Category: Spark SQL

This tutorial covers using Spark SQL with a JSON file input data source.

Overview

We will show examples of JSON as input source to Spark SQL’s SQLContext.  This Spark SQL tutorial with JSON has two parts.  Part 1 focus is the “happy path” when using JSON with Spark SQL.  Part 2 covers a “gotcha” or something you might not expect when using Spark SQL JSON data source.

By the way, If you are not familiar with Spark SQL, a couple of references include a summary of Spark SQL chapter post and the first Spark SQL CSV tutorial.

Methodology

We build upon the previous baby_names.csv file as well as a simple file to get us started which I’ve called customers.json.  Here is a gist of customers.json.

Spark SQL JSON Example Tutorial Part 1

1. Start the spark shell

$SPARK_HOME/bin/spark–shell

2. Load the JSON using the jsonFile function from the provided sqlContext.  Following assumes you have customers.json in same directory as from where the spark-shell script was called.

RBH12103:spark-1.4.1-bin-hadoop2.4 tmcgrath$ bin/spark-shell
Welcome to
      ____              __
     / __/__  ___ _____/ /__
    _\ \/ _ \/ _ `/ __/  '_/
   /___/ .__/\_,_/_/ /_/\_\   version 1.4.1
      /_/

Using Scala version 2.10.4 (Java HotSpot(TM) 64-Bit Server VM, Java 1.7.0_79)
Type in expressions to have them evaluated.
Type :help for more information.
Spark context available as sc.
SQL context available as sqlContext.

scala> val customers = sqlContext.jsonFile("customers.json")
warning: there were 1 deprecation warning(s); re-run with -deprecation for details
customers: org.apache.spark.sql.DataFrame = [address: struct<city:string,state:string,street:string,zip:string>, first_name: string, last_name: string]

3. Register the data as a temp table to ease our future SQL queries

scala> customers.registerTempTable("customers")

4. We are now in position to run some SQL

scala> val firstCityState = sqlContext.sql("SELECT first_name, address.city, address.state FROM customers")
firstCityState: org.apache.spark.sql.DataFrame = [first_name: string, city: string, state: string]

scala> firstCityState.collect.foreach(println)
[James,New Orleans,LA]
[Josephine,Brighton,MI]
[Art,Bridgeport,NJ]

Spark SQL JSON Example Tutorial Part 2

If you run the customers.json from part 1 through http://jsonlint.com, it will not validate.  You might be surprised to know that creating invalid JSON for Part 1 was intentional.  Why?  We needed JSON source which works well with Spark SQL out of the box.

[{
	"Year": "2013",
	"First Name": "DAVID",
	"County": "KINGS",
	"Sex": "M",
	"Count": "272"
}, {
	"Year": "2013",
	"First Name": "JAYDEN",
	"County": "KINGS",
	"Sex": "M",
	"Count": "268"
}, {
	"Year": "2013",
	"First Name": "JAYDEN",
	"County": "QUEENS",
	"Sex": "M",
	"Count": "219"
}, {
	"Year": "2013",
	"First Name": "MOSHE",
	"County": "KINGS",
	"Sex": "M",
	"Count": "219"
}, {
	"Year": "2013",
	"First Name": "ETHAN",
	"County": "QUEENS",
	"Sex": "M",
	"Count": "216"
}]

scala> val jsonRDD = sc.wholeTextFiles("baby_names.json").map(x => x._2)
jsonRDD: org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[10] at map at <console>:21

scala> val namesJson = sqlContext.read.json(jsonRDD)
namesJson: org.apache.spark.sql.DataFrame = [Count: string, County: string, First Name: string, Sex: string, Year: string]

scala> namesJson.printSchema
root
 |-- Count: string (nullable = true)
 |-- County: string (nullable = true)
 |-- First Name: string (nullable = true)
 |-- Sex: string (nullable = true)
 |-- Year: string (nullable = true)

scala>

In this Spark tutorial, we will use Spark SQL with a CSV input data source.  We will continue to use the baby names CSV source file as used in the previous Spark tutorials.  This tutorial presumes the reader is familiar with using SQL with relational databases and would like to know how to use with Spark.

Overview

Earlier versions of Spark SQL required a certain kind of Resilient Distributed Data set called SchemaRDD.   Starting with Spark 1.3, Schema RDD was renamed to DataFrame.

DataFrames are composed of Row objects accompanied with schema which describes the data types of each column. A DataFrame may be considered similar to a table in a traditional relational database. A DataFrame may be created from a variety of input sources including CSV text files.

