Big-Data-HPC-Platform/docs/IMDB/IMDB.xml

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<book lang="en_US" xml:base="../">
  <title>Six Degrees of Kevin Bacon</title>

  <bookinfo>
    <title>Six Degrees of Kevin Bacon: ECL Programming Example</title>

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    <author>
      <surname>Boca Raton Documentation Team</surname>
    </author>

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      <para>Other products, logos, and services may be trademarks or
      registered trademarks of their respective companies.</para>

      <para>All names and example data used in this manual are fictitious. Any
      similarity to actual persons, living or dead, is purely
      coincidental.</para>

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  <chapter id="Working_with_Data">
    <title><emphasis role="bold">Working with Data</emphasis></title>

    <sect1 id="Working_with_data_Intro" role="nobrk">
      <title>Introduction</title>

      <para>This exercise shows the methodology to extract useful information
      from data. Finding interesting links and relationships from large or
      massive datasets is a typical use of the HPCCSystems High Performance
      Computing Cluster (HPCC) platform.</para>

      <para>In this example, we will download the data files from the Internet
      Movie Database (IMDB) and see one technique to extract links and find
      relationships.</para>

      <para>Since the concept of actors and movies is conceptually simple;
      everyone should understand the data and relationships intuitively.
      However, the data is comprehensive enough to provide a solid example and
      inspiration for new users to gain skills to attack their own real-world
      problems with an HPCC.</para>

      <para>In this example, we will:</para>

      <itemizedlist mark="bullet">
        <listitem>
          <para>Download raw data files and supporting documentation about the
          data</para>
        </listitem>

        <listitem>
          <para>Analyze the data file to understand its format and
          contents</para>
        </listitem>

        <listitem>
          <para>Spray the file to a Data Refinery (Thor) cluster</para>
        </listitem>

        <listitem>
          <para>Examine the data and determine the pre-processing
          needed</para>
        </listitem>

        <listitem>
          <para>Pre-process the data to produce a new data file</para>
        </listitem>
      </itemizedlist>

      <para><informaltable colsep="1" frame="all">
          <?dbfo keep-together="always"?>

          <tgroup cols="2">
            <colspec colwidth="52.60pt" />

            <colspec />

            <tbody>
              <row>
                <entry><inlinegraphic fileref="images/OSSgr3.png" /></entry>

                <entry>While this example will run on a single-node HPCC, you
                will see a dramatic difference in performance on a multi-node
                system. The true power of an HPCC is its ability to work on
                different portions of the data file in parallel. This is known
                as Massively Parallel Processing (MPP).</entry>
              </row>
            </tbody>
          </tgroup>
        </informaltable></para>
    </sect1>

    <sect1 id="Processing_the_data">
      <title>Processing the Data</title>

      <sect2 id="Get_a_data_file">
        <title><emphasis>We get a data file</emphasis></title>

        <para>The Internet Movie Database (IMDB) database is a freely
        downloadable set of data files about Movies.</para>

        <para>It can be downloaded in many formats, including text file
        format. The set includes approximately 48 files about Actors,
        Actresses, Directors, Producers, and other aspects of motions
        pictures.</para>

        <para>It is manageable in size (~400MB) and is sufficient in size to
        exercise an HPCC platform but not too big to download<emphasis
        role="bold">.</emphasis></para>

        <para>The plain text data files are available from the following ftp
        sites:</para>

        <itemizedlist mark="bullet">
          <listitem>
            <para><ulink
            url="ftp://ftp.fu-berlin.de/pub/misc/movies/database/">ftp://ftp.fu-berlin.de/pub/misc/movies/database/</ulink>
            (Germany)</para>
          </listitem>

          <listitem>
            <para><ulink
            url="ftp://ftp.funet.fi/pub/mirrors/ftp.imdb.com/pub/">ftp://ftp.funet.fi/pub/mirrors/ftp.imdb.com/pub/</ulink>
            (Finland)</para>
          </listitem>

          <listitem>
            <para><ulink
            url="ftp://ftp.sunet.se/pub/tv+movies/imdb/">ftp://ftp.sunet.se/pub/tv+movies/imdb/</ulink>
            (Sweden)</para>
          </listitem>
        </itemizedlist>

        <para>The files are compressed using GNUzip to save space and
        bandwidth.</para>

        <para>We will focus initially on two of the larger data sets in the
        IMDB database</para>

        <blockquote>
          <itemizedlist mark="bullet">
            <listitem>
              <para>The Actors Dataset (Approximately 4 million
              Records)</para>
            </listitem>

            <listitem>
              <para>The Actresses Dataset (Approximately 2 million
              Records)</para>
            </listitem>
          </itemizedlist>
        </blockquote>

        <itemizedlist mark="bullet">
          <listitem>
            <para>Download the plain text data files
            (<emphasis>actors.list.gz</emphasis> and
            <emphasis>actresses.list.gz</emphasis> )to your local drive using
            any ftp interface you choose.</para>
          </listitem>

