I am trying to read a HBase table using scala and then add a new column as tags based on the content in the rows in HBase Table. I have read the table as spark RDD. I also have a hashmap of which key value pairs are as follows:
keys are to be matched with the entries of spark rdd(generated from HBase table) and if match is found, the value from the hashmap is to be added into a new column.
The function to write to hbase table in the a new column name is this:
def convert (a:Int,s:String) : Tuple2[ImmutableBytesWritable,Put]={
val p = new Put(a.toString.getBytes())
p.add(Bytes.toBytes("columnfamily"),Bytes.toBytes("col_2"), s.toString.getBytes())//a.toString.getBytes())
println("the value of a is: " + a)
new Tuple2[ImmutableBytesWritable,Put](new ImmutableBytesWritable(Bytes.toBytes(a)), p);
}
new PairRDDFunctions(newrddtohbaseLambda.map(x=>convert(x, ranjan))).saveAsHadoopDataset(jobConfig)
Then to read string from hashmap and compare and add back the code is this:
csvhashmap.keys.foreach{i=> if (arrayRDD.zipWithIndex.foreach{case(a,j) => a.split(" ").exists(i contains _); p = j.toInt}==true){new PairRDDFunctions(convert(p,csvhashmap(i))).saveAsHadoopDataset(jobConfig)}}
here csvhashmap is the above described hashmap, "words" is the rdd where we are trying to match the string. When the above command is run, I get the following error:
error: type mismatch;
found : (org.apache.hadoop.hbase.io.ImmutableBytesWritable, org.apache.hadoop.hbase.client.Put)
required: org.apache.spark.rdd.RDD[(?, ?)]
How to get rid of it? I have tried many things to change the data type but each time I get some error. Also, I have checked for the individual functions inside the above snippet and they are just fine. When I integrate them together, I got the above error. Any help would be appreciated.
Related
I'm reading a HDFS directory
val schema = spark.read.schema(schema).json("/HDFS path").schema
val df= spark.read.schema(schema).json ("/HDFS path")
Here selecting only PK and timestamp from JSON file
Val df2= df.select($"PK1",$"PK2",$"PK3" ,$"ts")
Then
Using windows function to get updated PK on the base of timestamp
val dfrank = df2.withColumn("rank",row_number().over(
Window.partitionBy($"PK1",$"PK2",$"PK3" ).orderBy($"ts".desc))
)
.filter($"rank"===1)
From this window function getting only updated primary keys & timestamp of updated JSON.
Now I have to add one more column where I want to get only JSON with updated PK and Timestamp
How I can do that
Trying below but getting wrong JSON instead of updated JSON
val df3= dfrank.withColumn("JSON",lit(dfrank.toJSON.first()))
Result shown in image.
Here, you convert the entire dataframe to JSON and collect it to the driver with toJSON (that's going to crash with a large dataframe) and add a column that contains a JSON version of the first row of the dataframe to your dataframe. I don't think this is what you want.
From what I understand, you have a dataframe and for each row, you want to create a JSON column that contains all of its columns. You could create a struct with all your columns and then use to_json like this:
val df3 = dfrank.withColumn("JSON", to_json(struct(df.columns.map(col) : _*)))
I have a config defined which contains a list of column for each table to be used as a dedup key
for ex:
config 1 :
val lst = List(section_xid, learner_xid)
these are the column that needs to be used as a dedup keys. This list is dynamic some table will have 1 value some will have 2 or 3 values in it
what I am trying to do is build a single key column from this list
df.
.withColumn( "dedup_key_sk", uuid(md5(concat($"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", $"col2"):_*))
For this to work I had to convert list to Df and each value in list needs to be in separate columns I was not able to figure that out.
tried doing this but didn't work
val res = sc.parallelize(List((lst))).toDF
ANy input here will be appreciated . Thank you
The list of strings can be mapped to a list of columns (using functions.col). This list of columns can then be used with concat:
val lst: List[String] = List("section_xid", "learner_xid")
df.withColumn("dedup_key_sk", concat(lst.map(col):_*)).show()
So, I'm trying to read an existing file, save that into a DataFrame, once that's done I make a "union" between that existing DataFrame and a new one I have already created, both have the same columns and share the same schema.
ALSO I CANNOT GIVE SIGNIFICANT NAME TO VARS NOR GIVE ANYMORE DATA BECAUSE OF RESTRICTIONS
val dfExist = spark.read.format("csv").option("header", "true").option("delimiter", ",").schema(schema).load(filePathAggregated3)
val df5 = df4.union(dfExist)
Once that's done I get the "start_ts" (a timestamp on Epoch format) that's duplicate in the union between the above dataframes (df4 and dfExist) and also I get rid of some characters I don't want
val df6 = df5.select($"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 am trying add an extra "tag" column to an Hbase table. Tagging is done on the basis of words present in the rows of the table. Say for example, If "Dark" appears in a certain row, then its tag will be added as "Horror". I have read all the rows from the table in a spark RDD and have matched them with words based on which we would tag. A snippet to code looks like this:
var hBaseRDD2=sc.newAPIHadoopRDD(conf,classOf[TableInputFormat],classOf[org.apache.hadoop.hbase.io.ImmutableBytesWritable], classOf[org.apache.hadoop.hbase.client.Result])
val transformedRDD = hBaseRDD2.map(tuple => {
(Bytes.toString(tuple._2.getValue(Bytes.toBytes("Moviesdata"),Bytes.toBytes("MovieName"))),
Bytes.toString(tuple._2.getValue(Bytes.toBytes("Moviesdata"),Bytes.toBytes("MovieSummary"))),
Bytes.toString(tuple._2.getValue(Bytes.toBytes("Moviesdata"),Bytes.toBytes("MovieActor")))
)
})
Here, "moviesdata" is the columnfamily of the HBase table and "MovieName"&"MovieSummary" & "MovieActor" are column names. "transformedRDD" in the above snippet is of type RDD[String,String,String]. It has been converted into type RDD[String] by:
val arrayRDD: RDD[String] = transformedRDD.map(x => (x._1 + " " + x._2 + " " + x._3))
From this, all words have been extracted by doing this:
val words = arrayRDD.map(x => x.split(" "))
The words which we would are looking for in the HBase Table rows are in a csv file. One of the column, let's say "synonyms" column, of the csv has the words which we would look for. Another column in the csv is a "target_tag" column, which has the words which would be tagged to the row corresponding to which there is match.
Read the csv by:
val csv = sc.textFile("/tag/moviestagdata.csv")
reading the synonyms column: (synonyms column is the second column, therefore "p(1)" in the below snippet)
val synonyms = csv.map(_.split(",")).map( p=>p(1))
reading the target_tag column: (target_tag is the 3rd column)
val targettag = csv.map(_.split(",")).map(p=>p(2))
Some rows in synonyms and targetag have more than one strings and are seperated by "###". The snippet to seperate them is this:
val splitsyno = synonyms.map(x => x.split("###"))
val splittarget = targettag.map(x=>x.split("###"))
Now, to match each string from "splitsyno", we need to traverse every row, and further a row might have many strings, hence, to create a set of every string, I did this:(an empty set was created)
splitsyno.map(x=>x.foreach(y=>set += y)
To match every string with those in "words" created up above, I did this:
val check = words.exists(set contains _)
Now, the problem which I am facing is that I don't exactly know that strings from what rows in csv are matching to strings from what rows in HBase table. This is needed as I would need to find corresponding target string and which row in HBase table to add to. How should I get it done? Any help would be highly appreciated.
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: _*)