hadoop streaming编程小demo(python版)

大数据团队搞数据质量评测。自动化质检和监控平台是用django,MR也是通过python实现的。(后来发现有orc压缩问题,python不知道怎么解决,正在改成java版本)

这里展示一个python编写MR的例子吧。

抄一句话:Hadoop Streaming是Hadoop提供的一个编程工具,它允许用户使用任何可执行文件或者脚本文件作为Mapper和Reducer。

1、首先,先介绍一下背景,我们的数据是存放在hive里的。hive建表语句如下:

我们将会解析元数据,和HDFS上的数据进行merge,方便处理。这里的partition_key用的是year/month/day。

hive (gulfstream_ods)> desc g_order;
OK
col_name        data_type       comment
order_id                bigint                  订单id                
driver_id               bigint                  司机id,司机抢单前该值为0      
driver_phone            string                  司机电话                
passenger_id            bigint                  乘客id                
passenger_phone         string                  乘客电话                
car_id                  int                     接驾车辆id              
area                    int                     城市id                
district                string                  城市区号                
type                    int                     订单时效,0 实时  1预约      
current_lng             decimal(19,6)           乘客发单时的经度            
current_lat             decimal(19,6)           乘客发单时的纬度            
starting_name           string                  起点名称                
starting_lng            decimal(19,6)           起点经度                
starting_lat            decimal(19,6)           起点纬度                
dest_name               string                  终点名称                
dest_lng                decimal(19,6)           终点经度                
dest_lat                decimal(19,6)           终点纬度                
driver_start_distance   int                     司机与出发地的路面距离,单位:米    
start_dest_distance     int                     出发地与终点的路面距离,单位:米    
departure_time          string                  出发时间(预约单的预约时间,实时单为发单时间)
strive_time             string                  抢单成功时间              
consult_time            string                  协商时间                
arrive_time             string                  司机点击‘我已到达’的时间       
setoncar_time           string                  上车时间(暂时不用)          
begin_charge_time       string                  司机点机‘开始计费’的时间       
finish_time             string                  完成时间                
year                    string                                      
month                   string                                      
day                     string                                      
                 
# Partition Information          
# col_name              data_type               comment             
                 
year                    string                                      
month                   string                                      
day                     string              

2、我们解析元数据

这里是解析元数据的过程。之后我们把元数据序列化后存入文件desc.gulfstream_ods.g_order,我们将会将此配置文件连同MR脚本一起上传到hadoop集群。

import subprocess
from subprocess import Popen


def desc_table(db, table):
    process = Popen('hive -e "desc %s.%s"' % (db, table),
            shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
    stdout, stderr = process.communicate()
    is_column = True
    structure_list = list()
    column_list = list()
    for line in stdout.split('\n'):
        value_list = list()
        if not line or len(line.split()) < 2:
            break
        if is_column:
            column_list = line.split()
            is_column = False
            continue
        else:
            value_list = line.split()
        structure_dict = dict(zip(column_list, value_list))
        structure_list.append(structure_dict)

    return structure_list

3、下面是hadoop streaming执行脚本。

#!/bin/bash
source /etc/profile
source ~/.bash_profile

#hadoop目录
echo "HADOOP_HOME: "$HADOOP_HOME
HADOOP="$HADOOP_HOME/bin/hadoop"

DB=$1
TABLE=$2
YEAR=$3
MONTH=$4
DAY=$5
echo $DB--$TABLE--$YEAR--$MONTH--$DAY

if [ "$DB" = "gulfstream_ods" ]
then
    DB_NAME="gulfstream"
else
    DB_NAME=$DB
fi
TABLE_NAME=$TABLE

#输入路径
input_path="/user/xiaoju/data/bi/$DB_NAME/$TABLE_NAME/$YEAR/$MONTH/$DAY/*"
#标记文件后缀名
input_mark="_SUCCESS"
echo $input_path
#输出路径
output_path="/user/bigdata-t/QA/yangfan/$DB_NAME/$TABLE_NAME/$YEAR/$MONTH/$DAY"
output_mark="_SUCCESS"
echo $output_path
#性能约束参数
capacity_mapper=500
capacity_reducer=200
map_num=10
reducer_num=10
queue_name="root.dashujudidiyanjiuyuan-zhinengpingtaibu.datapolicy-develop"
#启动job name
job_name="DW_Monitor_${DB_NAME}_${TABLE_NAME}_${YEAR}${MONTH}${DAY}"
mapper="python mapper.py $DB $TABLE_NAME"
reducer="python reducer.py"

