1) This document provides examples of how to use Spark DataFrames and SQL to load and analyze Iris flower data. It shows how to load data from files and Kafka, define schemas, select, filter, sort, group, and join dataframes. 2) Methods like spark.read, dataframe.select(), dataframe.filter(), and dataframe.groupBy() are used to load and query the data. StructType and case classes define the schema. SQL statements can also be used via the sqlContext. 3) User defined functions (UDFs) are demonstrated to handle custom data types like maps. The examples provide an overview of basic Spark DataFrame and SQL functionality.

1. Basic
Using Spark DataFrame
For SQL
charsyam@naver.com

2. Create DataFrame From File
val path = “abc.txt”
val df = spark.read.text(path)

3. Create DataFrame From Kafka
val rdd = KafkaUtils.createRDD[String, String](...)
val logsDF = rdd.map { _.value }.toDF

4. Spark DataFrame Column
1) col("column name")
2) $"column name"
1) And 2) are the same.

5. Simple Iris TSV Logs
http://www.math.uah.edu/stat/data/Fisher.txt
Type PW PL SW SL
0 2 14 33 50
1 24 56 31 67
1 23 51 31 69
0 2 10 36 46
1 20 52 30 65
1 19 51 27 58

6. Load TSV with StructType
import org.apache.spark.sql.types._
var irisSchema = StructType(Array(
StructField("Type", IntegerType, true),
StructField("PetalWidth", IntegerType, true),
StructField("PetalLength", IntegerType, true),
StructField("SepalWidth", IntegerType, true),
StructField("SepalLength", IntegerType, true)
))

7. Load TSV with Encoder #1
import org.apache.spark.sql.Encoders
case class IrisSchema(Type: Int, PetalWidth: Int, PetalLength: Int,
SepalWidth: Int, SepalLength: Int)
var irisSchema = Encoders.product[IrisSchema].schema

8. Load TSV
var irisDf = spark.read.format("csv"). // Use "csv" regardless of TSV or CSV.
option("header", "true"). // Does the file have a header line?
option("delimiter", "t"). // Set delimiter to tab or comma.
schema(irisSchema). // Schema that was built above.
load("Fisher.txt")
irisDf.show(5)

9. Load TSV - Show Results
scala> irisDf.show(5)
+----+----------+-----------+----------+-----------+
|Type|PetalWidth|PetalLength|SepalWidth|SepalLength|
+----+----------+-----------+----------+-----------+
| 0| 2| 14| 33| 50|
| 1| 24| 56| 31| 67|
| 1| 23| 51| 31| 69|
| 0| 2| 10| 36| 46|
| 1| 20| 52| 30| 65|
+----+----------+-----------+----------+-----------+
only showing top 5 rows

10. Using sqlContext sql
Super easy way
val view = df.createOrReplaceTempView("tmp_iris")
val resultDF = df.sqlContext.sql("select type, PetalWidth from tmp_iris")

11. Simple Select
SQL:
Select type, petalwidth + sepalwidth as sum_width from …
val sumDF = df.withColumn("sum_width", col("PetalWidth") + col("SepalWidth"))
val resultDF = sumDF.selectExpr("Type", "sum_width")
val resultDF = sumDF.selectExpr("*") ← select *

12. Select with where
SQL:
Select type, petalwidth from … where petalwidth > 10
val whereDF = df.filter($"petalwidth" > 10)
val whereDF = df.where($"petalwidth" > 10)
//filter and where are the same
val resultDF = whereDF.selectExpr("Type", "petalwidth")

13. Select with order by
SQL:
Select petalwidth, sepalwidth from … order by petalwidth, sepalwidth desc
1) val sortDF = df.sort($"petalwidth", $"sepalwidth".desc)
2) val sortDF = df.sort($"petalwidth", desc("sepalwidth"))
3) val sortDF = df.orderBy($"petalwidth", desc("sepalwidth"))
1), 2) And 3) are the same.
val resultDF = sortDF.selectExpr("petalwidth", "sepalwidth")

14. Select with Group by
SQL:
Select type, max(petalwidth) A, min(sepalwidth) B from … group by type
val groupDF = df.groupBy($"type").agg(max($"petalwidth").as("A"),
min($"sepalwidth").as("B"))
val resultDF = groupDF.selectExpr("type", "A", "B")

15. Tip - Support MapType<String, String> like Hive
SQL in Hive:
Create table test (type map<string, string>);
Hive support str_to_map, but spark not support for dataframe(spark support
str_to_map for hiveQL).
Using udf to solve this.
val string_line = "A=1,B=2,C=3"
Val df = logsDF.withColumn("type", str_to_map(string_line))

16. UDF - str_to_map
val str_to_map = udf {
text : String =>
val pairs = text.split("delimiter1|delimiter2").grouped(2)
pairs.map { case Array(k, v) => k -> v}.toMap
}

17. Thank you.