August 17, 2023

tMahoutClustering – Docs for ESB 5.x

tMahoutClustering

tMahoutClustering_icon32_white.png

Warning

This component will be available in the Palette of
the studio on the condition that you have subscribed to any Talend Platform product with Big Data.

tMahoutClustering properties

Component family

MapReduce

 

Function

tMahoutClustering groups data
together into clusters based on some similarities. The component
offers several similarity methods that can be used in different
clustering algorithms.

tMahoutClustering uses clustering
algorithms from Mahout libraries. All processes are run in a given
distributed file system.

Note

Currently, the studio supports Mahout 0.9.

This component, along with the MapReduce family it belongs to, appears only when you are
creating a Map/Reduce Job.

Purpose

tMahoutClustering helps you to
group unlabeled numerical data into clusters that can reveal
interesting patterns or helps identifying abnormal data items in the
data set.

Basic settings

Schema and Edit schema

A schema is a row description. It defines the number of fields to be processed and passed on
to the next component. The schema is either Built-In or
stored remotely in the Repository.

Since version 5.6, both the Built-In mode and the Repository mode are
available in any of the Talend solutions.

Click Edit schema to make changes to the schema. If the
current schema is of the Repository type, three options are
available:

  • View schema: choose this option to view the
    schema only.

  • Change to built-in property: choose this option
    to change the schema to Built-in for local
    changes.

  • Update repository connection: choose this option to change
    the schema stored in the repository and decide whether to propagate the changes to
    all the Jobs upon completion. If you just want to propagate the changes to the
    current Job, you can select No upon completion and
    choose this schema metadata again in the [Repository
    Content]
    window.

The output schema of tMahoutClustering provides one read-only column,
ClusterID.

 

 

Built-in: You create and store
the schema locally for this component only. Related topic: see
Talend Studio
User Guide.

 

 

Repository: You have already
created and stored the schema in the Repository. You can reuse it in
other projects and job designs. Related topic: see Talend Studio
User Guide.

File configuration

Input HDFS file

Browse to the HDFS file that holds the numerical data to be
processed.

 

Field separator

Enter a character, string or regular expression to separate fields
in the input and output data.

 

Cluster columns

In the Input Column, select the
column(s) from the main flow on which you want to define clustering
algorithms. These columns are used to calculate the clusters.

You can add only numerical columns to this table.

Clustering Configuration

Clustering type

Select the relevant clustering algorithm from the list:

Canopy: this algorithm uses an approximate distance
metric and two distance thresholds T 1 and T 2 ,where T 1 >T 2.
It starts with a set of data points in any order, picks a point
called the centroid of the cluster and approximately measures its
distance to all other points. It puts all points that are within
distance threshold T 1 into a canopy. It removes from the main set
all points that are within distance threshold T 2. This way points
that are very close to the centroid will avoid all further
processing. The algorithm then chooses a second centroid among the
data points in the principal set. It continues until the initial set
is empty, accumulating a set of Canopies, each containing one or
more points. A given point may occur in more than one Canopy.

Canopy clustering is often used as an initial step in more
rigorous clustering techniques, such as K-Means clustering . By
starting with Canopy clustering the number of more expensive
distance measurements can be significantly reduced by ignoring
points outside of the initial canopies.

K-Means: it sorts a given data set into a number of
clusters, the number of which you must define. The algorithm chooses
k random points, used as
centroids of k clusters.

The algorithm then associates each data point belonging to a given
data set to the nearest cluster center.

Fuzzy K-Means: also called
Fuzzy C-Means: it belongs to
the family of fuzzy-logic clustering algorithms. It works like
K-Means but recomputes the
cluster centers using the probability of a point belonging to two or
more clusters.

 

Distance measure

Select from the list the distance measure you want to use for
clustering:

Euclidean: defines the “ordinary”
distance between two points, as if measured with a ruler.

Manhattan: defines the distance
between two points if a grid-like path is followed.

Chebyshev: defines the maximum
distance between two vectors taken on any of the coordinate
dimensions.

Cosine: uses the cosine of the
angle between the two vectors representing the points to be
compared.

