Example - Reading a Greenplum Database Table into Spark (Scala)

This example utilizes the public Airline On-Time Statistics and Delay Cause data set. This data set records flights by date, airline, originating and destination airports, and many other flight details.

In this example, you:

  • Follow Greenplum Database tutorials to load the flight record data set into Greenplum Database.
  • Use spark-shell and the Greenplum-Spark Connector to read a fact table from Greenplum Database into Spark.
  • Perform transformations and actions on the data within Spark.

Prerequisites

Before starting this exercise, ensure that you are able to:

  • Access your Greenplum Database and Spark clusters
  • Identify your Greenplum Database master node hostname or IP address and port
  • Identify your Greenplum Database user/role name and password
  • Identify the absolute path to the Greenplum-Spark Connector JAR file on your system

Procedure

Perform the following procedure to load flight record data into Greenplum Database, read this data into Spark, and use Spark to transform and view the table data.

  1. Log in to your Greenplum Database master node and set up your environment. For example:

    $ ssh gpadmin@<gpmaster>
gpadmin@gpmaster$ . /usr/local/greenplum-db/greenplum_path.sh

  2. Download and unpack the flight record example data set:

    gpadmin@gpmaster$ git clone https://github.com/greenplum-db/gpdb-sandbox-tutorials.git
gpadmin@gpmaster$ cd gpdb-sandbox-tutorials
gpadmin@gpmaster$ tar zxf faa.tar.gz

  3. Perform the Create Users and Roles, Create and Prepare Database, Create Tables, and Data Loading exercises in the Greenplum Database tutorial. When you complete these exercises, you will have created:

    • A user named user2.
    • A Greenplum Database database named tutorial.
    • A schema named faa.
    • Several Greenplum Database tables in the faa schema and loaded them with flight data.

  4. Identify your Greenplum Database user/role name and password:

    If you are performing this exercise in the Greenplum Sandbox VM, you will use the Greenplum-Spark Connector as user2 (password pivotal). If you are performing this exercise in your own Greenplum Database instance, you may have a different user name.

  5. Assign required privileges to the Greenplum Database user to access the tutorial database, faa schema, and relevant tables. For example, if your Greenplum Database user name is user2, the Greenplum Database administrator would:

    gpadmin@gpmaster$ psql -d tutorial

    tutorial=# GRANT USAGE, CREATE ON SCHEMA faa TO user2;
tutorial=# GRANT SELECT ON faa.otp_c TO user2;
tutorial=# ALTER USER user2 CREATEEXTTABLE(type = 'writable', protocol = 'gpfdist');
tutorial=# \q

  6. Verify that the flight data loaded correctly:

    1. Connect to the tutorial database as user user2. Recall that the password for user2 is pivotal:

      gpadmin@gpmaster$ psql -d tutorial -U user2
Password for user user2:

    2. List all of the tables in the tutorial database schema named faa:

      tutorial=> \dt faa.*
                           List of relations
 Schema |         Name         | Type  |  Owner  |       Storage        
--------+----------------------+-------+---------+----------------------
 faa    | d_airlines           | table | user1   | heap
 faa    | d_airports           | table | user1   | heap
 faa    | d_cancellation_codes | table | user1   | heap
 faa    | d_delay_groups       | table | user1   | heap
 faa    | d_distance_groups    | table | user1   | heap
 faa    | d_wac                | table | user1   | heap
 faa    | faa_load_errors      | table | user1   | heap
 faa    | faa_otp_load         | table | gpadmin | heap
 faa    | otp_c                | table | gpadmin | append only columnar
 faa    | otp_c_1_prt_mth_1    | table | gpadmin | append only columnar
 faa    | otp_c_1_prt_mth_10   | table | gpadmin | append only columnar
 faa    | otp_c_1_prt_mth_11   | table | gpadmin | append only columnar
...
(27 rows)

      The \dt output lists 27 tables when the flight data was correctly loaded.

