1. 连接spark

from pyspark.sql import SparkSession

spark=SparkSession \
        .builder \
        .appName('my_first_app_name') \
        .getOrCreate()

2. 创建dataframe

2.1. 从变量创建

# 生成以逗号分隔的数据
stringCSVRDD = spark.sparkContext.parallelize([
    (123, "Katie", 19, "brown"),
    (234, "Michael", 22, "green"),
    (345, "Simone", 23, "blue")
])
# 指定模式, StructField(name,dataType,nullable)
# 其中：
#   name: 该字段的名字，
#   dataType：该字段的数据类型，
#   nullable: 指示该字段的值是否为空
from pyspark.sql.types import StructType, StructField, LongType, StringType  # 导入类型

schema = StructType([
    StructField("id", LongType(), True),
    StructField("name", StringType(), True),
    StructField("age", LongType(), True),
    StructField("eyeColor", StringType(), True)
])

# 对RDD应用该模式并且创建DataFrame
swimmers = spark.createDataFrame(stringCSVRDD,schema)

# 利用DataFrame创建一个临时视图
swimmers.registerTempTable("swimmers")

# 查看DataFrame的行数
swimmers.count()

2.2. 从变量创建

# 使用自动类型推断的方式创建dataframe

data = [(123, "Katie", 19, "brown"),
        (234, "Michael", 22, "green"),
        (345, "Simone", 23, "blue")]
df = spark.createDataFrame(data, schema=['id', 'name', 'age', 'eyccolor'])
df.show()
df.count()

2.3. 读取json

# 读取spark下面的示例数据

file = r"D:\hadoop_spark\spark-2.1.0-bin-hadoop2.7\examples\src\main\resources\people.json"
df = spark.read.json(file)
df.show()

2.4. 读取csv

# 先创建csv文件
import pandas as pd
import numpy as np
df=pd.DataFrame(np.random.rand(5,5),columns=['a','b','c','d','e']).\
    applymap(lambda x: int(x*10))
file=r"D:\hadoop_spark\spark-2.1.0-bin-hadoop2.7\examples\src\main\resources\random.csv"
df.to_csv(file,index=False)

# 再读取csv文件
monthlySales = spark.read.csv(file, header=True, inferSchema=True)
monthlySales.show()

2.5. 读取MySQL

# 此时需要将mysql-jar驱动放到spark-2.2.0-bin-hadoop2.7\jars下面
# 单机环境可行，集群环境不行
# 重新执行
df = spark.read.format('jdbc').options(
    url='jdbc:mysql://127.0.0.1',
    dbtable='mysql.db',
    user='root',
    password='123456' 
    ).load()
df.show()

# 也可以传入SQL语句

sql="(select * from mysql.db where db='wp230') t"
df = spark.read.format('jdbc').options(
    url='jdbc:mysql://127.0.0.1',
    dbtable=sql,
    user='root',
    password='123456' 
    ).load()
df.show()

2.6. 从pandas.dataframe创建

# 如果不指定schema则用pandas的列名
df = pd.DataFrame(np.random.random((4,4)))
spark_df = spark.createDataFrame (df,schema=['a','b','c','d'])  

2.7. 从列式存储的parquet读取

# 读取example下面的parquet文件
file=r"D:\apps\spark-2.2.0-bin-hadoop2.7\examples\src\main\resources\users.parquet"
df=spark.read.parquet(file)
df.show()

2.8. 从hive读取

# 如果已经配置spark连接hive的参数，可以直接读取hive数据
spark = SparkSession \
        .builder \
        .enableHiveSupport() \      
        .master("172.31.100.170:7077") \
        .appName("my_first_app_name") \
        .getOrCreate()

df=spark.sql("select * from hive_tb_name")
df.show()

3. 保存数据

3.1. 写到csv

# 创建dataframe
import numpy as np
df = pd.DataFrame(np.random.random((4, 4)),columns=['a', 'b', 'c', 'd'])
spark_df = spark.createDataFrame(df)

# 写到csv
file=r"D:\apps\spark-2.2.0-bin-hadoop2.7\examples\src\main\resources\test.csv"
spark_df.write.csv(path=file, header=True, sep=",", mode='overwrite')

3.2. 保存到parquet

# 创建dataframe
import numpy as np
df = pd.DataFrame(np.random.random((4, 4)),columns=['a', 'b', 'c', 'd'])
spark_df = spark.createDataFrame(df)

# 写到parquet
file=r"D:\apps\spark-2.2.0-bin-hadoop2.7\examples\src\main\resources\test.parquet"
spark_df.write.parquet(path=file,mode='overwrite')

3.3. 写到hive

# 打开动态分区
spark.sql("set hive.exec.dynamic.partition.mode = nonstrict")
spark.sql("set hive.exec.dynamic.partition=true")

# 使用普通的hive-sql写入分区表
spark.sql("""
    insert overwrite table ai.da_aipurchase_dailysale_hive 
    partition (saledate) 
    select productid, propertyid, processcenterid, saleplatform, sku, poa, salecount, saledate 
    from szy_aipurchase_tmp_szy_dailysale distribute by saledate
    """)

# 或者使用每次重建分区表的方式
jdbcDF.write.mode("overwrite").partitionBy("saledate").insertInto("ai.da_aipurchase_dailysale_hive")
jdbcDF.write.saveAsTable("ai.da_aipurchase_dailysale_hive", None, "append", partitionBy='saledate')

# 不写分区表，只是简单的导入到hive表
jdbcDF.write.saveAsTable("ai.da_aipurchase_dailysale_for_ema_predict", None, "overwrite", None)

3.4. 写到hdfs

# 数据写到hdfs，而且以csv格式保存
jdbcDF.write.mode("overwrite").options(header="true").csv("/home/ai/da/da_aipurchase_dailysale_for_ema_predict.csv")

3.5. 写到mysql

# 会自动对齐字段，也就是说，spark_df 的列不一定要全部包含MySQL的表的全部列才行

# overwrite 清空表再导入
spark_df.write.mode("overwrite").format("jdbc").options(
    url='jdbc:mysql://127.0.0.1',
    user='root',
    password='123456',
    dbtable="test.test",
    batchsize="1000",
).save()

# append 追加方式
spark_df.write.mode("append").format("jdbc").options(
    url='jdbc:mysql://127.0.0.1',
    user='root',
    password='123456',
    dbtable="test.test",
    batchsize="1000",
).save()