Spark SQL大數據處理並寫入Elasticsearch

SparkSQL(Spark用於處理結構化數據的模塊)

經過SparkSQL導入的數據能夠來自MySQL數據庫、Json數據、Csv數據等，經過load這些數據能夠對其作一系列計算

下面經過程序代碼來詳細查看SparkSQL導入數據並寫入到ES中：

數據集：北京市PM2.5數據

Spark版本：2.3.2

Python版本：3.5.2

mysql-connector-java-8.0.11 下載

ElasticSearch：6.4.1

Kibana：6.4.1

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

具體代碼：

 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對應健康級別
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     獲取Spark SQL分析後的數據
44     :return:
45     """
46     # load所需字段的數據到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     # 經過udf將字符型health_level轉換爲column
57     level_function_udf = udf(get_health_level, StringType())
58 
59     # 新建列healthy_level 並healthy_level分組
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 並計算它們對應的值
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     將SparkSQL計算結果寫入到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

將mysql-connector-java-8.0.11和elasticsearch-spark-20_2.11-6.4.1.jar放到Spark的jars目錄下，提交spark任務便可。

 

注意:

(1) 若是提示：ClassNotFoundException Failed to find data source: org.elasticsearch.spark.sql.，則表示spark沒有發現jar包，此時需從新編譯pyspark：

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

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

　　則表示ES-Hadoop jar包有多餘的,可能既有elasticsearch-hadoop，又有elasticsearch-spark，此時刪除多餘的jar包，從新編譯pyspark 便可

 

執行效果：

 

更多源碼請關注個人github， https://github.com/a342058040/Spark-for-Python ，Spark相關技術全程用python實現，持續更新