Spark SQL大数据处理并写入Elasticsearch

SparkSQL(Spark用于处理结构化数据的模块)

通过SparkSQL导入的数据可以来自MySQL数据库、Json数据、Csv数据等，通过load这些数据可以对其做一系列计算

下面通过程序代码来详细查看SparkSQL导入数据并写入到ES中：

数据集：北京市PM2.5数据

Spark版本：2.3.2

Python版本：3.5.2

mysql-connector-java-8.0.11 下载

ElasticSearch：6.4.1

Kibana：6.4.1

elasticsearch-spark-20_2.11-6.4.1.jar 下载

具体代码：

 # coding: utf-8
 import sys
 import os

 pre_current_dir = os.path.dirname(os.getcwd())
 sys.path.append(pre_current_dir)
 from pyspark.sql import SparkSession
 from pyspark.sql.types import *
 from pyspark.sql.functions import udf
 from settings import ES_CONF

 current_dir = os.path.dirname(os.path.realpath(__file__))

 spark = SparkSession.builder.appName("weather_result").getOrCreate()

 def get_health_level(value):
     """
     PM2.5对应健康级别
     :param value:
     :return:
     """
     if 0 <= value <= 50:
         return "Very Good"
     elif 50 < value <= 100:
         return "Good"
     elif 100 < value <= 150:
         return "Unhealthy for Sensi"
     elif value <= 200:
         return "Unhealthy"
     elif 200 < value <= 300:
         return "Very Unhealthy"
     elif 300 < value <= 500:
         return "Hazardous"
     elif value > 500:
         return "Extreme danger"
     else:
         return None

 def get_weather_result():
     """
     获取Spark SQL分析后的数据
     :return:
     """
     # load所需字段的数据到DF
     df_2017 = spark.read.format("csv") \
         .option("header", "true") \
         .option("inferSchema", "true") \
         .load("file://{}/data/Beijing2017_PM25.csv".format(current_dir)) \
         .select("Year", "Month", "Day", "Hour", "Value", "QC Name")

     # 查看Schema
     df_2017.printSchema()

     # 通过udf将字符型health_level转换为column
     level_function_udf = udf(get_health_level, StringType())

     # 新建列healthy_level 并healthy_level分组
     group_2017 = df_2017.withColumn(
         "healthy_level", level_function_udf(df_2017['Value'])
     ).groupBy("healthy_level").count()

     # 新建列days和percentage 并计算它们对应的值
     result_2017 = group_2017.select("healthy_level", "count") \
         .withColumn("days", group_2017['count'] / 24) \
         .withColumn("percentage", group_2017['count'] / df_2017.count())
     result_2017.show()

     return result_2017

 def write_result_es():
     """
     将SparkSQL计算结果写入到ES
     :return:
     """
     result_2017 = get_weather_result()
     # ES_CONF配置 ES的node和index
     result_2017.write.format("org.elasticsearch.spark.sql") \
         .option("es.nodes", "{}".format(ES_CONF['ELASTIC_HOST'])) \
         .mode("overwrite") \
         .save("{}/pm_value".format(ES_CONF['WEATHER_INDEX_NAME']))

 write_result_es()
 spark.stop()

将mysql-connector-java-8.0.11和elasticsearch-spark-20_2.11-6.4.1.jar放到Spark的jars目录下，提交spark任务即可。

注意:

(1) 如果提示：ClassNotFoundException Failed to find data source: org.elasticsearch.spark.sql.，则表示spark没有发现jar包，此时需重新编译pyspark：

cd /opt/spark-2.3.2-bin-hadoop2.7/python
python3 setup.py sdist
pip install dist/*.tar.gz

(2) 如果提示：Multiple ES-Hadoop versions detected in the classpath; please use only one ，

　　则表示ES-Hadoop jar包有多余的,可能既有elasticsearch-hadoop，又有elasticsearch-spark，此时删除多余的jar包，重新编译pyspark 即可

执行效果：

更多源码请关注我的github， https://github.com/a342058040/Spark-for-Python ，Spark相关技术全程用python实现，持续更新