I have two RDDs- one from hdfs file system and the other created from a string as shown below-
val txt=sc.textFile("/tmp/textFile.txt")
val str="This\nfile is\nallowed"
val strRDD=sc.parallelize(List(str))
Now, I want two compare the data in these two RDDs:
OR
The result should be an empty RDD but that is not the case. Can someone please explain how I should compare the data of these two RDDs?
Values of the two rdds that you've created looks to be same but are not same. It is evident if you do the count of elements in both rdds as
txt.collect().count(!_.isEmpty)
//res0: Int = 3
strRDD.collect().count(!_.isEmpty)
//res1: Int = 1
The result should be an empty RDD but that is not the case.
Thats the reason the results of txt.subtract(strRDD) and strRDD.subtract(txt) are not same
val txt=sc.textFile("/tmp/textFile.txt") gives each line as separate element in txt RDD
val str="This\nfile is\nallowed"
val strRDD=sc.parallelize(List(str)) gives one \n separated element in strRDD RDD
I hope the explanation is clear
I have a Spark Dataframe with some missing values. I would like to perform a simple imputation by replacing the missing values with the mean for that column. I am very new to Spark, so I have been struggling to implement this logic. This is what I have managed to do so far:
a) To do this for a single column (let's say Col A), this line of code seems to work:
df.withColumn("new_Col", when($"ColA".isNull, df.select(mean("ColA"))
.first()(0).asInstanceOf[Double])
.otherwise($"ColA"))
b) However, I have not been able to figure out, how to do this for all the columns in my dataframe. I was trying out the Map function, but I believe it loops through each row of a dataframe
c) There is a similar question on SO - here. And while I liked the solution (using Aggregated tables and coalesce), I was very keen to know if there is a way to do this by looping through each column (I come from R, so looping through each column using a higher order functional like lapply seems more natural to me).
Thanks!
Spark >= 2.2
You can use org.apache.spark.ml.feature.Imputer (which supports both mean and median strategy).
Scala :
import org.apache.spark.ml.feature.Imputer
val imputer = new Imputer()
.setInputCols(df.columns)
.setOutputCols(df.columns.map(c => s"${c}_imputed"))
.setStrategy("mean")
imputer.fit(df).transform(df)
Python:
from pyspark.ml.feature import Imputer
imputer = Imputer(
inputCols=df.columns,
outputCols=["{}_imputed".format(c) for c in df.columns]
)
imputer.fit(df).transform(df)
Spark < 2.2
Here you are:
import org.apache.spark.sql.functions.mean
df.na.fill(df.columns.zip(
df.select(df.columns.map(mean(_)): _*).first.toSeq
).toMap)
where
df.columns.map(mean(_)): Array[Column]
computes an average for each column,
df.select(_: *).first.toSeq: Seq[Any]
collects aggregated values and converts row to Seq[Any] (I know it is suboptimal but this is the API we have to work with),
df.columns.zip(_).toMap: Map[String,Any]
creates aMap: Map[String, Any] which maps from the column name to its average, and finally:
df.na.fill(_): DataFrame
fills the missing values using:
fill: Map[String, Any] => DataFrame
from DataFrameNaFunctions.
To ingore NaN entries you can replace:
df.select(df.columns.map(mean(_)): _*).first.toSeq
with:
import org.apache.spark.sql.functions.{col, isnan, when}
df.select(df.columns.map(
c => mean(when(!isnan(col(c)), col(c)))
): _*).first.toSeq
For imputing the median (instead of the mean) in PySpark < 2.2
## filter numeric cols
num_cols = [col_type[0] for col_type in filter(lambda dtype: dtype[1] in {"bigint", "double", "int"}, df.dtypes)]
### Compute a dict with <col_name, median_value>
median_dict = dict()
for c in num_cols:
median_dict[c] = df.stat.approxQuantile(c, [0.5], 0.001)[0]
Then, apply na.fill
df_imputed = df.na.fill(median_dict)
For PySpark, this is the code I used:
mean_dict = { col: 'mean' for col in df.columns }
col_avgs = df.agg( mean_dict ).collect()[0].asDict()
col_avgs = { k[4:-1]: v for k,v in col_avgs.iteritems() }
df.fillna( col_avgs ).show()
The four steps are:
Create the dictionary mean_dict mapping column names to the aggregate operation (mean)
Calculate the mean for each column, and save it as the dictionary col_avgs
The column names in col_avgs start with avg( and end with ), e.g. avg(col1). Strip the parentheses out.