          <listitem>
            <para>Extract the two data files (<emphasis>actors.list</emphasis>
            and <emphasis>actresses.list</emphasis> ) using any GNUzip
            interface.</para>
          </listitem>
        </itemizedlist>
      </sect2>

      <sect2 id="Analyze_the_data">
        <title><emphasis>Analyze the data file to understand its format and
        its contents</emphasis></title>

        <para>Here is the sample of the data in the Actors.list file from
        IMDB</para>

        <para><programlisting>Koolout' Starks, Johnny Nothing Like the Holidays (2008) [Alexis' Thug] &lt;35&gt;
Subtle Seduction (2008) [Officer Ward]
The Godfather of Green Bay (2005) (as Johnny Starks) [Marcus] &lt;18&gt;

La Chispa', Tony Caceria de judiciales (1997) &lt;11&gt;
Violencia en la sierra (1995) [Victoriano] &lt;4&gt;</programlisting></para>

        <para>Notice the actors text file is structured as follows</para>

        <para><programlisting>Blankline
Actorname_i Moviename (year) [role] &lt;listing position&gt;
            Moviename (year) [role] &lt;listing position&gt;
            Moviename (year) [role] &lt;listing position&gt;
              :
Blankline
Actorname_j \t Moviename (year) [role] &lt;listing position&gt;
              :
Blankline</programlisting></para>
      </sect2>

      <sect2 id="Load_the_Incoming_Data" role="brk">
        <title><emphasis>Load the Incoming Data File to your Landing
        Zone</emphasis></title>

        <para>In this step, you will copy the data files to a location from
        which it can be sprayed to your HPCC cluster. A Landing Zone is a
        storage location attached to your HPCC. It has a utility running to
        facilitate file spraying to a cluster.</para>

        <para>For smaller data files, maximum of 2GB, you can use the
        upload/download file utility in ECL Watch. The sample data files are
        ~400 mb.</para>

        <para>Next you will distribute (or Spray) the dataset to all the nodes
        in the HPCC cluster. The power of the HPCC comes from its ability to
        assign multiple processors to work on different portions of the data
        file in parallel.</para>

        <orderedlist>
          <listitem>
            <para>Download the sample data files from the ftp sites as
            described in the previous section, if you have not done so
            already.</para>
          </listitem>

          <listitem>
            <para>Extract them to a folder on your local machine.</para>
          </listitem>

          <listitem>
            <para>In your browser, go to the <emphasis role="bold">ECL
            Watch</emphasis> URL. For example, http://nnn.nnn.nnn.nnn:8010,
            where nnn.nnn.nnn.nnn is your ESP Server's IP address.</para>

            <para><informaltable colsep="1" frame="all" rowsep="1">
                <?dbfo keep-together="always"?>

                <tgroup cols="2">
                  <colspec colwidth="49.50pt" />

                  <colspec />

                  <tbody>
                    <row>
                      <entry><inlinegraphic
                      fileref="images/caution.png" /></entry>

                      <entry>Your IP address could be different from the ones
                      provided in the example images. Please use the IP
                      address provided by <emphasis
                      role="bold">your</emphasis> installation.</entry>
                    </row>
                  </tbody>
                </tgroup>
              </informaltable></para>
          </listitem>

          <listitem>
            <?dbfo keep-together="always"?>

            <para>From ECL Watch page, click on the <emphasis
            role="bold">Files </emphasis>icon, then on the <emphasis
            role="bold">Landing Zones</emphasis> link.</para>

            <para><figure>
                <title>Upload/download</title>

                <mediaobject>
                  <imageobject>
                    <imagedata fileref="images/LZimg03-1.jpg"
                               vendor="eclwatchSS" />
                  </imageobject>
                </mediaobject>
              </figure></para>

            <para>Once you click on the <emphasis
            role="bold">Upload</emphasis> file link, a file dialog
            displays.</para>

            <para></para>
          </listitem>

          <listitem>
            <para>Browse the files on your local machine, then use
            multi-select to choose the files to upload and then press the
            <emphasis role="bold">Open</emphasis> button.</para>

            <para>The files you selected should appear . The data files are
            named: <emphasis>actors.list</emphasis> and
            <emphasis>actresses.list</emphasis> <emphasis role="bold">
            </emphasis></para>

            <figure>
              <title>Dropzones and Files</title>

              <mediaobject>
                <imageobject>
                  <imagedata fileref="images/IMDB_upload.jpg"
                             vendor="eclwatchSS" />
                </imageobject>
              </mediaobject>
            </figure>
          </listitem>

          <listitem>
            <para>Press the <emphasis role="bold">Start</emphasis> button to
            upload the files.</para>

            <para>You can monitor priogress as it uploads.</para>

            <figure>
              <title>Upload Progress</title>

              <mediaobject>
                <imageobject>
                  <imagedata fileref="images/IMDB_uploadProgress.jpg"
                             vendor="eclwatchSS" />
                </imageobject>
              </mediaobject>
            </figure>
          </listitem>
        </orderedlist>
      </sect2>

      <sect2 id="Spray_the_Data_to_THOR">
        <title>Spray the Data File to your <emphasis>Data Refinery (Thor)
        Cluster</emphasis></title>

