peewee是一个轻量级的ORM。用的是大名鼎鼎的sqlalchemy内核，采用纯python编写，显得十分轻便。为了后续方便查看，在这里简单记录下~~

peewee不仅轻量级，还提供了多种数据库的访问，如SqliteDatabase（file or memory）、MYSQLDatabase、PostgresqlDatabase；

接下来就从API上路吧~~~

1. class  fn---To express functions in peewee, use the fn object。

For example:

Functions can be used as any part of a query:

select
where
group_by
order_by
having
update query
insert query

# user‘s username starts with a ‘g‘ or a ‘G‘:
fn.Lower(fn.Substr(User.username, 1, 1)) == ‘g‘

2.表达式支持的操作符

a:

Comparison
Meaning
==
x equals y
<
x is less than y
<=
x is less than or equal to y
>
x is greater than y
>=
x is greater than or equal to y
!=
x is not equal to y
<<
x IN y, where y is a list or query
>>
x IS y, where y is None/NULL
%
x LIKE y where y may contain wildcards
**
x ILIKE y where y may contain wildcards
 b:

Employee.select().where(Employee.salary.between(50000, 60000))
note: 由于sqlite的like函数在默认下是大小写不敏感的，如果想实现大小写搜索，需要用’*‘做通配符。
3.实现用户自定义的操作
Here is how you might add support for modulo and regexp in SQLite:

from peewee import *
from peewee import Expression # the building block for expressions

OP_MOD = 'mod'
OP_REGEXP = 'regexp'

def mod(lhs, rhs):
    return Expression(lhs, OP_MOD, rhs)

def regexp(lhs, rhs):
    return Expression(lhs, OP_REGEXP, rhs)

SqliteDatabase.register_ops({OP_MOD: '%', OP_REGEXP: 'REGEXP'}) #添加 %、regexp操作
Now you can use these custom operators to build richer queries:

# users with even ids
User.select().where(mod(User.id, 2) == 0)

# users whose username starts with a number
User.select().where(regexp(User.username, '[0-9].*'))
4.Joining tables
There are three types of joins by default:

JOIN_INNER (default)
JOIN_LEFT_OUTER
JOIN_FULL
Here are some examples:

User.select().join(Blog).where(
    (User.is_staff == True) & (Blog.status == LIVE))

Blog.select().join(User).where(
    (User.is_staff == True) & (Blog.status == LIVE))

subquery：
staff = User.select().where(User.is_staff == True)
Blog.select().where(
    (Blog.status == LIVE) & (Blog.user << staff))

补充：在没有通过ForeignKeyField产生外键的多个models中，也可以做join操作，如：
# No explicit foreign key between these models.
OutboundShipment.select().join(InboundShipment, on=(
    OutboundShipment.barcode == InboundShipment.barcode))

5.Performing advanced queries
To create arbitrarily complex queries, simply use python’s bitwise “and” and “or” operators:

sq = User.select().where(
    (User.is_staff == True) |
    (User.is_superuser == True))
The WHERE clause will look something like:

WHERE (is_staff = ? OR is_superuser = ?)
In order to negate an expression, use the bitwise “invert” operator:

staff_users = User.select().where(User.is_staff == True)
Tweet.select().where(
    ~(Tweet.user << staff_users))
This query generates roughly the following SQL:

SELECT t1.* FROM blog AS t1
WHERE
    NOT t1.user_id IN (
        SELECT t2.id FROM user AS t2 WHERE t2.is_staff = ?)
Rather complex lookups are possible:

sq = User.select().where(
    ((User.is_staff == True) | (User.is_superuser == True)) &
    (User.join_date >= datetime(2009, 1, 1))
This generates roughly the following SQL:

SELECT * FROM user
WHERE (
    (is_staff = ? OR is_superuser = ?) AND
    (join_date >= ?))


6.Aggregating records
#Suppose you have some users and want to get a list of them along with the count of tweets each has made. First I will show y#ou the shortcut:

query = User.select().annotate(Tweet)
This is equivalent to the following:

query = User.select(
    User, fn.Count(Tweet.id).alias('count')
).join(Tweet).group_by(User)

#You can also specify a custom aggregator. In the following query we will annotate the users with the date of their most rece#nt tweet:

query = User.select().annotate(
    Tweet, fn.Max(Tweet.created_date).alias('latest'))

#Conversely, sometimes you want to perform an aggregate query that returns a scalar value, like the “max id”. Queries like #this can be executed by using the aggregate() method:

most_recent_tweet = Tweet.select().aggregate(fn.Max(Tweet.created_date))
7.Window functions
#peewee comes with basic support for SQL window functions, which can be created by calling fn.over() and passing in your parti#tioning or ordering parameters.

# Get the list of employees and the average salary for their dept.
query = (Employee
         .select(
             Employee.name,
             Employee.department,
             Employee.salary,
             fn.Avg(Employee.salary).over(
                 partition_by=[Employee.department]))
         .order_by(Employee.name))

# Rank employees by salary.
query = (Employee
         .select(
             Employee.name,
             Employee.salary,
             fn.rank().over(
                 order_by=[Employee.salary])))
有待继续考究啊~~ url：<a target=_blank href=http://www.mamicode.com/"http://www.postgresql.org/docs/9.1/static/tutorial-window.html">http://www.postgresql.org/docs/9.1/static/tutorial-window.html