    3. Examine the definition of the table named otp_c:

      tutorial=>  \d faa.otp_c
       Append-Only Columnar Table "faa.otp_c"
        Column        |       Type       | Modifiers 
----------------------+------------------+-----------
 flt_year             | smallint         | 
 flt_quarter          | smallint         | 
 flt_month            | smallint         | 
 flt_dayofmonth       | smallint         | 
 flt_dayofweek        | smallint         | 
 flightdate           | date             | 
 uniquecarrier        | text             | 
 airlineid            | integer          | 
 carrier              | text             | 
 flightnum            | text             | 
 origin               | text             | 
 origincityname       | text             | 
 originstate          | text             | 
 originstatename      | text             | 
 dest                 | text             | 
 destcityname         | text             | 
 deststate            | text             | 
 deststatename        | text             | 
 crsdeptime           | text             | 
 deptime              | integer          | 
 depdelay             | double precision | 
 depdelayminutes      | double precision | 
 departuredelaygroups | smallint         | 
 taxiout              | smallint         | 
 wheelsoff            | text             | 
 wheelson             | text             | 
 taxiin               | smallint         | 
 crsarrtime           | text             | 
 arrtime              | text             | 
 arrdelay             | double precision | 
 arrdelayminutes      | double precision | 
 arrivaldelaygroups   | smallint         | 
 cancelled            | smallint         | 
 cancellationcode     | text             | 
 diverted             | smallint         | 
 crselapsedtime       | integer          | 
 actualelapsedtime    | double precision | 
 airtime              | double precision | 
 flights              | smallint         | 
 distance             | double precision | 
 distancegroup        | smallint         | 
 carrierdelay         | smallint         | 
 weatherdelay         | smallint         | 
 nasdelay             | smallint         | 
 securitydelay        | smallint         | 
 lateaircraftdelay    | smallint         | 
Checksum: t
Number of child tables: 17 (Use \d+ to list them.)
Distributed by: (uniquecarrier, flightnum)
Partition by: (flightdate)

      The table named otp_c is a column-oriented, partitioned fact table.

  7. Log in to your Spark client node:

    $ ssh user@<spark-client>
user@spark-client$

  8. Construct the JDBC connection string URL to access Greenplum Database. For example, if your Greenplum Database master host name is gpmaster.domain, the Greenplum Database master process is running on the default port, and you want to access the Greenplum database named tutorial:

    jdbc:postgresql://gpmaster.domain/tutorial

    Save this URL string, you will use it in an upcoming step.

  9. Start spark-shell. Replace <gsc-jar> with the full path to your Greenplum-Spark Connector JAR file:

    user@spark-client$ spark-shell --jars <gsc-jar>
< ... spark-shell startup output messages ... >
scala>

    You enter the spark-shell interactive scala shell.

  10. Prepare to read the otp_c table into Spark. In a text editor, construct a Map of read options for the greenplum data source. You want to load the Greenplum Database table named otp_c in the schema named faa, specifying airlineid as the partition column. For example, if you are the user user2 with password pivotal:

    val gscOptionMap = Map(
      "url" -> "jdbc:postgresql://gpmaster.domain/tutorial",
      "user" -> "user2",
      "password" -> "pivotal",
      "dbschema" -> "faa",
      "dbtable" -> "otp_c",
      "partitionColumn" -> "airlineid"
)

  11. Copy/paste the options map setting to your scala> shell terminal window. You must first enter :paste to switch to scala> paste mode:

    scala> :paste
// Entering paste mode (ctrl-D to finish)

val gscOptionMap = Map(
      "url" -> "jdbc:postgresql://gpmaster.domain/tutorial",
      "user" -> "user2",
      "password" -> "pivotal",
      "dbschema" -> "faa",
      "dbtable" -> "otp_c",
      "partitionColumn" -> "airlineid"
)

  12. Exit scala> paste mode by entering control-D:

    control-D
// Exiting paste mode, now interpreting.

gscOptionMap: scala.collection.immutable.Map[String,String] = Map(url -> jdbc:postgresql://gpmaster.domain/tutorial, partitionColumn -> airlineid, dbschema -> faa, dbtable -> otp_c, user -> user2, password -> pivotal)

    scala displays the option map you just constructed.

