[Linux]shell多进程并发—详细版

业务背景

schedule.sh脚本负责调度用户轨迹工程脚本的执行，截取部分代码如下：

#!/bin/bash
source /etc/profile;

export userTrackPathCollectHome=/home/pms/bigDataEngine/analysis/script/usertrack/master/pathCollect


##############################
#
# 流程A
#
##############################

# 验证机器搭配的相关商品数据源是否存在
lines=`hadoop fs -ls /user/pms/recsys/algorithm/schedule/warehouse/ruleengine/artificial/product/$yesterday | wc -l`
if [ $lines -le 0 ] ;then
    echo ‘Error! artificial product is not exist‘
    exit 1
else
    echo ‘artificial product is ok!!!!!!‘
fi

# 验证机器搭配的相关商品数据源是否存在
lines=`hadoop fs -ls /user/pms/recsys/algorithm/schedule/warehouse/mix/artificial/product/$yesterday | wc -l`
if [ $lines -le 0 ] ;then
    echo ‘Error! mix product is not exist‘
    exit 1
else
    echo ‘mix product is ok!!!!!!‘
fi


##############################
#
# 流程B
#
##############################

# 生成团购信息表,目前只抓取团购ID、商品ID两项
sh $userTrackPathCollectHome/scripts/extract_groupon_info.sh
lines=`hadoop fs -ls /user/hive/pms/extract_groupon_info | wc -l `
if [ $lines -le 0 ] ;then
    echo ‘Error! groupon info is not exist‘
    exit 4
else
    echo ‘groupon info is ok!!!!!‘
fi

# 生成系列商品，总文件大小在320M左右
sh $userTrackPathCollectHome/scripts/extract_product_serial.sh
lines=`hadoop fs -ls /user/hive/pms/product_serial_id | wc -l `
if [ $lines -le 0 ] ;then
    echo ‘Error! product serial is not exist‘
    exit 5
else
    echo ‘product serial is ok!!!!!‘
fi

# 预处理生成extract_trfc_page_kpi表--用于按照pageId进行汇总统计所在页面的pv数、uv数
sh $userTrackPathCollectHome/scripts/extract_trfc_page_kpi.sh $date
lines=`hadoop fs -ls /user/hive/pms/extract_trfc_page_kpi/ds=$date | wc -l`
if [ $lines -le 0 ] ;then
    echo ‘Error! extract_trfc_page_kpi is not exist‘
    exit 6
else
    echo ‘extract_trfc_page_kpi is ok!!!!!!‘
fi

# 同步term_category到hive，并将前台类目转换为后台类目
sh $userTrackPathCollectHome/scripts/extract_term_category.sh
lines=`hadoop fs -ls /user/hive/pms/temp_term_category | wc -l`
if [ $lines -le 0 ] ;then
    echo ‘Error! temp_term_category is not exist‘
    exit 7
else
    echo ‘temp_term_category is ok!!!!!!‘
fi


##############################
#
# 流程C
#
##############################

# 生成extract_track_info表
sh $userTrackPathCollectHome/scripts/extract_track_info.sh
lines=`hadoop fs -ls /user/hive/warehouse/extract_track_info | wc -l `
if [ $lines -le 0 ] ;then
    echo ‘Error! extract_track_info is not exist‘
    exit 1
else
    echo ‘extract_track_info is ok!!!!!‘
fi
...

