'write'
# # self.store = args['target']
# self.store['lock'] = True
# # self.writer = transport.instance(**args['target'])
# #
# # If the table doesn't exists maybe create it ?
# #
# self.rows = args['rows']
# # self.rows = args['rows'].fillna('')
# def log(self,**_args) :
# if ETL.logger :
# ETL.logger.info(**_args)
# def run(self):
# _info = {"values":self.rows} if 'couch' in self.PROVIDER else self.rows
# writer = transport.factory.instance(**self.store)
# writer.write(_info)
# writer.close()
# class ETL (Process):
# logger = None
# def __init__(self,**_args):
# super().__init__()
# self.name = _args['id'] if 'id' in _args else 'UNREGISTERED'
# # if 'provider' not in _args['source'] :
# # #@deprecate
# # self.reader = transport.factory.instance(**_args['source'])
# # else:
# # #
# # # This is the new interface
# # _args['source']['context'] = 'read'
# # self.reader = transport.instance(**_args['source'])
# #
# # do we have an sql query provided or not ....
# # self.sql = _args['source']['sql'] if 'sql' in _args['source'] else None
# # self.cmd = _args['source']['cmd'] if 'cmd' in _args['source'] else None
# # self._oargs = _args['target'] #transport.factory.instance(**_args['target'])
# self._source = _args ['source']
# self._target = _args['target']
# self._source['context'] = 'read'
# self._target['context'] = 'write'
# self.JOB_COUNT = _args['jobs']
# self.jobs = []
# # self.logger = transport.factory.instance(**_args['logger'])
# def log(self,**_args) :
# if ETL.logger :
# ETL.logger.info(**_args)
# def run(self):
# # if self.cmd :
# # idf = self.reader.read(**self.cmd)
# # else:
# # idf = self.reader.read()
# # idf = pd.DataFrame(idf)
# # # idf = idf.replace({np.nan: None}, inplace = True)
# # idf.columns = [str(name).replace("b'",'').replace("'","").strip() for name in idf.columns.tolist()]
# # self.log(rows=idf.shape[0],cols=idf.shape[1],jobs=self.JOB_COUNT)
# #
# # writing the data to a designated data source
# #
# try:
# _log = {"name":self.name,"rows":{"input":0,"output":0}}
# _reader = transport.factory.instance(**self._source)
# if 'table' in self._source :
# _df = _reader.read()
# else:
# _df = _reader.read(**self._source['cmd'])
# _log['rows']['input'] = _df.shape[0]
# #
# # Let's write the input data-frame to the target ...
# _writer = transport.factory.instance(**self._target)
# _writer.write(_df)
# _log['rows']['output'] = _df.shape[0]
# # self.log(module='write',action='partitioning',jobs=self.JOB_COUNT)
# # rows = np.array_split(np.arange(0,idf.shape[0]),self.JOB_COUNT)
# # #
# # # @TODO: locks
# # for i in np.arange(self.JOB_COUNT) :
# # # _id = ' '.join([str(i),' table ',self.name])
# # indexes = rows[i]
# # segment = idf.loc[indexes,:].copy() #.to_dict(orient='records')
# # _name = "partition-"+str(i)
# # if segment.shape[0] == 0 :
# # continue
# # proc = Post(target = self._oargs,rows = segment,name=_name)
# # self.jobs.append(proc)
# # proc.start()
# # self.log(module='write',action='working',segment=str(self.name),table=self.name,rows=segment.shape[0])
# # while self.jobs :
# # jobs = [job for job in proc if job.is_alive()]
# # time.sleep(1)
# except Exception as e:
# print (e)
# self.log(**_log)
# def is_done(self):
# self.jobs = [proc for proc in self.jobs if proc.is_alive()]
# return len(self.jobs) == 0
# def instance (**_args):
# """
# path to configuration file
# """
# _path = _args['path']
# _config = {}
# jobs = []
# if os.path.exists(_path) :
# file = open(_path)
# _config = json.loads(file.read())
# file.close()
# if _config and type
# def _instance(**_args):
# """
# :path ,index, id
# :param _info list of objects with {source,target}`
# :param logger
# """
# logger = _args['logger'] if 'logger' in _args else None
# if 'path' in _args :
# _info = json.loads((open(_args['path'])).read())
# if 'index' in _args :
# _index = int(_args['index'])
# _info = _info[_index]
# elif 'id' in _args :
# _info = [_item for _item in _info if '_id' in _item and _item['id'] == _args['id']]
# _info = _info[0] if _info else _info
# else:
# _info = _args['info']
# if logger and type(logger) != str:
# ETL.logger = logger
# elif logger == 'console':
# ETL.logger = transport.factory.instance(provider='console',context='write',lock=True)
# if type(_info) in [list,dict] :
# _info = _info if type(_info) != dict else [_info]
# #
# # The assumption here is that the objects within the list are {source,target}
# jobs = []
# for _item in _info :
# _item['jobs'] = 5 if 'procs' not in _args else int(_args['procs'])
# _job = ETL(**_item)
# _job.start()
# jobs.append(_job)
# return jobs
# else:
# return None
# if __name__ == '__main__' :
# _info = json.loads(open (SYS_ARGS['config']).read())
# index = int(SYS_ARGS['index']) if 'index' in SYS_ARGS else None
# procs = []
# for _config in _info :
# if 'source' in SYS_ARGS :
# _config['source'] = {"type":"disk.DiskReader","args":{"path":SYS_ARGS['source'],"delimiter":","}}
# _config['jobs'] = 3 if 'jobs' not in SYS_ARGS else int(SYS_ARGS['jobs'])
# etl = ETL (**_config)
# if index is None:
# etl.start()
# procs.append(etl)
# elif _info.index(_config) == index :
# # print (_config)
# procs = [etl]
# etl.start()
# break
# #
# #
# N = len(procs)
# while procs :
# procs = [thread for thread in procs if not thread.is_done()]
# if len(procs) < N :
# print (["Finished ",(N-len(procs)), " remaining ", len(procs)])
# N = len(procs)
# time.sleep(1)
# # print ("We're done !!")

