I have started studying pyspark and for some reason I'm not able to get the concept of Resiliency property of RDD. My understanding is RDD is a data structure like dataframe in pandas and is immutable. But I wrote a code (shown below) and it works.
file = sc.textfile('file name')
filterData = file.map(lambda x: x.split(','))
filterData = filterData.reduceByKey(lambda x,y: x+y)
filterData = filterData.sortBy(lambda x: x[1])
result = filterData.collect()
Doesn't this violate the immutable property as you can see I'm modifying the same RDD again and again.
File is a csv file with 2 columns. column 1 is an id and column 2 is just some integer.
Can you guys please explain where I'm going wrong with my understanding.
I have the following code where I want to get Dataframe dfDateFiltered from dfBackendInfo containing all rows with RowCreationTime greater than timestamp "latestRowCreationTime"
val latestRowCreationTime = dfVersion.agg(max("BackendRowCreationTime")).first.getTimestamp(0)
val dfDateFiltered = dfBackendInfo.filter($"RowCreationTime" > latestRowCreationTime)
The problem I see is that the first line adds a job in Databricks cluster making it slower.
Is there anyway if I could use a better way to filter (for ex. just using transformation instead of action)?
Below are the schemas of the 2 Dataframes:
case class Version(BuildVersion:String,
MainVersion:String,
Hotfix:String,
BackendRowCreationTime:Timestamp)
case class BackendInfo(SerialNumber:Integer,
NumberOfClients:Long,
BuildVersion:String,
MainVersion:String,
Hotfix:String,
RowCreationTime:Timestamp)
The below code worked:
val dfLatestRowCreationTime1 = dfVersion.agg(max($"BackendRowCreationTime").as("BackendRowCreationTime")).limit(1)
val latestRowCreationTime = dfLatestRowCreationTime1.withColumn("BackendRowCreationTime", when($"BackendRowCreationTime".isNull, DefaultTime))
val dfDateFiltered = dfBackendInfo.join(latestRowCreationTime, dfBackendInfo.col("RowCreationTime").gt(latestRowCreationTime.col("BackendRowCreationTime")))
I've been breaking my head about this one for a couple of days now. It feels like it should be intuitively easy... Really hope someone can help!
I've built an org.nd4j.linalg.api.ndarray.INDArray of word occurrence from some semi-structured data like this:
import org.nd4j.linalg.factory.Nd4j
import org.nd4s.Implicits._
val docMap = collection.mutable.Map[Int,Map[Int,Int]] //of the form Map(phrase -> Map(phrasePosition -> word)
val words = ArrayBuffer("word_1","word_2","word_3",..."word_n")
val windows = ArrayBuffer("$phrase,$phrasePosition_1","$phrase,$phrasePosition_2",..."$phrase,$phrasePosition_n")
var matrix = Nd4j.create(windows.length*words.length).reshape(windows.length,words.length)
for (row <- matrix.shape(0)){
for(column <- matrix.shape(1){
//+1 to (row,column) if word occurs at phrase, phrasePosition indicated by window_n.
}
}
val finalmatrix = matrix.T.dot(matrix) // to get co-occurrence matrix
So far so good...
Downstream of this point I need to integrate the data into an existing pipeline in Spark, and use that implementation of pca etc, so I need to create a DataFrame, or at least an RDD. If I knew the number of words and/or windows in advance I could do something like:
case class Row(window : String, word_1 : Double, word_2 : Double, ...etc)
val dfSeq = ArrayBuffer[Row]()
for (row <- matrix.shape(0)){
dfSeq += Row(windows(row),matrix.get(NDArrayIndex.point(row), NDArrayIndex.all()))
}
sc.parallelize(dfSeq).toDF("window","word_1","word_2",...etc)
but the number of windows and words is determined at runtime. I'm looking for a WindowsxWords org.apache.spark.sql.DataFrame as output, input is a WindowsxWords org.nd4j.linalg.api.ndarray.INDArray
Thanks in advance for any help you can offer.
Ok, so after several days work it looks like the simple answer is: there isn't one. In fact, it looks like trying to use Nd4j in this context at all is a bad idea for several reasons:
It's (really) hard to get data out of the native INDArray format once you've put it in.
Even using something like guava, the .data() method brings everything on heap which will quickly become expensive.
You've got the added hassle of having to compile an assembly jar or use hdfs etc to handle the library itself.
I did also consider using Breeze which may actually provide a viable solution but carries some of the same problems and can't be used on distributed data structures.
Unfortunately, using native Spark / Scala datatypes, although easier once you know how, is - for someone like me coming from Python + numpy + pandas heaven at least - painfully convoluted and ugly.
Nevertheless, I did implement this solution successfully:
import org.apache.spark.mllib.linalg.{Vectors,Vector,Matrix,DenseMatrix,DenseVector}
import org.apache.spark.mllib.linalg.distributed.RowMatrix
//first make a pseudo-matrix from Scala Array[Double]:
var rowSeq = Seq.fill(windows.length)(Array.fill(words.length)(0d))
//iterate through 'rows' and 'columns' to fill it:
for (row 0 until windows.length){
for (column 0 until words.length){
// rowSeq(row)(column) += 1 if word occurs at phrase, phrasePosition indicated by window_n.
