I need a window function that partitions by some keys (=column names), orders by another column name and returns the rows with top x ranks.
This works fine for ascending order:
def getTopX(df: DataFrame, top_x: String, top_key: String, top_value:String): DataFrame ={
val top_keys: List[String] = top_key.split(", ").map(_.trim).toList
val w = Window.partitionBy(top_keys(1),top_keys.drop(1):_*)
.orderBy(top_value)
val rankCondition = "rn < "+top_x.toString
val dfTop = df.withColumn("rn",row_number().over(w))
.where(rankCondition).drop("rn")
return dfTop
}
But when I try to change it to orderBy(desc(top_value)) or orderBy(top_value.desc) in line 4, I get a syntax error. What's the correct syntax here?
There are two versions of orderBy, one that works with strings and one that works with Column objects (API). Your code is using the first version, which does not allow for changing the sort order. You need to switch to the column version and then call the desc method, e.g., myCol.desc.
Now, we get into API design territory. The advantage of passing Column parameters is that you have a lot more flexibility, e.g., you can use expressions, etc. If you want to maintain an API that takes in a string as opposed to a Column, you need to convert the string to a column. There are a number of ways to do this and the easiest is to use org.apache.spark.sql.functions.col(myColName).
Putting it all together, we get
.orderBy(org.apache.spark.sql.functions.col(top_value).desc)
Say for example, if we need to order by a column called Date in descending order in the Window function, use the $ symbol before the column name which will enable us to use the asc or desc syntax.
Window.orderBy($"Date".desc)
After specifying the column name in double quotes, give .desc which will sort in descending order.
Column
col = new Column("ts")
col = col.desc()
WindowSpec w = Window.partitionBy("col1", "col2").orderBy(col)
Related
I have a fixed date "2000/01/01" and a dataframe:
data1 = [{'index':1,'offset':50}]
data_p = sc.parallelize(data1)
df = spark.createDataFrame(data_p)
I want to create a new column by adding the offset column to this fixed date
I tried different method but cannot pass the column iterator and expr error as:
function is neither a registered temporary function nor a permanent function registered in the database 'default'
The only solution I can think of is
df = df.withColumn("zero",lit(datetime.strptime('2000/01/01', '%Y/%m/%d')))
df.withColumn("date_offset",expr("date_add(zero,offset)")).drop("zero")
Since I cannot use lit and datetime.strptime in the expr, I have to use this approach which creates a redundant column and redundant operations.
Any better way to do it?
As you have marked it as pyspark question so in python you can do below
df_a3.withColumn("date_offset",F.lit("2000-01-01").cast("date") + F.col("offset").cast("int")).show()
Edit- As per comment below lets assume there was an extra column of type then based on it below code can be used
df_a3.withColumn("date_offset",F.expr("case when type ='month' then add_months(cast('2000-01-01' as date),offset) else date_add(cast('2000-01-01' as date),cast(offset as int)) end ")).show()
I have a List:hdtList which contain columns that represent the columns of a Hive table:
forecast_id bigint,period_year bigint,period_num bigint,period_name string,drm_org string,ledger_id bigint,currency_code string,source_system_name string,source_record_type string,gl_source_name string,gl_source_system_name string,year string
I have a List: partition_columns which contains two elements: source_system_name, period_year
Using the List: partition_columns, I am trying to match them and move the corresponding columns in List: hdtList to the end of it as below:
val (pc, notPc) = hdtList.partition(c => partition_columns.contains(c.takeWhile(x => x != ' ')))
But when I print them as: println(notPc.mkString(",") + "," + pc.mkString(","))
I see the output unordered as below:
forecast_id bigint,period_num bigint,period_name string,drm_org string,ledger_id bigint,currency_code string,source_record_type string,gl_source_name string,gl_source_system_name string,year string,period string,period_year bigint,source_system_name string
The columns period_year comes first and the source_system_name last. Is there anyway I can make data as below so that the order of columns in the List: partition_columns is maintained.
forecast_id bigint,period_num bigint,period_name string,drm_org string,ledger_id bigint,currency_code string,source_record_type string,gl_source_name string,gl_source_system_name string,year string,period string,source_system_name string,period_year bigint
I know there is an option to reverse a List but I'd like to learn if I can implement a collection that maintains that order of insert.
It doesn't matter which collections you use; you only use partition_columns to call contains which doesn't depend on its order, so how could it be maintained?
But your code does maintain order: it's just hdtList's.
Something like
// get is ugly, but safe here
val pc1 = partition_columns.map(x => pc.find(y => y.startsWith(x)).get)
after your code will give you desired order, though there's probably more efficient way to do it.
