Hellow everyone!
I have two DataFrames in apache spark (2.3) and I want to join them properly. I will explain below what I mean with 'properly'. First of all the two dataframes holds the following information:
nodeDf: ( id, year, title, authors, journal, abstract )
edgeDf: ( srcId, dstId, label )
The label could be 0 or 1 in case node1 is connected with node2 or not.
I want to combine this two dataframes to get one dataframe withe the following information:
JoinedDF: ( id_from, year_from, title_from, journal_from, abstract_from, id_to, year_to, title_to, journal_to, abstract_to, time_dist )
time_dist = abs(year_from - year_to)
When I said 'properly' I meant that the query must be as fast as it could be and I don't want to contain null rows or cels ( value on a row ).
I have tried the following but I took me 500 -540 sec to execute the query and the final dataframe contains null values. I don't even know if the dataframes ware joined correctly.
I want to mention that the node file from which I create the nodeDF has 27770 rows and the edge file (edgeDf) has 615512 rows.
Code:
val spark = SparkSession.builder().master("local[*]").appName("Logistic Regression").getOrCreate()
val sc = spark.sparkContext
val data = sc.textFile("resources/data/training_set.txt").map(line =>{
val fields = line.split(" ")
(fields(0),fields(1), fields(2).toInt)
})
val data2 = sc.textFile("resources/data/test_set.txt").map(line =>{
val fields = line.split(" ")
(fields(0),fields(1))
})
import spark.implicits._
val trainingDF = data.toDF("srcId","dstId", "label")
val testDF = data2.toDF("srcId","dstId")
val infoRDD = spark.read.option("header","false").option("inferSchema","true").format("csv").load("resources/data/node_information.csv")
val infoDF = infoRDD.toDF("srcId","year","title","authors","jurnal","abstract")
println("Showing linksDF sample...")
trainingDF.show(5)
println("Rows of linksDF: ",trainingDF.count())
println("Showing infoDF sample...")
infoDF.show(2)
println("Rows of infoDF: ",infoDF.count())
println("Joining linksDF and infoDF...")
var joinedDF = trainingDF.as("a").join(infoDF.as("b"),$"a.srcId" === $"b.srcId")
println(joinedDF.count())
joinedDF = joinedDF.select($"a.srcId",$"a.dstId",$"a.label",$"b.year",$"b.title",$"b.authors",$"b.jurnal",$"b.abstract")
joinedDF.show(5)
val graphX = new GraphX()
val pageRankDf =graphX.computePageRank(spark,"resources/data/training_set.txt",0.0001)
println("Joining joinedDF and pageRankDf...")
joinedDF = joinedDF.as("a").join(pageRankDf.as("b"),$"a.srcId" === $"b.nodeId")
var dfWithRanks = joinedDF.select("srcId","dstId","label","year","title","authors","jurnal","abstract","rank").withColumnRenamed("rank","pgRank")
dfWithRanks.show(5)
println("Renameming joinedDF...")
dfWithRanks = dfWithRanks
.withColumnRenamed("srcId","id_from")
.withColumnRenamed("dstId","id_to")
.withColumnRenamed("year","year_from")
.withColumnRenamed("title","title_from")
.withColumnRenamed("authors","authors_from")
.withColumnRenamed("jurnal","jurnal_from")
.withColumnRenamed("abstract","abstract_from")
var infoDfRenamed = dfWithRanks
.withColumnRenamed("id_from","id_from")
.withColumnRenamed("id_to","id_to")
.withColumnRenamed("year_from","year_to")
.withColumnRenamed("title_from","title_to")
.withColumnRenamed("authors_from","authors_to")
.withColumnRenamed("jurnal_from","jurnal_to")
.withColumnRenamed("abstract_from","abstract_to").select("id_to","year_to","title_to","authors_to","jurnal_to","jurnal_to")
var finalDF = dfWithRanks.as("a").join(infoDF.as("b"),$"a.id_to" === $"b.srcId")
finalDF = finalDF
.withColumnRenamed("year","year_to")
.withColumnRenamed("title","title_to")
.withColumnRenamed("authors","authors_to")
.withColumnRenamed("jurnal","jurnal_to")
.withColumnRenamed("abstract","abstract_to")
println("Dropping unused columns from joinedDF...")
finalDF = finalDF.drop("srcId")
finalDF.show(5)
Here are my results!
Avoid all calculations and code related to pgRank! Is there any proper way to do this join works?