120
transport/iowrapper.py
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"""
This class is a wrapper around read/write classes of cloud,sql,nosql,other packages
The wrapper allows for application of plugins as pre-post conditions.
NOTE: Plugins are converted to a pipeline, so we apply a pipeline when reading or writing:
- upon initialization we will load plugins
- on read/write we apply a pipeline (if passed as an argument)
"""
from transport.plugins import plugin, PluginLoader
import transport
from transport import providers
from multiprocessing import Process
import time
class IO:
"""
Base wrapper class for read/write and support for logs
"""
def __init__(self,_agent,plugins):
self._agent = _agent
if plugins :
self._init_plugins(plugins)
else:
self._plugins = None
def _init_plugins(self,_args):
"""
This function will load pipelined functions as a plugin loader
"""
if 'path' in _args and 'names' in _args :
self._plugins = PluginLoader(**_args)
else:
self._plugins = PluginLoader()
[self._plugins.set(_pointer) for _pointer in _args]
#
# @TODO: We should have a way to log what plugins are loaded and ready to use
def meta (self,**_args):
if hasattr(self._agent,'meta') :
return self._agent.meta(**_args)
return []
def close(self):
if hasattr(self._agent,'close') :
self._agent.close()
def apply(self):
"""
applying pre/post conditions given a pipeline expression
"""
for _pointer in self._plugins :
_data = _pointer(_data)
def apply(self,_query):
if hasattr(self._agent,'apply') :
return self._agent.apply(_query)
return None
def submit(self,_query):
return self.delegate('submit',_query)
def delegate(self,_name,_query):
if hasattr(self._agent,_name) :
pointer = getattr(self._agent,_name)
return pointer(_query)
return None
class IReader(IO):
"""
This is a wrapper for read functionalities
"""
def __init__(self,_agent,pipeline=None):
super().__init__(_agent,pipeline)
def read(self,**_args):
if 'plugins' in _args :
self._init_plugins(_args['plugins'])
_data = self._agent.read(**_args)
if self._plugins and self._plugins.ratio() > 0 :
_data = self._plugins.apply(_data)
#
# output data
return _data
class IWriter(IO):
def __init__(self,_agent,pipeline=None):
super().__init__(_agent,pipeline)
def write(self,_data,**_args):
if 'plugins' in _args :
self._init_plugins(_args['plugins'])
if self._plugins and self._plugins.ratio() > 0 :
_data = self._plugins.apply(_data)
self._agent.write(_data,**_args)
#
# The ETL object in its simplest form is an aggregation of read/write objects
# @TODO: ETL can/should aggregate a writer as a plugin and apply it as a process
class IETL(IReader) :
"""
This class performs an ETL operation by ineriting a read and adding writes as pipeline functions
"""
def __init__(self,**_args):
super().__init__(transport.get.reader(**_args['source']))
if 'target' in _args:
self._targets = _args['target'] if type(_args['target']) == list else [_args['target']]
else:
self._targets = []
self.jobs = []
#
# If the parent is already multiprocessing
self._hasParentProcess = False if 'hasParentProcess' not in _args else _args['hasParentProcess']
def read(self,**_args):
_data = super().read(**_args)
for _kwargs in self._targets :
self.post(_data,**_kwargs)
return _data
def post (self,_data,**_args) :
"""
This function returns an instance of a process that will perform the write operation
:_args parameters associated with writer object
"""
writer = transport.get.writer(**_args)
writer.write(_data)
writer.close()

12
transport/nosql/__init__.py
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@ -1,12 +0,0 @@
"""
Steve L. Nyemba, nyemba@gmail.com
This namespace implements support for cloud databases couchdb,mongodb, cloudant ...
"""
# from transport.nosql import couchdb
# from transport.nosql import mongodb
from . import mongodb
from . import couchdb
# import mongodb
# import couchdb
cloudant = couchdb

213
transport/nosql/couchdb.py
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"""
Data-Transport
Steve L. Nyemba, The Phi Technology
This file is a wrapper around couchdb using IBM Cloudant SDK that has an interface to couchdb
"""
import cloudant
import json
import sys
# from transport.common import Reader, Writer
from datetime import datetime
class Couch:
"""
This class is a wrapper for read/write against couchdb. The class captures common operations for read/write.
@param url host & port reference default http://localhost:5984
@param doc user id involved
@param dbname database name (target)
"""
def __init__(self,**args):
url = args['url'] if 'url' in args else 'http://localhost:5984'
self._id = args['doc']
dbname = args['dbname']
if 'username' not in args and 'password' not in args :
self.server = cloudant.CouchDB(None,None,url=url)
else:
self.server = cloudant.CouchDB(args['username'],args['password'],url=url)
self.server.connect()
if dbname in self.server.all_dbs() :
self.dbase = self.server.get(dbname,dbname,True)
#
# @TODO Check if the database exists ...
#
doc = cloudant.document.Document(self.dbase,self._id) #self.dbase.get(self._id)
if not doc.exists():
doc = self.dbase.create_document({"_id":self._id})
doc.save()
else:
self.dbase = None
"""
Insuring the preconditions are met for processing
"""
def isready(self):
p = self.server.metadata() != {}
if p == False or not self.dbase:
return False
#
# At this point we are sure that the server is connected
# We are also sure that the database actually exists
#
doc = cloudant.document.Document(self.dbase,self._id)
# q = self.dbase.all_docs(key=self._id)['rows']
# if not q :
if not doc.exists():
return False
return True
def view(self,**args):
"""
The function will execute a view (provivded a user is authenticated)
:id design document _design/xxxx (provide full name with _design prefix)
:view_name name of the view i.e
:key(s) key(s) to be used to filter the content
"""
document = cloudant.design_document.DesignDocument(self.dbase,args['id'])
document.fetch()
params = {'group_level':1,'group':True}
if 'key' in args :
params ['key'] = args['key']
elif 'keys' in args :
params['keys'] = args['keys']
return document.get_view(args['view_name'])(**params)['rows']
class Reader(Couch):
"""
This function will read an attachment from couchdb and return it to calling code. The attachment must have been placed before hand (otherwise oops)
@T: Account for security & access control
"""
def __init__(self,**args):
"""
@param filename filename (attachment)
"""
#
# setting the basic parameters for
Couch.__init__(self,**args)
if 'filename' in args :
self.filename = args['filename']
else:
self.filename = None
def stream(self):
#
# @TODO Need to get this working ...
#
document = cloudant.document.Document(self.dbase,self._id)
# content = self.dbase.fetch_attachment(self._id,self.filename).split('\n') ;
content = self.get_attachment(self.filename)
for row in content:
yield row
def read(self,**args):
if self.filename is not None:
self.stream()
else:
return self.basic_read()
def basic_read(self):
document = cloudant.document.Document(self.dbase,self._id)
# document = self.dbase.get(self._id)
if document.exists() :
document.fetch()
document = dict(document)
del document['_rev']
else:
document = {}
return document
class Writer(Couch):
"""
This class will write on a couchdb document provided a scope
The scope is the attribute that will be on the couchdb document
"""
def __init__(self,**args):
"""
@param uri host & port reference
@param uid user id involved
@param filename filename (attachment)
@param dbname database name (target)
"""
super().__init__(self,**args)
def set (self,info):
document = cloudant.document.Document(self.dbase,self._id)
if document.exists() :
keys = list(set(document.keys()) - set(['_id','_rev','_attachments']))
for id in keys :
document.field_set(document,id,None)
for id in info :
value = info[id]
document.info(document,id,value)
document.save()
pass
else:
_document = dict({"_id":self._id},**args)
document.create_document(_document)
def write(self,info):
"""
write a given attribute to a document database
@info object to be written to the to an attribute. this
"""
# document = self.dbase.get(self._id)
document = cloudant.document.Document(self.dbase,self._id) #.get(self._id)
if document.exists() is False :
document = self.dbase.create_document({"_id":self._id})
# label = params['label']
# row = params['row']
# if label not in document :
# document[label] = []
# document[label].append(row)
for key in info :
if key in document and type(document[key]) == list :
document[key] += info[key]
else:
document[key] = info[key]
document.save()
# self.dbase.bulk_docs([document])
# self.dbase.save_doc(document)
def upload(self,**args):
"""
:param name name of the file to be uploaded
:param data content of the file (binary or text)
:param content_type (default)
"""
mimetype = args['content_type'] if 'content_type' in args else 'text/plain'
document = cloudant.document.Document(self.dbase,self.uid)
document.put_attachment(self.dbase,args['filename'],mimetype,args['content'])
document.save()
def archive(self,params=None):
"""
This function will archive the document onto itself.
"""
# document = self.dbase.all_docs(self._id,include_docs=True)
document = cloudant.document.Document(self.dbase,self.filename)
document.fetch()
content = {}
# _doc = {}
for id in document:
if id not in ['_id','_rev','_attachments'] :
content[id] = document[id]
del document[id]
content = json.dumps(content)
# document= _doc
now = str(datetime.today())
name = '-'.join([document['_id'] , now,'.json'])
self.upload(filename=name,data=content,content_type='application/json')
# self.dbase.bulk_docs([document])
# self.dbase.put_attachment(document,content,name,'application/json')
# document.put_attachment(self.dbase,name,'application/json',content)
# document.save()

