工作中，如果我们本地要操作的数据量大，那么主机是跑不起来python脚本的，这个时候，就要用到服务器(也叫堡垒机)了。那么如何用HIVE调用堡垒机上的python脚本呢？今天小白就总结一下步骤和一些注意事项~

1.首先将Python脚本上传到堡垒机上

2.上传后，在HIVE中编写shell JOB

#这里设置地址等变量，可以重复使用

file_path="/home/chen_lib" #服务器大目录

file_name_t="traindatas.csv" #训练数据

file_name_y="df2.csv" #结果数据集

python_name="Untitled1.py" #python脚本

#传入数据 由于两个库不通，所以直接把测试数据放到服务器上，就不做从表读入的操作

#若要将HIVE中的表读入到服务器上，执行下面命令

hive -e "set hive.resultset.use.unique.column.names=false;set hive.cli.print.header=true;

select * from table " >> $file_path/$file_name_t

#执行python脚本，将结果传入到服务器的df表中

python2.7 $file_path/$python_name $file_path $file_path

#从服务器上读取表到HIVE表中

hive -e "LOAD DATA LOCAL INPATH '$file_path/$file_name_y' OVERWRITE INTO TABLE tablename partition (d='${zdt.format("yyyy-MM-dd")}')"

echo "导入数据完成！"

3.建HIVE表时要注意：

1)文件的分割要用‘，’，因为是csv文件，否则数据读入就是空的

2)建表是要建成textfile，如果建成orc会报错：

Caused by: java.io.IOException: Malformed ORC file

原因是：ORC格式是列式存储的表，不能直接从本地文件导入数据，只有当数据源表也是ORC格式存储时，才可以直接加载，否则会出现上述报错。

USE database;

CREATE TABLE tablename(

hotelid int COMMENT 'field1 comment',

max_quantity int COMMENT 'field2 comment',

section_query_min int COMMENT 'field2 comment',

section_query_max intCOMMENT 'NULL'

)

COMMENT 'owner:chen'

PARTITIONED BY (d string COMMENT 'date')

row format delimited fields terminated by ','

STORED AS textfile;

4.Python脚本中有几点需要注意一下

1)输出的路径要与HIVE中的路径一致

2)传出的表df2要去掉列名，不然也会被读入到hive表中

3)传出csv文件要用‘，’分割，否则，如果用‘\’,会输出到一列中去，无法读入hive表中

# coding: utf-8

import pandas as pd

import numpy as np

file_path="/home/hotel/chen_lib/"

file_name_t="traindatas.csv"

file_name_y="df2.csv"

data_ctrip = pd.read_csv(file_path+file_name_t,header = 'infer')

ret1=[]

#循环取出每一行的最大值

for row in range(data_ctrip.shape[0]):

ret = []

quantitylist=[data_ctrip.loc[row,'quantity0_1'],data_ctrip.loc[row,'quantity1_2'],data_ctrip.loc[row,'quantity2_3'],data_ctrip.loc[row,'quantity3_4'],data_ctrip.loc[row,'quantity4_5'],data_ctrip.loc[row,'quantity5_6'],data_ctrip.loc[row,'quantity6_7'],data_ctrip.loc[row,'quantity7_8'],data_ctrip.loc[row,'quantity8_9'],data_ctrip.loc[row,'quantity9_10'],

data_ctrip.loc[row,'quantity10_11'],data_ctrip.loc[row,'quantity11_12'],data_ctrip.loc[row,'quantity12_13'],data_ctrip.loc[row,'quantity13_14'],data_ctrip.loc[row,'quantity14_15'],data_ctrip.loc[row,'quantity15_16'],data_ctrip.loc[row,'quantity16_17'],data_ctrip.loc[row,'quantity17_18'],data_ctrip.loc[row,'quantity18_19'],data_ctrip.loc[row,'quantity19_20']]

sectionlist=[data_ctrip.loc[row,'section0_1'],data_ctrip.loc[row,'section1_2'],data_ctrip.loc[row,'section2_3'],data_ctrip.loc[row,'section3_4'],data_ctrip.loc[row,'section4_5'],data_ctrip.loc[row,'section5_6'],data_ctrip.loc[row,'section6_7'],data_ctrip.loc[row,'section7_8'],data_ctrip.loc[row,'section8_9'],data_ctrip.loc[row,'section9_10'],

data_ctrip.loc[row,'section10_11'],data_ctrip.loc[row,'section11_12'],data_ctrip.loc[row,'section12_13'],data_ctrip.loc[row,'section13_14'],data_ctrip.loc[row,'section14_15'],data_ctrip.loc[row,'section15_16'],data_ctrip.loc[row,'section16_17'],data_ctrip.loc[row,'section17_18'],data_ctrip.loc[row,'section18_19'],data_ctrip.loc[row,'section19_20']]

max_quantity = max(quantitylist) #取出最大的间夜

max_quantity_index = quantitylist.index(max_quantity)#取出最大间夜对应的索引

section_query = sectionlist[max_quantity_index]#取出最大间夜对应的区间

section_query_min = int(section_query.split("-", 1)[0])#取出价格区间的最小值

section_query_max = int(section_query.split("-", 1)[1])#取出价格区间的最大值

ret.append([data_ctrip.loc[row,'hotelid'],max_quantity,section_query_min,section_query_max])

ret1.extend(ret)#追加行

df1 = pd.DataFrame(ret1)

df1.to_csv('file_path+file_name_y',sep=',',index=False,header=False)

以上就是在用堡垒机和HIVE来执行时的步骤和可能遇到的问题啦~

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