        <para>To use the data file in our HPCC system, we must "spray" it to
        all the nodes. A <emphasis>spray</emphasis> or
        <emphasis>import</emphasis> is the relocation of a data file from one
        location (such as a Landing Zone) to multiple file parts on nodes in a
        cluster.</para>

        <para>The distributed or sprayed file is given a
        <emphasis>logical-file-name</emphasis> as follows<emphasis
        role="bold">: ~thor::in::IMDB::actors.list </emphasis> The system
        maintains a list of logical files and the corresponding physical file
        locations of the file parts.</para>

        <para></para>

        <itemizedlist mark="bullet">
          <listitem>
            <para>Open ECL Watch using the following URL:</para>

            <para><emphasis role="bold">http://nnn.nnn.nnn.nnn:pppp(where
            nnn.nnn.nnn.nnn is your ESP Server's IP Address and pppp is the
            port. The default port is 8010)</emphasis></para>
          </listitem>

          <listitem>
            <para>Click on the <emphasis role="bold">Files</emphasis> icon,
            then click the <emphasis role="bold">Landing Zones</emphasis> link
            from the navigation.</para>
          </listitem>

          <listitem>
            <para>Select the two files (actors.list and actresses.list ) then
            press the Delimited button.</para>

            <para>The <emphasis role="bold">Spray Delimited</emphasis> dialog
            displays.</para>

            <para><figure>
                <title>Spray Delimited</title>

                <mediaobject>
                  <imageobject>
                    <imagedata fileref="images/IMDB_01.jpg"
                               vendor="eclwatchSS" />
                  </imageobject>
                </mediaobject>
              </figure></para>

            <para></para>
          </listitem>

          <listitem>
            <para>Select mythor in the <emphasis role="bold">Group</emphasis>
            drop-list.</para>

            <para>The IP Address is automatically filled and the Local Path is
            partially filled with the default folder on your landing zone.
            Note: The VM and Community Edition typically only has one landing
            zone defined.</para>
          </listitem>

          <listitem>
            <para>Complete the Name prefix <emphasis
            role="bold">~thor::in::IMDB</emphasis></para>
          </listitem>

          <listitem>
            <para>Fill in the rest of the parameters (if they are not filled
            in already).</para>

            <para><itemizedlist>
                <listitem>
                  <para>Max Record Length 8192</para>
                </listitem>

                <listitem>
                  <para>Separator \,</para>
                </listitem>

                <listitem>
                  <para>Line Terminator \n,\r\n</para>
                </listitem>

                <listitem>
                  <para>Quote: '</para>
                </listitem>
              </itemizedlist></para>
          </listitem>

          <listitem>
            <?dbfo keep-together="always"?>

            <para>Make sure the <emphasis role="bold">Overwrite</emphasis> and
            <emphasis role="bold">Replicate</emphasis><emphasis role="bold">
            </emphasis>boxes are checked.</para>

            <para><emphasis role="bold">Note:</emphasis> The Replicate option
            is only available on systems where replication has been
            enabled.</para>

            <para></para>
          </listitem>

          <listitem>
            <?dbfo keep-together="always"?>

            <para>Press the <emphasis role="bold">Spray</emphasis><emphasis
            role="bold"> </emphasis>button.</para>

            <para>A tab opens for each file. On these tabs, you can monitor
            the progress of each DFU Spray.</para>

            <para><figure>
                <title>View Progress</title>

                <mediaobject>
                  <imageobject>
                    <imagedata fileref="images/IMDB_03a.jpg"
                               vendor="eclwatchSS" />
                  </imageobject>
                </mediaobject>
              </figure></para>
          </listitem>

          <listitem>
            <para>After both sprays are complete, we can query the logical
            files on the HPCC to see the files we sprayed.</para>
          </listitem>

          <listitem>
            <para>Click on the <emphasis role="bold">Logical Files</emphasis>
            link</para>

            <para>The files display in the Logical Files list:</para>

            <para><figure>
                <title>Display Logical Files</title>

                <mediaobject>
                  <imageobject>
                    <imagedata fileref="images/IMDB_05.jpg"
                               vendor="eclwatchSS" />
                  </imageobject>
                </mediaobject>
              </figure></para>
          </listitem>
        </itemizedlist>
      </sect2>

      <sect2 id="Working_with_the_Data">
        <title>Working With the Data</title>

        <para>In this portion of the example, we will write ECL code to make
        sure we can read the sprayed data file .We will define and execute
        simple queries on it so we can evaluate it and determine any necessary
        pre-processing.</para>

        <itemizedlist mark="bullet">
          <listitem>
            <para>Start the ECL IDE (Start &gt;&gt; All Programs &gt;&gt; HPCC
            Systems &gt;&gt; ECL IDE )</para>
          </listitem>

          <listitem>
            <para>Log in to your environment.</para>
          </listitem>

          <listitem>
            <para>Expand the <emphasis role="bold">examples</emphasis> ECL
            folder in the Repository toolbox.</para>
          </listitem>

