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：
1 # coding: utf-8
2 import sys
3 import os
4
5 pre_current_dir = os.path.dirname(os.getcwd())
6 sys.path.append(pre_current_dir)
7 from pyspark.sql import SparkSession
8 from pyspark.sql.types import *
9 from pyspark.sql.functions import udf
10 from settings import ES_CONF
11
12 current_dir = os.path.dirname(os.path.realpath(__file__))
13
14 spark = SparkSession.builder.appName("weather_result").getOrCreate()
15
16
17 def get_health_level(value):
18 """
19 PM2.5对应健康级别
20 :param value:
21 :return:
22 """
23 if 0 <= value <= 50:
24 return "Very Good"
25 elif 50 < value <= 100:
26 return "Good"
27 elif 100 < value <= 150:
28 return "Unhealthy for Sensi"
29 elif value <= 200:
30 return "Unhealthy"
31 elif 200 < value <= 300:
32 return "Very Unhealthy"
33 elif 300 < value <= 500:
34 return "Hazardous"
35 elif value > 500:
36 return "Extreme danger"
37 else:
38 return None
39
40
41 def get_weather_result():
42 """
43 获取Spark SQL分析后的数据
44 :return:
45 """
46 # load所需字段的数据到DF
47 df_2017 = spark.read.format("csv") \
48 .option("header", "true") \
49 .option("inferSchema", "true") \
50 .load("file://{}/data/Beijing2017_PM25.csv".format(current_dir)) \
51 .select("Year", "Month", "Day", "Hour", "Value", "QC Name")
52
53 # 查看Schema
54 df_2017.printSchema()
55
56 # 通过udf将字符型health_level转换为column
57 level_function_udf = udf(get_health_level, StringType())
58
59 # 新建列healthy_level 并healthy_level分组
60 group_2017 = df_2017.withColumn(
61 "healthy_level", level_function_udf(df_2017['Value'])
62 ).groupBy("healthy_level").count()
63
64 # 新建列days和percentage 并计算它们对应的值
65 result_2017 = group_2017.select("healthy_level", "count") \
66 .withColumn("days", group_2017['count'] / 24) \
67 .withColumn("percentage", group_2017['count'] / df_2017.count())
68 result_2017.show()
69
70 return result_2017
71
72
73 def write_result_es():
74 """
75 将SparkSQL计算结果写入到ES
76 :return:
77 """
78 result_2017 = get_weather_result()
79 # ES_CONF配置 ES的node和index
80 result_2017.write.format("org.elasticsearch.spark.sql") \
81 .option("es.nodes", "{}".format(ES_CONF['ELASTIC_HOST'])) \
82 .mode("overwrite") \
83 .save("{}/pm_value".format(ES_CONF['WEATHER_INDEX_NAME']))
84
85
86 write_result_es()
87 spark.stop()View Code
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
