I have a list of strings, which represents the names of various columns I want to add together to make another column:
val myCols = List("col1", "col2", "col3")
I want to convert the list to columns, then add the columns together to make a final column. I've looked for a number of ways to do this, and the closest I can come to the answer is:
df.withColumn("myNewCol", myCols.foldLeft(lit(0))(col(_) + col(_)))
I get a compile error where it says it is looking for a string, when all I really want is a column. What's wrong? How to fix it?
When I tried it out in spark-shell it gave me the error that says exactly what the error is and where.
scala> myCols.foldLeft(lit(0))(col(_) + col(_))
<console>:26: error: type mismatch;
found : org.apache.spark.sql.Column
required: String
myCols.foldLeft(lit(0))(col(_) + col(_))
^
Just think of the first pair that is given to the function of foldLeft. It's going to be lit(0) of type Column and col1 of type String. There's no col function that accepts a Column.
Try reduce instead:
myCols.map(col).reduce(_ + _)
From the official documentation of reduce:
Applies a binary operator to all elements of this collection, going right to left.
the result of inserting op between consecutive elements of this collection, going right to left:
op(x_1, op(x_2, ..., op(x_{n-1}, x_n)...))
where x1, ..., xn are the elements of this collection.
Here is how you can add columns dynamically based on the column names on a List. When all columns are numeric the result is a number. The 1st variable on foldLeft is of same type as return. foldLeft would work as much as reduce.
val employees = //a dataframe with 2 numeric columns "salary","exp"
val initCol = lit(0)
val cols = Seq("salary","exp")
val col1 = cols.foldLeft(initCol)((x,y) => x + col(y))
employees.select(col1).show()
Related
I have a dataset that is like the following:
val df = Seq("samb id 12", "car id 13", "lxu id 88").toDF("list")
I want to create a column that will be a string containing only the values after Id. The result would be something like:
val df_result = Seq(("samb id 12",12), ("car id 13",13), ("lxu id 88",88)).toDF("list", "id_value")
For that, I am trying to use substring. For the the parameter of the starting position to extract the substring, I am trying to use locate. But it gives me an error saying that it should be an Int and not a column type.
What I am trying is like:
df
.withColumn("id_value", substring($"list", locate("id", $"list") + 2, 2))
The error I get is:
error: type mismatch;
found : org.apache.spark.sql.Column
required: Int
.withColumn("id_value", substring($"list", locate("id", $"list") + 2, 2))
^
How can I fix this and continue using locate() as a parameter?
UPDATE
Updating to give an example in which #wBob answer doesn't work for my real world data: my data is indeed a bit more complicated than the examples above.
It is something like this:
val df = Seq(":option car, lorem :ipsum: :ison, ID R21234, llor ip", "lst ID X49329xas ipsum :ion: ip_s-")
The values are very long strings that don't have a specific pattern.
Somewhere in the string that is always a part written ID XXXXX. The XXXXX varies, but it is always the same size (5 characters) and always after a ID .
I am not being able to use neither split nor regexp_extract to get something in this pattern.
It is not clear if you want the third item or the first number from the list, but here are a couple of examples which should help:
// Assign sample data to dataframe
val df = Seq("samb id 12", "car id 13", "lxu id 88").toDF("list")
df
.withColumn("t1", split($"list", "\\ ")(2))
.withColumn("t2", regexp_extract($"list", "\\d+", 0))
.withColumn("t3", regexp_extract($"list", "(id )(\\d+)", 2))
.withColumn("t4", regexp_extract($"list", "ID [A-Z](\\d{5})", 1))
.show()
You can use functions like split and regexp_extract with withColumn to create new columns based on existing values. split splits out the list into an array based on the delimiter you pass in. I have used space here, escaped with two slashes to split the array. The array is zero-based hence specifying 2 gets the third item in the array. regexp_extract uses regular expressions to extract from strings. here I've used \\d which represents digits and + which matches the digit 1 or many times. The third column, t3, again uses regexp_extract with a similar RegEx expression, but using brackets to group up sections and 2 to get the second group from the regex, ie the (\\d+). NB I'm using additional slashes in the regex to escape the slashes used in the \d.
