Use Spark for Scala to load data and run SQL queries

This notebook introduces basic Spark concepts and helps you to start using Spark for Scala.

Some familiarity with Scala is recommended. This notebook runs on Scala 2.11 with Spark.

In this notebook, you'll use the publicly available mtcars data set from Motor Trend magazine to learn some basic Scala. You'll learn how to load data, create a Spark DataFrame, aggregate data, run mathematical formulas, and run SQL queries against the data.

1. Load a DataFrame

A DataFrame is a distributed collection of data that is organized into named columns. The local Scala DataFrame called mtcars includes observations on the following 11 variables:

[, 1] mpg Miles / (US) gallon
[, 2] cyl Number of cylinders
[, 3] disp Displacement (cu. in.)
[, 4] hp Gross horsepower
[, 5] drat Rear axle ratio
[, 6] wt Weight (1000 lbs)
[, 7] qsec 1/4 mile time (seconds)
[, 8] vs 0 = V-engine, 1 = straight engine
[, 9] am Transmission (0 = automatic, 1 = manual)
[,10] gear Number of forward gears
[,11] carb Number of carburetors

In [1]:
import sys.process._
import java.net.URL
import java.io.File

def fileDownloader(url: String, filename: String) = {
    new URL(url) #> new File(filename) !!
}

fileDownloader("https://ibm.box.com/shared/static/f1dhhjnzjwxmy2c1ys2whvrgz05d1pui.csv", "mtcars.csv")
warning: there was one feature warning; re-run with -feature for details
fileDownloader: (url: String, filename: String)String
res0: String = ""

2. Initialize an SQLContext

To work with a DataFrame, you need an SQLContext. You create this SQLContext by using SQLContext(sc). A SparkContext named sc, which has been created for you, is used to initialize the SQLContext:

In [5]:
import au.com.bytecode.opencsv.CSVParser
val sqlContext = new org.apache.spark.sql.SQLContext(sc)
import sqlContext.implicits._
Waiting for a Spark session to start...
sqlContext = org.apache.spark.sql.SQLContext@22663e10
warning: there was one deprecation warning; re-run with -deprecation for details
Out[5]:
org.apache.spark.sql.SQLContext@22663e10

3. Create a Spark DataFrame

Using the SQLContext and the loaded local DataFrame, create a Spark DataFrame and print the schema, or structure, of the DataFrame:

In [6]:
// Define the schema using a case class.
case class Cars(car: String, mpg: String, cyl: String, disp: String, hp: String, drat: String,
 wt: String, qsec: String, vs: String, am: String, gear: String, carb: String);
defined class Cars
In [7]:
val csv = sc.textFile("mtcars.csv")
val headerAndRows = csv.map(line => line.split(",").map(_.trim))
val header = headerAndRows.first
val data = headerAndRows.filter(_(0) != header(0))
val mtcars = data.map(p => Cars(p(0), p(1), p(2), p(3), p(4), p(5), p(6), p(7), p(8), p(9), p(10), p(11))).toDF()
mtcars.printSchema
root
 |-- car: string (nullable = true)
 |-- mpg: string (nullable = true)
 |-- cyl: string (nullable = true)
 |-- disp: string (nullable = true)
 |-- hp: string (nullable = true)
 |-- drat: string (nullable = true)
 |-- wt: string (nullable = true)
 |-- qsec: string (nullable = true)
 |-- vs: string (nullable = true)
 |-- am: string (nullable = true)
 |-- gear: string (nullable = true)
 |-- carb: string (nullable = true)

csv = mtcars.csv MapPartitionsRDD[1] at textFile at <console>:46
headerAndRows = MapPartitionsRDD[2] at map at <console>:47
header = Array(car, mpg, cyl, disp, hp, drat, wt, qsec, vs, am, gear, carb)
data = MapPartitionsRDD[3] at filter at <console>:49
mtcars = [car: string, mpg: string ... 10 more fields]
Out[7]:
[car: string, mpg: string ... 10 more fields]

Display the content of the Spark DataFrame:

