Spark SQL and DataFrame Guide(1.4.1)——之DataFrames

还能够使用HiveContext，它能够提供比SQLContext很多其它的功能。比如能够使用更完整的HiveQL解析器写查询，使用Hive UDFs。从Hive表中读取数据等。

使用HiveContext并不须要安装hive，Spark默认将HiveContext单独打包避免对hive过多的依赖

2.创建DataFrames
使用JSON文件创建：

from pyspark.sql import SQLContext
sqlContext = SQLContext(sc)

df = sqlContext.read.json("examples/src/main/resources/people.json")

# Displays the content of the DataFrame to stdout
df.show()

注意：
这里你可能须要将文件存入HDFS（这里的文件在Spark安装文件夹中，1.4版本号）

hadoop fs -mkdir examples/src/main/resources/
hadoop fs -put /appcom/spark/examples/src/main/resources/*         /user/hdpuser/examples/src/main/resources/

3.DataFrame操作

from pyspark.sql import SQLContext
sqlContext = SQLContext(sc)

# Create the DataFrame
df = sqlContext.read.json("examples/src/main/resources/people.json")

# Show the content of the DataFrame
df.show()
## age  name
## null Michael
## 30   Andy
## 19   Justin

# Print the schema in a tree format
df.printSchema()
## root
## |-- age: long (nullable = true)
## |-- name: string (nullable = true)

# Select only the "name" column
df.select("name").show()
## name
## Michael
## Andy
## Justin

# Select everybody, but increment the age by 1
df.select(df[‘name‘], df[‘age‘] + 1).show()
## name    (age + 1)
## Michael null
## Andy    31
## Justin  20

# Select people older than 21
df.filter(df[‘age‘] > 21).show()
## age name
## 30  Andy

# Count people by age
df.groupBy("age").count().show()
## age  count
## null 1
## 19   1
## 30   1

4.使用编程执行SQL查询
SQLContext能够使用编程执行SQL查询并返回DataFrame。

from pyspark.sql import SQLContext
sqlContext = SQLContext(sc)
df = sqlContext.sql("SELECT * FROM table")

5.和RDD交互

将RDD转换成DataFrames有两种方法：

• 利用反射来判断包括特定类型对象的RDD的schema。这样的方法会简化代码而且在你已经知道schema的时候非常适用。
• 使用编程接口。构造一个schema并将其应用在已知的RDD上。

一、利用反射判断Schema
Spark SQL能够将含Row对象的RDD转换成DataFrame。并判断数据类型。通过将一个键值对（key/value）列表作为kwargs传给Row类来构造Rows。

key定义了表的列名，类型通过看第一列数据来判断。

（所以这里RDD的第一列数据不能有缺失）未来版本号中将会通过看很多其它数据来判断数据类型。像如今对JSON文件的处理一样。

# sc is an existing SparkContext.
from pyspark.sql import SQLContext, Row
sqlContext = SQLContext(sc)

# Load a text file and convert each line to a Row.
lines = sc.textFile("examples/src/main/resources/people.txt")
parts = lines.map(lambda l: l.split(","))
people = parts.map(lambda p: Row(name=p[0], age=int(p[1])))

# Infer the schema, and register the DataFrame as a table.
schemaPeople = sqlContext.createDataFrame(people)
schemaPeople.registerTempTable("people")

# SQL can be run over DataFrames that have been registered as a table.
teenagers = sqlContext.sql("SELECT name FROM people WHERE age >= 13 AND age <= 19")

# The results of SQL queries are RDDs and support all the normal RDD operations.
teenNames = teenagers.map(lambda p: "Name: " + p.name)
for teenName in teenNames.collect():
  print teenName

二、编程指定Schema
通过编程指定Schema须要3步：

1. 从原来的RDD创建一个元祖或列表的RDD。
2. 用StructType 创建一个和步骤一中创建的RDD中元祖或列表的结构相匹配的Schema。

3. 通过SQLContext提供的createDataFrame方法将schema 应用到RDD上。

# Import SQLContext and data types
from pyspark.sql import SQLContext
from pyspark.sql.types import *

# sc is an existing SparkContext.
sqlContext = SQLContext(sc)

# Load a text file and convert each line to a tuple.
lines = sc.textFile("examples/src/main/resources/people.txt")
parts = lines.map(lambda l: l.split(","))
people = parts.map(lambda p: (p[0], p[1].strip()))

# The schema is encoded in a string.
schemaString = "name age"

fields = [StructField(field_name, StringType(), True) for field_name in schemaString.split()]
schema = StructType(fields)

# Apply the schema to the RDD.
schemaPeople = sqlContext.createDataFrame(people, schema)

# Register the DataFrame as a table.
schemaPeople.registerTempTable("people")

# SQL can be run over DataFrames that have been registered as a table.
results = sqlContext.sql("SELECT name FROM people")

# The results of SQL queries are RDDs and support all the normal RDD operations.
names = results.map(lambda p: "Name: " + p.name)
for name in names.collect():
  print name

Spark SQL and DataFrame Guide(1.4.1)——之DataFrames

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