Notebook Docker 安装spark环境

环境

Notebook docker环境

https://registry.hub.docker.com/r/jupyter/datascience-notebook/

下载安装包

spark安装包

http://mirror.bit.edu.cn/apache/spark/spark-3.0.0/spark-3.0.0-bin-hadoop3.2.tgz

pyspark安装包

http://mirror.bit.edu.cn/apache/spark/spark-3.0.0/pyspark-3.0.0.tar.gz

Py4j安装包

https://pypi.org/simple/py4j/

下载0.10.9版本

JDK安装包

Jdk 1.8

安装

spark环境安装

解压spark-3.0.0-bin-hadoop3.2.tgz 至 /var/spark目录，配置docker环境变量

SPARK_HOME=/var/spark/spark-3.0.0-bin-hadoop3.2

Java 环境安装

解压jdk至/var/spark/jdk1.8.0_191

配置环境变量

JAVA_HOME=/var/spark/jdk1.8.0_191

PATH=%PATH%:/var/spark/jdk1.8.0_191/bin

Pyspark安装

解压pyspark-3.0.0 并跳转至pyspark-3.0.0目录，执行python setup.py install,执行安装，默认会自动安装py4j，如果自动安装失败，手动安装上一步下载的py4j安装包再次执行python setup.py install命令

测试

新建python文件

from pyspark import SparkContext     
from pyspark.sql import SparkSession 
from pyspark.sql.types import StructType, StructField, LongType, StringType
from pyspark.sql import Row
from pyspark.sql import Column
import pandas as pd
import numpy as np

spark=SparkSession .builder .appName(‘newapp‘) .getOrCreate()

stringCSVRDD = spark.sparkContext.parallelize([
                    (123, "Katie", 19, "brown"),
                    (456, "Michael", 22, "green"),
                    (789, "Simone", 23, "blue")])
schema = StructType([StructField("id", LongType(), True),
                        StructField("name", StringType(), True),
                        StructField("age", LongType(), True),
                        StructField("eyeColor", StringType(), True)])
swimmers = spark.createDataFrame(stringCSVRDD,schema)
swimmers.registerTempTable("swimmers")
# 使用Sql语句
data=spark.sql("select * from swimmers")
# 将数据转换List，这样就可以查看dataframe的数据元素的样式
print(data.collect())
# 以表格形式展示数据
data.show()