Fill the columns of the dataframe with the averages using col_avgs
I am working with Spark dataframes. I have a categorical variable in my dataframe with many levels. I am attempting a simple transformation of this variable - Only pick the top few levels which has greater than n observations (say,1000). Club all other levels into an "Others" category.
I am fairly new to Spark, so I have been struggling to implement this. This is what I have been able to achieve so far:
# Extract all levels having > 1000 observations (df is the dataframe name)
val levels_count = df.groupBy("Col_name").count.filter("count >10000").sort(desc("count"))
# Extract the level names
val level_names = level_count.select("Col_name").rdd.map(x => x(0)).collect
This gives me an Array which has the level names that I would like to retain. Next, I should define the transformation function which can be applied to the column. This is where I am getting stuck. I believe we need to create a User defined function. This is what I tried:
# Define UDF
val var_transform = udf((x: String) => {
if (level_names contains x) x
else "others"
})
# Apply UDF to the column
val df_new = df.withColumn("Var_new", var_transform($"Col_name"))
However, when I try df_new.show it throws a "Task not serializable" exception. What am I doing wrong? Also, is there a better way to do this?
Thanks!
Here is a solution that would be, in my opinion, better for such a simple transformation: stick to the DataFrame API and trust catalyst and Tungsten to be optimised (e.g. making a broadcast join):
val levels_count = df
.groupBy($"Col_name".as("new_col_name"))
.count
.filter("count >10000")
val df_new = df
.join(levels_count,$"Col_name"===$"new_col_name", joinType="leftOuter")
.drop("Col_name")
.withColumn("new_col_name",coalesce($"new_col_name", lit("other")))
I’m using Scala and want to build my own DataFrame function. For example, I want to treat a column like an array , iterate through each element and make a calculation.
To start off, I’m trying to implement my own getMax method. So column x would have the values [3,8,2,5,9], and the expected output of the method would be 9.
Here is what it looks like in Scala
def getMax(inputArray: Array[Int]): Int = {
var maxValue = inputArray(0)
for (i <- 1 until inputArray.length if inputArray(i) > maxValue) {
maxValue = inputArray(i)
}
maxValue
}
This is what I have so far, and get this error
"value length is not a member of org.apache.spark.sql.column",
and I don't know how else to iterate through the column.
def getMax(col: Column): Column = {
var maxValue = col(0)
for (i <- 1 until col.length if col(i) > maxValue){
maxValue = col(i)
}
maxValue
}
Once I am able to implement my own method, I will create a column function
val value_max:org.apache.spark.sql.Column=getMax(df.col(“value”)).as(“value_max”)
And then I hope to be able to use this in a SQL statement, for example
val sample = sqlContext.sql("SELECT value_max(x) FROM table")
and the expected output would be 9, given input column [3,8,2,5,9]
I am following an answer from another thread Spark Scala - How do I iterate rows in dataframe, and add calculated values as new columns of the data frame where they create a private method for standard deviation.
The calculations I will do will be more complex than this, (e.g I will be comparing each element in the column) , am I going in the correct directions or should I be looking more into User Defined Functions?
In a Spark DataFrame, you can't iterate through the elements of a Column using the approaches you thought of because a Column is not an iterable object.
However, to process the values of a column, you have some options and the right one depends on your task:
1) Using the existing built-in functions
Spark SQL already has plenty of useful functions for processing columns, including aggregation and transformation functions. Most of them you can find in the functions package (documentation here). Some others (binary functions in general) you can find directly in the Column object (documentation here). So, if you can use them, it's usually the best option. Note: don't forget the Window Functions.
2) Creating an UDF
If you can't complete your task with the built-in functions, you may consider defining an UDF (User Defined Function). They are useful when you can process each item of a column independently and you expect to produce a new column with the same number of rows as the original one (not an aggregated column). This approach is quite simple: first, you define a simple function, then you register it as an UDF, then you use it. Example:
def myFunc: (String => String) = { s => s.toLowerCase }
import org.apache.spark.sql.functions.udf
val myUDF = udf(myFunc)
val newDF = df.withColumn("newCol", myUDF(df("oldCol")))
For more information, here's a nice article.
3) Using an UDAF
If your task is to create aggregated data, you can define an UDAF (User Defined Aggregation Function). I don't have a lot of experience with this, but I can point you to a nice tutorial:
https://ragrawal.wordpress.com/2015/11/03/spark-custom-udaf-example/
4) Fall back to RDD processing
If you really can't use the options above, or if you processing task depends on different rows for processing one and it's not an aggregation, then I think you would have to select the column you want and process it using the corresponding RDD. Example:
val singleColumnDF = df("column")
val myRDD = singleColumnDF.rdd
// process myRDD
So, there was the options I could think of. I hope it helps.