 

Canopy threshold1

The threshold of distance T1 used for the Canopy algorithm.

 

Canopy threshold2

The threshold of distance T2 used for the Canopy algorithm.

 

Number of clusters

Enter the maximum number of clusters that can be generated by a
clustering algorithm. Some clusters may not have data.

 

Max iterations

Enter the maximum number of iterations to be carried out for a
clustering algorithm.

 

Convergence delta

Enter a rate of convergence for the algorithm. It must be between
0.0 and 1.0. The greater the rate is, the faster the algorithm is
but results will be less precise.

 

Fuzziness

Enter the fuzziness parameter for the Fuzzy
K-Means
algorithm. It must be greater or equal to
1.0.

When the fuzziness is close to 1, then the cluster center closest
to the point is given much more weight than the others, and the
algorithm is similar to K-Means.

Global Variables

ERROR_MESSAGE: the error message generated by the
component when an error occurs. This is an After variable and it returns a string. This
variable functions only if the Die on error check box is
cleared, if the component has this check box.

A Flow variable functions during the execution of a component while an After variable
functions after the execution of the component.

To fill up a field or expression with a variable, press Ctrl +
Space
to access the variable list and choose the variable to use from it.

For further information about variables, see Talend Studio
User Guide.

Usage

tMahoutClustering must be the
start component in a Job. You can select an input HDFS file from its
basic settings.

Scenario: Grouping customer numerical data into clusters on HDFS

The scenario is inspired from a research paper on model-based clustering. Its data can
be found at Wholesale
customers Data Set
. The research paper is available at Enhancing
the selection of a model-based clustering with external categorical
variables
. This scenario is included in the Data Quality
Demos
project you can import into your Talend Studio.
For further information, see the Talend Studio User
Guide
.

The Job in this scenario connects to a given Hadoop distributed file system (HDFS),
groups customers of a “wholesale distributor” into two clusters using the algorithms in
tMahoutClustering and outputs data on a given
HDFS.

The data set has 440 samples that refer to clients of a wholesale distributor. It
includes the annual spending in monetary units on diverse product categories like fresh
and grocery products or milk.

The data set refers to customers from different channels – Horeca
(Hotel/Restaurant/Cafe) or Retail (sale of goods in small quantities) channel, and from
different regions (Lisbon/Oporto/other).

use_case-tmahoutclustering_input.png

This Job uses:

  • tMahoutClustering to compute the clusters for
    the input data set.

  • two tAggregateRow components to count the
    number of clients in both clusters based on the region and
    channel columns.

  • three tMap components to map the channel and
    region input flows into two separate output flows. The components are also used
    to map the single clusterID column received from tMahoutClustering to two-column data flow that feed
    the region and the channel clusters.

  • two tHDFSOutput components to write data to
    HDFS in two output files.

Prerequisites: Before being able to use the tMahoutClustering component, you must have a functional
Hadoop system.

Setting up the Job

  1. Drop the following components from the Palette onto the design workspace: tMahoutClustering, three tMap, two tAggregateRow and
    two tHDFSOutput components.

    use_case-tmahoutclustering.png
  2. Set the components as shown in the capture and connect them together using
    Main links.

Setting up Hadoop connection

  1. Click Run to open its view and then click the
    Hadoop Configuration tab to display its
    view for configuring the Hadoop connection for this Job.

    This view looks like the image below:

    use_case-hadoop_config-common.png
  2. From the Property type list, select Built-in. If you have created the connection to be
    used in Repository, then select Repository and thus the Studio will reuse that set of
    connection information for this Job.

    For further information about how to create an Hadoop connection in
    Repository, see the chapter describing the Hadoop
    cluster
    node of the Talend Big Data Getting Started Guide.

  3. In the Version area, select the Hadoop
    distribution to be used and its version. If you cannot find from the list the
    distribution corresponding to yours, select Custom so as to connect to a Hadoop distribution not officially
    supported in the Studio.

    For a step-by-step example about how to use this Custom option, see Connecting to a custom Hadoop distribution.