In [8]:
mtcars.show(32)
+-------------------+----+---+-----+---+----+-----+-----+---+---+----+----+
|                car| mpg|cyl| disp| hp|drat|   wt| qsec| vs| am|gear|carb|
+-------------------+----+---+-----+---+----+-----+-----+---+---+----+----+
|          Mazda RX4|  21|  6|  160|110| 3.9| 2.62|16.46|  0|  1|   4|   4|
|      Mazda RX4 Wag|  21|  6|  160|110| 3.9|2.875|17.02|  0|  1|   4|   4|
|         Datsun 710|22.8|  4|  108| 93|3.85| 2.32|18.61|  1|  1|   4|   1|
|     Hornet 4 Drive|21.4|  6|  258|110|3.08|3.215|19.44|  1|  0|   3|   1|
|  Hornet Sportabout|18.7|  8|  360|175|3.15| 3.44|17.02|  0|  0|   3|   2|
|            Valiant|18.1|  6|  225|105|2.76| 3.46|20.22|  1|  0|   3|   1|
|         Duster 360|14.3|  8|  360|245|3.21| 3.57|15.84|  0|  0|   3|   4|
|          Merc 240D|24.4|  4|146.7| 62|3.69| 3.19|   20|  1|  0|   4|   2|
|           Merc 230|22.8|  4|140.8| 95|3.92| 3.15| 22.9|  1|  0|   4|   2|
|           Merc 280|19.2|  6|167.6|123|3.92| 3.44| 18.3|  1|  0|   4|   4|
|          Merc 280C|17.8|  6|167.6|123|3.92| 3.44| 18.9|  1|  0|   4|   4|
|         Merc 450SE|16.4|  8|275.8|180|3.07| 4.07| 17.4|  0|  0|   3|   3|
|         Merc 450SL|17.3|  8|275.8|180|3.07| 3.73| 17.6|  0|  0|   3|   3|
|        Merc 450SLC|15.2|  8|275.8|180|3.07| 3.78|   18|  0|  0|   3|   3|
| Cadillac Fleetwood|10.4|  8|  472|205|2.93| 5.25|17.98|  0|  0|   3|   4|
|Lincoln Continental|10.4|  8|  460|215|   3|5.424|17.82|  0|  0|   3|   4|
|  Chrysler Imperial|14.7|  8|  440|230|3.23|5.345|17.42|  0|  0|   3|   4|
|           Fiat 128|32.4|  4| 78.7| 66|4.08|  2.2|19.47|  1|  1|   4|   1|
|        Honda Civic|30.4|  4| 75.7| 52|4.93|1.615|18.52|  1|  1|   4|   2|
|     Toyota Corolla|33.9|  4| 71.1| 65|4.22|1.835| 19.9|  1|  1|   4|   1|
|      Toyota Corona|21.5|  4|120.1| 97| 3.7|2.465|20.01|  1|  0|   3|   1|
|   Dodge Challenger|15.5|  8|  318|150|2.76| 3.52|16.87|  0|  0|   3|   2|
|        AMC Javelin|15.2|  8|  304|150|3.15|3.435| 17.3|  0|  0|   3|   2|
|         Camaro Z28|13.3|  8|  350|245|3.73| 3.84|15.41|  0|  0|   3|   4|
|   Pontiac Firebird|19.2|  8|  400|175|3.08|3.845|17.05|  0|  0|   3|   2|
|          Fiat X1-9|27.3|  4|   79| 66|4.08|1.935| 18.9|  1|  1|   4|   1|
|      Porsche 914-2|  26|  4|120.3| 91|4.43| 2.14| 16.7|  0|  1|   5|   2|
|       Lotus Europa|30.4|  4| 95.1|113|3.77|1.513| 16.9|  1|  1|   5|   2|
|     Ford Pantera L|15.8|  8|  351|264|4.22| 3.17| 14.5|  0|  1|   5|   4|
|       Ferrari Dino|19.7|  6|  145|175|3.62| 2.77| 15.5|  0|  1|   5|   6|
|      Maserati Bora|  15|  8|  301|335|3.54| 3.57| 14.6|  0|  1|   5|   8|
|         Volvo 142E|21.4|  4|  121|109|4.11| 2.78| 18.6|  1|  1|   4|   2|
+-------------------+----+---+-----+---+----+-----+-----+---+---+----+----+

Try different ways of retrieving subsets of the data. For example, get the first 5 values in the mpg column:

In [9]:
mtcars.select("mpg").show(5)
+----+
| mpg|
+----+
|  21|
|  21|
|22.8|
|21.4|
|18.7|
+----+
only showing top 5 rows

Filter the DataFrame to retain only rows with mpg values that are less than 18:

