My objective is to get groups of userids from the dataframe and if consecutive rows of Depts match, then the MergeCol column would be set to 1.
I tried to do this within a for loop, getting distinct userids and the getting all the corresponding records from the dataframe for one userid and then loop over.
Its throwing null pointer exception inside the for loop.
When searched in StackOverflow, found out that dataframes cannot be used within for each loop.
Any work around anyone can suggest?
val distinctIdUser = inputTableDf.select("idUser").distinct()
distinctIdUser.foreach{ row =>
val id_user = row.getAs[Long]("idUser")
val subset = inputTableDf.filter($"idUser" === id_user)
val window = Window.orderBy("tsStart")
subset.withColumn("MergeCol",when(compareCol(col("Dept"),lead(col("Dept").over(window), 1)))
}
Related
I need to Check the duplicate filename in my table and if file count is 0 then i need to load a file in my table using sparkSql. I wrote below code.
val s1=spark.sql("select count(filename) from mytable where filename='myfile.csv'") //giving '2'
s1: org.apache.spark.sql.DataFrame = [count(filename): bigint]
s1.show //giving 2 as output
//s1 is giving me the filecount from my table then i need to compare this count value using if statement.
I'm using below code.
val s2=s1.count //not working always giving 1
val s2=s1.head.count() // error: value count is not a member of org.apache.spark.sql.Row
val s2=s1.size //value size is not a member of Unit
if(s1>0){ //code } //value > is not a member of org.apache.spark.sql.DataFrame
can someone please give me a hint how should i do this.How can i get the dataframe value and can use as variable to check the condition.
i.e.
if(value of s1(i.e.2)>0){
//my code
}
You need to extract the value itself. Count will return the number of rows in the df, which is just one row.
So you can keep your original query and extract the value after with first and getInt methods
val s1 = spark.sql("select count(filename) from mytable where filename='myfile.csv'")`
val valueToCompare = s1.first().getInt(0)
And then:
if(valueToCompare>0){
//my code
}
Another option is performing the count outside the query, then the count will give you the desired value:
val s1 = spark.sql("select filename from mytable where filename='myfile.csv'")
if(s1.count>0){
//my code
}
I like the most the second option, but there is no reason other than that i think it is more clear
spark.sql("select count(filename) from mytable where filename='myfile.csv'") returns a dataframe and you need to extract both the first row and the first column of that row. It is much simpler to directly filter the dataset and count the number of rows in Scala:
val s1 = df.filter($"filename" === "myfile.csv").count
if (s1 > 0) {
...
}
where df is the dataset that corresponds to the mytable table.
If you got the table from some other source and not by registering a view, use SparkSession.table() to get a dataframe using the instance of SparkSession that you already have. For example, in Spark shell the pre-set variable spark holds the session and you'll do:
val df = spark.table("mytable")
val s1 = df.filter($"filename" === "myfile.csv").count
So, I'm trying to read an existing file, save that into a DataFrame, once that's done I make a "union" between that existing DataFrame and a new one I have already created, both have the same columns and share the same schema.