    Along with the evolution of Hadoop, please note the
    following changes:

    • If you use Hortonworks Data Platform
      V2.2
      , the configuration files of your cluster might be using
      environment variables such as ${hdp.version}. If this is your situation, you need to set
      the mapreduce.application.framework.path property in the
      Hadoop properties table with the path
      value explicitly pointing to the MapReduce framework archive of your
      cluster. For
      example:

    • If you use Hortonworks Data Platform
      V2.0.0
      , the type of the operating system for running the
      distribution and a Talend Job must be the same,
      such as Windows or Linux. Otherwise, you have to use Talend Jobserver to execute the Job in the same
      type of operating system in which the Hortonworks
      Data Platform V2.0.0
      distribution you are using is run. For
      further information about Talend Jobserver, see
      Talend
      Installation and Upgrade Guide
      .

  4. In the Name node field, enter the location of
    the master node, the NameNode, of the distribution to be used. For example,
    hdfs://tal-qa113.talend.lan:8020.

    If you are using a MapR distribution, you can simply leave maprfs:/// as it is in this field; then the MapR
    client will take care of the rest on the fly for creating the connection. The
    MapR client must be properly installed. For further information about how to set
    up a MapR client, see the following link in MapR’s documentation: http://doc.mapr.com/display/MapR/Setting+Up+the+Client

  5. In the Job tracker field, enter the location
    of the JobTracker of your distribution. For example, tal-qa114.talend.lan:8050.

    Note that the notion Job in this term JobTracker designates the MR or the
    MapReduce jobs described in Apache’s documentation on http://hadoop.apache.org/.

    If you use YARN in your Hadoop cluster such as Hortonworks Data Platform V2.0.0 or Cloudera CDH4.3 + (YARN mode), you need to specify the location
    of the Resource Manager instead of the
    Jobtracker. Then you can continue to set the following parameters depending on
    the configuration of the Hadoop cluster to be used (if you leave the check box
    of a parameter clear, then at runtime, the configuration about this parameter in
    the Hadoop cluster to be used will be ignored ):

    • Select the Set resourcemanager scheduler
      address
      check box and enter the Scheduler address in
      the field that appears.

    • Select the Set jobhistory address
      check box and enter the location of the JobHistory server of the
      Hadoop cluster to be used. This allows the metrics information of
      the current Job to be stored in that JobHistory server.

    • Select the Set staging directory
      check box and enter this directory defined in your Hadoop cluster
      for temporary files created by running programs. Typically, this
      directory can be found under the yarn.app.mapreduce.am.staging-dir property in the
      configuration files such as yarn-site.xml or mapred-site.xml of your distribution.

    • Select the Use datanode hostname
      check box to allow the Job to access datanodes via their hostnames.
      This actually sets the dfs.client.use.datanode.hostname property to
      true. When connecting to a
      S3N filesystem, you must select this check box.

  6. If you are accessing the Hadoop cluster running with Kerberos security, select this check
    box, then, enter the Kerberos principal name for the NameNode in the field displayed. This
    enables you to use your user name to authenticate against the credentials stored in
    Kerberos.

    In addition, since this component performs Map/Reduce computations, you also need to
    authenticate the related services such as the Job history server and the Resource manager or
    Jobtracker depending on your distribution in the corresponding field. These principals can
    be found in the configuration files of your distribution. For example, in a CDH4
    distribution, the Resource manager principal is set in the yarn-site.xml file and the Job history principal in the mapred-site.xml file.

    If you need to use a Kerberos keytab file to log in, select Use a
    keytab to authenticate
    . A keytab file contains pairs of Kerberos principals
    and encrypted keys. You need to enter the principal to be used in the Principal field and the access path to the keytab file itself in the
    Keytab field.

    Note that the user that executes a keytab-enabled Job is not necessarily the one a
    principal designates but must have the right to read the keytab file being used. For
    example, the user name you are using to execute a Job is user1 and the principal to be used is guest; in this situation, ensure that user1 has the right to read the keytab file to be used.

  7. In the User name field, enter the login user
    name for your distribution. If you leave it empty, the user name of the machine
    hosting the Studio will be used.

