
Security News
Potemkin Understanding in LLMs: New Study Reveals Flaws in AI Benchmarks
New research reveals that LLMs often fake understanding, passing benchmarks but failing to apply concepts or stay internally consistent.
A thread safe sql executor for Python with connection pool. It helps you automatically manage connections and transactions. Support MySQL, PostgreSQL, SQLite etc.
Usage Sample ''''''''''''
.. code:: python
import sqlexec as db
if __name__ == '__main__':
db.init('test.db', driver='sqlite3', show_sql=True, debug=True)
# or
db.init("postgres://user:password@127.0.0.1:5432/testdb", driver='psycopg2', pool_size=5, show_sql=True, debug=True)
# or
db.init(host='127.0.0.1', port='5432', user='xxx', password='xxx', database='testdb', show_sql=True, driver='psycopg2')
effected_rowcount = db.insert(table='person', name='zhangsan', age=15)
# if driver is 'pymysql' or 'mysql.connector' of MySQL, the select_key is 'SELECT LAST_INSERT_ID()'
select_key = "SELECT currval('person_id_seq')"
id = db.save(select_key=select_key, table='person', name='lisi', age=26)
id = db.save_sql(select_key, 'INSERT INTO person(name, age) VALUES(?,?)', 'wangwu', 38)
id = db.save_sql(select_key, 'INSERT INTO person(name, age) VALUES(:name, :age)', name='zhaoliu', age=45)
count = db.get('select count(1) from person')
# result: 4
count = db.sql('select count(1) from person').get()
# result: 4
persons = db.select('select id, name, age from person')
# result:
# (3, 'zhangsan', 15)
# (4, 'lisi', 26)
# (5, 'wangwu', 38)
# (6, 'zhaoliu', 45)
persons = db.sql('select id, name, age from person').select()
# result:
# (3, 'zhangsan', 15)
# (4, 'lisi', 26)
# (5, 'wangwu', 38)
# (6, 'zhaoliu', 45)
persons = db.table('person').select('id', 'name', 'age')
# result:
# (3, 'zhangsan', 15)
# (4, 'lisi', 26)
# (5, 'wangwu', 38)
# (6, 'zhaoliu', 45)
persons = db.select_one('select id, name, age from person where name = ?', 'zhangsan')
# result:
# (3, 'zhangsan', 15)
persons = db.sql('select id, name, age from person where name = ?').select_one('zhangsan')
# result:
# (3, 'zhangsan', 15)
persons = db.select('select id, name, age from person where name = :name', name='zhangsan')
# result:
# [(3, 'zhangsan', 15)]
persons = db.sql('select id, name, age from person where name = :name').select(name='zhangsan')
# result:
# [(3, 'zhangsan', 15)]
persons = db.sql('select id, name, age from person where name = :name').param(name='zhangsan').select()
# result:
# [(3, 'zhangsan', 15)]
persons = db.table('person').where(name__eq='zhangsan').select('id', 'name', 'age')
# result:
# [(3, 'zhangsan', 15)]
persons = db.query('select id, name, age from person')
# result:
# {'id': 3, 'name': 'zhangsan', 'age': 15}
# {'id': 4, 'name': 'lisi', 'age': 26}
# {'id': 5, 'name': 'wangwu', 'age': 38}
# {'id': 6, 'name': 'zhaoliu', 'age': 45}
persons = db.sql('select id, name, age from person').query()
# result:
# {'id': 3, 'name': 'zhangsan', 'age': 15}
# {'id': 4, 'name': 'lisi', 'age': 26}
# {'id': 5, 'name': 'wangwu', 'age': 38}
# {'id': 6, 'name': 'zhaoliu', 'age': 45}
persons = db.query_one('select id, name, age from person where name = ?', 'zhangsan')
# result:
# {'id': 3, 'name': 'zhangsan', 'age': 15}
persons = db.sql('select id, name, age from person where name = ?').query_one('zhangsan')
# result:
# {'id': 3, 'name': 'zhangsan', 'age': 15}
persons = db.query('select id, name, age from person where name = :name', name='zhangsan')
# result:
# [{'id': 3, 'name': 'zhangsan', 'age': 15}]
persons = db.sql('select id, name, age from person where name = :name').query(name='zhangsan')
# result:
# [{'id': 3, 'name': 'zhangsan', 'age': 15}]
persons = db.sql('select id, name, age from person where name = :name').param(name='zhangsan').query()
# result:
# [{'id': 3, 'name': 'zhangsan', 'age': 15}]
persons = db.table('person').columns('id', 'name', 'age').where(name='zhangsan').query()
# result:
# [{'id': 3, 'name': 'zhangsan', 'age': 15}]
effected_rowcount = db.table('person').where(name='zhangsan').update(name='xxx', age=45)
effected_rowcount = db.table('person').where(id=6).delete()
count = db.table('person').count())
# result: 3
effected_rowcount = db.execute('delete from person where id = :id', id=5)
count = db.get('select count(1) from person')
# result: 2
effected_rowcount = db.sql('delete from person where id = ?').execute(4)
count = db.sql('select count(1) from person').get()
# result: 1
effected_rowcount = db.sql('delete from person where id = :id').execute(id=3)
count = db.sql('select count(1) from person').get()
# result: 0
# select data save as csv
db.sql('select name, age from person WHERE name = ?').load('张三').to_csv('test.csv')
db.sql('select name, age from person WHERE name = ?').param('张三').to_csv('test.csv')
# insert from csv
db.table('person').insert_from_csv('test.csv')
# select data transform to DataFrame of pandas
df = db.sql('select name, age from person WHERE name = :name').load(name='张三').to_df()
df = db.sql('select name, age from person WHERE name = :name').param(name='张三').to_df()
# insert from DataFrame of pandas
db.table('person').insert_from_df(dataframe)
# select data save as json
db.sql('select name, age from person WHERE name = ?').load('张三').to_json('test.json')
db.sql('select name, age from person WHERE name = ?').param('张三').to_json('test.json')
# insert from json
db.table('person').insert_from_json('test.json')
db.close()
Transaction '''''''''''
.. code:: python
from sqlexec import with_transaction, transaction
@with_transaction
def test_transaction():
insert_func(....)
update_func(....)
def test_transaction2():
with transaction():
insert_func(....)
update_func(....)
If you want to operate MySQL database like Mybatis, may be you need MySqlx: https://pypi.org/project/mysqlx
If you want to operate PostgreSQL database like Mybatis, may be you need PgSqlx: https://pypi.org/project/pgsqlx
If you want to execute SQL like Mybatis, may be you need sqlx-batis: https://pypi.org/project/sqlx-batis
FAQs
A thread safe sql executor for Python with connection pool. It helps you automatically manage connections and transactions. Support MySQL, PostgreSQL, SQLite etc.
We found that sqlx-exec demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
Security News
New research reveals that LLMs often fake understanding, passing benchmarks but failing to apply concepts or stay internally consistent.
Security News
Django has updated its security policies to reject AI-generated vulnerability reports that include fabricated or unverifiable content.
Security News
ECMAScript 2025 introduces Iterator Helpers, Set methods, JSON modules, and more in its latest spec update approved by Ecma in June 2025.