Spark scala join RDD between 2 datasets - scala

Supposed i have two dataset as following:
Dataset 1:
id, name, score
1, Bill, 200
2, Bew, 23
3, Amy, 44
4, Ramond, 68
Dataset 2:
id,message
1, i love Bill
2, i hate Bill
3, Bew go go !
4, Amy is the best
5, Ramond is the wrost
6, Bill go go
7, Bill i love ya
8, Ramond is Bad
9, Amy is great
I wanted to join above two datasets and counting the top number of person's name that appears in dataset2 according to the name in dataset1 the result should be:
Bill, 4
Ramond, 2
..
..
I managed to join both of them together but not sure how to count how many time it appear for each person.
Any suggestion would be appreciated.
Edited:
my join code:
val rdd = sc.textFile("dataset1")
val rdd2 = sc.textFile("dataset2")
val rddPair1 = rdd.map { x =>
var data = x.split(",")
new Tuple2(data(0), data(1))
}
val rddPair2 = rdd2.map { x =>
var data = x.split(",")
new Tuple2(data(0), data(1))
}
rddPair1.join(rddPair2).collect().foreach(f =>{
println(f._1+" "+f._2._1+" "+f._2._2)
})

Using RDDs, achieving the solution you desire, would be complex. Not so much using dataframes.
First step would be to read the two files you have into dataframes as below
val df1 = sqlContext.read.format("com.databricks.spark.csv")
.option("header", true)
.load("dataset1")
val df2 = sqlContext.read.format("com.databricks.spark.csv")
.option("header", true)
.load("dataset1")
so that you should be having
df1
+---+------+-----+
|id |name |score|
+---+------+-----+
|1 |Bill |200 |
|2 |Bew |23 |
|3 |Amy |44 |
|4 |Ramond|68 |
+---+------+-----+
df2
+---+-------------------+
|id |message |
+---+-------------------+
|1 |i love Bill |
|2 |i hate Bill |
|3 |Bew go go ! |
|4 |Amy is the best |
|5 |Ramond is the wrost|
|6 |Bill go go |
|7 |Bill i love ya |
|8 |Ramond is Bad |
|9 |Amy is great |
+---+-------------------+
join, groupBy and count should give your desired output as
df1.join(df2, df2("message").contains(df1("name")), "left").groupBy("name").count().as("count").show(false)
Final output would be
+------+-----+
|name |count|
+------+-----+
|Ramond|2 |
|Bill |4 |
|Amy |2 |
|Bew |1 |
+------+-----+

Related

Scala -- apply a custom if-then on a dataframe

I have this kind of dataset:
val cols = Seq("col_1","col_2")
val data = List(("a",1),
("b",1),
("a",2),
("c",3),
("a",3))
val df = spark.createDataFrame(data).toDF(cols:_*)
+-----+-----+
|col_1|col_2|
+-----+-----+
|a |1 |
|b |1 |
|a |2 |
|c |3 |
|a |3 |
+-----+-----+
I want to add an if-then column based on the existing columns.
df
.withColumn("col_new",
when(col("col_2").isin(2, 5), "str_1")
.when(col("col_2").isin(4, 6), "str_2")
.when(col("col_2").isin(1) && col("col_1").contains("a"), "str_3")
.when(col("col_2").isin(3) && col("col_1").contains("b"), "str_1")
.when(col("col_2").isin(1,2,3), "str_4")
.otherwise(lit("other")))
Instead of the list of when-then statements, I would prefer to apply a custom function. In Python I would run a lambda & map.
thank you!