          <listitem>
            <?dbfo keep-together="always"?>

            <para>Expand the <emphasis role="bold">IMDB </emphasis>folder
            inside.</para>

            <para>All the ECL files needed to complete this tutorial are
            located in the IMDB folder.</para>

            <figure>
              <title>IMDB ECL Files</title>

              <mediaobject>
                <imageobject>
                  <imagedata fileref="images/IMDB_06_new.jpg" />
                </imageobject>
              </mediaobject>
            </figure>
          </listitem>

          <listitem>
            <para>Open the CleanActor ECL file and examine the code.</para>

            <para>This code reads and processes the raw text file. The
            comments below describe the processing:</para>

            <para><programlisting>IMPORT Std;

EXPORT STRING CleanActor(STRING infld) := FUNCTION
  //this can be refined later
  s1 := Std.Str.FindReplace(infld, '\'',''); // replace apostrophe
  s2 := Std.Str.FindReplace(s1, '\t','');    //replace tabs
  s3 := Std.Str.FindReplace(s2, '----','');  // replace multiple -----
  return TRIM(s3, LEFT, RIGHT);
END;
</programlisting></para>
          </listitem>
        </itemizedlist>

        <sect3 role="brk">
          <title>Examine the Data</title>

          <para>In this section, we will look at the data and determine if
          there is any pre-processing we want to perform. This is the step in
          the development process where we convert the raw data into a form we
          can actually use.</para>

          <variablelist>
            <varlistentry>
              <term>Note:</term>

              <listitem>
                <para>The IMDB.FileActors.ecl file specifies the size of the
                header in the files (actors.list and actresses.list.) The
                HEADING() value in the example code was accurate at the time
                we downloaded the IMDB data, but could change at any time. We
                suggest opening in a text editor and checking the line number
                where the header ends and actual data begins (as shown
                below).</para>
              </listitem>
            </varlistentry>
          </variablelist>

          <figure>
            <title>actors.list in text editor</title>

            <mediaobject>
              <imageobject>
                <imagedata fileref="images/IMDB_fileheading.jpg" />
              </imageobject>
            </mediaobject>
          </figure>

          <para></para>

          <itemizedlist mark="bullet">
            <listitem>
              <para>Open a new Builder window (CTRL+N) and write the following
              code:</para>

              <para><programlisting>IMPORT IMDB;

OUTPUT(IMDB.FileActors);
</programlisting></para>
            </listitem>

            <listitem>
              <para>Press the syntax check button on the main toolbar (or
              press F7).</para>

              <para>It is always a good idea to check syntax before
              submitting.</para>
            </listitem>

            <listitem>
              <?dbfo keep-together="always"?>

              <para>Make sure the selected cluster is your
              <emphasis>thor</emphasis> cluster, then press the <emphasis
              role="bold">Submit</emphasis> button.</para>

              <para><figure>
                  <title>Submit to Thor</title>

                  <mediaobject>
                    <imageobject>
                      <imagedata fileref="images/IMDB_10.jpg" />
                    </imageobject>
                  </mediaobject>
                </figure></para>
            </listitem>

            <listitem>
              <para>When the Workunit completes it displays a green checkmark.
              <inlinegraphic fileref="images/DT173-15.jpg" /></para>

              <para><emphasis role="bold">Note:</emphasis> Depending on the
              size of your cluster and the speed of your server(s), this
              process could take several minutes. If you are running this on a
              virtual machine, it could take as long as 45 minutes to
              complete.</para>
            </listitem>

            <listitem>
              <?dbfo keep-together="always"?>

              <para>Select the Workunit tab (the one with the number and the
              checkmark next to it) and select the <emphasis
              role="bold">Result 1</emphasis> tab.</para>

              <para><figure>
                  <title>Select Workunit</title>

                  <mediaobject>
                    <imageobject>
                      <imagedata fileref="images/IMDB_07.jpg" />
                    </imageobject>
                  </mediaobject>
                </figure></para>
            </listitem>

            <listitem>
              <?dbfo keep-together="always"?>

              <para>Scroll down to see more records.</para>

              <para><figure>
                  <title>See more records</title>

                  <mediaobject>
                    <imageobject>
                      <imagedata fileref="images/IMDB_08.jpg" />
                    </imageobject>
                  </mediaobject>
                </figure></para>
            </listitem>
          </itemizedlist>

          <itemizedlist mark="bullet">
            <listitem>
              <para>Close the Builder Window.</para>
            </listitem>
          </itemizedlist>
        </sect3>
      </sect2>

      <sect2 id="Processing_the_Data_E-T-L">
        <title><emphasis role="bold">Processing the Data : Extract,
        </emphasis><emphasis>Transform, and Load</emphasis></title>

        <para><emphasis>In this section, we will write code to transform the
        original actor data as follows:</emphasis></para>

        <itemizedlist mark="bullet">
          <listitem>
            <para>From the raw actors data, we will do an ETL operation
            (Extract, Transform, Load) to build an <emphasis
            role="bold">actor_movie </emphasis>relation set.</para>
          </listitem>

