


What is Schema?
No matter what application we make, as long as we deal with user input, there is one principle - never trust the user's input data. This means that we need to strictly verify user input. In web development, input data is generally sent to the back-end API in JSON form, and the API needs to verify the input data. Usually I add a lot of judgments and various ifs, which makes the code very ugly. Is there a more elegant way to verify user data? Schema comes in handy. This article mainly introduces the detailed explanation of the method of using Python to operate Schema in MySQL database design. It is quite good. I will share it with you here for the reference of friends who need it.
㈠MySQLdb part
Table structure:
mysql> use sakila; mysql> desc actor; +-------------+----------------------+------+-----+-------------------+-----------------------------+ | Field | Type | Null | Key | Default | Extra | +-------------+----------------------+------+-----+-------------------+-----------------------------+ | actor_id | smallint(5) unsigned | NO | PRI | NULL | auto_increment | | first_name | varchar(45) | NO | | NULL | | | last_name | varchar(45) | NO | MUL | NULL | | | last_update | timestamp | NO | | CURRENT_TIMESTAMP | on update CURRENT_TIMESTAMP | +-------------+----------------------+------+-----+-------------------+-----------------------------+ 4 rows in set (0.00 sec)
Database connection module:
[root@DataHacker ~]# cat dbapi.py #!/usr/bin/env ipython #coding = utf-8 #Author: linwaterbin@gmail.com #Time: 2014-1-29 import MySQLdb as dbapi USER = 'root' PASSWD = 'oracle' HOST = '127.0.0.1' DB = 'sakila' conn = dbapi.connect(user=USER,passwd=PASSWD,host=HOST,db=DB)
1 Print column metadata
[root@DataHacker ~]# cat QueryColumnMetaData.py #!/usr/bin/env ipython from dbapi import * cur = conn.cursor() statement = """select * from actor limit 1""" cur.execute(statement) print "output column metadata....." print for record in cur.description: print record cur.close() conn.close()
1.) After calling execute(), the cursor should set its description attribute
2.) It is a tuple with a total of 7 columns: column name, type, display size, internal size , precision, range, and a flag indicating whether to accept null values
[root@DataHacker ~]# chmod +x QueryColumnMetaData.py [root@DataHacker ~]# ./QueryColumnMetaData.py output column metadata..... ('actor_id', 2, 1, 5, 5, 0, 0) ('first_name', 253, 8, 45, 45, 0, 0) ('last_name', 253, 7, 45, 45, 0, 0) ('last_update', 7, 19, 19, 19, 0, 0)
##2 Accessing column values by column name
In [1]: from dbapi import * In [2]: cur = conn.cursor() In [3]: v_sql = "select actor_id,last_name from actor limit 2" In [4]: cur.execute(v_sql) Out[4]: 2L In [5]: results = cur.fetchone() In [6]: print results[0] 58 In [7]: print results[1] AKROYDWe can use the cursorclass attribute to return it as a dictionary
In [2]: import MySQLdb.cursors In [3]: import MySQLdb In [4]: conn = MySQLdb.connect(user='root',passwd='oracle',host='127.0.0.1',db='sakila',cursorclass=MySQLdb.cursors.DictCursor) In [5]: cur = conn.cursor() In [6]: v_sql = "select actor_id,last_name from actor limit 2" In [7]: cur.execute(v_sql) Out[7]: 2L In [8]: results = cur.fetchone() In [9]: print results['actor_id'] 58 In [10]: print results['last_name'] AKROYD
#㈡ SQLAlchemy--SQL Alchemist
Although SQL has International standards, but unfortunately, each database manufacturer has different interpretations of these standards, and they all implement their own private syntax based on the standards. In order to hide the differences between different SQL "dialects", people have developed tools such as SQLAlchemy
[root@DataHacker Desktop]# cat sa.py import sqlalchemy as sa engine = sa.create_engine('mysql://root:oracle@127.0.0.1/testdb',pool_recycle=3600) metadata = sa.MetaData()
example 1: Table definition
In [3]: t = Table('t',metadata, ...: Column('id',Integer), ...: Column('name',VARCHAR(20)), ...: mysql_engine='InnoDB', ...: mysql_charset='utf8' ...: ) In [4]: t.create(bind=engine)
example 2: Table deletion
有2种方式,其一: In [5]: t.drop(bind=engine,checkfirst=True) 另一种是: In [5]: metadata.drop_all(bind=engine,checkfirst=True),其中可以借助tables属性指定要删除的对象
example 3: 5 kinds of constraints
3 .1 primary key 下面2种方式都可以,一个是列级,一个是表级 In [7]: t_pk_col = Table('t_pk_col',metadata,Column('id',Integer,primary_key=True),Column('name',VARCHAR(20))) In [8]: t_pk_col.create(bind=engine) In [9]: t_pk_tb = Table('t_pk_01',metadata,Column('id',Integer),Column('name',VARCHAR(20)),PrimaryKeyConstraint('id','name',name='prikey')) In [10]: t_pk_tb.create(bind=engine) 3.2 Foreign Key In [13]: t_fk = Table('t_fk',metadata,Column('id',Integer,ForeignKey('t_pk.id'))) In [14]: t_fk.create(bind=engine) In [15]: t_fk_tb = Table('t_fk_tb',metadata,Column('col1',Integer),Column('col2',VARCHAR(10)),ForeignKeyConstraint(['col1','col2'],['t_pk.id','t_pk.name'])) In [16]: t_fk_tb.create(bind=engine) 3.3 unique In [17]: t_uni = Table('t_uni',metadata,Column('id',Integer,unique=True)) In [18]: t_uni.create(bind=engine) In [19]: t_uni_tb = Table('t_uni_tb',metadata,Column('col1',Integer),Column('col2',VARCHAR(10)),UniqueConstraint('col1','col2')) In [20]: t_uni_tb.create(bind=engine) 3.4 check 虽然能成功,但MySQL目前尚未支持check约束。这里就不举例了。 3.5 not null In [21]: t_null = Table('t_null',metadata,Column('id',Integer,nullable=False)) In [22]: t_null.create(bind=engine)4 Default value