  8. In the Temp folder field, enter the path in
    HDFS to the folder where you store the temporary files generated during
    Map/Reduce computations.

  9. Leave the default value of the Path separator in server as
    it is, unless you have changed the separator used by your Hadoop distribution’s host machine
    for its PATH variable or in other words, that separator is not a colon (:). In that
    situation, you must change this value to the one you are using in that host.

  10. Leave the Clear temporary folder check box
    selected, unless you want to keep those temporary files.

  11. Leave the Compress intermediate map output to reduce
    network traffic
    check box selected, so as to spend shorter time
    to transfer the mapper task partitions to the multiple reducers.

    However, if the data transfer in the Job is negligible, it is recommended to
    clear this check box to deactivate the compression step, because this
    compression consumes extra CPU resources.

  12. If you need to use custom Hadoop properties, complete the Hadoop properties table with the property or
    properties to be customized. Then at runtime, these changes will override the
    corresponding default properties used by the Studio for its Hadoop
    engine.

    For further information about the properties required by Hadoop, see Apache’s
    Hadoop documentation on http://hadoop.apache.org, or
    the documentation of the Hadoop distribution you need to use.

  13. If the Hadoop distribution to be used is Hortonworks Data Platform V1.2 or Hortonworks
    Data Platform V1.3, you need to set proper memory allocations for the map and reduce
    computations to be performed by the Hadoop system.

    In that situation, you need to enter the values you need in the Mapred
    job map memory mb
    and the Mapred job reduce memory
    mb
    fields, respectively. By default, the values are both 1000 which are normally appropriate for running the
    computations.

    If the distribution is YARN, then the memory parameters to be set become Map (in Mb), Reduce (in Mb) and
    ApplicationMaster (in Mb), accordingly. These fields
    allow you to dynamically allocate memory to the map and the reduce computations and the
    ApplicationMaster of YARN.

For further information about this Hadoop
Configuration
tab, see the section describing how to configure the Hadoop
connection for a Talend Map/Reduce Job of the Talend Big Data Getting Started Guide.

For further information about the Resource Manager, its scheduler and the
ApplicationMaster, see YARN’s documentation such as http://hortonworks.com/blog/apache-hadoop-yarn-concepts-and-applications/.

For further information about how to determine YARN and MapReduce memory configuration
settings, see the documentation of the distribution you are using, such as the following
link provided by Hortonworks: http://docs.hortonworks.com/HDPDocuments/HDP2/HDP-2.0.6.0/bk_installing_manually_book/content/rpm-chap1-11.html.

Configuring the clustering process

  1. Double-click tMahoutClustering to open
    its Component view.

    use_case-tmahoutclustering3.png
  2. From the Schema list, select Built-In and then click the […] button next to Edit
    Schema
    and describe the data structure in the input
    file.

    use_case-tmahoutclustering4.png
  3. Add eight rows to the schema dialog box and define the input data as shown
    in the above capture.

    The component has one read-only column,
    clusterID.

  4. Click OK.

  5. In the File Configuration area:

    • Click the […] button next to
      the Input HDFS file and browse to
      the HDFS file on the Hadoop system that holds the input numerical
      data you want to cluster.

    • Set the field separator used to separate the columns in the
      clustered data.

    • In the Cluster columns table, add
      rows to the table and click in each row to select a column from the
      input schema.

  6. In the Clustering Configuration
    area:

    • From the Clustering Type list,
      select what algorithm you want to use to cluster the numerical data,
      Fuzzy K-means in this
      example.

    • From the Distance Measure list,
      select the distance measure you want to use for clustering.

    • In the Number of clusters field,
      enter 3.

    • Leave the values in Max
      iterations
      and Convergence
      delta
      as they are.

Mapping data

  1. Double-click tMap to open the Map Editor.

    use_case-tmahoutclustering2.png
  2. Drop the Region and the
    clusterID columns to the first output table that
    corresponds to the first tAggregateRow
    component.

    Drop the Channel and the
    clusterID columns to the second output table that
    corresponds to the second tAggregateRow
    component.

    Use the Schema editor section at the
    bottom of the editor to add necessary lines to the output tables.

