odps(MaxCompute) 上传或者下载本地数据到阿里云服务器

直接上代码了

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Jul 17 14:40:34 2019

odps 封装sql成dataframe调用

odps的dataframe 是odps.df.core.DataFrame，不喜欢它的语法咯，还是用原生的dataframe
"""
import pymysql
from sqlalchemy import create_engine

import time
import pandas as pd
from odps import ODPS
from odps.models import Schema, Column, Partition
from odps.df import DataFrame
from collections import defaultdict


odps = ODPS('username', 'password', 'data_platform',
            endpoint='http://service.odps.aliyun.com/api')

def odps_read_sql(code_sql):
    collection = []
    with odps.execute_sql(code_sql).open_reader(tunnel=True) as reader:
        for record in reader : # 处理每一个record
            row = dict(record)
            collection.append(row)
    df = pd.DataFrame(collection)
    return df 

#code_sql = """
#select * from t_demo_bx_voice limit 100
#"""
#df = odps_read_sql(code_sql)
## 直接通过读取表名，操作数据
def  odps_read_table(table_name,limit=10000):
    """判断读取的SQL行数，如果超过10万行，直接放弃"""
    """程序判定失败，受到迭代器影响，异步操作导致未能实现，属于一个小BUG，未解决"""
    #sql = "select count(*) as count from {table}".format(table = table_name)
    #df = odps_read_sql(sql)
    table = DataFrame(odps.get_table(table_name))
    col = table.dtypes.names[0]
    rows = table[col].count()
    count = rows
    print('table表的行数:{count}'.format(count=count))
    if  count <=limit :
        #df = table
        df = table.to_pandas()
    else :
        df = '表行数超过1万，请重新设置limit参数大小'
        print("超过limit:{limit}限制行数,请重新设置".format(limit=limit))
    return df
#table_name = 'tmp_page_godskill_order'
#odps_read_table(table_name)

def write_database_from_odps(table_name,limit=100000):
    table = odps_read_table(table_name,limit=limit)
    #df = table.to_pandas()
    df = table
    write_to_database(df,table_name)

## 通过odps向msyql数据库写入数据
##table_name = 'tmp_page_godskill_order'
##write_database_from_odps(table_name)



from pyhive import hive
import pandas as pd
def hive_read_sql(sql_code):
    connection = hive.Connection(host='10.111.3.61', port=10000, username='username')
    cur = connection.cursor()      
    cur.execute(sql_code)
    #columns = [col[0] for col in cur.description]
    #result = [dict(zip(columns, row)) for row in cur.fetchall()]
    #df = pd.DataFrame(result)
    #df.columns = columns 
    headers = [col[0] for col in cur.description]
    df= pd.DataFrame(cur.fetchall(), columns = headers)
    cur.close()
    return df
 
#sql = "select ds,type,user_id from test.tmp_p2p_message_jq limit 10"
#df  = hive_read_sql(sql)
#df.head()

"""
向odps写入数据
第一步：读取本地数据；
第二步：写入数据
"""

def read_csv(from_path):
    table = pd.read_csv(from_path,encoding='gbk') ##选取表
    return table

# 创建表# 写入表
def odps_write_dataframe(df,tb_name, schema_name=None, schema_type=None, schema_comment=None):
    if schema_name is None:
        schema_name = df.columns.values.tolist()
    if schema_type is None:
        schema_type = ['string'] * len(schema_name)
    if schema_comment is None:
        schema_comment = [''] * len(schema_name)

    df_schema = pd.DataFrame({'name': schema_name, 'type': schema_type, 'comment': schema_comment})
    df_schema = df_schema[['name', 'type', 'comment']]
    df_columns = df_schema.apply(lambda x: [Column(name=x[0], type=x[1], comment=x[2])], axis=1)

    columns = []
    for col in df_columns:
        columns = columns + col

    #partitions = [Partition(name='ds', type='string', comment='')]
    #schema = Schema(columns=columns, partitions=partitions)
    """
    判断表是否存在
    """
    try:
        table_name = odps.get_table(tb_name)
        print('表已存在,表结构是',table_name.schema)
        print('warning:表已存在,追加数据')
    except:
        print('info:表不存在,创建并写入数据')
    schema = Schema(columns=columns)
    table = odps.create_table(tb_name, schema=schema, if_not_exists=True)
    
    ## 写入数据
    t = odps.get_table(tb_name)
    records = df.values.tolist()
    with t.open_writer() as writer:
        writer.write(records)  # 这里records可以是可迭代对象
    return "数据集写入odps成功"

#odps_write_dataframe(df =df,tb_name='tmp_market_v1123_v1')



def write_excle(data,to_file_name):
    try:
        path = '/Users/username/Downloads'
        to_path = path+"/"+to_file_name
        writer = pd.ExcelWriter(to_path, engine='xlsxwriter')
        data.to_excel(writer,'Sheet1',index=False)
        writer.save()
        print('EXCEL保存成功success')
    except:
        print("EXCEL保存失败")


def write_to_database(table,table_name):
    
    try:
        engine = create_engine("mysql+pymysql://root:password@127.0.0.1:3306/db_name?charset=utf8mb4")
        table.to_sql(name=table_name,con=engine,if_exists='replace',index=False,chunksize=10000
                    )
        print('数据库写入成功')
    except :
        print ('数据库写入失败')



    
if __name__ == "__main__":
    start_time = time.time() # 开始时间
    end_time = time.time() #结束时间
    print("程序耗时%f秒." % (end_time - start_time))

使用方法

步骤一：将脚本添加到python的系统环境中

步骤二：直接使用

from py_function_tools import odps_read_sql,write_excle,write_to_database,hive_read_sql
from py_function_tools import odps_write_dataframe,odps_read_table,write_database_from_odps
import time
#df_v1 = odps_read_table(table_name) ## 读取odps的Table
#df_v1 = odps_read_sql(sql) # 读取odps数据集
#df_v2 = hive_read_sql(sql) # 读取hive数据集，需要手动指定表所在的数据库
#write_excle(df,file_name) #默认保存在downloads目录下
#write_to_database(df,table_name) #存到mysql中ypp数据库
#from pandas_dataframe_agg import dataframe_agg
#table =dataframe_agg(df,dimensions,func=func) # groupby 处理