          <listitem>
            <para>We will also construct a Kevin Bacon degrees of separation
            lookup set. This is the structure we will query to answer the
            question:</para>
          </listitem>
        </itemizedlist>

        <para><emphasis>H</emphasis><emphasis>ow many degrees of separation
        exist between Actor X and Kevin Bacon?</emphasis></para>

        <para></para>

        <para></para>

        <para></para>

        <para><emphasis role="bold">For example: </emphasis>Using Jon Lovitz
        as the actor, we want information as follows:</para>

        <para>Jon Lovitz ( (was in) Movie X ( (with) Actor2 ((who was in)
        Movie Y ( (with) Kevin Bacon</para>

        <para>We will then write this new file to our Thor cluster so it can
        be used in parameterized queries.</para>

        <para></para>

        <para></para>

        <para><itemizedlist mark="bullet">
            <listitem>
              <para>In the ECL IDE , go to the Repository panel and expand the
              IMDB folder.</para>
            </listitem>

            <listitem>
              <para>Open the ECL File ActorsInMovies.</para>

              <para>The code in this ECL file looks like this:</para>
            </listitem>
          </itemizedlist></para>

        <para><programlisting>/* ******************************************************************************
## Copyright 2011 HPCC Systems.  All rights reserved.
******************************************************************************* */

/**
  * Produce a slimmed down version of the IMDB actor AND actress files to 
  * permit more efficient join operations.
  * Filter out the movie records we do not want in building our KBacon Number sets.
  *
  */

IMPORT $ AS IMDB;
IMPORT Std;

// Filter out TV movies, Videos AND some documentary type collections
ds_IMDB := IMDB.FileActors(actorname!='' AND moviename != '' AND
                           Std.Str.Find(moviename,'Boffo',1) = 0 AND
                           Std.Str.Find(moviename,'Slasher Film',1) = 0 AND
                           movie_type != 'Video' AND isTVseries = 'N' AND
                           movie_type != 'For TV');

//Slim the records down to bare essentials for searching AND joining
slim_IMDB_rec := RECORD
  STRING50  actor;
  STRING150 movie;
END;

slim_IMDB_rec slim_it(ds_IMDB L):= TRANSFORM
  SELF.actor := Std.Str.FindReplace(L.actorname,'(I)','');
  SELF.movie := L.moviename;;
END;

IMDB_names := PROJECT(ds_IMDB, slim_it(LEFT));

export ActorsInMovies := IMDB_Names : persist('~temp::IMDB::ActorsInMovies');;
</programlisting></para>

        <para>This defines a relational data set:-- actor:movie. We will use
        this definition later.</para>
      </sect2>
    </sect1>

    <sect1 id="Getting_Useful_Info_from_Data">
      <title>Getting Useful Information from Data</title>

      <sect2 id="Links_and_Degrees_of_Separation">
        <title><emphasis>Links and Degrees of Separation</emphasis></title>

        <para>Now that we have our data in a useful format, have a relation
        defined, and the file is in place, we can write code to use the new
        data file.</para>

        <para>We want to know how many actors are a distance
        <emphasis>N</emphasis> from Kevin Bacon. To accomplish this, we will
        construct sets of Kevin Bacon's costars that are KBacon number
        apart.</para>

        <itemizedlist mark="bullet">
          <listitem>
            <para>Open the KevinBaconNumberSets ECL file.</para>
          </listitem>
        </itemizedlist>

        <para>This ECL code counts the number of actors with <emphasis>"bacon
        numbers"</emphasis> starting from 1 thru 7, that is up to 7 Levels of
        separation. We will use this later to do searches by building an
        index.</para>

        <para><programlisting>/* ******************************************************************************
ATTRIBUTE PURPOSE: 
 Produce a series of sets for Actors and Movies that are : distance-0
 away (KBacons Direct movies ), distance-2 Away KBacon's Costars Movies , 
 distance-3 away - Movies of Costars of Costars etc all the way upto level 7
 
 The nested attributes below are shown here together for the benefit of the reader. 
 
 Notes on variable naming convention used for costars and movies
 KBMovies               :  Movies  Kevin Bacon Worked in    (distance 0)
 KBCoStars              :  Stars who worked in KBMovies      (distance 1)
 KBCoStarMovies         :  Movies worked in by KBCoStars   
                             except KBMovies   (distance 1)
 KBCo2Stars             :  Stars(Actors) who worked in KBCoStarMovies (distance 2)
 KBCo2StarMovies        :  Movies worked  in by KBCo2Stars 
                             except KBCoStarMovies    (distance 2)
 KBCo3Stars             :  Stars(Actors) who worked in KBCo2StarMovies (distance 3)
 KBCo3StarMovies        :  Movies worked  in by KBCo3Stars  
                             except KBCo2StarMovies   (distance 3)
etc..
******************************************************************************* */

IMPORT Std;
IMPORT IMDB;

EXPORT KevinBaconNumberSets := MODULE
  // Constructing a proper name match function is an art within itself
  // For simplicity we will define a name as matching if both first and last name 
  //are found within the string