  3. Click OK to validate changes.

Aggregating and calculating output data

  1. Double-click the first tAggregateRow to
    display its Basic settings view and define
    the component properties.

    use_case-tmahoutclustering5.png
  2. Click the […] button next to Edit schema and define the output flow.

    use_case-tmahoutclustering6.png
  3. Move the columns in the input schema to the output schema and then use the
    [+] button to add a new column in the
    output schema. Call it count.

    When done, click OK to close the dialog
    box.

  4. In the Group by section, click the plus
    button to add an many lines as needed. Here you can define the group-by
    values.

    • Click in the first Output column
      row and select the output column that will hold the aggregated data,
      the region column in this example.

    • Click in the first Input column
      position
      row and select the input column from which
      you want to collect the values to be aggregated, the
      region column in this example.

  5. In the Operations section, click the plus
    button to add rows for the columns that will hold the aggregated data. Here
    you can define the calculation values.

    • Click in the Output column row
      and select the destination column from the list, the
      count column in this example.

    • Click in the Function column row
      and select any of the listed operations.

      In this example, we want to count the number of clients, based on
      their regions, to be listed only once in the output column.

    • Click in the Input column
      position
      row and select the input column from which
      you want to collect the values to be aggregated, the
      region column in this example.

  6. Double-click the second tAggregateRow
    component and define, the same way, its basic settings to count the number
    of clients in the second cluster based on the channel
    column.

    use_case-tmahoutclustering7.png

Mapping output data

  1. Double-click the second tMap to open the
    Map Editor.

    use_case-tmahoutclustering8.png
  2. Drop the region, the clusterID
    and the count columns to the output table that
    corresponds to the first HDFS file.

  3. Click OK to validate changes.

  4. Double-click the third tMap to open the
    Map Editor.

    use_case-tmahoutclustering9.png
  5. Drop the channel, the clusterID
    and the count columns to the output table that
    corresponds to the second HDFS file.

  6. Click OK to validate changes.

Writing output data in HDFS

  1. Double-click the first tHDFSOutput to
    open its Component view.

    use_case-tmahoutclustering10.png
  2. Click the […] button next to the
    Folder field and browse to the folder
    in which you want to write the region data.

  3. From the Type list, select the data
    format for the records to be written. In this example, select Text file.

  4. From the Action list, select the
    operation you need to perform on the file in question. If the file already
    exists, select Overwrite, otherwise select
    Create.

  5. Select the Merge result to single file
    check box and enter the path, or browse to the file you need to write the
    merged output data in.

  6. If the file for the merged data exists, select the Override target file check box to overwrite that
    file.

  7. Double-click the second tHDFSOutput to
    open its Component view.

    use_case-tmahoutclustering11.png
  8. Define the component settings similarly to write the data about the client
    channels from the second cluster to an output HDFS folder.

Finalizing and executing the Job

  • Save your Job and press F6 to execute
    it.

    The below figure shows part of the clustered data written to the HDFS
    folders.

    use_case-tmahoutclustering14.png

    tMahoutClustering reads data from the
    given Hadoop system and groups customer records into clusters. The other
    components in the Job analyze clustering results, show the number of
    customers in each cluster grouped by channels or regions and gives the
    cluster identification.

Visualizing output data

You can build a bar chart on each of the clusters to visualize the number of
customers grouped by different regions or channels.

  • Use tHDFSInput and tBarChart components in two Jobs to read the output HDFS
    files and generate a bar chart on the data to ease technical
    analysis.

    In the first Job, tHDFSInput reads the
    region output HDFS file and passes the flow to tBarChart. tBarChart reads
    data from the input flow and transforms it into a bar chart in a PNG image
    file.

    use_case-tmahoutclustering12.png

    In the second Job, tHDFSInput reads the
    channel output HDFS file and passes the flow to tBarChart which transforms the input data into a bar chart
    in a PNG image file.

    use_case-tmahoutclustering13.png

    Each bar chart has three columns, every column represents the number of
    records in one cluster.

    For further information about the tHDFSInput component,
    see tHDFSInput and for more information about the
    tBarChart, see tBarChart.


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