Kafka to HDFS Filter Application

Summary

Ingest filtered messages from kafka to hadoop HDFS for continuous ingestion to hadoop. The source code is available at: https://github.com/DataTorrent/app-templates/tree/master/kafka-to-hdfs-sync.

Please send feedback or feature requests to: feedback@datatorrent.com

This document has a step-by-step guide to configure, customize, and launch this application.

Steps to launch application

  1. Click on the AppHub tab from the top navigation bar. AppHub link from top navigation bar

  2. Page listing the applications available on AppHub is displayed. Search for Kafka to see all applications related to Kafka. AppHub search for Kafka Click on import button for Kafka to HDFS Filter App.

  3. Notification is displayed on the top right corner after application package is successfully imported. App import Notification

  4. Click on the link in the notification which navigates to the page for this application package. App details page Detailed information about the application package like version, last modified time, and short description is available on this page. Click on launch button for Kafka-to-HDFS-Filter application.

  5. Launch Kafka-to-HDFS-Filter dialogue is displayed. One can configure name of this instance of the application from this dialogue. Launch dialogue

  6. Select Use saved configuration option. This displays list of pre-saved configurations. Please select sandbox-memory-conf.xml or cluster-memory-conf.xml depending on whether your environment is the DataTorrent sandbox, or other cluster. Select saved configuration

  7. Select Specify custom properties option. Click on add default properties button. Specify custom properties

  8. This expands a key-value editor pre-populated with mandatory properties for this application. Change values as needed. Properties editor For example, suppose we wish to process all messages seperated by '|' from topic transactions at the kafka server running on kafka-source.node.com with port 9092, filters the messages based on the filter criteria ({$}.getAmount() >= 20000) and write them to output.txt under /user/appuser/output on HDFS. Properties should be set as follows:

    name value
    dt.operator.fileOutput.prop.filePath /user/appuser/output
    dt.operator.fileOutput.prop.outputFileName output.txt
    dt.operator.filter.prop.condition Filter condition
    dt.operator.kafkaInput.prop.clusters kafka-source.node.com:9092
    dt.operator.kafkaInput.prop.initialOffset EARLIEST
    dt.operator.kafkaInput.prop.topics transactions

    Details about configuration options are available in Configuration options section.

  9. Click on the Launch button on lower right corner of the dialog to launch the application. A notification is displayed on the top right corner after application is launched successfully and includes the Application ID which can be used to monitor this instance and find its logs. Application launch no tification

  10. Click on the Monitor tab from the top navigation bar. Monitor tab

  11. A page listing all running applications is displayed. Search for current application based on name or application id or any other relevant field. Click on the application name or id to navigate to application instance details page. Apps monitor listing

  12. Application instance details page shows key metrics for monitoring the application status. logical tab shows application DAG, Stram events, operator status based on logical operators, stream status, and a chart with key metrics. Logical tab

  13. Click on the physical tab to look at the status of physical instances of the operator, containers etc. Physical tab

Configuration options

Prerequistes

Kafka configured with version 0.9.

Mandatory properties

End user must specify the values for these properties.

Property Description Type Example
dt.operator.fileOutput.prop.filePath Output path for HDFS String /user/appuser/output
dt.operator.fileOutput.prop.outputFileName Output file name String output.txt
dt.operator.filter.prop.condition Filter condition Condition ({$}.getAmount() >= 20000)
dt.operator.kafkaInput.prop.clusters Comma separated list of kafka-brokers String node1.company.com:9098, node2.company.com:9098, node3.company.com:9098
dt.operator.kafkaInput.prop.initialOffset Initial offset to read from Kafka String
  • EARLIEST
  • LATEST
  • APPLICATION_OR_EARLIEST
  • APPLICATION_OR_LATEST
dt.operator.kafkaInput.prop.topics Topics to read from Kafka String event_data

Advanced properties

There are pre-saved configurations based on the application environment. Recommended settings for datatorrent sandbox edition are in sandbox-memory-conf.xml and for a cluster environment in cluster-memory-conf.xml. The messages or records emitted are specified by the value of the TUPLE_CLASS attribute in the configuration file namely PojoEvent in this case.

Property Description Type Default for
cluster-
memory
- conf.xml		 Default for
sandbox-
memory
-conf.xml
dt.operator.fileOutput.prop.maxLength Maximum length for output file after which file is rotated long Long.MAX_VALUE Long.MAX_VALUE
dt.operator.csvParser.prop.schema Schema for CSV Parser Schema {"separator": "|",
"quoteChar": "\"",
"lineDelimiter": "", "fields": [
{
"name": "accountNumber",
"type": "Integer"
},
{
"name": "name",
"type": "String"
},
{
"name": "amount",
"type": "Integer"
}
]}		 {"separator": "|",
"quoteChar": "\"",
"lineDelimiter": "", "fields": [
{
"name": "accountNumber",
"type": "Integer"
},
{
"name": "name",
"type": "String"
},
{
"name": "amount",
"type": "Integer"
}
]}
dt.operator.formatter.prop.schema Schema for CSV formatter Schema {"separator": "|",
"quoteChar": "\"",
"lineDelimiter": "", "fields": [
{
"name": "accountNumber",
"type": "Integer"
},
{
"name": "name",
"type": "String"
},
{
"name": "amount",
"type": "Integer"
}
]}		 {"separator": "|",
"quoteChar": "\"",
"lineDelimiter": "", "fields": [
{
"name": "accountNumber",
"type": "Integer"
},
{
"name": "name",
"type": "String"
},
{
"name": "amount",
"type": "Integer"
}
]}
dt.operator.formatter. port.in.attr.TUPLE_CLASS Fully qualified class name for the tuple class POJO(Plain old java objects) input to CSV formatter POJO com.datatorrent.apps.PojoEvent com.datatorrent.apps.PojoEvent
dt.operator.filter.port.input.attr.TUPLE_CLASS Fully qualified class name for the tuple class POJO(Plain old java objects) input to Filter POJO com.datatorrent.apps.PojoEvent com.datatorrent.apps.PojoEvent

You can override default values for advanced properties by specifying custom values for these properties in the step specify custom property step mentioned in steps to launch an application.

Steps to customize the application

  1. Make sure you have following utilities installed on your machine and available on PATH in environment variables

  2. Use following command to clone the examples repository:

    git clone git@github.com:DataTorrent/app-templates.git

  3. Change directory to 'examples/tutorials/kafka-to-hdfs-filter':

    cd examples/tutorials/kafka-to-hdfs-filter

  4. Import this maven project in your favorite IDE (e.g. eclipse).

  5. Change the source code as per your requirements. Some tips are given as commented blocks in the Application.java for this project

  6. Make respective changes in the test case and properties.xml based on your environment.

  7. Compile this project using maven:

    mvn clean package

    This will generate the application package with .apa extension in the target directory.

  8. Go to DataTorrent UI Management console on web browser. Click on the Develop tab from the top navigation bar. Develop tab

  9. Click on upload package button and upload the generated .apa file. Upload

  10. Application package page is shown with the listing of all packages. Click on the Launch button for the uploaded application package.
    Follow the steps for launching an application.