  NameMatch(string full_name, string fname,string lname) :=
    Std.Str.Find(full_name,fname,1) &gt; 0 AND 
    Std.Str.Find(full_name,lname,1) &gt; 0;

  //------ Get KBacon Movies
  AllKBEntries := IMDB.ActorsInMovies(NameMatch(actor,'Kevin','Bacon'));
  EXPORT KBMovies := DEDUP(AllKBEntries, movie, ALL); // Each movie should ONLY occur once

  //------ Get KBacon CoStars
  CoStars := IMDB.ActorsInMovies(Movie IN SET(KBMovies,Movie));
  EXPORT KBCoStars := DEDUP( CoStars(actor&lt;&gt;'Kevin Bacon'), actor, ALL);

  //------ Get KBacon Costars' Movies
  // CSM = First find all of the movies that a KBCoStar has been in

  CSM := DEDUP(JOIN(IMDB.ActorsInMovies,KBCoStars, LEFT.actor=RIGHT.actor,
                    TRANSFORM(LEFT), LOOKUP),
               movie,ALL);

  // Now we need to remove all of those that KB was in himself
  // We can use a set; KB has not been in (quite!) that many movies

  EXPORT KBCoStarMovies := CSM(movie NOT IN SET(KBMovies,movie));

  //------ Bacon # 2 Actors
  // To be a Co2Star of Kevin Bacon you must have appeared in a movie with a 
  //CoStar of Kevin Bacon
  // This corresponds to having a Bacon number of 2
  // We are now getting towards the expensive part of the process
  KBCo2S := DEDUP(JOIN(IMDB.ActorsInMovies, KBCoStarMovies, LEFT.movie=RIGHT.movie,
                       TRANSFORM(LEFT), LOOKUP),
                  actor, ALL);

  // KCCo2S = ALL Actors appearing in Movies of KBacon's CoActors
  // The above is all the people in the movies; but some will have been co-stars of KB 
  //directly - these must be removed
  // The LEFT ONLY join removes items in one list from another

  EXPORT KBCo2Stars := JOIN(KBCo2S, KBCoStars, LEFT.actor=RIGHT.actor, 
                            TRANSFORM(LEFT), LEFT ONLY);

  //------- bacon # 2 Movies
  // Co2SM = what movies have all the Co2Stars been in? 
  Co2SM := DEDUP(JOIN(IMDB.ActorsInMovies, KBCo2Stars, LEFT.actor=RIGHT.actor,
                      TRANSFORM(LEFT), LOOKUP),
                 movie, ALL);
  // Co2SM = ALL Movies KBCo2Stars have been in
  // Of course some of these movies will have CoStars in too and thus will already have 
  //been listed. Note this list will not contain any Kevin Bacon movies OR the movie would 
  //have been reached earlier!

  Export KBCo2StarMovies := JOIN(Co2SM, KBCoStarMovies, LEFT.movie=RIGHT.movie,
                                 TRANSFORM(LEFT),LEFT ONLY);

  //------ bacon #3 Actors
  // Find people with a Bacon number of 3
  // This code is very similar to KBCo2Stars; one might be tempted to common up into a 
  // function or macro. However it is worth looking at the attribute counts first; we may be
  // down to a small enough set that we can start using in-memory functions (e.g.,SET) again.

  KBCo3S := DEDUP(JOIN(IMDB.ActorsInMovies, KBCo2StarMovies, LEFT.movie=RIGHT.movie,
                       TRANSFORM(LEFT), LOOKUP),
                  actor, ALL);

  // KBCo3S = ALL CoStars  in KBCo2Star Movies
  // The above is all the people in the movies; but some will have been co2stars of KB 
  // directly - these must be removed. The LEFT ONLY join removes items in one list from 
  // another. There should not be any direct CoStars in this list (or the movie would have
  // been a CoStarMovie not a CoCoStarMovie)

  EXPORT KBCo3Stars := JOIN(KBCo3S, KBCo2Stars, LEFT.actor=RIGHT.actor, 
                            TRANSFORM(LEFT),LEFT ONLY);

  //----- bacon #3 Movies
  // So what movies have all the KBCo3Stars been in?

  Co3SM := DEDUP(JOIN(IMDB.ActorsInMovies, KBCo3Stars, LEFT.actor=RIGHT.actor,
                      TRANSFORM(LEFT), LOOKUP),
                 movie, ALL);

  // Co3SM = ALL Movies KBCo3Stars have been in
  // Of course some of these movies will have KBCo2Stars in too and thus will already have 
  // been listed. Note We ONLY have to remove one level back from the list; previous levels 
  // cannot be reached by definition

  EXPORT KBCo3StarMovies := JOIN(Co3SM, KBCo2StarMovies, LEFT.movie=RIGHT.movie,
                                 TRANSFORM(LEFT),LEFT ONLY);

  //------bacon #4 Actors
  KBCo4S := DEDUP(JOIN(IMDB.ActorsInMovies, KBCo3StarMovies, LEFT.movie=RIGHT.movie,
                       TRANSFORM(LEFT), LOOKUP),
                  actor, ALL);

  EXPORT KBCo4Stars := JOIN(KBCo4S, KBCo3Stars, LEFT.actor=RIGHT.actor,
                            TRANSFORM(LEFT),LEFT ONLY);

  //----- bacon #4 Movies
  // So what movies have all the Co4Stars been in?