My results:
If your real data is more complicated please post a few simple examples where this code does not work and explain why.
I have a config defined which contains a list of column for each table to be used as a dedup key
for ex:
config 1 :
val lst = List(section_xid, learner_xid)
these are the column that needs to be used as a dedup keys. This list is dynamic some table will have 1 value some will have 2 or 3 values in it
what I am trying to do is build a single key column from this list
df.
.withColumn( "dedup_key_sk", uuid(md5(concat($"lst(0)",$"lst(1)"))) )
how do I make this dynamic which will work for any number of columns in list .
I tried doing this
df.withColumn("dedup_key_sk", concat(Seq($"col1", $"col2"):_*))
For this to work I had to convert list to Df and each value in list needs to be in separate columns I was not able to figure that out.
tried doing this but didn't work
val res = sc.parallelize(List((lst))).toDF
ANy input here will be appreciated . Thank you
The list of strings can be mapped to a list of columns (using functions.col). This list of columns can then be used with concat:
val lst: List[String] = List("section_xid", "learner_xid")
df.withColumn("dedup_key_sk", concat(lst.map(col):_*)).show()
I would like to aggregate a Spark data frame using an array of column names as input, and at the same time retain the original names of the columns.
df.groupBy($"id").sum(colNames:_*)
This works but fails to preserve the names. Inspired by the answer found here I unsucessfully tried this:
df.groupBy($"id").agg(sum(colNames:_*).alias(colNames:_*))
error: no `: _*' annotation allowed here
It works to take a single element like
df.groupBy($"id").agg(sum(colNames(2)).alias(colNames(2)))
How can make this happen for the entire array?
Just provide an sequence of columns with aliases:
val colNames: Seq[String] = ???
val exprs = colNames.map(c => sum(c).alias(c))
df.groupBy($"id").agg(exprs.head, exprs.tail: _*)
I need a window function that partitions by some keys (=column names), orders by another column name and returns the rows with top x ranks.
This works fine for ascending order:
def getTopX(df: DataFrame, top_x: String, top_key: String, top_value:String): DataFrame ={
val top_keys: List[String] = top_key.split(", ").map(_.trim).toList
val w = Window.partitionBy(top_keys(1),top_keys.drop(1):_*)
.orderBy(top_value)
val rankCondition = "rn < "+top_x.toString
val dfTop = df.withColumn("rn",row_number().over(w))
.where(rankCondition).drop("rn")
return dfTop
}
But when I try to change it to orderBy(desc(top_value)) or orderBy(top_value.desc) in line 4, I get a syntax error. What's the correct syntax here?
There are two versions of orderBy, one that works with strings and one that works with Column objects (API). Your code is using the first version, which does not allow for changing the sort order. You need to switch to the column version and then call the desc method, e.g., myCol.desc.
Now, we get into API design territory. The advantage of passing Column parameters is that you have a lot more flexibility, e.g., you can use expressions, etc. If you want to maintain an API that takes in a string as opposed to a Column, you need to convert the string to a column. There are a number of ways to do this and the easiest is to use org.apache.spark.sql.functions.col(myColName).
Putting it all together, we get
.orderBy(org.apache.spark.sql.functions.col(top_value).desc)
Say for example, if we need to order by a column called Date in descending order in the Window function, use the $ symbol before the column name which will enable us to use the asc or desc syntax.
Window.orderBy($"Date".desc)
After specifying the column name in double quotes, give .desc which will sort in descending order.