Align multiple dataframes in pyspark

I have these 4 spark dataframes:
order,device,count_1
101,201,2
102,202,4
order,device,count_2
101,201,10
103,203,100
order,device,count_3
104,204,111
103,203,10
order,device,count_4
101,201,4
104,204,11
I want to create a resultant dataframe as:
order,device,count_1,count_2,count_3,count_4
101,201,2,10,,4,
102,202,4,,,,
103,203,,100,10,,
104,204,,,111,11
Is this a case of UNION or JOIN or APPEND? How to get the final resultant df?
You can think of UNION as combining tables by rows, so the number of rows will likely increase. JOIN combines tables by columns. I'm not sure what you mean by APPEND, but in this case, you would want JOIN.
Try:
val df1 = Seq((101,201,2), (102,202,4)).toDF("order" ,"device", "count_1")
val df2 = Seq((101,201,10), (103,203,100)).toDF("order" ,"device", "count_2")
val df3 = Seq((104,204,111), (103,203,10)).toDF("order" ,"device", "count_3")
val df4 = Seq((101,201,4), (104,204,11)).toDF("order" ,"device", "count_4")
val df12 = df1.join(df2, Seq("order", "device"),"fullouter")
df12.show(false)
val df123 = df12.join(df3, Seq("order", "device"),"fullouter")
df123.show(false)
val df1234 = df123.join(df4, Seq("order", "device"),"fullouter")
df1234.show(false)
returns:
+-----+------+-------+-------+-------+-------+
|order|device|count_1|count_2|count_3|count_4|
+-----+------+-------+-------+-------+-------+
|101 |201 |2 |10 |null |4 |
|102 |202 |4 |null |null |null |
|103 |203 |null |100 |10 |null |
|104 |204 |null |null |111 |11 |
+-----+------+-------+-------+-------+-------+
As you can see the comments are flawed and the 1st answer incorrect.
Did in Scala, should be easy to do in pyspark.

How to count the frequency of words with CountVectorizer in spark ML?

The below code gives a count vector for each row in the DataFrame:
import org.apache.spark.ml.feature.{CountVectorizer, CountVectorizerModel}
val df = spark.createDataFrame(Seq(
(0, Array("a", "b", "c")),
(1, Array("a", "b", "b", "c", "a"))
)).toDF("id", "words")
// fit a CountVectorizerModel from the corpus
val cvModel: CountVectorizerModel = new CountVectorizer()
.setInputCol("words")
.setOutputCol("features")
.fit(df)
cvModel.transform(df).show(false)
The result is:
+---+---------------+-------------------------+
|id |words |features |
+---+---------------+-------------------------+
|0 |[a, b, c] |(3,[0,1,2],[1.0,1.0,1.0])|
|1 |[a, b, b, c, a]|(3,[0,1,2],[2.0,2.0,1.0])|
+---+---------------+-------------------------+
How to get total counts of each words, like:
+---+------+------+
|id |words |counts|
+---+------+------+
|0 |a | 3 |
|1 |b | 3 |
|2 |c | 2 |
+---+------+------+
Shankar's answer only gives you the actual frequencies if the CountVectorizer model keeps every single word in the corpus (e.g. no minDF or VocabSize limitations). In these cases you can use Summarizer to directly sum each Vector. Note: this requires Spark 2.3+ for Summarizer.
import org.apache.spark.ml.stat.Summarizer.metrics
// You need to select normL1 and another item (like mean) because, for some reason, Spark
// won't allow one Vector to be selected at a time (at least in 2.4)
val totalCounts = cvModel.transform(df)
.select(metrics("normL1", "mean").summary($"features").as("summary"))
.select("summary.normL1", "summary.mean")
.as[(Vector, Vector)]
.first()
._1
You'll then have to zip totalCounts with cvModel.vocabulary to get the words themselves.
You can simply explode and groupBy to get the count of each word
cvModel.transform(df).withColumn("words", explode($"words"))
.groupBy($"words")
.agg(count($"words").as("counts"))
.withColumn("id", row_number().over(Window.orderBy("words")) -1)
.show(false)
Output:
+-----+------+---+
|words|counts|id |
+-----+------+---+
|a |3 |1 |
|b |3 |2 |
|c |2 |3 |
+-----+------+---+

How can I do map reduce on spark dataframe group by conditional columns?