  Co4SM := DEDUP(JOIN(IMDB.ActorsInMovies, KBCo4Stars, LEFT.actor=RIGHT.actor,
                      TRANSFORM(LEFT), LOOKUP),
                 movie, ALL);

  // Co4SM = ALL Movies KBCo4Stars have been in
  // Of course some of these movies will have Co3Stars in too and thus will already have 
  // been listed. Note We ONLY have to remove one level back from the list; previous levels
  // cannot be reached by definition

  EXPORT KBCo4StarMovies := JOIN(Co4SM, KBCo3StarMovies, LEFT.movie=RIGHT.movie,
                                 TRANSFORM(LEFT),LEFT ONLY);

  //----- bacon #5 Stars
  KBCo5S := DEDUP(JOIN(IMDB.ActorsInMovies, KBCo4StarMovies, LEFT.movie=RIGHT.movie,
                       TRANSFORM(LEFT), LOOKUP),
                  actor, ALL);

  EXPORT KBCo5Stars := JOIN(KBCo5S, KBCo4Stars, LEFT.actor=RIGHT.actor, 
                            TRANSFORM(LEFT),LEFT ONLY);

//----- bacon #5 Movies
  Co5SM := DEDUP(JOIN(IMDB.ActorsInMovies, KBCo5Stars, LEFT.actor=RIGHT.actor,
                      TRANSFORM(LEFT), LOOKUP),
                 movie,ALL);

  EXPORT KBCo5StarMovies := JOIN(Co5SM, KBCo4StarMovies, LEFT.movie=RIGHT.movie,
                                 TRANSFORM(LEFT),LEFT ONLY);

  //----- bacon #6 Stars
  // Find people with a Bacon number of 6
  // KBCo5 is getting small again - can move back down to the SET?

  KBCo6S  := DEDUP(IMDB.ActorsInMovies(movie IN SET(KBCo5StarMovies, movie)),
                   actor, ALL);

  EXPORT KBCo6Stars := JOIN(KBCo6S, KBCo5Stars, LEFT.actor=RIGHT.actor,
                            TRANSFORM(LEFT),LEFT ONLY);

  //----- bacon #6 Movies
  Co6SM := DEDUP(IMDB.ActorsInMovies(actor IN SET(KBCo6Stars, actor)), movie, ALL);

  EXPORT KBCo6StarMovies := Co6SM(movie NOT IN SET(KBCo5StarMovies, movie));

  //----- bacon #7 Movies
  // Find people with a Bacon number of 7
  KBCo7S := DEDUP(IMDB.ActorsInMovies(movie IN SET(KBCo6StarMovies,movie)), actor, ALL);
  EXPORT KBCo7Stars := KBCo7S(actor NOT IN SET(KBCo6Stars, actor));

  //----- We just have to count them all !! (How many holes in Albert Hall?)
  EXPORT doCounts := PARALLEL(
    OUTPUT(COUNT(KBMovies), NAMED('KBMovies')),
    OUTPUT(COUNT(KBCoStars), NAMED('KBCoStars')),
    OUTPUT(COUNT(KBCoStarMovies), NAMED('KBCoStarMovies')),
    OUTPUT(COUNT(KBCo2Stars), NAMED('KBCo2Stars')),
    OUTPUT(COUNT(KBCo2StarMovies), NAMED('KBCo2StarMovies')),
    OUTPUT(COUNT(KBCo3Stars), NAMED('KBCo3Stars')),
    OUTPUT(COUNT(KBCo3StarMovies), NAMED('KBCo3StarMovies')),
    OUTPUT(COUNT(KBCo4Stars), NAMED('KBCo4Stars')),
    OUTPUT(COUNT(KBCo4StarMovies), NAMED('KBCo4StarMovies')),
    OUTPUT(COUNT(KBCo5Stars), NAMED('KBCo5Stars')),
    OUTPUT(COUNT(KBCo5StarMovies), NAMED('KBCo5StarMovies')),
    OUTPUT(COUNT(KBCo6Stars), NAMED('KBCo6Stars')),
    OUTPUT(COUNT(KBCo6StarMovies), NAMED('KBCo6StarMovies')),
    OUTPUT(COUNT(KBCo7Stars), NAMED('KBCo7Stars')),
    OUTPUT(KBCo7Stars)
  );

END;</programlisting></para>

        <itemizedlist mark="bullet">
          <listitem>
            <para>Open a new Builder Window and type:</para>
          </listitem>
        </itemizedlist>

        <para><programlisting>IMPORT IMDB;
IMDB.KevinBaconNumberSets.doCounts;</programlisting></para>

