Scala - DataFrame-原创手记-慕课网

基本概念

What's DataFrame

A DataFrame is equivalent to a relational table in Spark SQL [1]。

DataFrame的前身是SchemaRDD，从Spark 1.3.0开始SchemaRDD更名为DataFrame [2]。其实从使用上来看，跟RDD的区别主要是有了Schema，这样就能根据不同行和列得到对应的值。

Why DataFrame, Motivition

比RDD有更多的操作，而且执行计划上也比RDD有更多的优化。能够方便处理大规模结构化数据。

How to use DataFrame

创建DataFrame

创建一个空的DataFrame
这里schema是一个StructType类型的

sqlContext.createDataFrame(sc.emptyRDD[Row], schema)

从一个List创建

def listToDataFrame(list: ListBuffer[List[Any]], schema:StructType): DataFrame = {    val rows = list.map{x => Row(x:_*)}    val rdd = sqlContext.sparkContext.parallelize(rows)
    
    sqlContext.createDataFrame(rdd, schema)
}

直接通过RDD生成

val departments = sc.parallelize(Array(
  (31, "Sales"), 
  (33, "Engineering"), 
  (34, "Clerical"),
  (35, "Marketing")
)).toDF("DepartmentID", "DepartmentName")val employees = sc.parallelize(Array[(String, Option[Int])](
  ("Rafferty", Some(31)), ("Jones", Some(33)), ("Heisenberg", Some(33)), ("Robinson", Some(34)), ("Smith", Some(34)), 
  ("Williams", null)
)).toDF("LastName", "DepartmentID")

读取json文件创建[5]

json文件

{"name":"Michael"}
{"name":"Andy", "age":30}
{"name":"Justin", "age":19}

创建DataFrame

val df = sqlContext.jsonFile("/path/to/your/jsonfile")
df: org.apache.spark.sql.DataFrame = [age: bigint, name: string]

从parquet文件读出创建

val df:DataFrame = sqlContext.read.parquet("/Users/robin/workspace/cooked_data/bt")

从MySQL读取表chuang创建[5]

val jdbcDF = sqlContext.load("jdbc", Map("url" -> "jdbc:mysql://localhost:3306/db?user=aaa&password=111", "dbtable" -> "your_table"))

从Hive创建[5]

Spark提供了一个HiveContext的上下文，其实是SQLContext的一个子类，但从作用上来说，sqlContext也支持Hive数据源。只要在部署Spark的时候加入Hive选项，并把已有的hive-site.xml文件挪到$SPARK_HOME/conf路径下，就可以直接用Spark查询包含已有元数据的Hive表了

sqlContext.sql("select count(*) from hive_people")

从CSV文件创建

有个spark-csv的library

可以从maven引入，也可以k从spark-shell $SPARK_HOME/bin/spark-shell --packages com.databricks:spark-csv_2.11:1.5.0

val df = sqlContext.read.format("com.databricks.spark.csv").
     option("header", "true").
     option("inferSchema","true").
     load("/Users/username/tmp/person.csv")

DataFrame基本操作

官方例子

// To create DataFrame using SQLContextval people = sqlContext.read.parquet("...")val department = sqlContext.read.parquet("...")

people.filter("age > 30")
 .join(department, people("deptId") === department("id"))
 .groupBy(department("name"), "gender")
 .agg(avg(people("salary")), max(people("age")))

Filter

把id为null的行都filter掉

df.withColumn("id", when(expr("id is null"), 0).otherwise(1)).show

Join连接

inner join [4]

val employees = sc.parallelize(Array[(String, Option[Int])](
  ("Rafferty", Some(31)), ("Jones", Some(33)), ("Heisenberg", Some(33)), ("Robinson", Some(34)), ("Smith", Some(34)), ("Williams", null)
)).toDF("LastName", "DepartmentID")val departments = sc.parallelize(Array(
  (31, "Sales"), (33, "Engineering"), (34, "Clerical"),
  (35, "Marketing")
)).toDF("DepartmentID", "DepartmentName")

departments.show()

+------------+--------------+
|DepartmentID|DepartmentName|
+------------+--------------+
|          31|         Sales|
|          33|   Engineering|
|          34|      Clerical|
|          35|     Marketing|
+------------+--------------+

employees.join(departments, "DepartmentID").show()
+------------+----------+--------------+
|DepartmentID|  LastName|DepartmentName|
+------------+----------+--------------+
|          31|  Rafferty|         Sales|
|          33|     Jones|   Engineering|
|          33|Heisenberg|   Engineering|
|          34|  Robinson|      Clerical|
|          34|     Smith|      Clerical|
|        null|  Williams|          null|
+------------+----------+--------------+

left outer join [4]

employees.join(departments, Seq("DepartmentID"), "left_outer").show()
+------------+----------+--------------+
|DepartmentID|  LastName|DepartmentName|
+------------+----------+--------------+
|          31|  Rafferty|         Sales|
|          33|     Jones|   Engineering|
|          33|Heisenberg|   Engineering|
|          34|  Robinson|      Clerical|
|          34|     Smith|      Clerical|
|        null|  Williams|          null|
+------------+----------+--------------+

val d1 = df.groupBy("startDate","endDate").agg(max("price") as "price").show

Join expression 用表达式连接 [3]

val products = sc.parallelize(Array(
  ("steak", "1990-01-01", "2000-01-01", 150),
  ("steak", "2000-01-02", "2020-01-01", 180),
  ("fish", "1990-01-01", "2020-01-01", 100)
)).toDF("name", "startDate", "endDate", "price")

products.show()

+-----+----------+----------+-----+
| name| startDate|   endDate|price|
+-----+----------+----------+-----+
|steak|1990-01-01|2000-01-01|  150|
|steak|2000-01-02|2020-01-01|  180|
| fish|1990-01-01|2020-01-01|  100|
+-----+----------+----------+-----+val orders = sc.parallelize(Array(
  ("1995-01-01", "steak"),
  ("2000-01-01", "fish"),
  ("2005-01-01", "steak")
)).toDF("date", "product")

orders.show()

+----------+-------+
|      date|product|
+----------+-------+
|1995-01-01|  steak|
|2000-01-01|   fish|
|2005-01-01|  steak|
+----------+-------+

orders.join(products, $"product" === $"name" && $"date" >= $"startDate" && $"date" <= $"endDate") .show()
+----------+-------+-----+----------+----------+-----+
|      date|product| name| startDate|   endDate|price|
+----------+-------+-----+----------+----------+-----+
|2000-01-01|   fish| fish|1990-01-01|2020-01-01|  100|
|1995-01-01|  steak|steak|1990-01-01|2000-01-01|  150|
|2005-01-01|  steak|steak|2000-01-02|2020-01-01|  180|
+----------+-------+-----+----------+----------+-----+

Join types: inner, outer, left_outer, right_outer, leftsemi
Join with dataframe alias

val joinedDF = testDF.as('a).join(genmodDF.as('b), $"a.PassengerId" === $"b.PassengerId")

joinedDF.select($"a.PassengerId", $"b.PassengerId").take(10)val joinedDF = testDF.join(genmodDF, testDF("PassengerId") === genmodDF("PassengerId"), "inner")

作者：虎耳
链接：https://www.jianshu.com/p/8ac9778eb4bd