My spark dataframe looks like this:
+------+------+-------+------+
|userid|useid1|userid2|score |
+------+------+-------+------+
|23 |null |dsad |3 |
|11 |44 |null |4 |
|231 |null |temp |5 |
|231 |null |temp |2 |
+------+------+-------+------+
I want to do the calculation for each pair of userid and useid1/userid2 (whichever is not null).
And if it's useid1, I multiply the score by 5, if it's userid2, I multiply the score by 3.
Finally, I want to add all score for each pair.
The result should be:
+------+--------+-----------+
|userid|useid1/2|final score|
+------+--------+-----------+
|23 |dsad |9 |
|11 |44 |20 |
|231 |temp |21 |
+------+------+-------------+
How can I do this?
For the groupBy part, I know dataframe has the groupBy function, but I don't know if I can use it conditionally, like if userid1 is null, groupby(userid, userid2), if userid2 is null, groupby(userid, useid1).
For the calculation part, how to multiply 3 or 5 based on the condition?
The below solution will help to solve your problem.
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.expressions.Window
import org.apache.spark.sql.functions._
val groupByUserWinFun = Window.partitionBy("userid","useid1/2")
val finalScoreDF = userDF.withColumn("useid1/2", when($"userid1".isNull, $"userid2").otherwise($"userid1"))
.withColumn("finalscore", when($"userid1".isNull, $"score" * 3).otherwise($"score" * 5))
.withColumn("finalscore", sum("finalscore").over(groupByUserWinFun))
.select("userid", "useid1/2", "finalscore").distinct()
using when method in spark SQL, select userid1 or 2 and multiply with values based on the condition
Output:
+------+--------+----------+
|userid|useid1/2|finalscore|
+------+--------+----------+
| 11 | 44| 20.0|
| 23 | dsad| 9.0|
| 231| temp| 21.0|
+------+--------+----------+
Group by will work:
val original = Seq(
(23, null, "dsad", 3),
(11, "44", null, 4),
(231, null, "temp", 5),
(231, null, "temp", 2)
).toDF("userid", "useid1", "userid2", "score")
// action
val result = original
.withColumn("useid1/2", coalesce($"useid1", $"userid2"))
.withColumn("score", $"score" * when($"useid1".isNotNull, 5).otherwise(3))
.groupBy("userid", "useid1/2")
.agg(sum("score").alias("final score"))
result.show(false)
Output:
+------+--------+-----------+
|userid|useid1/2|final score|
+------+--------+-----------+
|23 |dsad |9 |
|231 |temp |21 |
|11 |44 |20 |
+------+--------+-----------+
coalesce will do the needful.
df.withColumn("userid1/2", coalesce(col("useid1"), col("useid1")))
basically this function return first non-null value of the order
documentation :
COALESCE(T v1, T v2, ...)
Returns the first v that is not NULL, or NULL if all v's are NULL.
needs an import import org.apache.spark.sql.functions.coalesce