An easy example is given in the excellent documentation, where a whole section is dedicated to UDFs:
import org.apache.spark.sql._
val df = Seq(("id1", 1), ("id2", 4), ("id3", 5)).toDF("id", "value")
val spark = df.sparkSession
spark.udf.register("simpleUDF", (v: Int) => v * v)
df.select($"id", callUDF("simpleUDF", $"value"))
My file is,
sunny,hot,high,FALSE,no
sunny,hot,high,TRUE,no
overcast,hot,high,FALSE,yes
rainy,mild,high,FALSE,yes
rainy,cool,normal,FALSE,yes
rainy,cool,normal,TRUE,no
overcast,cool,normal,TRUE,yes
Here there are 7 rows & 5 columns(0,1,2,3,4)
I want the output as,
Map(0 -> Set("sunny","overcast","rainy"))
Map(1 -> Set("hot","mild","cool"))
Map(2 -> Set("high","normal"))
Map(3 -> Set("false","true"))
Map(4 -> Set("yes","no"))
The output must be the type of [Map[Int,Set[String]]]
EDIT: Rewritten to present the map-reduce version first, as it's more suited to Spark
Since this is Spark, we're probably interested in parallelism/distribution. So we need to take care to enable that.
Splitting each string into words can be done in partitions. Getting the set of values used in each column is a bit more tricky - the naive approach of initialising a set then adding every value from every row is inherently serial/local, since there's only one set (per column) we're adding the value from each row to.
However, if we have the set for some part of the rows and the set for the rest, the answer is just the union of these sets. This suggests a reduce operation where we merge sets for some subset of the rows, then merge those and so on until we have a single set.
So, the algorithm:
Split each row into an array of strings, then change this into an
array of sets of the single string value for each column - this can
all be done with one map, and distributed.
Now reduce this using an
operation that merges the set for each column in turn. This also can
be distributed
turn the single row that results into a Map
It's no coincidence that we do a map, then a reduce, which should remind you of something :)
Here's a one-liner that produces the single row:
val data = List(
"sunny,hot,high,FALSE,no",
"sunny,hot,high,TRUE,no",
"overcast,hot,high,FALSE,yes",
"rainy,mild,high,FALSE,yes",
"rainy,cool,normal,FALSE,yes",
"rainy,cool,normal,TRUE,no",
"overcast,cool,normal,TRUE,yes")
val row = data.map(_.split("\\W+").map(s=>Set(s)))
.reduce{(a, b) => (a zip b).map{case (l, r) => l ++ r}}
Converting it to a Map as the question asks:
val theMap = row.zipWithIndex.map(_.swap).toMap
Zip the list with the index, since that's what we need as the key of
the map.
The elements of each tuple are unfortunately in the wrong
order for .toMap, so swap them.
Then we have a list of (key, value)
pairs which .toMap will turn into the desired result.
These don't need to change AT ALL to work with Spark. We just need to use a RDD, instead of the List. Let's convert data into an RDD just to demo this:
val conf = new SparkConf().setAppName("spark-scratch").setMaster("local")
val sc= new SparkContext(conf)
val rdd = sc.makeRDD(data)
val row = rdd.map(_.split("\\W+").map(s=>Set(s)))
.reduce{(a, b) => (a zip b).map{case (l, r) => l ++ r}}
(This can be converted into a Map as before)
An earlier oneliner works neatly (transpose is exactly what's needed here) but is very difficult to distribute (transpose inherently needs to visit every row)
data.map(_.split("\\W+")).transpose.map(_.toSet)
(Omitting the conversion to Map for clarity)
Split each string into words.
Transpose the result, so we have a list that has a list of the first words, then a list of the second words, etc.
Convert each of those to a set.
Maybe this do the trick:
val a = Array(
"sunny,hot,high,FALSE,no",
"sunny,hot,high,TRUE,no",
"overcast,hot,high,FALSE,yes",
"rainy,mild,high,FALSE,yes",
"rainy,cool,normal,FALSE,yes",
"rainy,cool,normal,TRUE,no",
"overcast,cool,normal,TRUE,yes")
val b = new Array[Map[String, Set[String]]](5)
for (i <- 0 to 4)
b(i) = Map(i.toString -> (Set() ++ (for (s <- a) yield s.split(",")(i))) )
println(b.mkString("\n"))