$HADOOP fs -rmr $output_path
$HADOOP jar $HADOOP_HOME/share/hadoop/tools/lib/hadoop-streaming-2.7.2.jar \
    -jobconf mapred.job.name="$job_name" \
    -jobconf mapred.job.queue.name=$queue_name \
    -jobconf mapred.map.tasks=$map_num \
    -jobconf mapred.reduce.tasks=$reducer_num \
    -jobconf mapred.map.capacity=$capacity_mapper \
    -jobconf mapred.reduce.capacity=$capacity_reducer \
    -input $input_path \
    -output $output_path \
    -file ./mapper.py \
    -file ./reducer.py \
    -file ./utils.py \
    -file ./"desc.${DB}.${TABLE_NAME}" \
    -mapper "$mapper" \
    -reducer "$reducer"
if [ $? -ne 0 ]; then
        echo "$DB_NAME $TABLE_NAME $YEAR $MONTH $DAY run faild"
fi
$HADOOP fs -touchz "${output_path}/$output_mark"
rm -rf ./${DB_NAME}.${TABLE_NAME}.${YEAR}-${MONTH}-${DAY}
$HADOOP fs -get $output_path/part-00000 ./${DB_NAME}.${TABLE_NAME}.${YEAR}-${MONTH}-${DAY}

 4、这里是Wordcount的进阶版本,第一个功能是分区域统计订单量,第二个功能是在一天中分时段统计订单量。

mapper脚本

# -*- coding:utf-8 -*-
#!/usr/bin/env python
import sys
import json
import pickle
reload(sys)
sys.setdefaultencoding('utf-8')


# 将字段和元数据匹配, 返回迭代器
def read_from_input(file, separator, columns):
    for line in file:
        if line is None or line == '':
            continue
        data_list = mapper_input(line, separator)
        if not data_list:
            continue
        item = None
        # 最后3列, 年月日作为partitionkey, 无用
        if len(data_list) == len(columns) - 3:
            item = dict(zip(columns, data_list))
        elif len(data_list) == len(columns):
            item = dict(zip(columns, data_list))
        if not item:
            continue
        yield item


def index_columns(db, table):
    with open('desc.%s.%s' % (db, table), 'r') as fr:
        structure_list = deserialize(fr.read())
    return [column.get('col_name') for column in structure_list]


# map入口
def main(separator, columns):
    items = read_from_input(sys.stdin, separator, columns)
    mapper_result = {}
    for item in items:
        mapper_plugin_1(item, mapper_result)
        mapper_plugin_2(item, mapper_result)


def mapper_plugin_1(item, mapper_result):
    # key在现实中可以是不同appkey, 是用来分发到不同的reducer上的, 相同的route用来分发到相同的reducer
    key = 'route1'
    area = item.get('area')
    district = item.get('district')
    order_id = item.get('order_id')
    if not area or not district or not order_id:
        return
    mapper_output(key, {'area': area, 'district': district, 'order_id': order_id, 'count': 1})


def mapper_plugin_2(item, mapper_result):
    key = 'route2'
    strive_time = item.get('strive_time')
    order_id = item.get('order_id')
    if not strive_time or not order_id:
        return
    try:
        day_hour = strive_time.split(':')[0]
        mapper_output(key, {'order_id': order_id, 'strive_time': strive_time, 'count': 1, 'day_hour': day_hour})except Exception, ex:
        pass


def serialize(data, type='json'):
    if type == 'json':
        try:
            return json.dumps(data)
        except Exception, ex:
            return ''
    elif type == 'pickle':
        try:
            return pickle.dumps(data)
        except Exception, ex:
            return ''
    else:
        return ''


def deserialize(data, type='json'):
    if type == 'json':
        try:
            return json.loads(data)
        except Exception, ex:
            return []
    elif type == 'pickle':
        try:
            return pickle.loads(data)
        except Exception, ex:
            return []
    else:
        return []


def mapper_input(line, separator='\t'):
    try:
        return line.split(separator)
    except Exception, ex:
        return None


def mapper_output(key, data, separator='\t'):
    key = str(key)
    data = serialize(data)
    print '%s%s%s' % (key, separator, data)
    # print >> sys.stderr, '%s%s%s' % (key, separator, data)


if __name__ == '__main__':
    db = sys.argv[1]
    table = sys.argv[2]
    columns = index_columns(db, table)
    main('||', columns)

reducer脚本

#!/usr/bin/env python
# vim: set fileencoding=utf-8
import sys
reload(sys)
sys.setdefaultencoding('utf-8')
import json
import pickle
from itertools import groupby
from operator import itemgetter


def read_from_mapper(file, separator):
    for line in file:
        yield reducer_input(line)


def main(separator='\t'):
    reducer_result = {}
    line_list = read_from_mapper(sys.stdin, separator)
    for route_key, group in groupby(line_list, itemgetter(0)):
        if route_key is None:
            continue
        reducer_result.setdefault(route_key, {})
        if route_key == 'route1':
            reducer_plugin_1(route_key, group, reducer_result)
            reducer_output(route_key, reducer_result[route_key])
        if route_key == 'route2':
            reducer_plugin_2(route_key, group, reducer_result)
            reducer_output(route_key, reducer_result[route_key])


def reducer_plugin_1(route_key, group, reducer_result):
    for _, data in group:
        if data is None or len(data) == 0:
            continue
        if not data.get('area') or not data.get('district') or not data.get('count'):
            continue
        key = '_'.join([data.get('area'), data.get('district')])
        reducer_result[route_key].setdefault(key, 0)
        reducer_result[route_key][key] += int(data.get('count'))
        # print >> sys.stderr, '%s' % json.dumps(reducer_result[route_key])


def reducer_plugin_2(route_key, group, reducer_result):
    for _, data in group:
        if data is None or len(data) == 0:
            continue
        if not data.get('order_id') or not data.get('strive_time') or not data.get('count') or not data.get('day_hour'):
            continue
        key = data.get('day_hour')
        reducer_result[route_key].setdefault(key, {})
        reducer_result[route_key][key].setdefault('count', 0)
        reducer_result[route_key][key].setdefault('order_list', [])
        reducer_result[route_key][key]['count'] += int(data.get('count'))
        if len(reducer_result[route_key][key]['order_list']) < 100:
            reducer_result[route_key][key]['order_list'].append(data.get('order_id'))
        # print >> sys.stderr, '%s' % json.dumps(reducer_result[route_key])


def serialize(data, type='json'):
    if type == 'json':
        try:
            return json.dumps(data)
        except Exception, ex:
            return ''
    elif type == 'pickle':
        try:
            return pickle.dumps(data)
        except Exception, ex:
            return ''
    else:
        return ''


def deserialize(data, type='json'):
    if type == 'json':
        try:
            return json.loads(data)
        except Exception, ex:
            return []
    elif type == 'pickle':
        try:
            return pickle.loads(data)
        except Exception, ex:
            return []
    else:
        return []


def reducer_input(data, separator='\t'):
    data_list = data.strip().split(separator, 2)
    key = data_list[0]
    data = deserialize(data_list[1])
    return [key, data]


def reducer_output(key, data, separator='\t'):
    key = str(key)
    data = serialize(data)
    print '%s\t%s' % (key, data)
    # print >> sys.stderr, '%s\t%s' % (key, data)


if __name__ == '__main__':
    main()

5、上一个版本,遭遇了reduce慢的情况,原因有两个:一是因为route的设置,所有相同的route都将分发到同一个reducer,造成单个reducer处理压力大,性能下降。二是因为集群是搭建在虚拟机上的,性能本身就差。可以对这个问题进行改进。改进版本如下,方案是在mapper阶段先对数据进行初步的统计,缓解reducer的计算压力。

mapper脚本

# -*- coding:utf-8 -*-
#!/usr/bin/env python
import sys
import json
import pickle
reload(sys)
sys.setdefaultencoding('utf-8')


# 将字段和元数据匹配, 返回迭代器
def read_from_input(file, separator, columns):
    for line in file:
        if line is None or line == '':
            continue
        data_list = mapper_input(line, separator)
        if not data_list:
            continue
        item = None
        # 最后3列, 年月日作为partitionkey, 无用
        if len(data_list) == len(columns) - 3:
            item = dict(zip(columns, data_list))
        elif len(data_list) == len(columns):
            item = dict(zip(columns, data_list))
        if not item:
            continue
        yield item


def index_columns(db, table):
    with open('desc.%s.%s' % (db, table), 'r') as fr:
        structure_list = deserialize(fr.read())
    return [column.get('col_name') for column in structure_list]