ALSO I CANNOT GIVE SIGNIFICANT NAME TO VARS NOR GIVE ANYMORE DATA BECAUSE OF RESTRICTIONS
val dfExist = spark.read.format("csv").option("header", "true").option("delimiter", ",").schema(schema).load(filePathAggregated3)
val df5 = df4.union(dfExist)
Once that's done I get the "start_ts" (a timestamp on Epoch format) that's duplicate in the union between the above dataframes (df4 and dfExist) and also I get rid of some characters I don't want
val df6 = df5.select($"start_ts").collect()
val df7 = df6.diff(df6.distinct).distinct.mkString.replace("[", "").replace("]", "")
Now I use this "start_ts" duplicate to filter the DataFrame and create 2 new DataFrames selecting the items of this duplicate timestamp, and the items that are not like this duplicate timestamp
val itemsNotDup = df5.filter(!$"start_ts".like(df7)).select($"start_ts",$"avg_value",$"Number_of_val")
val items = df5.filter($"start_ts".like(df7)).select($"start_ts",$"avg_value",$"Number_of_val")
And then I save in 2 different lists the avg_value and the Number_of_values
items.map(t => t.getAs[Double]("avg_value")).collect().foreach(saveList => listDataDF += saveList.toString)
items.map(t => t.getAs[Long]("Number_of_val")).collect().foreach(saveList => listDataDF2 += saveList.toString)
Now I make some maths with the values on the lists (THIS IS WHERE I'M GETTING ISSUES)
val newAvg = ((listDataDF(0).toDouble*listDataDF2(0).toDouble) - (listDataDF(1).toDouble*listDataDF2(1).toDouble)) / (listDataDF2(0) + listDataDF2(1)).toInt
val newNumberOfValues = listDataDF2(0).toDouble + listDataDF2(1).toDouble
Then save the duplicate timestamp (df7), the avg and the number of values into a list as a single item, this list transforms into a DataFrame and then I transform I get a new DataFrame with the columns how are supposed to be.
listDataDF3 += df7 + ',' + newAvg.toString + ',' + newNumberOfValues.toString + ','
val listDF = listDataDF3.toDF("value")
val listDF2 = listDF.withColumn("_tmp", split($"value", "\\,")).select(
$"_tmp".getItem(0).as("start_ts"),
$"_tmp".getItem(1).as("avg_value"),
$"_tmp".getItem(2).as("Number_of_val")
).drop("_tmp")
Finally I join the DataFrame without duplicates with the new DataFrame which have the duplicate timestamp and the avg of the duplicate avg values and the sum of number of values.
val finalDF = itemsNotDup.union(listDF2)
finalDF.coalesce(1).write.mode(SaveMode.Overwrite).format("csv").option("header","true").save(filePathAggregated3)
When I run this code in SPARK it gives me the error, I supposed it was related to empty lists (since it's giving me the error when making some maths with the values of the lists) but If I delete the line where I write to CSV, the code runs perfectly, also I saved the lists and values of the math calcs into files and they are not empty.
My supposition, is that, is deleting the file before reading it (because of how spark distribute tasks between workers) and that's why the list is empty therefore I'm getting this error when trying to make maths with those values.
I'm trying to be as clear as possible but I cannot give much more details, nor show any of the output.
So, how can I avoid this error? also I've been only 1 month with scala/spark so any code recommendation will be nice as well.
Thanks beforehand.
This error comes because of the Data. Any of your list does not contains columns as expected. When you refer to that index, the List gives this error to you
It was a problem related to reading files, I made a check (df.rdd.isEmpty) and wether the DF was empty I was getting this error. Made this as an if/else statement to check if the DF is empty, and now it works fine.
I'm filtering Integer columns from the input parquet file with below logic and been trying to modify this logic to add additional validation to see if any one of the input columns have count equals to the input parquet file rdd count. I would want to filter out such column.
Update
The number of columns and names in the input file will not be static, it will change every time we get the file.
The objective is to also filter out column for which the count is equal to the input file rdd count. Filtering integer columns is already achieved with below logic.
e.g input parquet file count = 100
count of values in column A in the input file = 100
Filter out any such column.
Current Logic
//Get array of structfields
val columns = df.schema.fields.filter(x =>
x.dataType.typeName.contains("integer"))
//Get the column names
val z = df.select(columns.map(x => col(x.name)): _*)
//Get array of string
val m = z.columns
New Logic be like
val cnt = spark.read.parquet("inputfile").count()
val d = z.column.where column count is not equals cnt
I do not want to pass the column name explicitly to the new condition, since the column having count equal to input file will change ( val d = .. above)
How do we write logic for this ?