You can filter your data first and then join, in that case you will avoid nulls
df.filter($"ColumnName".isNotNull)
use <=> operator in your joining column condition
var joinedDF = trainingDF.as("a").join(infoDF.as("b"),$"a.srcId" <=> $"b.srcId")
There is a function in spark 2.1 or greater is eqNullSafe
var joinedDF = trainingDF.join(infoDF,trainingDF("srcId").eqNullSafe(infoDF("srcId")))
Related
I have 3 RDDs:
1st one is of form ((a,b),c).
2nd one is of form (b,d).
3rd one is of form (a,e).
How can I perform join in scala over these RDDs such that my final output is of the form ((a,b),c,d,e)?
you can do something like this:
val rdd1: RDD[((A,B),C)]
val rdd2: RDD[(B,D)]
val rdd3: RDD[(A,E)]
val tmp1 = rdd1.map {case((a,b),c) => (a, (b,c))}
val tmp2 = tmp1.join(rdd3).map{case(a, ((b,c), e)) => (b, (a,c,e))}
val res = tmp2.join(rdd2).map{case(b, ((a,c,e), d)) => ((a,b), c,d,e)}
With current implementations of join apis for paired rdds, its not possible to use condtions. And you would need conditions when joining to get the desired result.
But you can use dataframes/datasets for the joins, where you can use conditions. So use dataframes/datasets for the joins. If you want the result of join in dataframes then you can proceed with that. In case you want your results in rdds, then *.rdd can be used to convert the dataframes/datasets to RDD[Row]*
Below is the sample codes of it can be done in scala
//creating three rdds
val first = sc.parallelize(Seq((("a", "b"), "c")))
val second = sc.parallelize(Seq(("b", "d")))
val third = sc.parallelize(Seq(("a", "e")))
//coverting rdds to dataframes
val firstdf = first.toDF("key1", "value1")
val seconddf = second.toDF("key2", "value2")
val thirddf = third.toDF("key3", "value3")
//udf function for the join condition
import org.apache.spark.sql.functions._
def joinCondition = udf((strct: Row, key: String) => strct.toSeq.contains(key))
//joins with conditions
firstdf
.join(seconddf, joinCondition(firstdf("key1"), seconddf("key2"))) //joining first with second
.join(thirddf, joinCondition(firstdf("key1"), thirddf("key3"))) //joining first with third
.drop("key2", "key3") //dropping unnecessary columns
.rdd //converting dataframe to rdd
You should have output as
[[a,b],c,d,e]
I have a bunch of columns, sample like my data displayed as show below.
I need to check the columns for errors and will have to generate two output files.
I'm using Apache Spark 2.0 and I would like to do this in a efficient way.
Schema Details
---------------
EMPID - (NUMBER)
ENAME - (STRING,SIZE(50))
GENDER - (STRING,SIZE(1))
Data
----
EMPID,ENAME,GENDER
1001,RIO,M
1010,RICK,MM
1015,123MYA,F
My excepected output files should be as shown below:
1.
EMPID,ENAME,GENDER
1001,RIO,M
1010,RICK,NULL
1015,NULL,F
2.
EMPID,ERROR_COLUMN,ERROR_VALUE,ERROR_DESCRIPTION
1010,GENDER,"MM","OVERSIZED"
1010,GENDER,"MM","VALUE INVALID FOR GENDER"
1015,ENAME,"123MYA","NAME SHOULD BE A STRING"
Thanks
I have not really worked with Spark 2.0, so I'll try answering your question with a solution in Spark 1.6.
// Load you base data
val input = <<you input dataframe>>
//Extract the schema of your base data
val originalSchema = input.schema
// Modify you existing schema with you additional metadata fields
val modifiedSchema= originalSchema.add("ERROR_COLUMN", StringType, true)
.add("ERROR_VALUE", StringType, true)
.add("ERROR_DESCRIPTION", StringType, true)
// write a custom validation function
def validateColumns(row: Row): Row = {
var err_col: String = null
var err_val: String = null
var err_desc: String = null
val empId = row.getAs[String]("EMPID")
val ename = row.getAs[String]("ENAME")
val gender = row.getAs[String]("GENDER")
// do checking here and populate (err_col,err_val,err_desc) with values if applicable
Row.merge(row, Row(err_col),Row(err_val),Row(err_desc))
}
// Call you custom validation function
val validateDF = input.map { row => validateColumns(row) }
// Reconstruct the DataFrame with additional columns
val checkedDf = sqlContext.createDataFrame(validateDF, newSchema)
// Filter out row having errors
val errorDf = checkedDf.filter($"ERROR_COLUMN".isNotNull && $"ERROR_VALUE".isNotNull && $"ERROR_DESCRIPTION".isNotNull)
// Filter our row having no errors
val errorFreeDf = checkedDf.filter($"ERROR_COLUMN".isNull && !$"ERROR_VALUE".isNull && !$"ERROR_DESCRIPTION".isNull)
I have used this approach personally and it works for me. I hope it points you in the right direction.
