data-transport/README.md

205 lines
6.6 KiB
Markdown

# 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** and **SQL** data stores and leverages **pandas**.
The supported data store providers :
| Provider | Underlying Drivers | Description |
| :---- | :----: | ----: |
| sqlite| Native SQLite|SQLite3|
| postgresql| psycopg2 | PostgreSQL
| redshift| psycopg2 | Amazon Redshift
| s3| boto3 | Amazon Simple Storage Service
| netezza| nzpsql | IBM Neteeza
| Files: CSV, TSV| pandas| pandas data-frame
| Couchdb| cloudant | Couchbase/Couchdb
| mongodb| pymongo | Mongodb
| mysql| mysql| Mysql
| bigquery| google-bigquery| Google BigQuery
| mariadb| mysql| Mariadb
| rabbitmq|pika| RabbitMQ Publish/Subscribe
# Why Use Data-Transport ?
Mostly data scientists that don't really care about the underlying database and would like to manipulate data transparently.
1. Familiarity with **pandas data-frames**
2. Connectivity **drivers** are included
3. Mining data from various sources
4. Useful for data migrations or ETL
# Usage
## Installation
Within the virtual environment perform the following :
pip install git+https://github.com/lnyemba/data-transport.git
Once installed **data-transport** can be used as a library in code or a command line interface (CLI), as a CLI it is used for ETL and requires a configuration file.
## Data Transport as a Library (in code)
---
The data-transport can be used within code as a library, and offers the following capabilities:
* Read/Write against [mongodb](https://github.com/lnyemba/data-transport/wiki/mongodb)
* Read/Write against tranditional [RDBMS](https://github.com/lnyemba/data-transport/wiki/rdbms)
* Read/Write against [bigquery](https://github.com/lnyemba/data-transport/wiki/bigquery)
* ETL CLI/Code [ETL](https://github.com/lnyemba/data-transport/wiki/etl)
* Support for pre/post conditions i.e it is possible to specify queries to run before or after a read or write
The read/write functions make data-transport a great candidate for **data-science**; **data-engineering** or all things pertaining to data. It enables operations across multiple data-stores(relational or not)
## ETL
**Embedded in Code**
It is possible to perform ETL within custom code as follows :
```
import transport
import time
_info = [{source:{'provider':'sqlite','path':'/home/me/foo.csv','table':'me',"pipeline":{"pre":[],"post":[]}},target:{provider:'bigquery',private_key='/home/me/key.json','table':'me','dataset':'mydataset'}}, ...]
procs = transport.factory.instance(provider='etl',info=_info)
#
#
while procs:
procs = [pthread for pthread in procs if pthread.is_alive()]
time.sleep(1)
```
**Command Line Interface (CLI):**
---
The CLI program is called **transport** and it requires a configuration file. The program is intended to move data from one location to another. Supported data stores are in the above paragraphs.
```
[
{
"id":"logs",
"source":{
"provider":"postgresql","context":"read","database":"mydb",
"cmd":{"sql":"SELECT * FROM logs limit 10"}
},
"target":{
"provider":"bigquery","private_key":"/bgqdrive/account/bq-service-account-key.json",
"dataset":"mydataset"
}
},
]
```
Assuming the above content is stored in a file called **etl-config.json**, we would perform the following in a terminal window:
```
[steve@data-transport]$ transport --config ./etl-config.json [--index <value>]
```
**Reading/Writing Mongodb**
For this example we assume here we are tunneling through port 27018 and there is not access control:
```
import transport
reader = factory.instance(provider='mongodb',context='read',host='localhost',port='27018',db='example',doc='logs')
df = reader.read() #-- reads the entire collection
print (df.head())
#
#-- Applying mongodb command
PIPELINE = [{"$group":{"_id":None,"count":{"$sum":1}}}]
_command_={"cursor":{},"allowDiskUse":True,"aggregate":"logs","pipeline":PIPLINE}
df = reader.read(mongo=_command)
print (df.head())
reader.close()
```
**Read/Writing to Mongodb**
---
Scenario 1: Mongodb with security in place
1. Define an authentication file on disk
The semantics of the attributes are provided by mongodb, please visit [mongodb documentation](https://mongodb.org/docs). In this example the file is located on _/transport/mongo.json_
<div style="display:grid; grid-template-columns:60% auto; gap:4px">
<div>
<b>configuration file</b>
```
{
"username":"me","password":"changeme",
"mechanism":"SCRAM-SHA-1",
"authSource":"admin"
}
```
<b>Connecting to Mongodb </b>
```
import transport
PIPELINE = ... #-- do this yourself
MONGO_KEY = '/transport/mongo.json'
mreader = transport.factory.instance(provider=transport.providers.MONGODB,auth_file=MONGO_KEY,context='read',db='mydb',doc='logs')
_aggregateDF = mreader.read(mongo=PIPELINE) #--results of a aggregate pipeline
_collectionDF= mreader.read()
```
In order to enable write, change **context** attribute to **'read'**.
</div>
<div>
- The configuration file is in JSON format
- The commands passed to mongodb are the same as you would if you applied runCommand in mongodb
- The output is a pandas data-frame
- By default the transport reads, to enable write operations use **context='write'**
|parameters|description |
| --- | --- |
|db| Name of the database|
|port| Port number to connect to
|doc| Name of the collection of documents|
|username|Username |
|password|password|
|authSource|user database that has authentication info|
|mechanism|Mechnism used for authentication|
**NOTE**
Arguments like **db** or **doc** can be placed in the authentication file
</div>
</div>
**Limitations**
Reads and writes aren't encapsulated in the same object, this is to allow the calling code to deliberately perform actions and hopefully minimize accidents associated with data wrangling.
```
import transport
improt pandas as pd
writer = factory.instance(provider=transport.providers.MONGODB,context='write',host='localhost',port='27018',db='example',doc='logs')
df = pd.DataFrame({"names":["steve","nico"],"age":[40,30]})
writer.write(df)
writer.close()
```
#
# reading from postgresql
pgreader = factory.instance(type='postgresql',database=<database>,table=<table_name>)
pg.read() #-- will read the table by executing a SELECT
pg.read(sql=<sql query>)
#
# Reading a document and executing a view
#
document = dreader.read()
result = couchdb.view(id='<design_doc_id>',view_name=<view_name',<key=value|keys=values>)