Solutions - Problem 1¶

Get number of flights departed from each of the US airport in the month of 2008 January.

We have to use airport codes to determine US airport.
We need to use airtraffic data to get departure details.
To solve this problem we have to perform inner join.

Let us start spark context for this Notebook so that we can execute the code provided. You can sign up for our 10 node state of the art cluster/labs to learn Spark SQL using our unique integrated LMS.

from pyspark.sql import SparkSession

import getpass
username = getpass.getuser()

spark = SparkSession. \
    builder. \
    config('spark.ui.port', '0'). \
    config("spark.sql.warehouse.dir", f"/user/{username}/warehouse"). \
    enableHiveSupport(). \
    appName(f'{username} | Python - Joining Data Sets'). \
    master('yarn'). \
    getOrCreate()

If you are going to use CLIs, you can use Spark SQL using one of the 3 approaches.

Using Spark SQL

spark2-sql \
    --master yarn \
    --conf spark.ui.port=0 \
    --conf spark.sql.warehouse.dir=/user/${USER}/warehouse

Using Scala

spark2-shell \
    --master yarn \
    --conf spark.ui.port=0 \
    --conf spark.sql.warehouse.dir=/user/${USER}/warehouse

Using Pyspark

pyspark2 \
    --master yarn \
    --conf spark.ui.port=0 \
    --conf spark.sql.warehouse.dir=/user/${USER}/warehouse

spark.conf.set("spark.sql.shuffle.partitions", "2")

airtrafficPath = "/public/airtraffic_all/airtraffic-part/flightmonth=200801"

airtraffic = spark. \
    read. \
    parquet(airtrafficPath)

airtraffic. \
    select(
        "Year", "Month", "DayOfMonth", 
        "Origin", "Dest", "CRSDepTime"
    ). \
show()

+----+-----+----------+------+----+----------+
|Year|Month|DayOfMonth|Origin|Dest|CRSDepTime|
+----+-----+----------+------+----+----------+
|2008|    1|        16|   BGR| CVG|      1735|
|2008|    1|        17|   SYR| CVG|      1701|
|2008|    1|        17|   SAV| BOS|      1225|
|2008|    1|        17|   CVG| GRR|      1530|
|2008|    1|        17|   STL| CVG|      1205|
|2008|    1|        18|   STL| JFK|      1150|
|2008|    1|        18|   MCI| CVG|      1009|
|2008|    1|        19|   TUL| CVG|       835|
|2008|    1|        20|   JFK| PHL|      1935|
|2008|    1|        20|   RDU| CVG|       830|
|2008|    1|        21|   CVG| DTW|      1640|
|2008|    1|        21|   MSY| LGA|      1204|
|2008|    1|        21|   JFK| PHL|      1935|
|2008|    1|        21|   DCA| JFK|      1830|
|2008|    1|        21|   HSV| DCA|       700|
|2008|    1|        22|   ORD| CVG|      1910|
|2008|    1|        22|   CVG| JFK|      1320|
|2008|    1|        23|   LGA| SAV|       908|
|2008|    1|        23|   CLT| CVG|      1252|
|2008|    1|        23|   GSP| LGA|       635|
+----+-----+----------+------+----+----------+
only showing top 20 rows

airtraffic.count()

airportCodesPath = "/public/airtraffic_all/airport-codes"

def getValidAirportCodes(airportCodesPath):
    airportCodes = spark. \
        read. \
        option("sep", "\t"). \
        option("header", True). \
        option("inferSchema", True). \
        csv(airportCodesPath). \
        filter("!(State = 'Hawaii' AND IATA = 'Big') AND Country = 'USA'")
    return airportCodes

airportCodes = getValidAirportCodes(airportCodesPath)

airportCodes.count()

from pyspark.sql.functions import col, lit, count

airtraffic. \
    join(airportCodes, airportCodes["IATA"] == airtraffic["Origin"]). \
    select(col("Year"), col("Month"), col("DayOfMonth"), airportCodes["*"], col("CRSDepTime")). \
    show()

+----+-----+----------+-------------+-----+-------+----+----------+
|Year|Month|DayOfMonth|         City|State|Country|IATA|CRSDepTime|
+----+-----+----------+-------------+-----+-------+----+----------+
|2008|    1|        16|       Bangor|   ME|    USA| BGR|      1735|
|2008|    1|        17|     Syracuse|   NY|    USA| SYR|      1701|
|2008|    1|        17|     Savannah|   GA|    USA| SAV|      1225|
|2008|    1|        17|   Cincinnati|   OH|    USA| CVG|      1530|
|2008|    1|        17|    St. Louis|   MO|    USA| STL|      1205|
|2008|    1|        18|    St. Louis|   MO|    USA| STL|      1150|
|2008|    1|        18|  Kansas City|   MO|    USA| MCI|      1009|
|2008|    1|        19|        Tulsa|   OK|    USA| TUL|       835|
|2008|    1|        20|     New York|   NY|    USA| JFK|      1935|
|2008|    1|        20|      Raleigh|   NC|    USA| RDU|       830|
|2008|    1|        21|   Cincinnati|   OH|    USA| CVG|      1640|
|2008|    1|        21|  New Orleans|   LA|    USA| MSY|      1204|
|2008|    1|        21|     New York|   NY|    USA| JFK|      1935|
|2008|    1|        21|Washington DC| null|    USA| DCA|      1830|
|2008|    1|        21|   Huntsville|   AL|    USA| HSV|       700|
|2008|    1|        22|      Chicago|   IL|    USA| ORD|      1910|
|2008|    1|        22|   Cincinnati|   OH|    USA| CVG|      1320|
|2008|    1|        23|     New York|   NY|    USA| LGA|       908|
|2008|    1|        23|    Charlotte|   NC|    USA| CLT|      1252|
|2008|    1|        23|   Greenville|   SC|    USA| GSP|       635|
+----+-----+----------+-------------+-----+-------+----+----------+
only showing top 20 rows

airtraffic. \
    join(airportCodes, airportCodes["IATA"] == airtraffic["Origin"]). \
    groupBy("Origin"). \
    agg(count(lit(1)).alias("FlightCount")). \
    orderBy(col("FlightCount").desc()). \
    show()

+------+-----------+
|Origin|FlightCount|
+------+-----------+
|   ATL|      33897|
|   ORD|      29936|
|   DFW|      23861|
|   DEN|      19477|
|   LAX|      18945|
|   PHX|      17695|
|   IAH|      15531|
|   LAS|      15292|
|   DTW|      14357|
|   EWR|      12467|
|   SLC|      12401|
|   MSP|      11800|
|   SFO|      11573|
|   MCO|      11070|
|   CLT|      10752|
|   LGA|      10300|
|   JFK|      10023|
|   BOS|       9717|
|   BWI|       8883|
|   CVG|       8659|
+------+-----------+
only showing top 20 rows

Mastering Pyspark

Solutions - Problem 1¶