I'm new to Spark, and trying to figure out how I can add a column to a DataFrame where its value is fetched from a HashMap, where the key is another value on the same row which where the value is being set.
For example, I have a map defined as follows:
var myMap: Map<Integer,Integer> = generateMap();
I want to add a new column to my DataFrame where its value is fetched from this map, with the key a current column value. A solution might look like this:
val newDataFrame = dataFrame.withColumn("NEW_COLUMN", lit(myMap.get(col("EXISTING_COLUMN"))))
My issue with this code is that using the col function doesn't return a type of Int, like the keys in my HashMap.
Any suggestions?
I would create a dataframe from the map. Then do a join operation. It should be faster and can be reused.
A UDF (user-defined function) can also be used but they are black boxes to Catalyst, so I would be prudent in using them. Depending on where the content of the map is, it may also be complicated to pass it to a UDF.
As of the next version of Kotlin API for Apache Spark you will be able to simply create a udf which will be usable in almost this way.
val mapUDF by udf { input: Int -> myMap[input] }
dataFrame.withColumn("NEW_COLUMN", mapUDF(col("EXISTING_COLUMN")))
You need to use UDF.
val mapUDF = udf((i:Int)=>myMap.getOrElse(i,0))
val newDataFrame = dataFrame.withColumn("NEW_COLUMN", mapUDF(col("EXISTING_COLUMN")))
Related
I have a dataset Dataset<Row> which comes from reading a parquet file. Knowing that one column inside InfoMap is of type Map.
Now I want to update this column, but when I use withColumn, it tells me that I cannot put a hashmap inside because it's not a litteral.
I want to know what is the correct way to update a column of type Map for a dataset ?
Try using typedLit instead of lit
typedLit
"...The difference between this function and lit() is that this
function can handle parameterized scala types e.g.: List, Seq and Map"
data.withColumn("dictionary", typedLit(Map("foo" -> 1, "bar" -> 2)))
For a simple grouping operation apparently the returned type is no longer a DataFrame ??
val itemsQtyDf = pkgItemsDf.groupBy($"packageid").withColumn("totalqty",sum("qty"))
We can not however invoke the DataFrame ops after the groupBy - since it is a GroupedData:
Error:(26, 55) value withColumn is not a member of org.apache.spark.sql.GroupedData
So, then how to get my DataFrame back after a grouping? Is it necessary to use DataFrame.agg() instead??
Grouping only without an aggregate function implies you may want to use the distinct() function instead which does return a DataFrame. But your example shows you want sum("qty"), so just change your code to be like this:
pkgItemsDf.groupBy($"packageid").agg(sum("qty").alias("totalqty"))
I need to use an UDF in Spark that takes in a timestamp, an Integer and another dataframe and returns a tuple of 3 values.
I keep hitting error after error and I'm not sure I'm trying to fix it right anymore.
Here is the function:
def determine_price (view_date: org.apache.spark.sql.types.TimestampType , product_id: Int, price_df: org.apache.spark.sql.DataFrame) : (Double, java.sql.Timestamp, Double) = {
var price_df_filtered = price_df.filter($"mkt_product_id" === product_id && $"created"<= view_date)
var price_df_joined = price_df_filtered.groupBy("mkt_product_id").agg("view_price" -> "min", "created" -> "max").withColumn("last_view_price_change", lit(1))
var price_df_final = price_df_joined.join(price_df_filtered, price_df_joined("max(created)") === price_df_filtered("created")).filter($"last_view_price_change" === 1)
var result = (price_df_final.select("view_price").head().getDouble(0), price_df_final.select("created").head().getTimestamp(0), price_df_final.select("min(view_price)").head().getDouble(0))
return result
}
val det_price_udf = udf(determine_price)
the error it gives me is:
error: missing argument list for method determine_price
Unapplied methods are only converted to functions when a function type is expected.
You can make this conversion explicit by writing `determine_price _` or `determine_price(_,_,_)` instead of `determine_price`.
If I start adding the arguments I keep running in other errors such as Int expected Int.type found or object DataFrame is not a member of package org.apache.spark.sql
To give some context:
The idea is that I have a dataframe of prices, a product id and a date of creation and another dataframe containing product IDs and view dates.
I need to determine the price based on which was the last created price entry that is older than the view date.
Since each product ID has multiple view dates in the second dataframe. I thought an UDF is faster than a cross join. If anyone has a different idea, I'd be grateful.
You cannot pass the Dataframe inside UDF as UDF will be running on the Worker On a particular partition. And as you cannot use RDD on Worker( Is it possible to create nested RDDs in Apache Spark? ), similarly you cannot use the DataFrame on Worker too.!
You need to do a work around for this !