In [10]:
mtcars.filter(mtcars("mpg") < 18).show()
+-------------------+----+---+-----+---+----+-----+-----+---+---+----+----+
|                car| mpg|cyl| disp| hp|drat|   wt| qsec| vs| am|gear|carb|
+-------------------+----+---+-----+---+----+-----+-----+---+---+----+----+
|         Duster 360|14.3|  8|  360|245|3.21| 3.57|15.84|  0|  0|   3|   4|
|          Merc 280C|17.8|  6|167.6|123|3.92| 3.44| 18.9|  1|  0|   4|   4|
|         Merc 450SE|16.4|  8|275.8|180|3.07| 4.07| 17.4|  0|  0|   3|   3|
|         Merc 450SL|17.3|  8|275.8|180|3.07| 3.73| 17.6|  0|  0|   3|   3|
|        Merc 450SLC|15.2|  8|275.8|180|3.07| 3.78|   18|  0|  0|   3|   3|
| Cadillac Fleetwood|10.4|  8|  472|205|2.93| 5.25|17.98|  0|  0|   3|   4|
|Lincoln Continental|10.4|  8|  460|215|   3|5.424|17.82|  0|  0|   3|   4|
|  Chrysler Imperial|14.7|  8|  440|230|3.23|5.345|17.42|  0|  0|   3|   4|
|   Dodge Challenger|15.5|  8|  318|150|2.76| 3.52|16.87|  0|  0|   3|   2|
|        AMC Javelin|15.2|  8|  304|150|3.15|3.435| 17.3|  0|  0|   3|   2|
|         Camaro Z28|13.3|  8|  350|245|3.73| 3.84|15.41|  0|  0|   3|   4|
|     Ford Pantera L|15.8|  8|  351|264|4.22| 3.17| 14.5|  0|  1|   5|   4|
|      Maserati Bora|  15|  8|  301|335|3.54| 3.57| 14.6|  0|  1|   5|   8|
+-------------------+----+---+-----+---+----+-----+-----+---+---+----+----+

4. Aggregate data after grouping by columns

Spark DataFrames support a number of common functions to aggregate data after grouping. For example, you can compute the average weight of cars as a function of the number of cylinders:

In [11]:
import org.apache.spark.sql.functions._
mtcars.groupBy("cyl").agg(avg("wt")).show()
+---+-----------------+
|cyl|          avg(wt)|
+---+-----------------+
|  8|3.999214285714286|
|  6|3.117142857142857|
|  4|2.285727272727273|
+---+-----------------+

You can also sort the output from the aggregation to determine the most popular cylinder configuration in the DataFrame:

In [12]:
mtcars.groupBy("cyl").agg(count("wt")).sort($"count(wt)".desc).show()
+---+---------+
|cyl|count(wt)|
+---+---------+
|  8|       14|
|  4|       11|
|  6|        7|
+---+---------+

5. Operate on columns

Scala provides a number of functions that you can apply directly to columns for data processing. In the following example, a basic arithmetic function converts lbs to metric tons:

In [13]:
mtcars.withColumn("wtTon", mtcars("wt") * 0.45).select(("car"),("wt"),("wtTon")).show(6)
+-----------------+-----+-------+
|              car|   wt|  wtTon|
+-----------------+-----+-------+
|        Mazda RX4| 2.62|  1.179|
|    Mazda RX4 Wag|2.875|1.29375|
|       Datsun 710| 2.32|  1.044|
|   Hornet 4 Drive|3.215|1.44675|
|Hornet Sportabout| 3.44|  1.548|
|          Valiant| 3.46|  1.557|
+-----------------+-----+-------+
only showing top 6 rows

6. Run SQL queries from the Spark DataFrame

You can register a Spark DataFrame as a temporary table and then run SQL queries over the data. The sql function enables an application to run SQL queries programmatically and returns the result as a DataFrame:

In [14]:
mtcars.registerTempTable("cars")

val highgearcars = sqlContext.sql("SELECT car, gear FROM cars WHERE gear >= 5")
highgearcars.show()
+--------------+----+
|           car|gear|
+--------------+----+
| Porsche 914-2|   5|
|  Lotus Europa|   5|
|Ford Pantera L|   5|
|  Ferrari Dino|   5|
| Maserati Bora|   5|
+--------------+----+

highgearcars = [car: string, gear: string]
warning: there was one deprecation warning; re-run with -deprecation for details
Out[14]:
[car: string, gear: string]

That's it!

You successfully completed this notebook! You learned how to load a DataFrame, view and filter the data, aggregate the data, perform operations on the data in specific columns, and run SQL queries against the data. For more information about Spark, see the Spark Quick Start Guide.

Want to learn more?

Free courses on Big Data University:

Authors

Saeed Aghabozorgi, PhD, is a Data Scientist in IBM with a track record of developing enterprise-level applications that substantially increases clients' ability to turn data into actionable knowledge. He is a researcher in the data mining field and an expert in developing advanced analytic methods like machine learning and statistical modelling on large data sets.

Polong Lin is a Data Scientist at IBM in Canada. Under the Emerging Technologies division, Polong is responsible for educating the next generation of data scientists through Big Data University. Polong is a regular speaker in conferences and meetups, and holds an M.Sc. in Cognitive Psychology.

Copyright © 2016, 2018 Big Data University. This notebook and its source code are released under the terms of the MIT License.

Love this notebook? Don't have an account yet?
Share it with your colleagues and help them discover the power of Watson Studio! Sign Up