  13. Load the data from Greenplum Database table otp_c into a Spark DataFrame. Enter paste mode and copy/paste the command:

    scala> :paste
// Entering paste mode (ctrl-D to finish)

val gpdf = spark.read.format("greenplum")
        .options(gscOptionMap)
        .load()

control-D
// Exiting paste mode, now interpreting.

gpdf: org.apache.spark.sql.DataFrame = [flt_year: smallint, flt_quarter: smallint ... 44 more fields]

    The Greenplum Database table is not actually loaded until you perform an action on the returned DataFrame.

  14. Print the schema of the Greenplum Database table:

    scala> gpdf.printSchema()
root
 |-- flt_year: short (nullable = true)
 |-- flt_quarter: short (nullable = true)
 |-- flt_month: short (nullable = true)
 |-- flt_dayofmonth: short (nullable = true)
 |-- flt_dayofweek: short (nullable = true)
 |-- flightdate: date (nullable = true)
 |-- uniquecarrier: string (nullable = true)
 |-- airlineid: integer (nullable = true)
 |-- carrier: string (nullable = true)
 |-- flightnum: string (nullable = true)
 |-- origin: string (nullable = true)
 |-- origincityname: string (nullable = true)
 |-- originstate: string (nullable = true)
 |-- originstatename: string (nullable = true)
 |-- dest: string (nullable = true)
 |-- destcityname: string (nullable = true)
 |-- deststate: string (nullable = true)
 |-- deststatename: string (nullable = true)
 |-- crsdeptime: string (nullable = true)
 |-- deptime: integer (nullable = true)
 |-- depdelay: double (nullable = true)
 |-- depdelayminutes: double (nullable = true)
 |-- departuredelaygroups: short (nullable = true)
 |-- taxiout: short (nullable = true)
 |-- wheelsoff: string (nullable = true)
 |-- wheelson: string (nullable = true)
 |-- taxiin: short (nullable = true)
 |-- crsarrtime: string (nullable = true)
 |-- arrtime: string (nullable = true)
 |-- arrdelay: double (nullable = true)
 |-- arrdelayminutes: double (nullable = true)
 |-- arrivaldelaygroups: short (nullable = true)
 |-- cancelled: short (nullable = true)
 |-- cancellationcode: string (nullable = true)
 |-- diverted: short (nullable = true)
 |-- crselapsedtime: integer (nullable = true)
 |-- actualelapsedtime: double (nullable = true)
 |-- airtime: double (nullable = true)
 |-- flights: short (nullable = true)
 |-- distance: double (nullable = true)
 |-- distancegroup: short (nullable = true)
 |-- carrierdelay: short (nullable = true)
 |-- weatherdelay: short (nullable = true)
 |-- nasdelay: short (nullable = true)
 |-- securitydelay: short (nullable = true)
 |-- lateaircraftdelay: short (nullable = true)

    Compare this Spark output with that of the Greenplum Database \d faa.otp_c command that you invoked earlier. Note that the Greenplum Database data type names differ from those of Spark. For example, the distancegroup column is of Greenplum Database type smallint, while the Spark data type is short. For detailed information about how the Greenplum-Spark Connector maps data types between Greenplum Database and Spark, refer to the Greenplum Database -> Spark Data Type Mapping documentation.

  15. Use the .count() method to count the number of rows loaded:

    scala> gpdf.count()
res2: Long = 1024552

  16. Use the .select() and .filter() methods to show the origin city, month, and carrier of all flights cancelled in the month of December. Order the results by airline ID and origin city. Enter paste mode and copy/paste the command:

    scala> :paste
// Entering paste mode (ctrl-D to finish)

gpdf.select("origincityname", "flt_month", "airlineid", "carrier")
           .filter("cancelled = 1").filter("flt_month = 12")
           .orderBy("airlineid", "origincityname")
           .show()

control-D
// Exiting paste mode, now interpreting.