This intro to Spark SQL post will uses a CSV file from previous Spark SQL tutorials.

Download the CSV version of baby names file here:

https://health.data.ny.gov/api/views/jxy9-yhdk/rows.csv?accessType=DOWNLOAD

For this and other Spark tutorials, the file has been named baby_names.csv

Methodology

We’re going to use the spark shell and the spark-csv package available from Spark Packages to make our lives easier.  It is described as a “library for parsing and querying CSV data with Apache Spark, for Spark SQL and DataFrames”  This library is compatible with Spark 1.3 and above.

Spark SQL CSV Example Tutorial Part 1

1. Depending on your version of Scala, start the spark shell with a packages command line argument.

At time of this writing, scala 2.10 version:

SPARK_HOME/bin/spark-shell --packages com.databricks:spark-csv_2.10:1.3.0

where SPARK_HOME is the root directory of your Spark directory; i.e. ~/Development/spark-1.4.1-bin-hadoop2.4 or c:/dev/spark-1.4.1-bin-hadoop2.4

At time of this writing, scala 2.11 version:

SPARK_HOME/bin/spark-shell --packages com.databricks:spark-csv_2.11:1.3.0

2. Using the available sqlContext from the shell load the CSV read, format, option and load functions

Welcome to
      ____              __
     / __/__  ___ _____/ /__
    _\ \/ _ \/ _ `/ __/  '_/
   /___/ .__/\_,_/_/ /_/\_\   version 1.4.1
      /_/

Using Scala version 2.10.4 (Java HotSpot(TM) 64-Bit Server VM, Java 1.7.0_79)
Type in expressions to have them evaluated.
Type :help for more information.
Spark context available as sc.
SQL context available as sqlContext.

scala> val baby_names = sqlContext.read.format("com.databricks.spark.csv").option("header", "true").option("inferSchema", "true").load("baby_names.csv")
baby_names: org.apache.spark.sql.DataFrame = [Year: int, First Name: string, County: string, Sex: string, Count: int]

In the above code, we are specifying the desire to use com.databricks.spark.csv format from the package we passed to the shell in step 1.  “header” set to true signifies the first row has column names.  “inferSchema” instructs Spark to attempt to infer the schema of the CSV and finally load function passes in the path and name of the CSV source file.  In this example, we can tell the baby_names.csv file is in the same directory as where the spark-shell script was launched.

3. Register a temp table

scala> baby_names.registerTempTable("names")

4. We’re now ready to query using SQL such as finding the distinct years in the CSV

scala> val distinctYears = sqlContext.sql("select distinct Year from names")
distinctYears: org.apache.spark.sql.DataFrame = [Year: int]

scala> distinctYears.collect.foreach(println)
[2007]                                                                          
[2008]
[2009]
[2010]
[2011]
[2012]
[2013]

5. It might be handy to know the schema

scala> baby_names.printSchema
root
|-- Year: integer (nullable = true)
|-- First Name: string (nullable = true)
|-- County: string (nullable = true)
|-- Sex: string (nullable = true)
|-- Count: integer (nullable = true)

Spark SQL CSV Example Tutorial Part 2

But, let’s try some more advanced SQL, such as determining the names which appear most often in the data

scala> val popular_names = sqlContext.sql("select distinct(`First Name`), count(County) as cnt from names group by `First Name` order by cnt desc LIMIT 10")
popular_names: org.apache.spark.sql.DataFrame = [First Name: string, cnt: bigint]

scala> popular_names.collect.foreach(println)
[JACOB,284]
[EMMA,284]
[LOGAN,270]
[OLIVIA,268]
[ISABELLA,259]
[SOPHIA,249]
[NOAH,247]
[MASON,245]
[ETHAN,239]
[AVA,234]

But as we learned from Spark transformation and action tutorials, this doesn’t necessarily imply the most popular names.  We need to utilize the Count column value:

scala> val popular_names = sqlContext.sql("select distinct(`First Name`), sum(Count) as cnt from names group by `First Name` order by cnt desc LIMIT 10")
popular_names: org.apache.spark.sql.DataFrame = [First Name: string, cnt: bigint]

scala> popular_names.collect.foreach(println)
[MICHAEL,10391]
[ISABELLA,9106]
[MATTHEW,9002]
[JAYDEN,8948]
[JACOB,8770]
[JOSEPH,8715]
[SOPHIA,8689]
[DANIEL,8412]
[ANTHONY,8399]
[RYAN,8187]

Featured image credit https://flic.kr/p/3CrmX