Python 交互数据库总结

大数据量且快速，使用IO。

import io
from sqlalchemy import create_engine

def write_to_table(df, table_name,schema, if_exists='append'):
    #engine database
    db_engine = create_engine('//user:password@host/dbname')
    string_data_io = io.StringIO()
    df.to_csv(string_data_io, sep='|', index=False)
    pd_sql_engine = pd.io.sql.pandasSQL_builder(db_engine)
    table = pd.io.sql.SQLTable(table_name, pd_sql_engine, frame=df,
                               index=False, if_exists=if_exists,schema = '{schema}')
    table.create()
    string_data_io.seek(0)
#     string_data_io.readline()  # remove header
    with db_engine.connect() as connection:
        with connection.connection.cursor() as cursor:
            copy_cmd = "COPY {schema}.%s FROM STDIN DELIMITER '|' CSV HEADER" %table_name  # remove header
            cursor.copy_expert(copy_cmd, string_data_io)
        connection.connection.commit()

普通 慢 .tosql

def putsimilarData(df, name):
    engine = create_engine(.....)
    try:
        df.to_sql(name, engine, index=False, if_exists='append',schema='{schema}')
    except Exception as e:
        print(e)

df_predict = df_predict[['model_predict', 'upset_type', 'chnl_code']]
update_sql(df_predict)
logger.info('入库成功：{0},{1},{2}'.format(chnl_code,predict_sales,dates))


def update_sql(df):
    """
    :param df:三个值 geohash_daily_sales = '%s',upset_type = '%s' chnl_code='%s'
    :return:
    """
    client = psycopg2.connect(dbname=, user=, password=, host=,port=)
    cursor = client.cursor()
    init_sql = """ update table 
                   set    geohash_daily_sales = '%s',
                          upset_type = '%s'
                   where  chnl_code='%s' """
    for value in df.values.tolist():
        sql = init_sql % (tuple(value))
        cursor.execute(sql)  # 传值
        client.commit()  # 提交事务
    client.close()

## 可以用update代替，不再复用

def update_importance_sql(df,chnl_code,table='familymart_feature_importance'):
    """
        :param df:
        :param status: 0清空更新；1增量更新
        :return:
        """
    #engine = create_engine()

    # 分情况  增量直接写入 修改则是删除再写入
    test_sql = f""" select chnl_code
                    from {table}
                    where chnl_code ='{chnl_code}'
                """
    df_test = pd.read_sql(test_sql, client)
    client.close()

    if len(df_test) == 0:
        #基盘 增量直接写入
        putsimilarData(df, table)
    else:
        delete_sql = f"""delete from {table}
                        where chnl_code ='{chnl_code}'
                    """
        # 删除
        deletesimilarData(delete_sql)
        # 重新写入
        putsimilarData(df, table)

def deletesimilarData(sql):
    # # 获得连接
     conn = psycopg2.connect(dbname=...)
    # 获得游标对象
    cursor = conn.cursor()
    # 执行语句
    cursor.execute(sql)
    # 事物提交
    conn.commit()
    # 关闭数据库连接
    conn.close()

 4. 读

# pg
def connect_sql_poi(sql):
    '''连接MySQL或SqlServer数据库
    参数说明：
    RDBMS.要连接的数据库，mysql或者sqlsever
    database：数据库名
    sql：sql语句'''
    client = psycopg2.connect(dbname=。。。)
    df = pd.read_sql(sql, client)
    client.close()
    return df

#Hive
from pyhive import hive
def conn_hive(sql):
    connection = hive.connect(host=,
                          port=,
                          database=,
                          username=)
    cur = connection.cursor()
    cur.execute(sql)
    columns = [col[0] for col in cur.description]
    result = [dict(zip(columns, row)) for row in cur.fetchall()]
    Main = pd.Dataframe(result)
    Main.columns = columns 
    return Main