I have a question regarding Mechanics of Spark Dataframereader. I will appreciate if anybody can help me. Let me explain the Scenario here
I am creating a DataFrame from Dstream like this. This in Input Data
var config = new HashMap[String,String]();
config += ("zookeeper.connect" ->zookeeper);
config += ("partition.assignment.strategy" ->"roundrobin");
config += ("bootstrap.servers" ->broker);
config += ("serializer.class" -> "kafka.serializer.DefaultEncoder");
config += ("group.id" -> "default");
val lines = KafkaUtils.createDirectStream[String, Array[Byte], StringDecoder, DefaultDecoder](ssc,config.toMap,Set(topic)).map(_._2)
lines.foreachRDD { rdd =>
if(!rdd.isEmpty()){
val rddJson = rdd.map { x => MyFunctions.mapToJson(x) }
val sqlContext = SQLContextSingleton.getInstance(ssc.sparkContext)
val rddDF = sqlContext.read.json(rddJson)
rddDF.registerTempTable("inputData")
val dbDF = ReadDataFrameHelper.readDataFrameHelperFromDB(sqlContext, jdbcUrl, "ABCD","A",numOfPartiton,lowerBound,upperBound)
Here is the code of ReadDataFrameHelper
def readDataFrameHelperFromDB(sqlContext:HiveContext,jdbcUrl:String,dbTableOrQuery:String,
columnToPartition:String,numOfPartiton:Int,lowerBound:Int,highBound:Int):DataFrame={
val jdbcDF = sqlContext.read.jdbc(url = jdbcUrl, table = dbTableOrQuery,
columnName = columnToPartition,
lowerBound = lowerBound,
upperBound = highBound,
numPartitions = numOfPartiton,
connectionProperties = new java.util.Properties()
)
jdbcDF
}
Lastly I am doing a Join like this
val joinedData = rddDF.join(dbDF,rddDF("ID") === dbDF("ID")
&& rddDF("CODE") === dbDF("CODE"),"left_outer")
.drop(dbDF("code"))
.drop(dbDF("id"))
.drop(dbDF("number"))
.drop(dbDF("key"))
.drop(dbDF("loaddate"))
.drop(dbDF("fid"))
joinedData.show()
My input DStream will have 1000 rows and data will contains million of rows. So when I do this join, will spark load all the rows from database and read those rows or will this just read those specific rows from DB which have the code,id from the input DStream
As specified by zero323, i have also confirmed that data will be read full from the table. I checked the DB session logs and saw that whole dataset is getting loaded.
Thanks zero323
I have a dataframe df with columns
date: timestamp
status : String
name : String
I'm trying to find last status of the all the names
val users = df.select("name").distinct
val final_status = users.map( t =>
{
val _name = t.getString(0)
val record = df.where(col("name") === _name)
val lastRecord = userRecord.sort(desc("date")).first
lastRecord
})
This works with an array, but with spark dataframe it is throwing java.lang.NullPointerException
Update1 : Using removeDuplicates
df.sort(desc("date")).removeDuplicates("name")
Is this a good solution?
This
df.sort(desc("date")).removeDuplicates("name")
is not guaranteed to work. The solutions in response to this question should work for you
spark: How to do a dropDuplicates on a dataframe while keeping the highest timestamped row
I am a beginner of Apache Spark. I want to filter two RDD into result RDD with the below code
def runSpark(stList:List[SubStTime],icList:List[IcTemp]): Unit ={
val conf = new SparkConf().setAppName("OD").setMaster("local[*]")
val sc = new SparkContext(conf)
val st = sc.parallelize(stList).map(st => ((st.productId,st.routeNo),st)).groupByKey()
val ic = sc.parallelize(icList).map(ic => ((ic.productId,ic.routeNo),ic)).groupByKey()
//TODO
//val result = st.join(ic).mapValues( )
sc.stop()
}
here is what i want to do
List[ST] ->map ->Map(Key,st) ->groupByKey ->Map(Key,List[st])
List[IC] ->map ->Map(Key,ic) ->groupByKey ->Map(Key,List[ic])
STRDD join ICRDD get Map(Key,(List[st],List[ic]))
I have a function compare listST and listIC get the List[result] result contains both SubStTime and IcTemp information
def calcIcSt(st:List[SubStTime],ic:List[IcTemp]): List[result]
I don't know how to use mapvalues or other some way to get my result
Thanks
val result = st.join(ic).mapValues( x => calcIcSt(x._1,x._2) )