# map入口
def main(separator, columns):
    items = read_from_input(sys.stdin, separator, columns)
    mapper_result = {}
    for item in items:
        mapper_plugin_1(item, mapper_result)
        mapper_plugin_2(item, mapper_result)

    for route_key, route_value in mapper_result.iteritems():
        for key, value in route_value.iteritems():
            ret_dict = dict()
            ret_dict['route_key'] = route_key
            ret_dict['key'] = key
            ret_dict.update(value)
            mapper_output('route_total', ret_dict)


def mapper_plugin_1(item, mapper_result):
    # key在现实中可以是不同appkey, 是用来分发到不同的reducer上的, 相同的route用来分发到相同的reducer
    key = 'route1'
    area = item.get('area')
    district = item.get('district')
    order_id = item.get('order_id')
    if not area or not district or not order_id:
        returntry:
        # total统计
        mapper_result.setdefault(key, {})
        mapper_result[key].setdefault('_'.join([area, district]), {})
        mapper_result[key]['_'.join([area, district])].setdefault('count', 0)
        mapper_result[key]['_'.join([area, district])].setdefault('order_id', [])
        mapper_result[key]['_'.join([area, district])]['count'] += 1
        if len(mapper_result[key]['_'.join([area, district])]['order_id']) < 10:
            mapper_result[key]['_'.join([area, district])]['order_id'].append(order_id)
    except Exception, ex:
        pass


def mapper_plugin_2(item, mapper_result):
    key = 'route2'
    strive_time = item.get('strive_time')
    order_id = item.get('order_id')
    if not strive_time or not order_id:
        return
    try:
        day_hour = strive_time.split(':')[0]# total统计
        mapper_result.setdefault(key, {})
        mapper_result[key].setdefault(day_hour, {})
        mapper_result[key][day_hour].setdefault('count', 0)
        mapper_result[key][day_hour].setdefault('order_id', [])
        mapper_result[key][day_hour]['count'] += 1
        if len(mapper_result[key][day_hour]['order_id']) < 10:
            mapper_result[key][day_hour]['order_id'].append(order_id)
    except Exception, ex:
        pass


def serialize(data, type='json'):
    if type == 'json':
        try:
            return json.dumps(data)
        except Exception, ex:
            return ''
    elif type == 'pickle':
        try:
            return pickle.dumps(data)
        except Exception, ex:
            return ''
    else:
        return ''


def deserialize(data, type='json'):
    if type == 'json':
        try:
            return json.loads(data)
        except Exception, ex:
            return []
    elif type == 'pickle':
        try:
            return pickle.loads(data)
        except Exception, ex:
            return []
    else:
        return []


def mapper_input(line, separator='\t'):
    try:
        return line.split(separator)
    except Exception, ex:
        return None


def mapper_output(key, data, separator='\t'):
    key = str(key)
    data = serialize(data)
    print '%s%s%s' % (key, separator, data)
    # print >> sys.stderr, '%s%s%s' % (key, separator, data)


if __name__ == '__main__':
    db = sys.argv[1]
    table = sys.argv[2]
    columns = index_columns(db, table)
    main('||', columns)

reducer脚本

#!/usr/bin/env python
# vim: set fileencoding=utf-8
import sys
reload(sys)
sys.setdefaultencoding('utf-8')
import json
import pickle
from itertools import groupby
from operator import itemgetter


def read_from_mapper(file, separator):
    for line in file:
        yield reducer_input(line)


def main(separator='\t'):
    reducer_result = {}
    line_list = read_from_mapper(sys.stdin, separator)
    for route_key, group in groupby(line_list, itemgetter(0)):
        if route_key is None:
            continue
        reducer_result.setdefault(route_key, {})if route_key == 'route_total':
            reducer_total(route_key, group, reducer_result)
            reducer_output(route_key, reducer_result[route_key])


