bash循环执行程序

1 bash循环执行程序

#!/bin/sh

list1="1 2 3 4 5" # 这就是个string
list2="5 4 3 2 1"

list1_x=($list1)  # 转换成列表
list2_x=($list2)

length=${#list1_x[@]} # 获取列表内数据的个数

for ((i=0; i<${length}; i++));
do
    echo ${list1_x[$i]}
    echo ${list2_x[$i]}
done

2 循环任务列表执行hadoop脚本

#!/usr/bin/env bash
# cd into current path
path=$(cd `dirname $0`; pwd)
cd $path

# common
# AFS_BASE="path/user/xxxx/diaoyan/xxxxx"
AFS_BASE="path/user/fmflow/yunfan/job_output"
USER_BASE="path/user/xxxx/diaoyan/name"
LOCAL_HADOOP_BIN="/home/vca/xxxx/bin/hadoop"

# tools
HADOOP_LIB_PATH="path/user/xxxx/diaoyan/xxxx/lib/"
PYTHON_PATH=${HADOOP_LIB_PATH}"python.tar.gz"
USER_ENV=${USER_BASE}"/image_data/proto_trans.tar.gz"

# all the path you need
task_list="3153 3152 3115 3112"       # 任务列表
input_list="3195 3187 3182 3173"
output_list="se_0059 se_0104 se_0304 se_0204"

task_list=($task_list)                # 转换成列表
input_list=($input_list)
output_list=($output_list)
task_len=${#task_list[@]}
echo $task_len

for ((i=0; i<${task_len}; i++));      # 循环执行
do
    INPUT_DIR1=${AFS_BASE}"/task_"${task_list[$i]}"/"${task_list[$i]}
    INPUT_DIR2=${AFS_BASE}"/task_"${task_list[$i]}"/"${input_list[$i]}
    OUTPUT_PATH=${USER_BASE}"/txxxxx/"${output_list[$i]}

    echo $INPUT_DIR1, $INPUT_DIR2, $OUTPUT_PATH
    
    # show sth.
    echo "input path: ${INPUT_DIR}"
    echo "output path: ${OUTPUT_PATH}"
    $LOCAL_HADOOP_BIN fs -rmr ${OUTPUT_PATH}

    # run the routine

    ${LOCAL_HADOOP_BIN} streaming 
        -D fs.default.name="hadoop_path" 
        -D mapred.job.groups="group_name" 
        -D dfs.client.auth.method=4 
        -D mapred.job.priority=VERY_HIGH 
        -D mapred.job.name="search_development_name_vxxxxxc_image_extract" 
        -D abaci.split.optimize.enable=false 
        -D mapred.job.map.capacity=500 
        -D mapred.map.tasks=500 
        -D mapred.job.reduce.capacity=50 
        -D mapred.reduce.tasks=50 
        -D stream.memory.limit=800 
        -D mapred.reduce.tasks.speculative.execution=false 
        -D mapred.map.tasks.speculative.execution=false 
        -D mapred.max.map.failures.percent=20 
        -D mapred.max.reduce.failures.percent=20 
        -D stream.num.map.output.key.fields=1 
        -D num.key.fields.for.partition=1 
        -partitioner org.apache.hadoop.mapred.lib.KeyFieldBasedPartitioner 
        -mapper "./python/bin/python base64_decode.py" 
        -reducer "cat" 
        -input ${INPUT_DIR1},${INPUT_DIR2}                   
        -output ${OUTPUT_PATH}                
        -file ./base64_decode.py                
        -cacheArchive ${PYTHON_PATH}          
        -cacheArchive ${USER_ENV}                      
done