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:
Peewee expression | Equivalent SQL |
---|---|
fn.Count(Tweet.id).alias(‘count‘) | Count(t1."id") AS count |
fn.Lower(fn.Substr(User.username, 1, 1)) | Lower(Substr(t1."username", 1, 1)) |
fn.Rand().alias(‘random‘) | Rand() AS random |
fn.Stddev(Employee.salary).alias(‘sdv‘) | Stddev(t1."salary") AS sdv |
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 |
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 tablesThere 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 queriesTo 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.postgresql.org/docs/9.1/static/tutorial-window.html">http://www.postgresql.org/docs/9.1/static/tutorial-window.html</a>
python轻量级ORM---peewee,布布扣,bubuko.com
原文地址:http://blog.csdn.net/hzrandd/article/details/31742367