        <itemizedlist mark="bullet">
          <listitem>
            <para>Check the syntax then press the <emphasis
            role="bold">Submit</emphasis> button.</para>

            <para><emphasis role="bold">Note:</emphasis> Depending on the size
            of your cluster and the speed of your server(s), this process
            could take several minutes. If you are running this on a virtual
            machine, it could take as long as an hour to complete.</para>
          </listitem>

          <listitem>
            <para>When the process completes, each row shown below becomes
            it's own result tab. You will get a sample of the output as
            follows:</para>

            <para><emphasis role="bold">Note:</emphasis> The data files for
            this tutorial change frequently, your results may vary from those
            shown in this document.</para>
          </listitem>
        </itemizedlist>

        <para><informaltable>
            <?dbfo keep-together="always"?>

            <tgroup cols="2">
              <tbody>
                <row>
                  <entry>KB Movies</entry>

                  <entry>71</entry>
                </row>

                <row>
                  <entry>KB Co Stars</entry>

                  <entry>3520</entry>
                </row>

                <row>
                  <entry>KB Co Star Movies</entry>

                  <entry>33504</entry>
                </row>

                <row>
                  <entry>KB Co 2 Stars</entry>

                  <entry>430145</entry>
                </row>

                <row>
                  <entry>KB Co 2 Star Movies</entry>

                  <entry>251867</entry>
                </row>

                <row>
                  <entry>KB Co 3 Stars</entry>

                  <entry>896009</entry>
                </row>

                <row>
                  <entry>KB Co 3 Star Movies</entry>

                  <entry>51650</entry>
                </row>

                <row>
                  <entry>KB Co 4 Stars</entry>

                  <entry>102729</entry>
                </row>

                <row>
                  <entry>KB Co 4 Star Movies</entry>

                  <entry>2634</entry>
                </row>

                <row>
                  <entry>KB Co 5 Stars</entry>

                  <entry>6080</entry>
                </row>

                <row>
                  <entry>KB Co 5 Star Movies</entry>

                  <entry>190</entry>
                </row>

                <row>
                  <entry>KB Co 6 Stars</entry>

                  <entry>450</entry>
                </row>

                <row>
                  <entry>KB Co 6 Star Movies</entry>

                  <entry>14</entry>
                </row>

                <row>
                  <entry>KB Co 7 Stars</entry>

                  <entry>22</entry>
                </row>
              </tbody>
            </tgroup>
          </informaltable></para>
      </sect2>
    </sect1>
  </chapter>

  <chapter id="Next_Steps">
    <title><emphasis role="bold">Next Steps</emphasis></title>

    <para>Now that you have successfully processed the data and established
    links, what's next?</para>

    <para>Two more ECL files are included in the IMDB folder that you can use
    in conjunction with the examples you have already worked through in this
    tutorial:</para>

    <para>• KeysKevinBacon -- Builds an index of actors/actresses and the
    movies they have starred in.</para>

    <para>You must build this index before you can run queries to find the
    degree of separation between Kevin Bacon and an actor of your
    choice.</para>

    <para>To build the index, open a builder window and type the following
    code:</para>

    <para><programlisting>IMPORT IMDB;
IMDB.KeysKevinBacon.BuildAll;</programlisting></para>

    <para>Press the <emphasis role="bold">Submit</emphasis> button to run the
    ECL code and build the index.</para>

    <para>SearchKevinBaconLinks -- Searches the index you built to give you
    the degree of separation between an actor and Kevin Bacon.</para>

    <para>For example, to find the degree of separation between Kevin Bacon
    and Andi Everingham, open a builder window and type the following
    code:</para>

    <para><programlisting>IMPORT IMDB; 
IMDB.SearchKevinBaconLinks('Everingham, Andi');</programlisting></para>

    <para>Make sure the selected cluster is your <emphasis>hThor</emphasis>
    cluster, then press the <emphasis role="bold">Submit</emphasis> button to
    run the query.</para>

    <para>When it has completed, click on the Workunit ID tab.</para>

    <para>Two results are shown.</para>

    <para><emphasis role="bold">Result1</emphasis> shows the degree of
    separation between the actor and Kevin Bacon.</para>

    <para>Interpret the results as follows:</para>

    <para>Actor is at level 1 - The actor you chose and Kevin Bacon starred in
    a movie together.</para>

    <para>Actor is at level 2 - The actor you chose starred in a movie with an
    actor who starred in a movie with Kevin Bacon.</para>

    <para>The higher the level, the greater the degree of separation between
    the actor you chose and Kevin Bacon.</para>

    <para>In this example, the actor is at level 6, indicating that there are
    6 degrees of separation between Andi Everingham and Kevin Bacon.</para>

    <para><emphasis role="bold">Result2</emphasis> shows the level (degree of
    separation), the name of the actor and the movie they starred in.</para>

    <para>Each line shows an actor and the movie they starred in which links
    them to each other and eventually to Kevin Bacon.</para>

    <para>Have fun finding the degrees of separation between any actor and
    Kevin Bacon.</para>

    <para>Remember to build the index first.</para>
  </chapter>
</book>