}
}
//create Spark DenseMatrix
val rows : Array[Double] = rowSeq.transpose.flatten.toArray
val matrix = new DenseMatrix(windows.length,words.length,rows)
One of the main operations that I needed Nd4J for was matrix.T.dot(matrix) but it turns out that you can't multiply 2 matrices of Type org.apache.spark.mllib.linalg.DenseMatrix together, one of them (A) has to be a org.apache.spark.mllib.linalg.distributed.RowMatrix and - you guessed it - you can't call matrix.transpose() on a RowMatrix, only on a DenseMatrix! Since it's not really relevant to the question, I'll leave that part out, except to explain that what comes out of that step is a RowMatrix. Credit is also due here and here for the final part of the solution:
val rowMatrix : [RowMatrix] = transposeAndDotDenseMatrix(matrix)
// get DataFrame from RowMatrix via DenseMatrix
val newdense = new DenseMatrix(rowMatrix.numRows().toInt,rowMatrix.numCols().toInt,rowMatrix.rows.collect.flatMap(x => x.toArray)) // the call to collect() here is undesirable...
val matrixRows = newdense.rowIter.toSeq.map(_.toArray)
val df = spark.sparkContext.parallelize(matrixRows).toDF("Rows")
// then separate columns:
val df2 = (0 until words.length).foldLeft(df)((df, num) =>
df.withColumn(words(num), $"Rows".getItem(num)))
.drop("Rows")
Would love to hear improvements and suggestions on this, thanks.
I am trying to create multiple RDDs to process independently from below file based on the similar format of data .
Please find the file with different data formats
custid,starttime,rpdid,catry,auapp,sppp,retatype,status,process,fileavil
4fgdfg,00:56:30.034,BM_-unit1,GEN,TRUE,FALSE,NONE,A,45,TRUE
X95GEK,00:56:32.083,CBM_OMDD_RSVCM0CBM-unit0,GEN,TRUE,FALSE,NONE,A,GWC,TRUE
XWZ08K,00:57:01.947,GWC-0-UNIT-1,GEN,TRUE,FALSE,NONE,A,GWC,TRUE
custid,relstatus
fg3-03,R
dfsdf4-01,V
56fbfg,R
devid,reg,hold,devbrn,lname,lcon
CTUTANCM0CBM,TRUE,FALSE,13:17:36.934,CBM_BMI_25_5_2,13:43:21.370
In the above file, we have three different type of data formats exist and I want to split the file into three different RDDs as per the format.
Could you please suggest how to implement using Spark (Scala)?
Your file looks like it has 3 different csv files in it.
You can read it as a single file and extract 3 RDDs from it based on the number of fields you have in each row.
// Caching because you'll be filtering it thrice
val topRdd = sc.textFile("file").cache
topRdd.count
//res0: Long = 10
val rdd1 = topRdd.filter(_.split(",", -1).length == 10 )
val rdd2 = topRdd.filter(_.split(",", -1).length == 2 )
val rdd3 = topRdd.filter(_.split(",", -1).length == 6 )
rdd1.collect.foreach(println)
// custid,starttime,rpdid,catry,auapp,sppp,retatype,status,process,fileavil
// 4fgdfg,00:56:30.034,BM_-unit1,GEN,TRUE,FALSE,NONE,A,45,TRUE
// X95GEK,00:56:32.083,CBM_OMDD_RSVCM0CBM-unit0,GEN,TRUE,FALSE,NONE,A,GWC,TRUE
// XWZ08K,00:57:01.947,GWC-0-UNIT-1,GEN,TRUE,FALSE,NONE,A,GWC,TRUE
rdd2.collect.foreach(println)
// custid,relstatus
// fg3-03,R
// dfsdf4-01,V
// 56fbfg,R
rdd3.collect.foreach(println)
// devid,reg,hold,devbrn,lname,lcon
// CTUTANCM0CBM,TRUE,FALSE,13:17:36.934,CBM_BMI_25_5_2,13:43:21.370
Despite existing a lot of seemingly similar questions none answers my question.
I have a DataFrame already processed in order to be fed to a DecisionTreeClassifier and it contains a column label which is filled with either 0.0 or 1.0.
I need to bootstrap my data set, by randomly selecting with replacement the same amount of rows for each values of my label column.
I've looked at all the doc and all I could find are DataFrame.sample(...) and DataFrameStatFunctions.sampleBy(...) but the issue with those are that the number of sample retained is not guaranteed and the second one doesn't allow replacement! This wouldn't be an issue on larger data set but in around 50% of my cases I'll have one of the label values that have less than a hundred rows and I really don't want skewed data.
Despite my best efforts, I was unable to find a clean solution to this problem and I resolved myself. to collecting the whole DataFrame and doing the sampling "manually" in Scala before recreating a new DataFrame to train my DecisionTreeClassifier on. But this seem highly inefficient and cumbersome, I would much rather stay with DataFrame and keep all the benefits coming from that structure.
Here is my current implementation for reference and so you know exactly what I'd like to do:
val nbSamplePerClass = /* some int value currently ranging between 50 and 10000 */
val onesDataFrame = inputDataFrame.filter("label > 0.0")
val zeros = inputDataFrame.except(onesDataFrame).collect()
val ones = onesDataFrame.collect()
val nbZeros = zeros.count().toInt
val nbOnes = ones.count().toInt
def randomIndexes(maxIndex: Int) = (0 until nbSamplePerClass).map(
_ => new scala.util.Random().nextInt(maxIndex)).toSeq
val zerosSample = randomIndexes(nbZeros).map(idx => zeros(idx))
val onesSample = randomIndexes(nbOnes).map(idx => ones(idx))
val samples = scala.collection.JavaConversions.seqAsJavaList(zerosSample ++ onesSample)
val resDf = sqlContext.createDataFrame(samples, inputDataFrame.schema)
Does anyone know how I could implement such a sampling while only working with DataFrames?
I'm pretty sure that it would significantly speed up my code!
Thank you for your time.