append two dataframes and update data

Hello guys I want to update an old dataframe based on pos_id and article_id field.
If the tuple (pos_id,article_id) exist , I will add each column to the old one, if it doesn't exist I will add the new one. It worked fine. But I don't know how to deal with the case , when the dataframe is intially empty , in this case , I will add the new rows in the second dataframe to the old one. Here it is what I did
val histocaisse = spark.read
.format("csv")
.option("header", "true") //reading the headers
.load("C:/Users/MHT/Desktop/histocaisse_dte1.csv")
val hist = histocaisse
.withColumn("pos_id", 'pos_id.cast(LongType))
.withColumn("article_id", 'pos_id.cast(LongType))
.withColumn("date", 'date.cast(DateType))
.withColumn("qte", 'qte.cast(DoubleType))
.withColumn("ca", 'ca.cast(DoubleType))
val histocaisse2 = spark.read
.format("csv")
.option("header", "true") //reading the headers
.load("C:/Users/MHT/Desktop/histocaisse_dte2.csv")
val hist2 = histocaisse2.withColumn("pos_id", 'pos_id.cast(LongType))
.withColumn("article_id", 'pos_id.cast(LongType))
.withColumn("date", 'date.cast(DateType))
.withColumn("qte", 'qte.cast(DoubleType))
.withColumn("ca", 'ca.cast(DoubleType))
hist2.show(false)
+------+----------+----------+----+----+
|pos_id|article_id|date |qte |ca |
+------+----------+----------+----+----+
|1 |1 |2000-01-07|2.5 |3.5 |
|2 |2 |2000-01-07|14.7|12.0|
|3 |3 |2000-01-07|3.5 |1.2 |
+------+----------+----------+----+----+
+------+----------+----------+----+----+
|pos_id|article_id|date |qte |ca |
+------+----------+----------+----+----+
|1 |1 |2000-01-08|2.5 |3.5 |
|2 |2 |2000-01-08|14.7|12.0|
|3 |3 |2000-01-08|3.5 |1.2 |
|4 |4 |2000-01-08|3.5 |1.2 |
|5 |5 |2000-01-08|14.5|1.2 |
|6 |6 |2000-01-08|2.0 |1.25|
+------+----------+----------+----+----+
+------+----------+----------+----+----+
|pos_id|article_id|date |qte |ca |
+------+----------+----------+----+----+
|1 |1 |2000-01-08|5.0 |7.0 |
|2 |2 |2000-01-08|39.4|24.0|
|3 |3 |2000-01-08|7.0 |2.4 |
|4 |4 |2000-01-08|3.5 |1.2 |
|5 |5 |2000-01-08|14.5|1.2 |
|6 |6 |2000-01-08|2.0 |1.25|
+------+----------+----------+----+----+
Here is the solution , i found
val df = hist2.join(hist1, Seq("article_id", "pos_id"), "left")
.select($"pos_id", $"article_id",
coalesce(hist2("date"), hist1("date")).alias("date"),
(coalesce(hist2("qte"), lit(0)) + coalesce(hist1("qte"), lit(0))).alias("qte"),
(coalesce(hist2("ca"), lit(0)) + coalesce(hist1("ca"), lit(0))).alias("ca"))
.orderBy("pos_id", "article_id")
This case doesn't work when hist1 is empty .Any help please ?
Thanks a lot
Not sure if I understood correctly, but if the problem is sometimes the second dataframe is empty, and that makes the join crash, something you can try is this:
val checkHist1Empty = Try(hist1.first)
val df = checkHist1Empty match {
case Success(df) => {
hist2.join(hist1, Seq("article_id", "pos_id"), "left")
.select($"pos_id", $"article_id",
coalesce(hist2("date"), hist1("date")).alias("date"),
(coalesce(hist2("qte"), lit(0)) + coalesce(hist1("qte"), lit(0))).alias("qte"),
(coalesce(hist2("ca"), lit(0)) + coalesce(hist1("ca"), lit(0))).alias("ca"))
.orderBy("pos_id", "article_id")
}
case Failure(e) => {
hist2.select($"pos_id", $"article_id",
coalesce(hist2("date")).alias("date"),
coalesce(hist2("qte"), lit(0)).alias("qte"),
coalesce(hist2("ca"), lit(0)).alias("ca"))
.orderBy("pos_id", "article_id")
}
}
This basically checks if the hist1 is empty before performing the join. In case it is empty it generates the df based on the same logic but applied only to the hist2 dataframe. In case it contains information it applies the logic you had, which you said that works.
instead of doing a join, why don't you do a union of the two dataframes and then groupBy (pos_id,article_id) and apply udf to each column sum for qte and ca.
val df3 = df1.unionAll(df2)
val df4 = df3.groupBy("pos_id", "article_id").agg($"pos_id", $"article_id", max("date"), sum("qte"), sum("ca"))