大数据ETL实践探索（3）---- pyspark 之大数据ETL利器

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5.spark dataframe 数据导入Elasticsearch

5.1 dataframe 及环境初始化

初始化， spark 第三方网站下载包：elasticsearch-spark-20_2.11-6.1.1.jar
http://spark.apache.org/third-party-projects.html

import sys
import os
print(os.getcwd())
# 加载包得放在这里
os.environ['PYSPARK_SUBMIT_ARGS'] = '--jars elasticsearch-spark-20_2.11-6.1.1.jar pyspark-shell'

import os
from pyspark.sql import SparkSession
from pyspark import SparkConf
from pyspark.sql.types import *
from pyspark.sql import functions as F
from pyspark.storagelevel import StorageLevel
import json
import math
import numbers
import numpy as np
import pandas as pd

os.environ["PYSPARK_PYTHON"] = "/home/hadoop/anaconda/envs/playground_py36/bin/python"



try:
    spark.stop()
    print("Stopped a SparkSession")
except Exception as e:
    print("No existing SparkSession")

SPARK_DRIVER_MEMORY= "10G"
SPARK_DRIVER_CORE = "5"
SPARK_EXECUTOR_MEMORY= "3G"
SPARK_EXECUTOR_CORE = "1"


conf = SparkConf().\
        setAppName("insurance_dataschema").\
        setMaster('yarn-client').\
        set('spark.executor.cores', SPARK_EXECUTOR_CORE).\
        set('spark.executor.memory', SPARK_EXECUTOR_MEMORY).\
        set('spark.driver.cores', SPARK_DRIVER_CORE).\
        set('spark.driver.memory', SPARK_DRIVER_MEMORY).\
        set('spark.driver.maxResultSize', '0').\
        set("es.index.auto.create", "true").\
        set("es.resource", "tempindex/temptype").\
        set("spark.jars", "elasticsearch-hadoop-6.1.1.zip")  # set the spark.jars
    
        
spark = SparkSession.builder.\
    config(conf=conf).\
    getOrCreate()

sc=spark.sparkContext
hadoop_conf = sc._jsc.hadoopConfiguration()

hadoop_conf.set("mapreduce.fileoutputcommitter.algorithm.version", "2")

5.2 清洗及写入数据

• 数据加载

#数据加载
df = (spark
                 .read
                 .option("header","true")
                 .option("multiLine", "true")
                 .csv('EXPORT.csv')
                 .cache()
                )
print(df.count())


#

• 数据清洗，增加一列，或者针对某一列进行udf 转换

'''  
#加一列yiyong ，如果是众城数据则为zhongcheng
'''

from pyspark.sql.functions import udf


from pyspark.sql import functions
df = df.withColumn('customer',functions.lit("腾讯用户"))

• 使用udf 清洗时间格式及数字格式

#udf 清洗时间
#清洗日期格式字段
from dateutil import parser

def clean_date(str_date):
    try:
        if str_date:
            d = parser.parse(str_date)
            return d.strftime('%Y-%m-%d')
        else:
            return None
    except Exception as e:
         return None
        


func_udf_clean_date = udf(clean_date, StringType())

def is_number(s):
    try:
        float(s)
        return True
    except ValueError:
        pass
    return False

def clean_number(str_number):

    try:
        if str_number:

                if is_number(str_number):
                    return str_number
                else:
                    None
        else:
            return None
    except Exception as e:
        return None




func_udf_clean_number = udf(clean_number, StringType())

column_Date = [
"DATE_FROM",
"DATE_TO",
]


for column in column_Date:
      df=df.withColumn(column,  func_udf_clean_date(df[column]))

df.select(column_Date).show(2)

#数据写入

df.write.format("org.elasticsearch.spark.sql").\
option("es.nodes", "IP").\
option("es.port","9002").\
mode("Overwrite").\
save("is/doc")

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