pygrametl的使用--python

pygrametl是一个python的package用于ETL（Extract-Transform-Load ）

简例

import MySQLdb
from pygrametl.datasources import SQLSource

conn = MySQLdb.connect(host="localhost", user="root", passwd="123456", db="ustcck", charset="utf8")

sql = "SELECT * FROM student;"
newnames = 'ID', 'Name', 'Gender'
resultsSource = SQLSource(connection=conn, query=sql, names=newnames)
print type(resultsSource)

for row in resultsSource:
    print row
    print row["Name"]

1.安装以及安装测试

$ pip install pygrametl

>>> import pygrametl
>>>

ok了！

2.pygrametl 支持多种数据源

'BackgroundSource', 'CSVSource', 'CrossTabbingSource', 'DictReader', 'DynamicForEachSource', 'FilteringSource', 'HashJoiningSource', 'JoiningSource', 'MergeJoiningSource', 'Process', 'ProcessSource', 'Queue', 'RoundRobinSource', 'SQLSource', 'TransformingSource', 'TypedCSVSource', 'UnionSource'..........

如：

（1）

import psycopg2
import pygrametl
from pygrametl.datasources import SQLSource

conn = psycopg2.connect(database="db", user="dbuser", password="dbpass")

sql = "SELECT * FROM table;"
resultsSource = SQLSource(connection=conn, query=sql)

（2）

import pygrametl
from pygrametl.datasources import CSVSource

resultsSource = CSVSource(csvfile=open('ResultsFile.csv', 'r', 16384), delimiter=',')

3.Dimension（维度）

pygrametl 提供了数据仓库维度交互，提供了一个在table中执行增删改查操作的接口。

使用Dimension两步走：

（1）创建ConnectionWrapper

（2）必须指定table的名字，key以及表中其他的列

下面是一个使用Dimension将相应的数据的插入到对应维度的操作的例子：（假设table已经存在，维度有'productid', 'name', 'category', 'price'）

import psycopg2
import pygrametl
from pygrametl.tables import Dimension

products = [
    {'name' : 'Calvin and Hobbes 1', 'category' : 'Comic', 'price' : '10'},
    {'name' : 'Calvin and Hobbes 2', 'category' : 'Comic', 'price' : '10'},
    {'name' : 'Calvin and Hobbes 3', 'category' : 'Comic', 'price' : '10'},
    {'name' : 'Cake and Me', 'category' : 'Cookbook', 'price' : '15'},
    {'name' : 'French Cooking', 'category' : 'Cookbook', 'price' : '50'},
    {'name' : 'Sushi', 'category' : 'Cookbook', 'price' : '30'},
    {'name' : 'Nineteen Eighty-Four', 'category' : 'Novel', 'price' : '15'},
    {'name' : 'The Lord of the Rings', 'category' : 'Novel', 'price' : '60'}
]

pgconn = psycopg2.connect("""host='localhost' dbname='dw' user='dwuser'
                          password='dwpass'""")

conn = pygrametl.ConnectionWrapper(connection=pgconn)

productDimension = Dimension(
    name='product',
    key='productid',
    attributes=['name', 'category', 'price'],
    lookupatts=['name'])

for row in products:
    productDimension.insert(row)
conn.commit()
conn.close()

4.FactTable。给个例子你就知道了

例：

三步走：

（1）建立一个connection

（2）创建一个ConnectionWrapper实例

（3）创建 FactTable

import MySQLdb
import pygrametl
from pygrametl.tables import FactTable

conn = MySQLdb.connect(host="localhost", user="root", passwd="123", db="ustcck", charset="utf8")

conn = pygrametl.ConnectionWrapper(connection=conn)

factTable = FactTable(
    name='facttable',
    measures=['price'],
    keyrefs=['storeid', 'productid', 'dateid'])

# A list of facts are ready to inserted into the fact table
facts = [{'storeid': 1, 'productid': 13, 'dateid': 4, 'price': 50},
         {'storeid': 2, 'productid': 7, 'dateid': 4, 'price': 75},
         {'storeid': 1, 'productid': 7, 'dateid': 4, 'price': 50},
         {'storeid': 3, 'productid': 9, 'dateid': 4, 'price': 25}]


# The facts can be inserted using the insert method, before committing to DB
for row in facts:
    factTable.insert(row)
conn.commit()

# Lookup retunes all both keys and measures given only the keys
factTable.lookup({'storeid': 1, 'productid': 13, 'dateid': 4})

# If a set of facts contain facts already existing in the database can the
# ensure method be used instead of calling lookup and insert manually, we
# also rename 'itemid' to 'productid' using the name mapping feature
newFacts = [{'storeid': 2, 'itemid': 7, 'dateid': 4, 'price': 75},
            {'storeid': 1, 'itemid': 7, 'dateid': 4, 'price': 50},
            {'storeid': 1, 'itemid': 2, 'dateid': 7, 'price': 150},
            {'storeid': 3, 'itemid': 3, 'dateid': 6, 'price': 100}]

for row in newFacts:
    # The second argument forces FactTable.ensure to not only match the keys
    # for facts to be considered equal, but also checks if the measures are
    # the same for facts with the same key, and if not raises a ValueError
    factTable.ensure(row, True, {'productid': 'itemid'})
conn.commit()
conn.close()

5.Bulk Loading（大面积载入数据）

三个类可以用于Bulk Loading： BulkDimension, BulkFactTable, and CachedBulkDimension

#MySQLdb
def mysqlbulkloader(name, attributes, fieldsep, rowsep, nullval, filehandle):
    global connection
    cursor = connection.cursor()
    sql = "LOAD DATA LOCAL INFILE '%s' INTO TABLE %s FIELDS TERMINATED BY '%s' LINES TERMINATED BY '%s' (%s);" % \
            (filehandle, name, fieldsep, rowsep, ', '.join(attributes))
    cursor.execute(sql)

参数含义：Parameters
name – 表名
attributes –属性序列列表
fieldsep – 属性分隔符
rowsep – row分隔符
nullval – null的替代
filehandle – 文件名或者文件对象