263
transport/nosql/mongodb.py
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"""
Data Transport - 1.0
Steve L. Nyemba, The Phi Technology LLC
This file is a wrapper around mongodb for reading/writing content against a mongodb server and executing views (mapreduce)
"""
from pymongo import MongoClient
import bson
from bson.objectid import ObjectId
from bson.binary import Binary
# import nujson as json
from datetime import datetime
import pandas as pd
import numpy as np
# import gridfs
from gridfs import GridFS
import sys
import json
import re
from multiprocessing import Lock, RLock
from transport.common import IEncoder
class Mongo :
lock = RLock()
"""
Basic mongodb functions are captured here
"""
def __init__(self,**args):
"""
:dbname database name/identifier
:host host and port of the database by default localhost:27017
:username username for authentication
:password password for current user
"""
self.host = 'localhost' if 'host' not in args else args['host']
if ':' not in self.host and 'port' in args :
self.host = ':'.join([self.host,str(args['port'])])
self.mechanism= 'SCRAM-SHA-256' if 'mechanism' not in args else args['mechanism']
# authSource=(args['authSource'] if 'authSource' in args else self.dbname)
self._lock = False if 'lock' not in args else args['lock']
self.dbname = None
username = password = None
if 'auth_file' in args :
_info = json.loads((open(args['auth_file'])).read())
else:
_info = {}
_args = dict(args,**_info)
_map = {'dbname':'db','database':'db','table':'uid','collection':'uid','col':'uid','doc':'uid'}
for key in _args :
if key in ['username','password'] :
username = _args['username'] if key=='username' else username
password = _args['password'] if key == 'password' else password
continue
value = _args[key]
if key in _map :
key = _map[key]
self.setattr(key,value)
#
# Let us perform aliasing in order to remain backwards compatible
self.dbname = self.db if hasattr(self,'db')else self.dbname
self.collection = _args['table'] if 'table' in _args else (_args['doc'] if 'doc' in _args else (_args['collection'] if 'collection' in _args else None))
if username and password :
self.client = MongoClient(self.host,
username=username,
password=password ,
authSource=self.authSource,
authMechanism=self.mechanism)
else:
self.client = MongoClient(self.host,maxPoolSize=10000)
self.db = self.client[self.dbname]
def isready(self):
p = self.dbname in self.client.list_database_names()
q = self.collection in self.client[self.dbname].list_collection_names()
return p and q
def setattr(self,key,value):
_allowed = ['host','port','db','doc','collection','authSource','mechanism']
if key in _allowed :
setattr(self,key,value)
pass
def close(self):
self.client.close()
def meta(self,**_args):
return []
class Reader(Mongo):
"""
This class will read from a mongodb data store and return the content of a document (not a collection)
"""
def __init__(self,**args):
Mongo.__init__(self,**args)
def read(self,**args):
if 'mongo' in args or 'cmd' in args or 'pipeline' in args:
#
# @TODO:
cmd = {}
if 'aggregate' not in cmd and 'aggregate' not in args:
cmd['aggregate'] = self.collection
elif 'aggregate' in args :
cmd['aggregate'] = args['aggregate']
if 'pipeline' in args :
cmd['pipeline']= args['pipeline']
if 'pipeline' not in args or 'aggregate' not in cmd :
cmd = args['mongo'] if 'mongo' in args else args['cmd']
if "aggregate" in cmd :
if "allowDiskUse" not in cmd :
cmd["allowDiskUse"] = True
if "cursor" not in cmd :
cmd["cursor"] = {}
r = []
out = self.db.command(cmd)
#@TODO: consider using a yield (generator) works wonders
while True :
if 'values' in out :
r += out['values']
if 'cursor' in out :
key = 'firstBatch' if 'firstBatch' in out['cursor'] else 'nextBatch'
else:
key = 'n'
if 'cursor' in out and out['cursor'][key] :
r += list(out['cursor'][key])
elif key in out and out[key]:
r.append (out[key])
# yield out['cursor'][key]
if key not in ['firstBatch','nextBatch'] or ('cursor' in out and out['cursor']['id'] == 0) :
break
else:
out = self.db.command({"getMore":out['cursor']['id'],"collection":out['cursor']['ns'].split(".")[-1]})
return pd.DataFrame(r)
else:
if 'table' in args or 'collection' in args :
if 'table' in args:
_uid = args['table']
elif 'collection' in args :
_uid = args['collection']
else:
_uid = self.collection
else:
_uid = self.collection
collection = self.db[_uid]
_filter = args['filter'] if 'filter' in args else {}
_df = pd.DataFrame(collection.find(_filter))
columns = _df.columns.tolist()[1:]
return _df[columns]
def view(self,**args):
"""
This function is designed to execute a view (map/reduce) operation
"""
pass
class Writer(Mongo):
"""
This class is designed to write to a mongodb collection within a database
"""
def __init__(self,**args):
Mongo.__init__(self,**args)
def upload(self,**args) :
"""
This function will upload a file to the current database (using GridFS)
:param data binary stream/text to be stored
:param filename filename to be used
:param encoding content_encoding (default utf-8)
"""
if 'encoding' not in args :
args['encoding'] = 'utf-8'
gfs = GridFS(self.db)
gfs.put(**args)
def archive(self):
"""
This function will archive documents to the
"""
collection = self.db[self.collection]
rows = list(collection.find())
for row in rows :
if type(row['_id']) == ObjectId :
row['_id'] = str(row['_id'])
stream = Binary(json.dumps(collection,cls=IEncoder).encode())
collection.delete_many({})
now = "-".join([str(datetime.now().year()),str(datetime.now().month), str(datetime.now().day)])
name = ".".join([self.collection,'archive',now])+".json"
description = " ".join([self.collection,'archive',str(len(rows))])
self.upload(filename=name,data=stream,description=description,content_type='application/json')
# gfs = GridFS(self.db)
# gfs.put(filename=name,description=description,data=stream,encoding='utf-8')
# self.write({{"filename":name,"file":stream,"description":descriptions}})
pass
def write(self,info,**_args):
"""
This function will write to a given collection i.e add a record to a collection (no updates)
@param info new record in the collection to be added
"""
# document = self.db[self.collection].find()
#collection = self.db[self.collection]
# if type(info) == list :
# self.db[self.collection].insert_many(info)
# else:
try:
if 'table' in _args or 'collection' in _args :
_uid = _args['table'] if 'table' in _args else _args['collection']
else:
_uid = self.collection if 'doc' not in _args else _args['doc']
if self._lock :
Mongo.lock.acquire()
if type(info) == list or type(info) == pd.DataFrame :
if type(info) == pd.DataFrame :
info = info.to_dict(orient='records')
# info if type(info) == list else info.to_dict(orient='records')
info = json.loads(json.dumps(info,cls=IEncoder))
self.db[_uid].insert_many(info)
else:
#
# sometimes a dictionary can have keys with arrays (odd shaped)
#
_keycount = len(info.keys())
_arraycount = [len(info[key]) for key in info if type(info[key]) in (list,np.array,np.ndarray)]
if _arraycount and len(_arraycount) == _keycount and np.max(_arraycount) == np.min(_arraycount) :
#
# In case an object with consistent structure is passed, we store it accordingly
#
self.write(pd.DataFrame(info),**_args)
else:
self.db[_uid].insert_one(json.loads(json.dumps(info,cls=IEncoder)))
finally:
if self._lock :
Mongo.lock.release()
def set(self,document):
"""
if no identifier is provided the function will delete the entire collection and set the new document.
Please use this function with great care (archive the content first before using it... for safety)
"""
collection = self.db[self.collection]
if collection.count_documents() > 0 and '_id' in document:
id = document['_id']
del document['_id']
collection.find_one_and_replace({'_id':id},document)
else:
#
# Nothing to be done if we did not find anything
#
pass
# collection.delete_many({})
# self.write(info)
def close(self):
Mongo.close(self)
# collecton.update_one({"_id":self.collection},document,True)

