Question

使用Scala，我如何将dataFrame拆分为具有相同列值的多个dataFrame（无论是数组还是集合）。例如，我想拆分以下DataFrame：

ID  Rate    State
1   24  AL
2   35  MN
3   46  FL
4   34  AL
5   78  MN
6   99  FL

为：

数据集1

ID  Rate    State
1   24  AL  
4   34  AL

数据集2

ID  Rate    State
2   35  MN
5   78  MN

数据集3

ID  Rate    State
3   46  FL
6   99  FL

Answer 1

您可以收集唯一的状态值，只需映射结果数组：

val states = df.select("State").distinct.collect.flatMap(_.toSeq)
val byStateArray = states.map(state => df.where($"State" <=> state))

或映射：

val byStateMap = states
    .map(state => (state -> df.where($"State" <=> state)))
    .toMap

Python中的相同内容：

from itertools import chain
from pyspark.sql.functions import col

states = chain(*df.select("state").distinct().collect())

# PySpark 2.3 and later
# In 2.2 and before col("state") == state) 
# should give the same outcome, ignoring NULLs 
# if NULLs are important 
# (lit(state).isNull() & col("state").isNull()) | (col("state") == state)
df_by_state = {state: 
  df.where(col("state").eqNullSafe(state)) for state in states}

这里显而易见的问题是它需要对每个级别进行全数据扫描，因此这是一项昂贵的操作。如果您正在寻找分割输出的方法，请参阅How do I split an RDD into two or more RDDs?

特别是您可以编写由感兴趣的列划分的Dataset：

val path: String = ???
df.write.partitionBy("State").parquet(path)

并在需要时回读：

// Depend on partition prunning
for { state <- states } yield spark.read.parquet(path).where($"State" === state)

// or explicitly read the partition
for { state <- states } yield spark.read.parquet(s"$path/State=$state")

根据数据的大小，输入的分割，存储和持久性级别的数量，它可能比多个过滤器更快或更慢。

Answer 2

如果将数据帧作为临时表，则非常简单（如果spark版本为2）。

df1.createOrReplaceTempView("df1")

现在你可以进行查询了，

var df2 = spark.sql("select * from df1 where state = 'FL'")
var df3 = spark.sql("select * from df1 where state = 'MN'")
var df4 = spark.sql("select * from df1 where state = 'AL'")

现在你得到了df2，df3，df4。如果您想将它们作为列表，可以使用

df2.collect()
df3.collect()

甚至是地图/过滤功能。请参阅https://spark.apache.org/docs/latest/sql-programming-guide.html#datasets-and-dataframes

灰

Answer 3

you can use .. 
var stateDF = df.select("state").distinct()  // to get states in a df
val states = stateDF.rdd.map(x=>x(0)).collect.toList //to get states in a list

for (i <- states)  //loop to get each state
{
var finalDF = sqlContext.sql("select * from table1 where state = '" + state
+"' ")
}

如何将数据框拆分为具有相同列值的数据框？

3 个答案: