I am trying to use spark.read to get file count inside my UDF, but when i execute the program hangs at that point.
i am calling an UDF in withcolumn of dataframe. the udf has to read a file and return a count of it. But it is not working. i am passing a variable value to UDF function. when i remove the spark.read code and simply return a number it works. but spark.read is not working through UDF
def prepareRowCountfromParquet(jobmaster_pa: String)(implicit spark: SparkSession): Int = {
print("The variable value is " + jobmaster_pa)
print("the count is " + spark.read.format("csv").option("header", "true").load(jobmaster_pa).count().toInt)
spark.read.format("csv").option("header", "true").load(jobmaster_pa).count().toInt
}
val SRCROWCNT = udf(prepareRowCountfromParquet _)
df
.withColumn("SRC_COUNT", SRCROWCNT(lit(keyPrefix)))
SRC_COUNT column should get lines of the file
UDFs cannot use spark context as it exists only in the driver and it isn't serializable.
generally speaking, you need to read all the csvs, calc the counts using a groupBy and then you can do a left join to the df
Related
I have a spark scala dataframe and need to filter the elements based on condition and select the count.
val filter = df.groupBy("user").count().alias("cnt")
val **count** = filter.filter(col("user") === ("subscriber").select("cnt")
The error i am facing is value select is not a member of org.apache.spark.sql.Column
Also for some reasons count is Dataset[Row]
Any thoughts to get the count in a single line?
DataSet[Row] is DataFrame
RDD[Row] is DataFrame so no need to worry.. its dataframe
see this for better understanding... Difference between DataFrame, Dataset, and RDD in Spark
Regarding select is not a member of org.apache.spark.sql.Column its purely compile error.
val filter = df.groupBy("user").count().alias("cnt")
val count = filter.filter (col("user") === ("subscriber"))
.select("cnt")
will work since you are missing ) braces which is closing brace for filter.
You are missing ")" before .select, Please check below code.
Column class don't have .select method, you have to invoke select on Dataframe.
val filter = df.groupBy("user").count().alias("cnt")
val **count** = filter.filter(col("user") === "subscriber").select("cnt")
I'm trying to do a count in scala with dataframe. My data has 3 columns and I've already loaded the data and split by tab. So I want to do something like this:
val file = file.map(line=>line.split("\t"))
val x = file1.map(line=>(line(0), line(2).toInt)).reduceByKey(_+_,1)
I want to put the data in dataframe, and having some trouble on the syntax
val file = file.map(line=>line.split("\t")).toDF
val file.groupby(line(0))
.count()
Can someone help check if this is correct?
spark needs to know the schema of the df
there are many ways to specify the schema, here is one option:
val df = file
.map(line=>line.split("\t"))
.map(l => (l(0), l(1).toInt)) //at this point spark knows the number of columns and their types
.toDF("a", "b") //give the columns names for ease of use
df
.groupby('a)
.count()
I am reading 2 different .csv files which has only column as below:
val dF1 = sqlContext.read.csv("some.csv").select($"ID")
val dF2 = sqlContext.read.csv("other.csv").select($"PID")
trying to search if dF2("PID") exists in dF1("ID"):
val getIdUdf = udf((x:String)=>{dF1.collect().map(_(0)).toList.contains(x)})
val dfFinal = dF2.withColumn("hasId", getIdUdf($"PID"))
This gives me null pointer exception.
but if I convert dF1 outside and use list in udf it works:
val dF1 = sqlContext.read.csv("some.csv").select($"ID").collect().map(_(0)).toList
val getIdUdf = udf((x:String)=>{dF1.contains(x)})
val dfFinal = dF2.withColumn("hasId", getIdUdf($"PID"))
I know I can use join to get this done but want to know what is the reason of null pointer exception here.
Thanks.
Please check this question about accessing dataframe inside the transformation of another dataframe. This is exactly what you are doing with your UDF, and this is not possible in spark. Solution is either to use join, or collect outside of transformation and broadcast.
I have a scenario where I want to convert the result of a dataframe which is in the format Array[Row] to RDD[Row]. I have tried using parallelize, but I don't want to use it as it needs to contain entire data in a single system which is not feasible in production box.
val Bid = spark.sql("select Distinct DeviceId, ButtonName from stb").collect()
val bidrdd = sparkContext.parallelize(Bid)
How do I achieve this? I tried the approach given in this link (How to convert DataFrame to RDD in Scala?), but it didn't work for me.
val bidrdd1 = Bid.map(x => (x(0).toString, x(1).toString)).rdd
It gives an error value rdd is not a member of Array[(String, String)]
The variable Bid which you've created here is not a DataFrame, it is an Array[Row], that's why you can't use .rdd on it. If you want to get an RDD[Row], simply call .rdd on the DataFrame (without calling collect):
val rdd = spark.sql("select Distinct DeviceId, ButtonName from stb").rdd
Your post contains some misconceptions worth noting:
... a dataframe which is in the format Array[Row] ...
Not quite - the Array[Row] is the result of collecting the data from the DataFrame into Driver memory - it's not a DataFrame.
... I don't want to use it as it needs to contain entire data in a single system ...
Note that as soon as you use collect on the DataFrame, you've already collected entire data into a single JVM's memory. So using parallelize is not the issue.
I have an RDD whose elements are of type (Long, String). For some reason, I want to save the whole RDD into the HDFS, and later also read that RDD back in a Spark program. Is it possible to do that? And if so, how?
It is possible.
In RDD you have saveAsObjectFile and saveAsTextFile functions. Tuples are stored as (value1, value2), so you can later parse it.
Reading can be done with textFile function from SparkContext and then .map to eliminate ()
So:
Version 1:
rdd.saveAsTextFile ("hdfs:///test1/");
// later, in other program
val newRdds = sparkContext.textFile("hdfs:///test1/part-*").map (x => {
// here remove () and parse long / strings
})
Version 2:
rdd.saveAsObjectFile ("hdfs:///test1/");
// later, in other program - watch, you have tuples out of the box :)
val newRdds = sparkContext.sc.sequenceFile("hdfs:///test1/part-*", classOf[Long], classOf[String])
I would recommend to use DataFrame if your RDD is in tabular format. a data frame is a table, or two-dimensional array-like structure, in which each column contains measurements on one variable, and each row contains one case.
a DataFrame has additional metadata due to its tabular format, which allows Spark to run certain optimizations on the finalized query.
where a RDD is a Resilient Distributed Dataset that is more of a blackbox or core abstraction of data that cannot be optimized.
However, you can go from a DataFrame to an RDD and vice-versa, and you can go from an RDD to a DataFrame (if the RDD is in a tabular format) via toDF method.
The following is the example to create/store a DataFrame in CSV and Parquet format in HDFS,
val conf = {
new SparkConf()
.setAppName("Spark-HDFS-Read-Write")
}
val sqlContext = new SQLContext(sc)
val sc = new SparkContext(conf)
val hdfs = "hdfs:///"
val df = Seq((1, "Name1")).toDF("id", "name")
// Writing file in CSV format
df.write.format("com.databricks.spark.csv").mode("overwrite").save(hdfs + "user/hdfs/employee/details.csv")
// Writing file in PARQUET format
df.write.format("parquet").mode("overwrite").save(hdfs + "user/hdfs/employee/details")
// Reading CSV files from HDFS
val dfIncsv = sqlContext.read.format("com.databricks.spark.csv").option("inferSchema", "true").load(hdfs + "user/hdfs/employee/details.csv")
// Reading PQRQUET files from HDFS
val dfInParquet = sqlContext.read.parquet(hdfs + "user/hdfs/employee/details")