1
transport/other/__init__.py
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from . import files, http, rabbitmq, callback, files, console

49
transport/other/callback.py
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"""
This module uses callback architectural style as a writer to enable user-defined code to handle the output of a reader
The intent is to allow users to have control over the output of data to handle things like logging, encryption/decryption and other
"""
import queue
from threading import Thread, Lock
# from transport.common import Reader,Writer
import numpy as np
import pandas as pd
class Writer :
lock = Lock()
_queue = {'default':queue.Queue()}
def __init__(self,**_args):
self._cache = {}
self._callback = _args['callback'] if 'callback' in _args else None
self._id = _args['id'] if 'id' in _args else 'default'
if self._id not in Writer._queue :
Writer._queue[self._id] = queue.Queue()
thread = Thread(target=self._forward)
thread.start()
def _forward(self):
_q = Writer._queue[self._id]
_data = _q.get()
_q.task_done()
self._callback(_data)
def has(self,**_args) :
return self._callback is not None
def close(self):
"""
This will empty the queue and have it ready for another operation
"""
_q = Writer._queue[self._id]
with _q.mutex:
_q.queue.clear()
_q.all_tasks_done.notify_all()
def write(self,_data,**_args):
_id = _args['id'] if 'id' in _args else self._id
_q = Writer._queue[_id]
_q.put(_data)
_q.join()
# self.callback = print

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transport/other/console.py
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"""
This class uses classback pattern to allow output to be printed to the console (debugging)
"""
from . import callback
class Writer (callback.Writer):
def __init__(self,**_args):
super().__init__(callback=print)

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transport/other/files.py
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"""
This file is a wrapper around pandas built-in functionalities to handle character delimited files
"""
import pandas as pd
import numpy as np
import os
class File :
def __init__(self,**params):
"""
@param path absolute path of the file to be read
"""
self.path = params['path'] if 'path' in params else None
self.delimiter = params['delimiter'] if 'delimiter' in params else ','
def isready(self):
return os.path.exists(self.path)
def meta(self,**_args):
return []
class Reader (File):
"""
This class is designed to read data from disk (location on hard drive)
@pre : isready() == True
"""
def __init__(self,**_args):
super().__init__(**_args)
def read(self,**args):
_path = self.path if 'path' not in args else args['path']
_delimiter = self.delimiter if 'delimiter' not in args else args['delimiter']
return pd.read_csv(_path,delimiter=self.delimiter)
def stream(self,**args):
raise Exception ("streaming needs to be implemented")
class Writer (File):
"""
This function writes output to disk in a designated location. The function will write a text to a text file
- If a delimiter is provided it will use that to generate a xchar-delimited file
- If not then the object will be dumped as is
"""
# THREAD_LOCK = RLock()
def __init__(self,**_args):
super().__init__(**_args)
self._mode = 'w' if 'mode' not in _args else _args['mode']
def write(self,info,**_args):
"""
This function writes a record to a designated file
@param label <passed|broken|fixed|stats>
@param row row to be written
"""
try:
_delim = self.delimiter if 'delimiter' not in _args else _args['delimiter']
_path = self.path if 'path' not in _args else _args['path']
_mode = self._mode if 'mode' not in _args else _args['mode']
info.to_csv(_path,index=False,sep=_delim)
pass
except Exception as e:
#
# Not sure what should be done here ...
print (e)
pass
finally:
# DiskWriter.THREAD_LOCK.release()
pass

