Question

我的pyspark脚本工作正常。此脚本将从mysql获取数据并在HDFS中创建配置单元表。

pyspark脚本位于下方。

#!/usr/bin/env python
import sys
from pyspark import SparkContext, SparkConf
from pyspark.sql import HiveContext
conf = SparkConf()
sc = SparkContext(conf=conf)
sqlContext = HiveContext(sc)

#Condition to specify exact number of arguments in the spark-submit command line
if len(sys.argv) != 8:
    print "Invalid number of args......"
    print "Usage: spark-submit import.py Arguments"
    exit()
table = sys.argv[1]
hivedb = sys.argv[2]
domain = sys.argv[3]
port=sys.argv[4]
mysqldb=sys.argv[5]
username=sys.argv[6]
password=sys.argv[7]

df = sqlContext.read.format("jdbc").option("url", "{}:{}/{}".format(domain,port,mysqldb)).option("driver", "com.mysql.jdbc.Driver").option("dbtable","{}".format(table)).option("user", "{}".format(username)).option("password", "{}".format(password)).load()

#Register dataframe as table
df.registerTempTable("mytempTable")

# create hive table from temp table:
sqlContext.sql("create table {}.{} as select * from mytempTable".format(hivedb,table))

sc.stop()

现在使用pyspark脚本调用此shell脚本。对于这个shell脚本，我将表名作为参数传递给文件。

shell script在下面。

#!/bin/bash

source /home/$USER/spark/source.sh
[ $# -ne 1 ] && { echo "Usage : $0 table ";exit 1; }

args_file=$1

TIMESTAMP=`date "+%Y-%m-%d"`
touch /home/$USER/logs/${TIMESTAMP}.success_log
touch /home/$USER/logs/${TIMESTAMP}.fail_log
success_logs=/home/$USER/logs/${TIMESTAMP}.success_log
failed_logs=/home/$USER/logs/${TIMESTAMP}.fail_log

#Function to get the status of the job creation
function log_status
{
       status=$1
       message=$2
       if [ "$status" -ne 0 ]; then
                echo "`date +\"%Y-%m-%d %H:%M:%S\"` [ERROR] $message [Status] $status : failed" | tee -a "${failed_logs}"
                #echo "Please find the attached log file for more details"
                exit 1
                else
                    echo "`date +\"%Y-%m-%d %H:%M:%S\"` [INFO] $message [Status] $status : success" | tee -a "${success_logs}"
                fi
}
while read -r table ;do 
  spark-submit --name "${table}" --master "yarn-client" --num-executors 2 --executor-memory 6g  --executor-cores 1 --conf "spark.yarn.executor.memoryOverhead=609" /home/$USER/spark/sql_spark.py ${table} ${hivedb} ${domain} ${port} ${mysqldb} ${username} ${password} > /tmp/logging/${table}.log 2>&1
  g_STATUS=$?
  log_status $g_STATUS "Spark job ${table} Execution"
done < "${args_file}"

echo "************************************************************************************************************************************************************************"

我可以使用上面的shell脚本为args_file中的每个表收集日志。

现在我在mysql中有超过200个表。我修改了pyspark脚本，如下所示。我创建了一个函数来重温args_file并执行代码。

New spark script

#!/usr/bin/env python
import sys
from pyspark import SparkContext, SparkConf
from pyspark.sql import HiveContext
conf = SparkConf()
sc = SparkContext(conf=conf)
sqlContext = HiveContext(sc)

#Condition to specify exact number of arguments in the spark-submit command line
if len(sys.argv) != 8:
    print "Invalid number of args......"
    print "Usage: spark-submit import.py Arguments"
    exit()
args_file = sys.argv[1]
hivedb = sys.argv[2]
domain = sys.argv[3]
port=sys.argv[4]
mysqldb=sys.argv[5]
username=sys.argv[6]
password=sys.argv[7]

def testing(table, hivedb, domain, port, mysqldb, username, password):

    print "*********************************************************table = {} ***************************".format(table)
    df = sqlContext.read.format("jdbc").option("url", "{}:{}/{}".format(domain,port,mysqldb)).option("driver", "com.mysql.jdbc.Driver").option("dbtable","{}".format(table)).option("user", "{}".format(username)).option("password", "{}".format(password)).load()

    #Register dataframe as table
    df.registerTempTable("mytempTable")

    # create hive table from temp table:
    sqlContext.sql("create table {}.{} stored as parquet as select * from mytempTable".format(hivedb,table))

input = sc.textFile('/user/XXXXXXX/spark_args/%s' %args_file).collect()

for table in input:
 testing(table, hivedb, domain, port, mysqldb, username, password)

sc.stop()

现在我想在args_file中收集单个表的日志。但我只得到一个日志文件，其中包含所有表的日志。

我如何达到我的要求？或者我正在做的方法是完全错误的

新的shell脚本：

spark-submit --name "${args_file}" --master "yarn-client" --num-executors 2 --executor-memory 6g  --executor-cores 1 --conf "spark.yarn.executor.memoryOverhead=609" /home/$USER/spark/sql_spark.py ${table} ${hivedb} ${domain} ${port} ${mysqldb} ${username} ${password} > /tmp/logging/${args_file}.log 2>&1

Answer 1

您可以执行的操作是编写一个python脚本，该脚本将采用单个日志文件并将日志文件切换为prints table名称之前的行。

例如：

*************************************table=table1***************

然后下一个日志文件从

开始

*************************************table=table2****************

等等。您还可以将表名作为文件名

使用shell脚本在python中收集函数的日志

1 个答案: