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87 lines
2.5 KiB
Markdown
87 lines
2.5 KiB
Markdown
# Introduction
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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** and **SQL** data stores and leverages **pandas**
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The supported data store providers :
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| Provider | Underlying Drivers | Description |
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| :---- | :----: | ----: |
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| sqlite| Native SQLite|SQLite3|
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| postgresql| psycopg2 | PostgreSQL
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| redshift| psycopg2 | Amazon Redshift
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| s3| boto3 | Amazon Simple Storage Service
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| netezza| nzpsql | IBM Neteeza
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| Files: CSV, TSV| pandas| pandas data-frame
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| Couchdb| cloudant | Couchbase/Couchdb
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| mongodb| pymongo | Mongodb
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| mysql| mysql| Mysql
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| bigquery| google-bigquery| Google BigQuery
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| mariadb| mysql| Mariadb
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| rabbitmq|pika| RabbitMQ Publish/Subscribe
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# Why Use Data-Transport ?
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Mostly data scientists that don't really care about the underlying database and would like to manipulate data transparently.
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1. Familiarity with **pandas data-frames**
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2. Connectivity **drivers** are included
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3. Useful for data migrations or ETL
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# Usage
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## Installation
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Within the virtual environment perform the following :
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pip install git+https://dev.the-phi.com/git/steve/data-transport.git
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## In code (Embedded)
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**Reading/Writing Mongodb**
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For this example we assume here we are tunneling through port 27018 and there is not access control:
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```
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import transport
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reader = factory.instance(provider='mongodb',context='read',host='localhost',port='27018',db='example',doc='logs')
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df = reader.read() #-- reads the entire collection
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print (df.head())
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#
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#-- Applying mongodb command
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PIPELINE = [{"$group":{"_id":None,"count":{"$sum":1}}}]
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_command_={"cursor":{},"allowDiskUse":True,"aggregate":"logs","pipeline":PIPLINE}
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df = reader.read(mongo=_command)
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print (df.head())
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reader.close()
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```
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**Writing to Mongodb**
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---
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```
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import transport
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improt pandas as pd
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writer = factory.instance(provider='mongodb',context='write',host='localhost',port='27018',db='example',doc='logs')
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df = pd.DataFrame({"names":["steve","nico"],"age":[40,30]})
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writer.write(df)
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writer.close()
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```
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#
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# reading from postgresql
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pgreader = factory.instance(type='postgresql',database=<database>,table=<table_name>)
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pg.read() #-- will read the table by executing a SELECT
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pg.read(sql=<sql query>)
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#
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# Reading a document and executing a view
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#
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document = dreader.read()
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result = couchdb.view(id='<design_doc_id>',view_name=<view_name',<key=value|keys=values>)
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