According to my understanding of your question, your are trying filter in columns with integer as dataType and whose distinct count is not equal to the count of rows in another input parquet file. If my understanding is correct, you can add column count filter in your existing filter as
val cnt = spark.read.parquet("inputfile").count()
val columns = df.schema.fields.filter(x =>
x.dataType.typeName.contains("string") && df.select(x.name).distinct().count() != cnt)
Rest of the codes should follow as it is.
I hope the answer is helpful.
Jeanr and Ramesh suggested the right approach and here is what I did to get the desired output, it worked :)
cnt = (inputfiledf.count())
val r = df.select(df.col("*")).where(df.col("MY_COLUMN_NAME").<(cnt))
I want to iterate across the columns of dataframe in my Spark program and calculate min and max value.
I'm new to Spark and scala and not able to iterate over the columns once I fetch it in a dataframe.
I have tried running the below code but it needs column number to be passed to it, question is how do I fetch it from dataframe and pass it dynamically and store the result in a collection.
val parquetRDD = spark.read.parquet("filename.parquet")
parquetRDD.collect.foreach ({ i => parquetRDD_subset.agg(max(parquetRDD(parquetRDD.columns(2))), min(parquetRDD(parquetRDD.columns(2)))).show()})
Appreciate any help on this.
You should not be iterating on rows or records. You should be using aggregation function
import org.apache.spark.sql.functions._
val df = spark.read.parquet("filename.parquet")
val aggCol = col(df.columns(2))
df.agg(min(aggCol), max(aggCol)).show()
First when you do spark.read.parquet you are reading a dataframe.
Next we define the column we want to work on using the col function. The col function translate a column name to a column. You could instead use df("name") where name is the name of the column.
The agg function takes aggregation columns so min and max are aggregation functions which take a column and return a column with an aggregated value.
Update
According to the comments, the goal is to have min and max for all columns. You can therefore do this:
val minColumns = df.columns.map(name => min(col(name)))
val maxColumns = df.columns.map(name => max(col(name)))
val allMinMax = minColumns ++ maxColumns
df.agg(allMinMax.head, allMinMax.tail: _*).show()
You can also simply do:
df.describe().show()
which gives you statistics on all columns including min, max, avg, count and stddev
I am working on SPARK 1.6.1 version using SCALA and facing a unusual issue. When creating a new column using an existing column created during same execution getting "org.apache.spark.sql.AnalysisException".
WORKING:.
val resultDataFrame = dataFrame.withColumn("FirstColumn",lit(2021)).withColumn("SecondColumn",when($"FirstColumn" - 2021 === 0, 1).otherwise(10))
resultDataFrame.printSchema().
NOT WORKING
val resultDataFrame = dataFrame.withColumn("FirstColumn",lit(2021)).withColumn("SecondColumn",when($"FirstColumn" - **max($"FirstColumn")** === 0, 1).otherwise(10))
resultDataFrame.printSchema().
Here i am creating my SecondColumn using the FirstColumn created during the same execution. Question is why it does not work while using avg/max functions. Please let me know how can i resolve this problem.
If you want to use aggregate functions together with "normal" columns, the functions should come after a groupBy or with a Window definition clause. Out of these cases they make no sense. Examples:
val result = df.groupBy($"col1").max("col2").as("max") // This works
In the above case, the resulting DataFrame will have both "col1" and "max" as columns.
val max = df.select(min("col2"), max("col2"))
This works because there are only aggregate functions in the query. However, the following will not work:
val result = df.filter($"col1" === max($"col2"))
because I am trying to mix a non aggregated column with an aggregated column.
If you want to compare a column with an aggregated value, you can try a join:
val maxDf = df.select(max("col2").as("maxValue"))
val joined = df.join(maxDf)
val result = joined.filter($"col1" === $"maxValue").drop("maxValue")
Or even use the simple value:
val maxValue = df.select(max("col2")).first.get(0)
val result = filter($"col1" === maxValue)