如上，整个预处理环节脚本执行完，需要耗时55分钟。

优化

上面的脚本执行流程可以分为三个流程：

流程A->流程B->流程C

考虑到流程B中的每个子任务都互不影响，因此没有必要顺序执行，优化的思路是将流程B中这些互不影响的子任务并行执行。
其实linux中并没有并发执行这一特定命令，上面所说的并发执行实际上是将这些子任务放到后台执行，这样就可以实现所谓的“并发执行”，脚本改造如下：

#!/bin/bash
source /etc/profile;

export userTrackPathCollectHome=/home/pms/bigDataEngine/analysis/script/usertrack/master/pathCollect


##############################
#
# 流程A
#
##############################

# 验证机器搭配的相关商品数据源是否存在
lines=`hadoop fs -ls /user/pms/recsys/algorithm/schedule/warehouse/ruleengine/artificial/product/$yesterday | wc -l`
if [ $lines -le 0 ] ;then
    echo ‘Error! artificial product is not exist‘
    exit 1
else
    echo ‘artificial product is ok!!!!!!‘
fi

# 验证机器搭配的相关商品数据源是否存在
lines=`hadoop fs -ls /user/pms/recsys/algorithm/schedule/warehouse/mix/artificial/product/$yesterday | wc -l`
if [ $lines -le 0 ] ;then
    echo ‘Error! mix product is not exist‘
    exit 1
else
    echo ‘mix product is ok!!!!!!‘
fi


##############################
#
# 流程B
#
##############################

# 并发进程，生成团购信息表,目前只抓取团购ID、商品ID两项
{
    sh $userTrackPathCollectHome/scripts/extract_groupon_info.sh
    lines=`hadoop fs -ls /user/hive/pms/extract_groupon_info | wc -l `
    if [ $lines -le 0 ] ;then
        echo ‘Error! groupon info is not exist‘
        exit 4
    else
        echo ‘groupon info is ok!!!!!‘
    fi
}&

# 并发进程，生成系列商品，总文件大小在320M左右
{
    sh $userTrackPathCollectHome/scripts/extract_product_serial.sh
    lines=`hadoop fs -ls /user/hive/pms/product_serial_id | wc -l `
    if [ $lines -le 0 ] ;then
        echo ‘Error! product serial is not exist‘
        exit 5
    else
        echo ‘product serial is ok!!!!!‘
    fi
}&

# 并发进程，预处理生成extract_trfc_page_kpi表--用于按照pageId进行汇总统计所在页面的pv数、uv数
{
    sh $userTrackPathCollectHome/scripts/extract_trfc_page_kpi.sh $date
    lines=`hadoop fs -ls /user/hive/pms/extract_trfc_page_kpi/ds=$date | wc -l`
    if [ $lines -le 0 ] ;then
        echo ‘Error! extract_trfc_page_kpi is not exist‘
        exit 6
    else
        echo ‘extract_trfc_page_kpi is ok!!!!!!‘
    fi
}&

# 并发进程，同步term_category到hive，并将前台类目转换为后台类目
{
    sh $userTrackPathCollectHome/scripts/extract_term_category.sh
    lines=`hadoop fs -ls /user/hive/pms/temp_term_category | wc -l`
    if [ $lines -le 0 ] ;then
        echo ‘Error! temp_term_category is not exist‘
        exit 7
    else
        echo ‘temp_term_category is ok!!!!!!‘
    fi
}&


##############################
#
# 流程C
#
##############################

# 等待上面所有的后台进程执行结束
wait 
echo ‘end of backend jobs above!!!!!!!!!!!!!!!!!!!!!!!!!!!!‘


# 生成extract_track_info表
sh $userTrackPathCollectHome/scripts/extract_track_info.sh
lines=`hadoop fs -ls /user/hive/warehouse/extract_track_info | wc -l `
if [ $lines -le 0 ] ;then
    echo ‘Error! extract_track_info is not exist‘
    exit 1
else
    echo ‘extract_track_info is ok!!!!!‘
fi

上面的脚本中，将流程B中互不影响的子任务全部放到了后台执行，从而实现了“并发执行”，同时为了不破坏脚本的执行流程：

流程A->流程B->流程C

就需要在流程C执行之前加上：

# 等待上面所有的后台进程执行结束
wait 

其目的是等待流程B的所有后台进程全部执行完成，才执行流程C

结论

经过优化后，脚本的执行时间，从耗时55分钟，降到了耗时15分钟，效果很显著。