88
transport/other/http.py
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from flask import request, session
from datetime import datetime
import re
# from transport.common import Reader, Writer
import json
import requests
from io import StringIO
import pandas as pd
class Reader:
"""
This class is designed to read data from an Http request file handler provided to us by flask
The file will be heald in memory and processed accordingly
NOTE: This is inefficient and can crash a micro-instance (becareful)
"""
def __init__(self,**_args):
self._url = _args['url']
self._headers = None if 'headers' not in _args else _args['headers']
# def isready(self):
# return self.file_length > 0
def format(self,_response):
_mimetype= _response.headers['Content-Type']
if _mimetype == 'text/csv' or 'text/csv':
_content = _response.text
return pd.read_csv(StringIO(_content))
#
# @TODO: Add support for excel, JSON and other file formats that fit into a data-frame
#
return _response.text
def read(self,**_args):
if self._headers :
r = requests.get(self._url,headers = self._headers)
else:
r = requests.get(self._url,headers = self._headers)
return self.format(r)
class Writer:
"""
This class is designed to submit data to an endpoint (url)
"""
def __init__(self,**_args):
"""
@param key required session key
"""
self._url = _args['url']
self._name = _args['name']
self._method = 'post' if 'method' not in _args else _args['method']
# self.session = params['queue']
# self.session['sql'] = []
# self.session['csv'] = []
# self.tablename = re.sub('..+$','',params['filename'])
# self.session['uid'] = params['uid']
#self.xchar = params['xchar']
def format_sql(self,row):
values = "','".join([col.replace('"','').replace("'",'') for col in row])
return "".join(["INSERT INTO :table VALUES('",values,"');\n"]).replace(':table',self.tablename)
def isready(self):
return True
def write(self,_data,**_args):
#
#
_method = self._method if 'method' not in _args else _args['method']
_method = _method.lower()
_mimetype = 'text/csv'
if type(_data) == dict :
_mimetype = 'application/json'
_content = _data
else:
_content = _data.to_dict(orient='records')
_headers = {'Content-Type':_mimetype}
_pointer = getattr(requests,_method)
_pointer ({self._name:_content},headers=_headers)
# label = params['label']
# row = params ['row']
# if label == 'usable':
# self.session['csv'].append(self.format(row,','))
# self.session['sql'].append(self.format_sql(row))

272
transport/other/rabbitmq.py
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"""
Data Transport - 1.0
Steve L. Nyemba, The Phi Technology LLC
This file is a wrapper around rabbitmq server for reading and writing content to a queue (exchange)
"""
import pika
from datetime import datetime
import re
import json
import os
import sys
# if sys.version_info[0] > 2 :
# from transport.common import Reader, Writer
# else:
# from common import Reader, Writer
import json
from multiprocessing import RLock
class MessageQueue:
"""
This class hierarchy is designed to handle interactions with a queue server using pika framework (our tests are based on rabbitmq)
:host
:xid identifier of the exchange
:qid identifier of the queue
"""
def __init__(self,**params):
self.host= 'localhost' if 'host' not in params else params['host'] #-- location of the queue server
self.port= 5672 if 'port' not in params else params['port']
self.virtual_host = '/' if 'vhost' not in params else params['vhost']
self.exchange = params['exchange'] if 'exchange' in params else 'amq.direct' #-- exchange
self.queue = params['queue'] if 'queue' in params else 'demo'
self.connection = None
self.channel = None
self.name = self.__class__.__name__.lower() if 'name' not in params else params['name']
username = password = None
if 'username' in params :
username = params['username']
password = params['password']
if 'auth_file' in params :
_info = json.loads((open(params['auth_file'])).read())
username=_info['username']
password=_info['password']
self.virtual_host = _info['virtual_host'] if 'virtual_host' in _info else self.virtual_host
self.exchange = _info['exchange'] if 'exchange' in _info else self.exchange
self.queue = _info['queue'] if 'queue' in _info else self.queue
self.credentials= pika.PlainCredentials('guest','guest')
if 'username' in params :
self.credentials = pika.PlainCredentials(
params['username'],
('' if 'password' not in params else params['password'])
)
def init(self,label=None):
properties = pika.ConnectionParameters(host=self.host,port=self.port,virtual_host=self.virtual_host,
client_properties={'connection_name':self.name},
credentials=self.credentials)
self.connection = pika.BlockingConnection(properties)
self.channel = self.connection.channel()
self.info = self.channel.exchange_declare(exchange=self.exchange,exchange_type='direct',durable=True)
if label is None:
self.qhandler = self.channel.queue_declare(queue=self.queue,durable=True)
else:
self.qhandler = self.channel.queue_declare(queue=label,durable=True)
self.channel.queue_bind(exchange=self.exchange,queue=self.qhandler.method.queue)
def isready(self):
#self.init()
resp = self.connection is not None and self.connection.is_open
# self.close()
return resp
def finalize(self):
pass
def close(self):
if self.connection.is_closed == False :
self.channel.close()
self.connection.close()
class Writer(MessageQueue):
"""
This class is designed to publish content to an AMQP (Rabbitmq)
The class will rely on pika to implement this functionality
We will publish information to a given queue for a given exchange
"""
def __init__(self,**params):
#self.host= params['host']
#self.exchange = params['uid']
#self.queue = params['queue']
MessageQueue.__init__(self,**params);
self.init()
def write(self,data,_type='text/plain'):
"""
This function writes a stream of data to the a given queue
@param object object to be written (will be converted to JSON)
@TODO: make this less chatty
"""
stream = json.dumps(data) if isinstance(data,dict) else data
self.channel.basic_publish(
exchange=self.exchange,
routing_key=self.queue,
body=stream,
properties=pika.BasicProperties(content_type=_type,delivery_mode=2)
);
# self.close()
def flush(self):
self.init()
_mode = 1 #-- Non persistent
self.channel.queue_delete( queue=self.queue);
self.close()
class Reader(MessageQueue):
"""
This class will read from a queue provided an exchange, queue and host
@TODO: Account for security and virtualhosts
"""
def __init__(self,**params):
"""
@param host host
@param uid exchange identifier
@param qid queue identifier
"""
#self.host= params['host']
#self.exchange = params['uid']
#self.queue = params['qid']
MessageQueue.__init__(self,**params);
# self.init()
self.durable = False if 'durable' not in params else params['durable']
# if 'durable' in params :
# self.durable = True
# else:
# self.durable = False
self.size = -1
self.data = {}
# def init(self,qid):
# properties = pika.ConnectionParameters(host=self.host)
# self.connection = pika.BlockingConnection(properties)
# self.channel = self.connection.channel()
# self.channel.exchange_declare(exchange=self.exchange,type='direct',durable=True)
# self.info = self.channel.queue_declare(queue=qid,durable=True)
def callback(self,channel,method,header,stream):
"""
This is the callback function designed to process the data stream from the queue
"""
r = []
# if re.match("^\{|\[",stream) is not None:
if stream.startswith(b'{') or stream.startswith(b'['):
r = json.loads(stream)
else:
r = stream
qid = self.qhandler.method.queue
if qid not in self.data :
self.data[qid] = []
self.data[qid].append(r)
#
# We stop reading when the all the messages of the queue are staked
#
if self.size == len(self.data[qid]) or len(self.data[qid]) == self.info.method.message_count:
self.close()
def read(self,**args):
"""
This function will read, the first message from a queue
@TODO:
Implement channel.basic_get in order to retrieve a single message at a time
Have the number of messages retrieved be specified by size (parameter)
"""
r = {}
self.size = -1 if 'size' in args else int(args['size'])
#
# We enabled the reader to be able to read from several queues (sequentially for now)
# The qid parameter will be an array of queues the reader will be reading from
#
if isinstance(self.queue,str) :
self.queue = [self.queue]
for qid in self.queue:
self.init(qid)
# r[qid] = []
if self.qhandler.method.message_count > 0:
self.channel.basic_consume(queue=qid,on_message_callback=self.callback,auto_ack=False);
self.channel.start_consuming()
else:
pass
#self.close()
# r[qid].append( self.data)
return self.data
class QueueListener(MessageQueue):
lock = RLock()
"""
This class is designed to have an active listener (worker) against a specified Exchange/Queue
It is initialized as would any other object and will require a callback function to address the objects returned.
"""
def __init__(self,**args):
MessageQueue.__init__(self,**args)
self.listen = self.read
self.apply = args['apply'] if 'apply' in args else print
self.lock = False if 'lock' not in args else args['lock']
def finalize(self,channel,ExceptionReason):
pass
def callback(self,channel,method,header,stream) :
_info= {}
# if re.match("^\{|\[",stream) is not None:
if stream.startswith(b"[") or stream.startswith(b"{"):
_info = json.loads(stream)
else:
_info = stream
#
# At this point we should invoke the apply function with a lock if need be
# @TODO: Establish a vocabulary
if stream == b'QUIT' :
# channel.exit()
self.close()
if self.lock == True :
QueueListener.lock.acquire()
try:
#
# In case the user has not specified a function to apply the data against, it will simply be printed
#
self.apply(_info)
except Exception as e:
pass
if self.lock == True :
QueueListener.lock.release()
def read(self):
self.init(self.queue)
self.channel.basic_consume(self.queue,self.callback,auto_ack=True);
self.channel.start_consuming()
class Factory :
@staticmethod
def instance(**_args):
"""
:param count number of workers
:param apply function workers
"""
_apply = _args['apply']
_count = _args['count']
for i in np.arange(_count) :
_name = _args['name'] if 'name' in _args else 'worker_'+str(i)
transport.factory.instance(provider="rabbit",context="listener",apply=_apply,auth_file=_args['auth_file'])