Divided into 2 categories: pessimism (value provided by DB Server) and optimism (value provided by SQLAlshemy). Optimism can be divided into: insert and update
4.1 例子:insert In [23]: t_def_inser = Table('t_def_inser',metadata,Column('id',Integer),Column('name',VARCHAR(10),server_default='cc')) In [24]: t_def_inser.create(bind=engine) 3.2 例子:update In [25]: t_def_upda = Table('t_def_upda',metadata,Column('id',Integer),Column('name',VARCHAR(10),server_onupdate='DataHacker')) In [26]: t_def_upda.create(bind=engine) 3.3 例子:Passive In [27]: t_def_pass = Table('t_def_pass',metadata,Column('id',Integer),Column('name',VARCHAR(10),DefaultClause('cc'))) In [28]: t_def_pass.create(bind=engine)
㈢ Hidden Schema
#Whether the security of the data is exposed to a completely trustworthy object, this is something that no security-conscious DBA will risk. risk. A better way is to hide the Schema structure as much as possible and verify the integrity of the data entered by the user. Although this increases the operation and maintenance cost to a certain extent, it is not trivial to be safe.
Here we develop a command line tool to illustrate this problem
Requirements: hide the table structure, implement dynamic query, and simulate the results to mysql \G output
版本: [root@DataHacker ~]# ./sesc.py --version 1.0 查看帮助: [root@DataHacker ~]# ./sesc.py -h Usage: sesc.py [options] <arg1> <arg2> [<arg3>...] Options: --version show program's version number and exit -h, --help show this help message and exit -q TERM assign where predicate -c COL, --column=COL assign query column -t TABLE assign query table -f, --format -f must match up -o -o OUTFILE assign output file 我们要的效果: [root@DataHacker ~]# ./sesc.py -t actor -c last_name -q s% -f -o output.txt [root@DataHacker ~]# cat output.txt ************ 1 row ******************* actor_id: 180 first_name: JEFF last_name: SILVERSTONE last_update: 2006-02-15 04:34:33 ************ 2 row ******************* actor_id: 195 first_name: JAYNE last_name: SILVERSTONE last_update: 2006-02-15 04:34:33 ......<此处省略大部分输出>......
Please see the code
#!/usr/bin/env python import optparse from dbapi import * #构造OptionParser实例,配置期望的选项 parser = optparse.OptionParser(usage="%prog [options] <arg1> <arg2> [<arg3>...]",version='1.0',) #定义命令行选项,用add_option一次增加一个 parser.add_option("-q",action="store",type="string",dest="term",help="assign where predicate") parser.add_option("-c","--column",action="store",type="string",dest="col",help="assign query column") parser.add_option("-t",action="store",type="string",dest="table",help="assign query table") parser.add_option("-f","--format",action="store_true",dest="format",help="-f must match up -o") parser.add_option("-o",action="store",type="string",dest="outfile",help="assign output file") #解析命令行 options,args = parser.parse_args() #把上述dest值赋给我们自定义的变量 table = options.table column = options.col term = options.term format = options.format #实现动态读查询 statement = "select * from %s where %s like '%s'"%(table,column,term) cur = conn.cursor() cur.execute(statement) results = cur.fetchall() #模拟 \G 输出形式 if format is True: columns_query = "describe %s"%(table) cur.execute(columns_query) heards = cur.fetchall() column_list = [] for record in heards: column_list.append(record[0]) output = "" count = 1 for record in results: output = output + "************ %s row ************\n\n"%(count) for field_no in xrange(0, len(column_list)): output = output + column_list[field_no]+ ": " + str(record[field_no]) + "\n" output = output + "\n" count = count + 1 else: output = [] for record in xrange(0,len(results)): output.append(results[record]) output = ''.join(output) #把输出结果定向到指定文件 if options.outfile: outfile = options.outfile with open(outfile,'w') as out: out.write(output) else: print output #关闭游标与连接 conn.close() cur.close()
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Detailed explanation of the difference between table and schema in the databaseDetailed introduction to information_schema in MySQLMySQL distributed cluster MyCAT (2) schema Detailed code explanationThe above is the detailed content of Detailed explanation of MySQL database using Python to operate Schema method. For more information, please follow other related articles on the PHP Chinese website!

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