I have a DataFrame which I need to convert into JavaRDD<Row> and back to DataFrame I have the following code
DataFrame sourceFrame = hiveContext.read().format("orc").load("/path/to/orc/file");
//I do order by in above sourceFrame and then I convert it into JavaRDD
JavaRDD<Row> modifiedRDD = sourceFrame.toJavaRDD().map(new Function<Row,Row>({
public Row call(Row row) throws Exception {
if(row != null) {
//updated row by creating new Row
return RowFactory.create(updateRow);
}
return null;
});
//now I convert above JavaRDD<Row> into DataFrame using the following
DataFrame modifiedFrame = sqlContext.createDataFrame(modifiedRDD,schema);
sourceFrame and modifiedFrame schema is same when I call sourceFrame.show() output is expected I see every column has corresponding values and no column is empty but when I call modifiedFrame.show() I see all the columns values gets merged into first column value for e.g. assume source DataFrame has 3 column as shown below
_col1 _col2 _col3
ABC 10 DEF
GHI 20 JKL
When I print modifiedFrame which I converted from JavaRDD it shows in the following order
_col1 _col2 _col3
ABC,10,DEF
GHI,20,JKL
As shown above all the _col1 has all the values and _col2 and _col3 is empty. I don't know what is wrong.
As I mentioned in question's comment ;
It might occurs because of giving list as a one parameter.
return RowFactory.create(updateRow);
When investigated Apache Spark docs and source codes ; In that specifying schema example They assign parameters one by one for all columns respectively. Just investigate the some source code roughly RowFactory.java class and GenericRow class doesn't allocate that one parameter. So Try to give parameters respectively for row's column's.
return RowFactory.create(updateRow.get(0),updateRow.get(1),updateRow.get(2)); // List Example
You may try to convert your list to array and then pass as a parameter.
YourObject[] updatedRowArray= new YourObject[updateRow.size()];
updateRow.toArray(updatedRowArray);
return RowFactory.create(updatedRowArray);
By the way RowFactory.create() method is creating Row objects. In Apache Spark documentation about Row object and RowFactory.create() method;
Represents one row of output from a relational operator. Allows both generic access by ordinal, which will incur boxing overhead for
primitives, as well as native primitive access. It is invalid to use
the native primitive interface to retrieve a value that is null,
instead a user must check isNullAt before attempting to retrieve a
value that might be null.
To create a new Row, use RowFactory.create() in Java or Row.apply() in
Scala.
A Row object can be constructed by providing field values. Example:
import org.apache.spark.sql._
// Create a Row from values.
Row(value1, value2, value3, ...)
// Create a Row from a Seq of values.
Row.fromSeq(Seq(value1, value2, ...))
According to documentation; You can also apply your own required algorithm to seperate rows columns while creating Row objects respectively. But i think converting list to array and pass parameter as an array will work for you(I couldn't try please post your feedbacks, thanks).
In the Spark documentation, it is stated that the result of a Spark SQL query is a SchemaRDD. Each row of this SchemaRDD can in turn be accessed by ordinal. I am wondering if there is any way to access the columns using the field names of the case class on top of which the SQL query was built. I appreciate the fact that the case class is not associated with the result, especially if I have selected individual columns and/or aliased them: however, some way to access fields by name rather than ordinal would be convenient.
A simple way is to use the "language-integrated" select method on the resulting SchemaRDD to select the column(s) you want -- this still gives you a SchemaRDD, and if you select more than one column then you will still need to use ordinals, but you can always select one column at a time. Example:
// setup and some data
val sqlContext = new org.apache.spark.sql.SQLContext(sc)
import sqlContext._
case class Score(name: String, value: Int)
val scores =
sc.textFile("data.txt").map(_.split(",")).map(s => Score(s(0),s(1).trim.toInt))
scores.registerAsTable("scores")
// initial query
val original =
sqlContext.sql("Select value AS myVal, name FROM scores WHERE name = 'foo'")
// now a simple "language-integrated" query -- no registration required
val secondary = original.select('myVal)
secondary.collect().foreach(println)
Now secondary is a SchemaRDD with just one column, and it works despite the alias in the original query.
Edit: but note that you can register the resulting SchemaRDD and query it with straight SQL syntax without needing another case class.
original.registerAsTable("original")
val secondary = sqlContext.sql("select myVal from original")
secondary.collect().foreach(println)
Second edit: When processing an RDD one row at a time, it's possible to access the columns by name by using the matching syntax:
val secondary = original.map {case Row(myVal: Int, _) => myVal}
although this could get cumbersome if the right hand side of the '=>' requires access to a lot of the columns, as they would each need to be matched on the left. (This from a very useful comment in the source code for the Row companion object)