Spark SQL

This short Spark tutorial shows analysis of World Cup player data using Spark SQL with a JSON file input data source from Python perspective.

Spark SQL JSON with Python Overview

We are going to load a JSON input source to Spark SQL’s SQLContext.  This Spark SQL JSON with Python tutorial has two parts.  The first part shows examples of JSON input sources with a specific structure.  The second part warns you of something you might not expect when using Spark SQL with a JSON data source.

Methodology

We are going to use two JSON inputs.  We’ll start with a simple, trivial Spark SQL with JSON example and then move to the analysis of historical World Cup player data.

There are assumptions you have worked with Spark and Python in the past.  See Spark with Python Quick Start if you are new.

Spark SQL JSON with Python Example Tutorial Part 1

1. Start pyspark

$SPARK_HOME/bin/pyspark

2. Load a JSON file which comes with Apache Spark distributions by default.  We do this by using the jsonFile function from the provided sqlContext.

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

Using Python version 2.7.11 (default, Dec  6 2015 18:57:58)
SparkContext available as sc, HiveContext available as sqlContext.
>>> people = sqlContext.read.json("examples/src/main/resources/people.json")
>>> people.printSchema()
root
 | – age: long (nullable = true)
 | – name: string (nullable = true)

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

>>> people.registerTempTable("people")

4. Now, we can run some SQL

>>> sqlContext.sql("select name from people").show()
+-------+
|   name|
+-------+
|Michael|
|   Andy|
| Justin|
+-------+

Spark SQL JSON Example Tutorial Part 2

Take a closer look at the people.json file used in Part 1.  If you run it through http://jsonlint.com, it will not validate.  Please, let’s not debate this being a byproduct of JSON and you can’t technical validate any JSON.  Stay with me here.

[
  {
    "Competition": "World Cup",
    "Year": 1930,
    "Team": "Argentina",
    "Number": "",
    "Position": "GK",
    "FullName": "Ãngel Bossio",
    "Club": "Club Atlético Talleres de Remedios de Escalada",
    "ClubCountry": "Argentina",
    "DateOfBirth": "1905-5-5",
    "IsCaptain": false
  },
  {
    "Competition": "World Cup",
    "Year": 1930,
    "Team": "Argentina",
    "Number": "",
    "Position": "GK",
    "FullName": "Juan Botasso",
    "Club": "Quilmes Atlético Club",
    "ClubCountry": "Argentina",
    "DateOfBirth": "1908-10-23",
    "IsCaptain": false
  },
....
]

>>> jsonRDD = sc.wholeTextFiles("2014-world-cup.json").map(lambda x: x[1])

>>> import re
>>> js = jsonRDD.map(lambda x: re.sub(r"\s+", "", x, flags=re.UNICODE))

>>> wc_players = sqlContext.jsonRDD(js)

>>> wc_players.registerTempTable("players")
>>> sqlContext.sql("select distinct Team from players").show()
+--------------------+
|                Team|
+--------------------+
|              Mexico|
|            Portugal|
|            Colombia|
|          SouthKorea|
|         Netherlands|
|             Belgium|
|               Chile|
|              Brazil|
|BosniaandHerzegovina|
|          IvoryCoast|
|            Cameroon|
|             England|
|             Croatia|
|           Argentina|
|             Algeria|
|               Ghana|
|                Iran|
|             Nigeria|
|              Russia|
|              France|
+--------------------+

In this Spark tutorial, we will use Spark SQL with a CSV input data source using the Python API.  We will continue to use the Uber CSV source file as used in the Getting Started with Spark and Python tutorial presented earlier.

Also, this Spark SQL CSV tutorial assumes you are familiar with using SQL against relational databases directly or from Python.  So, in other words, you have experience with SQL and would like to know how to use with Spark.

Overview

Spark SQL uses a type of Resilient Distributed Dataset called DataFrames.  DataFrames are composed of Row objects accompanied by a schema which describes the data types of each column. A DataFrame may be considered similar to a table in a traditional relational database. As you might expect, DataFrames may be created from a variety of input sources including CSV text files.

This intro to Spark SQL post will use a CSV file from previous Spark Python tutorials found here:

https://raw.githubusercontent.com/fivethirtyeight/uber-tlc-foil-response/master/Uber-Jan-Feb-FOIL.csv

Methodology

We’re going to use the ipython notebook and the spark-csv package available from Spark Packages to make our lives easier.  The spark-csv package 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 with Python Example Tutorial Part 1

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

At time of this writing, scala 2.10 version:

$SPARK_HOME/bin/pyspark – packages com.databricks:spark-csv_2.10:1.3.0

At time of this writing, scala 2.11 version:

$SPARK_HOME/bin/pyspark – 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

>>> df = sqlContext.read.format('com.databricks.spark.csv').options(header='true', inferschema='true').load('Uber-Jan-Feb-FOIL.csv')

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 Uber-Jan-Feb-FOIL.csv file is in the same directory as where pyspark was launched.

3. Register a temp table

>>> df.registerTempTable("uber")

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

>>> distinct_bases = sqlContext.sql("select distinct dispatching_base_number from uber")
>>> for b in distinct_bases.collect(): print b
Row(dispatching_base_number=u'B02598')
Row(dispatching_base_number=u'B02764')
Row(dispatching_base_number=u'B02765')
Row(dispatching_base_number=u'B02617')
Row(dispatching_base_number=u'B02682')
Row(dispatching_base_number=u'B02512')

5. It might be handy to know the schema

>>> df.printSchema()

root
 | – dispatching_base_number: string (nullable = true)
 | – date: string (nullable = true)
 | – active_vehicles: integer (nullable = true)
 | – trips: integer (nullable = true)

Spark SQL CSV with Python Example Tutorial Part 2

But, let’s try some more advanced SQL, such as determining which Uber bases is the busiest based on the number of trips

>>> sqlContext.sql("""select distinct(`dispatching_base_number`), 
                                sum(`trips`) as cnt from uber group by `dispatching_base_number` 
                                order by cnt desc""").show()


+-----------------------+-------+
|dispatching_base_number|    cnt|
+-----------------------+-------+
|                 B02764|1914449|
|                 B02617| 725025|
|                 B02682| 662509|
|                 B02598| 540791|
|                 B02765| 193670|
|                 B02512|  93786|
+-----------------------+-------+

Or the 5 busiest days based on the number of trips in the time range of the data:

>>> sqlContext.sql("""select distinct(`date`), 
                                sum(`trips`) as cnt from uber group by `date` 
                                order by cnt desc limit 5""").show()


+---------+------+
|     date|   cnt|
+---------+------+
|2/20/2015|100915|
|2/14/2015|100345|
|2/21/2015| 98380|
|2/13/2015| 98024|
|1/31/2015| 92257|
+---------+------+

Python Spark SQL Resource

ipython notebook file https://github.com/tmcgrath/spark-with-python-course/blob/master/Spark-SQL-CSV-with-Python.ipynb

Further Spark Python References

Spark Python Tutorials

Spark Python Quick Start Tutorial

Featured image credit https://flic.kr/p/4FbcGr