def reducer_total(route_key, group, reducer_result):
    for _, data in group:
        if data is None or len(data) == 0:
            continue
        if data.get('route_key') == 'route1':
            reducer_result[route_key].setdefault(data.get('route_key'), {})
            reducer_result[route_key][data.get('key')].setdefault('count', 0)
            reducer_result[route_key][data.get('key')].setdefault('order_id', [])
            reducer_result[route_key][data.get('key')]['count'] += data.get('count')
            for order_id in data.get('order_id'):
                if len(reducer_result[route_key][data.get('key')]['order_id']) <= 10:
                    reducer_result[route_key][data.get('key')]['order_id'].append(order_id)
        elif data.get('route_key') == 'route2':
            reducer_result[route_key].setdefault(data.get('route_key'), {})
            reducer_result[route_key][data.get('key')].setdefault('count', 0)
            reducer_result[route_key][data.get('key')].setdefault('order_id', [])
            reducer_result[route_key][data.get('key')]['count'] += data.get('count')
            for order_id in data.get('order_id'):
                if len(reducer_result[route_key][data.get('key')]['order_id']) <= 10:
                    reducer_result[route_key][data.get('key')]['order_id'].append(order_id)
        else:
            pass


def serialize(data, type='json'):
    if type == 'json':
        try:
            return json.dumps(data)
        except Exception, ex:
            return ''
    elif type == 'pickle':
        try:
            return pickle.dumps(data)
        except Exception, ex:
            return ''
    else:
        return ''


def deserialize(data, type='json'):
    if type == 'json':
        try:
            return json.loads(data)
        except Exception, ex:
            return []
    elif type == 'pickle':
        try:
            return pickle.loads(data)
        except Exception, ex:
            return []
    else:
        return []


def reducer_input(data, separator='\t'):
    data_list = data.strip().split(separator, 2)
    key = data_list[0]
    data = deserialize(data_list[1])
    return [key, data]


def reducer_output(key, data, separator='\t'):
    key = str(key)
    data = serialize(data)
    print '%s\t%s' % (key, data)
    # print >> sys.stderr, '%s\t%s' % (key, data)


if __name__ == '__main__':
    main()

遇到的问题:

1、The DiskSpace /user/bigdata/qa quota of  is exceeded

在reducer结束后,遭遇如上问题,是因为HDFS  路径下的disk容量已经被沾满,释放容量即可;

本文参与腾讯云自媒体分享计划,欢迎正在阅读的你也加入,一起分享。

发表于

我来说两句

0 条评论
登录 后参与评论

相关文章

来自专栏Python数据科学

Python爬虫之一个海贼迷的呐喊

海贼王已经10年了,一路陪伴了我们的点点滴滴。它热血,激情,感人,欢乐,吸引了无数男女老少的紧紧跟随。

12730
来自专栏Golang语言社区

C++ 实现银行排队服务模拟

教程简介:使用 C++对银行排队服务进行模拟,以事件驱动为核心思想,手动实现模板链式队列、随机数产生器等内容,进而学习概率编程等知识。作为可选进阶,这个模型同时...

51350
来自专栏恰童鞋骚年

Unity3D游戏开发初探—4.开发一个“疯狂击箱子”游戏

  (1)如何在游戏脚本程序中创建对象而不是一开始就创建好对象?->使用GameObject的静态方法:CreatePrimitive()

15340
来自专栏iOSDevLog

Unity 3D 开发《王者荣耀》:英雄移动地图英雄

27530
来自专栏PPV课数据科学社区

数据流编程教程:R语言与DataFrame

DataFrame DataFrame 是一个表格或者类似二维数组的结构,它的各行表示一个实例,各列表示一个变量。 一. DataFrame数据流编程 ? 二....

496120
来自专栏牛客网

今日头条三面面经

4.       优先队列的底层数据结构?插入和删除一个节点的时间复杂度是多少? 

96820
来自专栏极客生活

python爬虫隔一段时间一乐之海子的诗

每隔一段时间(一周到一个月)拿出1到2天来做一个好玩的东西,不求回报,只为快感。 前两天刚买了一本电子书《海子的诗》,晚上读了快一半,好多诗里面都提及了麦子和...

10510
来自专栏云霄雨霁

数据库概论

25900
来自专栏华章科技

独家 | Python数据分析入门指南

有一个朋友最近问到这个问题,我觉得把它公开出来对其他人也会有帮助。这是给完全不了解Python而想找到从零到一的最简单的路径的人的建议:

11930
来自专栏牛客网

Java面经:去哪儿四轮面试真题分享

废话不多说,前几天参加去哪网面试,面经如下: 去哪网java实习生面总共分为四轮(我也不知道为什么这么多)。 一面(技术面) 1.自我介绍,并分析简历上的项目,...

38160

扫码关注云+社区

领取腾讯云代金券