+---------------+---------+---------+-------+
| origincityname|flt_month|airlineid|carrier|
+---------------+---------+---------+-------+
|    Detroit, MI|       12|    19386|     NW|
|    Detroit, MI|       12|    19386|     NW|
|  Milwaukee, WI|       12|    19386|     NW|
|Minneapolis, MN|       12|    19386|     NW|
|    Phoenix, AZ|       12|    19386|     NW|
|    Houston, TX|       12|    19393|     WN|
|    Houston, TX|       12|    19393|     WN|
|  Las Vegas, NV|       12|    19393|     WN|
|  Las Vegas, NV|       12|    19393|     WN|
| Manchester, NH|       12|    19393|     WN|
|      Omaha, NE|       12|    19393|     WN|
|    Phoenix, AZ|       12|    19393|     WN|
|   San Jose, CA|       12|    19393|     WN|
|      Tampa, FL|       12|    19393|     WN|
| Washington, DC|       12|    19393|     WN|
|  Anchorage, AK|       12|    19704|     CO|
|  Anchorage, AK|       12|    19704|     CO|
|     Austin, TX|       12|    19704|     CO|
|    Houston, TX|       12|    19704|     CO|
|    Houston, TX|       12|    19704|     CO|
+--------------------+---------+---------+-------+
only showing top 20 rows

  17. Use the .groupBy(), .agg(), and avg() methods to identify the average departure delay for each day of the week, sorting by the day of the week with the .sort() method: 

    scala> gpdf.groupBy("flt_dayofweek").agg(avg("depdelayminutes")).sort("flt_dayofweek").show()
+-------------+--------------------+
|flt_dayofweek|avg(depdelayminutes)|
+-------------+--------------------+
|            1|  14.738491569779914|
|            2|  11.237272024020244|
|            3|  11.198198256252295|
|            4|  12.056892575385985|
|            5|  12.455024249521957|
|            6|   12.69586361271813|
|            7|  14.818271192603715|
+-------------+--------------------+

  18. Use the startsWith() function to display the cancelled flights for the month of December for all origin cities whose name starts with the letters Mi. Enter paste mode and copy/paste the command:

    scala> :paste
// Entering paste mode (ctrl-D to finish)

gpdf.select("origincityname", "destcityname", "flightnum", "carrier", "airlineid", "flt_month")
    .filter("cancelled = 1")
    .filter("flt_month = 12")
    .filter($"origincityname".startsWith("Mi"))
    .orderBy("origincityname", "destcityname")
    .show()

    control-D
// Exiting paste mode, now interpreting.

+--------------------+--------------------+---------+-------+---------+---------+
|      origincityname|        destcityname|flightnum|carrier|airlineid|flt_month|
+--------------------+--------------------+---------+-------+---------+---------+
|           Miami, FL|         Chicago, IL|      846|     AA|    19805|       12|
|           Miami, FL|Greensboro/High P...|     4197|     MQ|    20398|       12|
|           Miami, FL|      Washington, DC|     1068|     AA|    19805|       12|
|       Milwaukee, WI|       Baltimore, MD|      817|     FL|    20437|       12|
|       Milwaukee, WI|          Denver, CO|     5838|     OO|    20304|       12|
|       Milwaukee, WI|         Memphis, TN|     3799|     9E|    20363|       12|
|       Milwaukee, WI|     Minneapolis, MN|     7177|     NW|    19386|       12|
|       Milwaukee, WI|          Newark, NJ|     2504|     OO|    20304|       12|
|     Minneapolis, MN|         Atlanta, GA|     1073|     DL|    19790|       12|
|     Minneapolis, MN|     Grand Forks, ND|     4003|     9E|    20363|       12|
|     Minneapolis, MN|         Houston, TX|     2399|     XE|    20374|       12|
|     Minneapolis, MN|         Lincoln, NE|     3798|     9E|    20363|       12|
|     Minneapolis, MN|           Miami, FL|      925|     AA|    19805|       12|
|     Minneapolis, MN|       Milwaukee, WI|     7165|     NW|    19386|       12|
|     Minneapolis, MN|      Rapid City, SD|     3931|     9E|    20363|       12|
|Mission/Mcallen/E...|         Memphis, TN|     4066|     9E|    20363|       12|
|        Missoula, MT|  Salt Lake City, UT|     4461|     OO|    20304|       12|
+--------------------+--------------------+---------+-------+---------+---------+

    Notice the $"origincityname" notation used in the third filter. $"<colname>" is an implicit method call shortcut for new ColumnName("<colname>").