I have two dataframes in scala both having data from two different tables but of same structure (srcdataframe and tgttable). I have to join these two based on composite primary key and select few columns and append two columns the code for which is as below:
for(i <- 2 until numCols) {
srcdataframe.as("A")
.join(tgttable.as("B"), $"A.INSTANCE_ID" === $"B.INSTANCE_ID" &&
$"A.CONTRACT_LINE_ID" === $"B.CONTRACT_LINE_ID", "inner")
.filter($"A." + srcColnm(i) =!= $"B." + srcColnm(i))
.select($"A.INSTANCE_ID",
$"A.CONTRACT_LINE_ID",
"$"+"\""+"A."+srcColnm(i)+"\""+","+"$"+"\""+"B."+srcColnm(i)+"\"")
.withColumn("MisMatchedCol",lit("\""+srcColnm(i)+"\""))
.withColumn("LastRunDate",current_timestamp.cast("long"))
.createOrReplaceTempView("IPF_1M_Mismatch");
hiveSQLContext.sql("Insert into table xxxx.f2f_Mismatch1 select t.* from (select * from IPF_1M_Mismatch) t");}
Here are the things am trying to do:
Inner join of srcdataframe and tgttable based on instance_id and contract_line_id.
Select only instance_id, contract_line_id, mismatched_col_values, hardcode of mismatched_col_nm, timestamp.
srcColnm(i) is an array of strings which contains the non-primary keys to be compared.
However, I am not able to resolve the variables inside the dataframe in the for loop. I tried looking up for solutions here and here. I got to know that it may be because of the way spark substitutes the variables only at compile time, in this case I'm not sure how to resolve it.
Instead of creating columns with $, you can simply use strings or the col() function. I would also recommend performing the join outside of the for as it's an expensive operation. Slightly changed code, the main difference to solve your problem is in the select:
val df = srcdataframe.as("A")
.join(tgttable.as("B"), Seq("INSTANCE_ID", "CONTRACT_LINE_ID"), "inner")
for(columnName <- srcColnm) {
df.filter(col("A." + columnName) =!= col("B." + columnName))
.select("INSTANCE_ID", "CONTRACT_LINE_ID", "A." + columnName, "B." + columnName)
.withColumn("MisMatchedCol", lit(columnName))
.withColumn("LastRunDate", current_timestamp().cast("long"))
.createOrReplaceTempView("IPF_1M_Mismatch")
// Hive command
}
Regarding the problem in select:
$ is short for the col() function, it's selecting a column in the dataframe by name. The problem in the select is that the two first arguments col("A.INSTANCE_ID") and col("A.CONTRACT_LINE_ID") are two columns ($replaced bycol()` for clarity).
However, the next two arguments are strings. It is not possible to mix these two, either all arguments should be columns or all are strings. As you used "A."+srcColnm(i) to build up the column name $ can't be used, however, you could have used col("A."+srcColnm(i)).
I have a situation where I need fetch only distict records which are grater than 0 and all records with value 0.
For Example I have column name called mid then it rows like "0,0,1,1,2,3,5,5,3" then I should fetch only "0,0,1,2,5,3".
In short distinct record plus all mid with value 0
I have used this
def distinctMIdCursor = dataSetCollection.distinct("mid",whereObject)
def distinctMIdList = distinctMIdCursor.asList()
but its fetching result like "0,1,2,5,3"
Actual result "0,1,2,5,3".
Expected result "0,0,1,2,5,3"
How to achieve it. What is better way?
You cannot achieve it with distinct because by doing so you are defying the whole purpose of using distinct. Instead you can write two queries and concat the result.
def nonZeroDistinctList = dataSetCollection.distinct("mid",{mid: {$ne:0}});
// map function to convert object list into mid value list
def allZeroList = dataSetCollection.find({mid:0}).map(function(doc){return doc.mid});
// concating the two lists
def result = nonZeroDistinctList + allZeroList ;
I am currently trying to create a concatenating string for each group. This string should be the concatenation of all the occurrences of the field.
For the moment my code looks like :
grouped = GROUP a by group_field;
b = FOREACH grouped {
unique_field = DISTINCT myfield;
tupl = TOTUPLE(unique_field) ;
FOREACH tupl GENERATE group as id, CONCAT( ? ) as my_new_string;
}
The thing is I absolutely do not know for each group the number of distinct fields or what they contains. I don't know how what to do to replace the ? and make it work.
TOTUPLE is not doing what you are expecting, it is making a one element tuple where that one element is the bag of unique_field.
Also, CONCAT only takes two things to concat and they must be explicitly defined. Let's say that you have a schema like A: {A1: chararray, A2: chararray, A3: chararray} and you want to concatinate all fields together. You will have to do this (which is obviously not ideal): CONCAT(CONCAT(A1, A2), A3).
Anyways, this problem can be easily solved with a python UDF.
myudfs.py
#!/usr/bin/python
#outputSchema('concated: string')
def concat_bag(BAG):
return ''.join(BAG)
This UDF would be used in your script like:
Register 'myudfs.py' using jython as myfuncs;
grouped = GROUP a by group_field;
b = FOREACH grouped {
unique_field = DISTINCT myfield;
GENERATE group as id, myfuncs.concat_bag(unique_field);
}
I just noticed the FOREACH tupl GENERATE ... line. That is not valid syntax. The last statement in a nested FOREACH should be a GENERATE.