129
transport/plugins/__init__.py
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"""
The functions within are designed to load external files and apply functions against the data
The plugins are applied as
- post-processing if we are reading data
- and pre-processing if we are writing data
The plugin will use a decorator to identify meaningful functions
@TODO: This should work in tandem with loggin (otherwise we don't have visibility into what is going on)
"""
import importlib as IL
import importlib.util
import sys
import os
class plugin :
"""
Implementing function decorator for data-transport plugins (post-pre)-processing
"""
def __init__(self,**_args):
"""
:name name of the plugin
:mode restrict to reader/writer
:about tell what the function is about
"""
self._name = _args['name']
self._about = _args['about']
self._mode = _args['mode'] if 'mode' in _args else 'rw'
def __call__(self,pointer,**kwargs):
def wrapper(_args,**kwargs):
return pointer(_args,**kwargs)
#
# @TODO:
# add attributes to the wrapper object
#
setattr(wrapper,'transport',True)
setattr(wrapper,'name',self._name)
setattr(wrapper,'mode',self._mode)
setattr(wrapper,'about',self._about)
return wrapper
class PluginLoader :
"""
This class is intended to load a plugin and make it available and assess the quality of the developed plugin
"""
def __init__(self,**_args):
"""
:path location of the plugin (should be a single file)
:_names of functions to load
"""
_names = _args['names'] if 'names' in _args else None
path = _args['path'] if 'path' in _args else None
self._names = _names if type(_names) == list else [_names]
self._modules = {}
self._names = []
if path and os.path.exists(path) and _names:
for _name in self._names :
spec = importlib.util.spec_from_file_location('private', path)
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module) #--loads it into sys.modules
if hasattr(module,_name) :
if self.isplugin(module,_name) :
self._modules[_name] = getattr(module,_name)
else:
print ([f'Found {_name}', 'not plugin'])
else:
#
# @TODO: We should log this somewhere some how
print (['skipping ',_name, hasattr(module,_name)])
pass
else:
#
# Initialization is empty
self._names = []
pass
def set(self,_pointer) :
"""
This function will set a pointer to the list of modules to be called
This should be used within the context of using the framework as a library
"""
_name = _pointer.__name__
self._modules[_name] = _pointer
self._names.append(_name)
def isplugin(self,module,name):
"""
This function determines if a module is a recognized plugin
:module module object loaded from importlib
:name name of the functiion of interest
"""
p = type(getattr(module,name)).__name__ =='function'
q = hasattr(getattr(module,name),'transport')
#
# @TODO: add a generated key, and more indepth validation
return p and q
def has(self,_name):
"""
This will determine if the module name is loaded or not
"""
return _name in self._modules
def ratio (self):
"""
This functiion determines how many modules loaded vs unloaded given the list of names
"""
_n = len(self._names)
return len(set(self._modules.keys()) & set (self._names)) / _n
def apply(self,_data):
for _name in self._modules :
_pointer = self._modules[_name]
#
# @TODO: add exception handling
_data = _pointer(_data)
return _data
# def apply(self,_data,_name):
# """
# This function applies an external module function against the data.
# The responsibility is on the plugin to properly return data, thus responsibility is offloaded
# """
# try:
# _pointer = self._modules[_name]
# _data = _pointer(_data)
# except Exception as e:
# pass
# return _data