Column
col = new Column("ts")
col = col.desc()
WindowSpec w = Window.partitionBy("col1", "col2").orderBy(col)
I am new to Spark. I have two tables in HDFS. One table(table 1) is a tag table,composed of some text, which could be some words or a sentence. Another table(table 2) has a text column. Every row could have more than one keyword in the table 1. my task is find out all the matched keywords in table 1 for the text column in table 2, and output the keyword list for every row in table 2.
The problem is I have to iterate every row in table 2 and table 1. If I produce a big list for table 1, and use a map function for table 2. I will still have to use a loop to iterate the list in the map function. And the driver shows the JVM memory limit error,even if the loop is not large(10 thousands time).
myTag is the tag list of table 1.
def ourMap(line: String, myTag: List[String]): String = {
var ret = line
val length = myTag.length
for (i <- 0 to length - 1) {
if (line.contains(myTag(i)))
ret = ret.replaceAll(myTag(i), "_")
}
ret
}
val matched = result.map(b => ourMap(b, tagList))
Any suggestion to finish this task? With or without Spark
Many thanks!
An example is as follows:
table1
row1|Spark
row2|RDD
table2
row1| Spark is a fast and general engine. RDD supports two types of operations.
row2| All transformations in Spark are lazy.
row3| It is for test. I am a sentence.
Expected result :
row1| Spark,RDD
row2| Spark
MAJOR EDIT:
The first table actually may contain sentences and not just simple keywords :
row1| Spark
row2| RDD
row3| two words
row4| I am a sentence
Here you go, considering the data sample that you have provided :
val table1: Seq[(String, String)] = Seq(("row1", "Spark"), ("row2", "RDD"), ("row3", "Hashmap"))
val table2: Seq[String] = Seq("row1##Spark is a fast and general engine. RDD supports two types of operations.", "row2##All transformations in Spark are lazy.")
val rdd1: RDD[(String, String)] = sc.parallelize(table1)
val rdd2: RDD[(String, String)] = sc.parallelize(table2).map(_.split("##").toList).map(l => (l.head, l.tail(0))).cache
We'll build an inverted index of the second data table which we will join to the first table :
val df1: DataFrame = rdd1.toDF("key", "value")
val df2: DataFrame = rdd2.toDF("key", "text")
val df3: DataFrame = rdd2.flatMap { case (row, text) => text.trim.split( """[^\p{IsAlphabetic}]+""")
.map(word => (word, row))
}.groupByKey.mapValues(_.toSet.toSeq).toDF("word", "index")
import org.apache.spark.sql.functions.explode
val results: RDD[(String, String)] = df3.join(df1, df1("value") === df3("word")).drop("key").drop("value").withColumn("index", explode($"index")).rdd.map {
case r: Row => (r.getAs[String]("index"), r.getAs[String]("word"))
}.groupByKey.mapValues(i => i.toList.mkString(","))
results.take(2).foreach(println)
// (row1,Spark,RDD)
// (row2,Spark)
MAJOR EDIT:
As mentioned in the comment : The specifications of the issue changed. Keywords are no longer simple keywords, they might be sentences. In that case, this approach wouldn't work, it's a different kind of problem. One way to do it is using Locality-sensitive hashing (LSH) algorithm for nearest neighbor search.
An implementation of this algorithm is available here.
The algorithm and its implementation are unfortunately too long to discuss on SO.
From what I could gather from your problem statement is that you are kind of trying to tag the data in Table 2 with the keywords which are present in Table 1. For this, instead of loading the Table1 as a list and then doing each keyword pattern matching for each row in Table2, do this :
Load Table1 as a hashSet.
Traverse the Table2 and for each word in that phrase, do a search in the above hashset. I assume the words that you shall have to search from here are less as compared to pattern matching for each keyword. Remember, search now is O(1) operation whereas pattern matching is not.
Also, in this process, you can also filter words like " is, are, when, if " etc as they shall never be used for tagging. So that reduces words you need to find in hashSet.
The hashSet can be loaded into memory(I think 10K keywords should not take more than few MBs). This variable can be shared across executors through broadcast variables.