从 spark dataframe 访问特定行

Question

I am a newbie to azure spark/ databricks and trying to access specific row eg 10th row in the dataframe.

This is what I did in notebook so far

1. Read a CSV file in a table

spark.read
  .format("csv")
  .option("header", "true")
  .load("/mnt/training/enb/commonfiles/ramp.csv")
  .write
  .mode("overwrite")
  .saveAsTable("ramp_csv")

2. Create a DataFrame for the "table" ramp_csv

val rampDF = spark.read.table("ramp_csv")

3. Read specific row

I am using the following logic in Scala

val myRow1st = rampDF.rdd.take(10).last

display(myRow1st)

and it should display 10th row but I am getting the following error

command-2264596624884586:9: error: overloaded method value display with alternatives:
  [A](data: Seq[A])(implicit evidence$1: reflect.runtime.universe.TypeTag[A])Unit <and>
  (dataset: org.apache.spark.sql.Dataset[_],streamName: String,trigger: org.apache.spark.sql.streaming.Trigger,checkpointLocation: String)Unit <and>
  (model: org.apache.spark.ml.classification.DecisionTreeClassificationModel)Unit <and>
  (model: org.apache.spark.ml.regression.DecisionTreeRegressionModel)Unit <and>
  (model: org.apache.spark.ml.clustering.KMeansModel)Unit <and>
  (model: org.apache.spark.mllib.clustering.KMeansModel)Unit <and>
  (documentable: com.databricks.dbutils_v1.WithHelpMethods)Unit
 cannot be applied to (org.apache.spark.sql.Row)
display(myRow1st)
^
Command took 0.12 seconds --

Could you please share what I am missing here? I tried few other things but it didn't work. Thanks in advance for help!

Answer 1

Here is the breakdown of what is happening in your code:

rampDF.rdd.take(10) returns Array[Row]

.last returns Row

display() takes a Dataset and you are passing it a Row . You can use .show(10) to display the first 10 rows in tabular form.

Another option is to do display(rampDF.limit(10))

Answer 2

I'd go with João's answer as well. But if you insist on getting the Nth row as a DataFrame and avoid collecting to the driver node (say when N is very big) you can do:

import org.apache.spark.sql.functions._
import spark.implicits._

val df = 1 to 100 toDF //sample data
val cols = df.columns

df
.limit(10)
.withColumn("id", monotonically_increasing_id())
.agg(max(struct(("id" +: cols).map(col(_)):_*)).alias("tenth"))
.select(cols.map(c => col("tenth."+c).alias(c)):_*)

This will return:

+-----+
|value|
+-----+
|   10|
+-----+

Answer 3

I also go with João Guitana's answer. An alternative to get specifically the 10'th record:

val df = 1 to 1000 toDF
val tenth = df.limit(10).collect.toList.last
tenth: org.apache.spark.sql.Row = [10]

That will return the 10th Row on that df