50
transport/providers/__init__.py
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"""
This file is intended to aggregate all we can about the framework in terms of support
"""
BIGQUERY='bigquery'
POSTGRESQL = 'postgresql'
MONGODB = 'mongodb'
HTTP='http'
BIGQUERY ='bigquery'
FILE = 'file'
ETL = 'etl'
SQLITE = 'sqlite'
SQLITE3= 'sqlite3'
DUCKDB = 'duckdb'
REDSHIFT = 'redshift'
NETEZZA = 'netezza'
MYSQL = 'mysql'
MARIADB= MYSQL
COUCHDB = 'couchdb'
CONSOLE = 'console'
ETL = 'etl'
TRANSPORT = ETL
NEXTCLOUD = 'nextcloud'
S3 = 's3'
CALLBACK = 'callback'
CONSOLE = 'console'
RABBITMQ = 'rabbitmq'
DATABRICKS = 'databricks'
MSSQL ='sqlserver'
SQLSERVER ='sqlserver'
#
# synonyms of the above
BQ = BIGQUERY
MONGO = MONGODB
FERRETDB= MONGODB
PG = POSTGRESQL
PSQL = POSTGRESQL
PGSQL = POSTGRESQL
AWS_S3 = 's3'
RABBIT = RABBITMQ
# QLISTENER = 'qlistener'

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transport/registry.py
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import os
import json
from info import __version__
import copy
import transport
"""
This class manages data from the registry and allows (read only)
@TODO: add property to the DATA attribute
"""
REGISTRY_PATH=os.sep.join([os.environ['HOME'],'.data-transport'])
#
# This path can be overriden by an environment variable ...
#
if 'DATA_TRANSPORT_REGISTRY_PATH' in os.environ :
REGISTRY_PATH = os.environ['DATA_TRANSPORT_REGISTRY_PATH']
REGISTRY_FILE= 'transport-registry.json'
DATA = {}
def isloaded ():
return DATA not in [{},None]
def exists (path=REGISTRY_PATH) :
"""
This function determines if there is a registry at all
"""
p = os.path.exists(path)
q = os.path.exists( os.sep.join([path,REGISTRY_FILE]))
return p and q
def load (_path=REGISTRY_PATH):
global DATA
if exists(_path) :
path = os.sep.join([_path,REGISTRY_FILE])
f = open(path)
DATA = json.loads(f.read())
f.close()
def init (email,path=REGISTRY_PATH,override=False):
"""
Initializing the registry and will raise an exception in the advent of an issue
"""
p = '@' in email
q = False if '.' not in email else email.split('.')[-1] in ['edu','com','io','ai','org']
if p and q :
_config = {"email":email,'version':__version__}
if not os.path.exists(path):
os.makedirs(path)
filename = os.sep.join([path,REGISTRY_FILE])
if not os.path.exists(filename) or override == True :
f = open(filename,'w')
f.write( json.dumps(_config))
f.close()
# _msg = f"""{CHECK_MARK} Successfully wrote configuration to {path} from {email}"""
else:
raise Exception (f"""Unable to write configuration, Please check parameters (or help) and try again""")
else:
raise Exception (f"""Invalid Input, {email} is not well formatted, provide an email with adequate format""")
def lookup (label):
global DATA
return label in DATA
def get (label='default') :
global DATA
return copy.copy(DATA[label]) if label in DATA else {}
def set (label, auth_file, default=False,path=REGISTRY_PATH) :
"""
This function will add a label (auth-file data) into the registry and can set it as the default
"""
if label == 'default' :
raise Exception ("""Invalid label name provided, please change the label name and use the switch""")
reg_file = os.sep.join([path,REGISTRY_FILE])
if os.path.exists (auth_file) and os.path.exists(path) and os.path.exists(reg_file):
f = open(auth_file)
_info = json.loads(f.read())
f.close()
f = open(reg_file)
_config = json.loads(f.read())
f.close()
#
# set the proposed label
_object = transport.factory.instance(**_info)
if _object :
_config[label] = _info
if default :
_config['default'] = _info
#
# now we need to write this to the location
f = open(reg_file,'w')
f.write(json.dumps(_config))
f.close()
else:
raise Exception( f"""Unable to load file locate at {path},\nLearn how to generate auth-file with wizard found at https://healthcareio.the-phi.com/data-transport""")
pass
else:
pass
pass

18
transport/sql/__init__.py
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@ -1,18 +0,0 @@
"""
This namespace/package wrap the sql functionalities for a certain data-stores
- netezza, postgresql, mysql and sqlite
- mariadb, redshift (also included)
"""
from . import postgresql, mysql, netezza, sqlite, sqlserver, duckdb
#
# Creating aliases for support of additional data-store providerss
#
mariadb = mysql
redshift = postgresql
sqlite3 = sqlite
# from transport import sql

