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Create a Parser for Your New Data Source by Using the CLI

As an alternative to using the HCP Management module to parse your new data source, you can use the CLI.

  1. Determine the format of the new data source’s log entries, so you can parse them:
    1. Use ssh to access the host for the new data source.
    2. Look at the different log files and determine which to parse:
      sudo su - 
      cd /var/log/$NEW_DATASOURCE 
      The file you want is typically the access.log, but your data source might use a different name.
    3. Generate entries for the log that needs to be parsed so that you can see the format of the entries:
      timestamp | time elapsed | remotehost | code/status | bytes | method | URL rfc931 peerstatus/peerhost | type
  2. Create a Kafka topic for the new data source:
    1. Log in to $KAFKA_HOST as root.
    2. Create a Kafka topic with the same name as the new data source:
      --zookeeper $ZOOKEEPER_HOST:2181 --create --topic $NEW_DATASOURCE 
      --partitions 1 --replication-factor 1
    3. Verify your new topic by listing the Kafka topics:
      /usr/hdp/current/kafka-broker/bin/kafka-topics.sh --zookeeper $ZOOKEEPER_HOST:2181 --list
  3. Create a Grok statement file that defines the Grok expression for the log type you identified in Step 1.
    You must include timestamp to ensure that the system uses the event time rather than the system time.
    Refer to the Grok documentation for additional details.
  4. Save the Grok pattern and load it into Hadoop Distributed File System (HDFS) in a named location:
    1. Create a local file for the new data source:
      touch /tmp/$DATASOURCE
    2. Open $DATASOURCE and add the Grok pattern defined in Step 3b:
      vi /tmp/$DATASOURCE
    3. Put the $DATASOURCE file into the HDFS directory where Metron stores its Grok parsers.
      Existing Grok parsers that ship with HCP are staged under /apps/metron/patterns:
      su - hdfs 
      hadoop fs -rmr /apps/metron/patterns/$DATASOURCE 
      hdfs dfs -put /tmp/$DATASOURCE /apps/metron/patterns/
  5. Define a parser configuration for the Metron Parsing Topology.
    1. As root, log into the host with HCP installed:
      ssh $HCP_HOST
    2. Create a $DATASOURCE parser configuration file at $METRON_HOME/config/zookeeper/parsers/$DATASOURCE.json:
      "parserClassName": "org.apache.metron.parsers.GrokParser",
      "filterClassName:": null,
      "sensorTopic": "$DATASOURCE",
      "outputTopic": null,
      "errorTopic": null,
      "readMetadata" : true,
      "mergeMetadata" : true,
      "numWorkers": null,
      "numAckers": null,
      "spoutParallelism": 1,
      "spoutNumTasks": 1,
      "parserParallelism": 1,
      "parserNumTasks": 1,
      "errorWriterParallism": 1,
      "errorWriterNumTasks": 1,
      "spoutConfig:" :{},
      "securityProtocol:" null,
      "stormConfig": {},
      "parserConfig": {   
         "grokPath": "/apps/metron/patterns/$DATASOURCE", 
         "patternLabel": "$DATASOURCE_DELIMITED", 
         "timestampField": "timestamp" 
      "fieldTransformations" : [ 
           "transformation" : "STELLAR" 
           ,"output" : [ "full_hostname", "domain_without_subdomains" ] 
           ,"config" : { 
                        "full_hostname" : "URL_TO_HOST(url)" 
                        ,"domain_without_subdomains" : 

      The name of the parser's class in the .jar file.

      The filter to use.
      This can be the fully qualified name of a class that implements the org.apache.metron.parsers.interfaces.MessageFilter<JSONObject> interface. Message filters enable you to ignore a set of messages by using custom logic. The existing implementation is STELLAR. The Stellar implementation enables you to apply a Stellar statement that returns a Boolean, which passes every message for which the statement returns true . The stellar statement is specified by the filter.query property in the parserConfig. For example, the following Stellar filter includes messages that contain a field1 field:
          "filterClassName" : "STELLAR"
         ,"parserConfig" : {
          "filter.query" : "exists(field1)"

      The Kafka topic on which the telemetry is being streamed.


      A Boolean indicating whether to read metadata and make it available to field transformations (false by default).

      There are two types of metadata supported in HCP:

      • Environmental metadata about the whole system

        For example, if you have multiple Kafka topics being processed by one parser, you might want to tag the messages with the Kafka topic.

      • Custom metadata from an individual telemetry source that you might want to use within Metron

      A Boolean indicating whether to merge metadata with the message (false by default).

      If this property is set to true, then every metadata field becomes part of the messages and, consequently, is also available for field transformations.


      The number of workers to use in the topology (default is the storm default of 1).


      The number of acker executors to use in the topology (default is the Storm default of 1).


      The Kafka spout parallelism (default to 1). You can override the default on the command line.


      The number of tasks for the spout (default to 1). You can override the default on the command line.


      The parser bolt parallelism (default to 1). This can be overridden on the command line.


      The number of tasks for the parser bolt (default to 1). This can be overridden on the command line.


      The error writer bolt parallelism (default to 1). This can be overridden on the command line.


      The number of tasks for the error writer bolt (default to 1). This can be overridden on the command line.


      A map representing a custom spout configuration (this is a map). This can be overridden on the command line.


      The security protocol to use for reading from Kafka (this is a string). This can be overridden on the command line and also specified in the spout configuration via the security.protocol key. If both are specified, then they are merged and the CLI will take precedence.


      The storm configuration to use (this is a map). This can be overridden on the command line. If both are specified, they are merged with CLI properties taking precedence.


      The path for the Grok statement.


      The top-level pattern of the Grok file.


      The configuration file.

      This configuration file also includes batch sizing and timeout settings for writer configuration. If you do not define these properties, the system uses their default values.

      • batchSize - Number of records to batch together before sending to the writer. Default is 15.
      • batchTimeout - Optional. The timeout after which a batch is flushed even if the batchSize is not met.
          "parserConfig" {
            "batchSize": 15, 
            "batchTimeout" : 0

      An array of complex objects representing the transformations to be performed on the message generated from the parser before writing to the Kafka topic.

      In this example, the Grok parser is designed to extract the URL, but the only information that you need is the domain (or even the domain without subdomains). To obtain this, you can use the Stellar Field Transformation (under the fieldTransformations element). The Stellar Field Transformation enables you to use the Stellar DSL (Domain Specific Language) to define extra transformations to be performed on the messages flowing through the topology.

    3. Use the following script to upload configurations to Apache ZooKeeper:
      $METRON_HOME/bin/zk_load_configs.sh --mode PUSH -i $METRON_HOME/config/zookeeper -z $ZOOKEEPER_HOST:2181
  6. Deploy the new parser topology to the cluster:
    1. Log in to the host that has Metron installed as root user.
    2. Deploy the new parser topology:
      $METRON_HOME/bin/start_parser_topology.sh -k $KAFKA_HOST:6667 -z $ZOOKEEPER_HOST:2181 -s $DATASOURCE
    3. Use the Apache Storm UI to verify that the new topology is listed and that it has no errors.
    This new data source processor topology ingests from the $DATASOURCE Kafka topic that you created earlier and then parses the event with the HCP Grok framework using the Grok pattern defined earlier.