139
transport/sql/common.py
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"""
This file encapsulates common operations associated with SQL databases via SQLAlchemy
"""
import sqlalchemy as sqa
from sqlalchemy import text
import pandas as pd
class Base:
def __init__(self,**_args):
self._host = _args['host'] if 'host' in _args else 'localhost'
self._port = None
self._database = _args['database']
self._table = _args['table'] if 'table' in _args else None
self._engine= sqa.create_engine(self._get_uri(**_args),future=True)
def _set_uri(self,**_args) :
"""
:provider provider
:host host and port
:account account user/pwd
"""
_account = _args['account'] if 'account' in _args else None
_host = _args['host']
_provider = _args['provider'].replace(':','').replace('/','').strip()
def _get_uri(self,**_args):
"""
This function will return the formatted uri for the sqlAlchemy engine
"""
raise Exception ("Function Needs to be implemented ")
def meta (self,**_args):
"""
This function returns the schema (table definition) of a given table
:table optional name of the table (can be fully qualified)
"""
_table = self._table if 'table' not in _args else _args['table']
_schema = []
if _table :
if sqa.__version__.startswith('1.') :
_handler = sqa.MetaData(bind=self._engine)
_handler.reflect()
else:
#
# sqlalchemy's version 2.+
_handler = sqa.MetaData()
_handler.reflect(bind=self._engine)
#
# Let us extract the schema with the native types
_map = {'BIGINT':'INTEGER','TEXT':'STRING','DOUBLE_PRECISION':'FLOAT','NUMERIC':'FLOAT','DECIMAL':'FLOAT','REAL':'FLOAT'}
_schema = [{"name":_attr.name,"type":_map.get(str(_attr.type),str(_attr.type))} for _attr in _handler.tables[_table].columns]
return _schema
def has(self,**_args):
return self.meta(**_args)
def apply(self,sql):
"""
Executing sql statement that returns query results (hence the restriction on sql and/or with)
:sql SQL query to be exectued
@TODO: Execution of stored procedures
"""
if sql.lower().startswith('select') or sql.lower().startswith('with') :
return pd.read_sql(sql,self._engine)
else:
_handler = self._engine.connect()
_handler.execute(text(sql))
_handler.commit ()
_handler.close()
return None
class SQLBase(Base):
def __init__(self,**_args):
super().__init__(**_args)
def get_provider(self):
raise Exception ("Provider Needs to be set ...")
def get_default_port(self) :
raise Exception ("default port needs to be set")
def _get_uri(self,**_args):
_host = self._host
_account = ''
if self._port :
_port = self._port
else:
_port = self.get_default_port()
_host = f'{_host}:{_port}'
if 'username' in _args :
_account = ''.join([_args['username'],':',_args['password'],'@'])
_database = self._database
_provider = self.get_provider().replace(':','').replace('/','')
# _uri = [f'{_provider}:/',_account,_host,_database]
# _uri = [_item.strip() for _item in _uri if _item.strip()]
# return '/'.join(_uri)
return f'{_provider}://{_host}/{_database}' if _account == '' else f'{_provider}://{_account}{_host}/{_database}'
class BaseReader(SQLBase):
def __init__(self,**_args):
super().__init__(**_args)
def read(self,**_args):
"""
This function will read a query or table from the specific database
"""
if 'sql' in _args :
sql = _args['sql']
else:
_table = _args['table'] if 'table' in _args else self._table
sql = f'SELECT * FROM {_table}'
return self.apply(sql)
class BaseWriter (SQLBase):
"""
This class implements SQLAlchemy support for Writting to a data-store (RDBMS)
"""
def __init__(self,**_args):
super().__init__(**_args)
def write(self,_data,**_args):
if type(_data) == dict :
_df = pd.DataFrame(_data)
elif type(_data) == list :
_df = pd.DataFrame(_data)
else:
_df = _data.copy()
#
# We are assuming we have a data-frame at this point
#
_table = _args['table'] if 'table' in _args else self._table
_mode = {'chunksize':2000000,'if_exists':'append','index':False}
for key in ['if_exists','index','chunksize'] :
if key in _args :
_mode[key] = _args[key]
# if 'schema' in _args :
# _mode['schema'] = _args['schema']
# if 'if_exists' in _args :
# _mode['if_exists'] = _args['if_exists']
_df.to_sql(_table,self._engine,**_mode)

24
transport/sql/duckdb.py
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"""
This module implements the handler for duckdb (in memory or not)
"""
from transport.sql.common import Base, BaseReader, BaseWriter
class Duck :
def __init__(self,**_args):
#
# duckdb with none as database will operate as an in-memory database
#
self.database = _args['database'] if 'database' in _args else ''
def get_provider(self):
return "duckdb"
def _get_uri(self,**_args):
return f"""duckdb:///{self.database}"""
class Reader(Duck,BaseReader) :
def __init__(self,**_args):
Duck.__init__(self,**_args)
BaseReader.__init__(self,**_args)
class Writer(Duck,BaseWriter):
def __init__(self,**_args):
Duck.__init__(self,**_args)
BaseWriter.__init__(self,**_args)

18
transport/sql/mysql.py
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"""
This file implements support for mysql and maria db (with drivers mysql+mysql)
"""
from transport.sql.common import BaseReader, BaseWriter
# import mysql.connector as my
class MYSQL:
def get_provider(self):
return "mysql+mysqlconnector"
def get_default_port(self):
return "3306"
class Reader(MYSQL,BaseReader) :
def __init__(self,**_args):
super().__init__(**_args)
class Writer(MYSQL,BaseWriter) :
def __init__(self,**_args):
super().__init__(**_args)

15
transport/sql/netezza.py
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import nzpy as nz
from transport.sql.common import BaseReader, BaseWriter
class Netezza:
def get_provider(self):
return 'netezza+nzpy'
def get_default_port(self):
return '5480'
class Reader(Netezza,BaseReader) :
def __init__(self,**_args):
super().__init__(**_args)
class Writer(Netezza,BaseWriter):
def __init__(self,**_args):
super().__init__(**_args)

22
transport/sql/postgresql.py
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from transport.sql.common import BaseReader , BaseWriter
from psycopg2.extensions import register_adapter, AsIs
import numpy as np
register_adapter(np.int64, AsIs)
class PG:
def __init__(self,**_args):
super().__init__(**_args)
def get_provider(self):
return "postgresql"
def get_default_port(self):
return "5432"
class Reader(PG,BaseReader) :
def __init__(self,**_args):
super().__init__(**_args)
class Writer(PG,BaseWriter):
def __init__(self,**_args):
super().__init__(**_args)

25
transport/sql/sqlite.py
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import sqlalchemy
import pandas as pd
from transport.sql.common import Base, BaseReader, BaseWriter
class SQLite (BaseReader):
def __init__(self,**_args):
super().__init__(**_args)
if 'path' in _args :
self._database = _args['path']
if 'database' in _args :
self._database = _args['database']
def _get_uri(self,**_args):
path = self._database
return f'sqlite:///{path}' # ensure this is the correct path for the sqlite file.
class Reader(SQLite,BaseReader):
def __init__(self,**_args):
super().__init__(**_args)
# def read(self,**_args):
# sql = _args['sql']
# return pd.read_sql(sql,self._engine)
class Writer (SQLite,BaseWriter):
def __init__(self,**_args):
super().__init__(**_args)

24
transport/sql/sqlserver.py
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"""
Handling Microsoft SQL Server via pymssql driver/connector
"""
import sqlalchemy
import pandas as pd
from transport.sql.common import Base, BaseReader, BaseWriter
class MsSQLServer:
def __init__(self,**_args) :
super().__init__(**_args)
pass
def get_provider(self):
# mssql+pymssql://scott:tiger@hostname:port/dbname"
return "mssql+pymssql"
def get_default_port(self):
return "1433"
class Reader (MsSQLServer,BaseReader):
def __init__(self,**_args):
super().__init__(**_args)
class Writer (MsSQLServer,BaseWriter):
def __init__(self,**_args):
super().__init__(**_args)
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