Introduction

Parsers are pluggable components which are used to transform raw data (textual or raw bytes) into JSON messages suitable for downstream enrichment and indexing.

There are two general types types of parsers:

• A parser written in Java which conforms to the MessageParser interface. This kind of parser is optimized for speed and performance and is built for use with higher velocity topologies. These parsers are not easily modifiable and in order to make changes to them the entire topology need to be recompiled.
• A general purpose parser. This type of parser is primarily designed for lower-velocity topologies or for quickly standing up a parser for a new telemetry before a permanent Java parser can be written for it. As of the time of this writing, we have:
  • Grok parser: org.apache.metron.parsers.GrokParser with possible parserConfig entries of
    • grokPath : The path in HDFS (or in the Jar) to the grok statement
    • patternLabel : The pattern label to use from the grok statement
    • multiLine : The raw data passed in should be handled as a long with multiple lines, with each line to be parsed separately. This setting’s valid values are ‘true’ or ‘false’. The default if unset is ‘false’. When set the parser will handle multiple lines with successfully processed lines emitted normally, and lines with errors sent to the error topic.
    • timestampField : The field to use for timestamp
    • timeFields : A list of fields to be treated as time
    • dateFormat : The date format to use to parse the time fields
    • timezone : The timezone to use. UTC is default.
    • The Grok parser supports either 1 line to parse per incoming message, or incoming messages with multiple log lines, and will produce a json message per line
  • CSV Parser: org.apache.metron.parsers.csv.CSVParser with possible parserConfig entries of
    • timestampFormat : The date format of the timestamp to use. If unspecified, the parser assumes the timestamp is ms since unix epoch.
    • columns : A map of column names you wish to extract from the CSV to their offsets (e.g. { 'name' : 1, 'profession' : 3} would be a column map for extracting the 2nd and 4th columns from a CSV)
    • separator : The column separator, , by default.
  • JSON Map Parser: org.apache.metron.parsers.json.JSONMapParser with possible parserConfig entries of
    • mapStrategy : A strategy to indicate how to handle multi-dimensional Maps. This is one of
      • DROP : Drop fields which contain maps
      • UNFOLD : Unfold inner maps. So { "foo" : { "bar" : 1} } would turn into {"foo.bar" : 1}
      • ALLOW : Allow multidimensional maps
      • ERROR : Throw an error when a multidimensional map is encountered
    • jsonpQuery : A JSON Path query string. If present, the result of the JSON Path query should be a list of messages. This is useful if you have a JSON document which contains a list or array of messages embedded in it, and you do not have another means of splitting the message.
    • wrapInEntityArray : "true" or "false". If jsonQuery is present and this flag is present and set to "true", the incoming message will be wrapped in a JSON entity and array. for example: {"name":"value"},{"name2","value2} will be wrapped as {"message" : [{"name":"value"},{"name2","value2}]}. This is using the default value for wrapEntityName if that property is not set.
    • wrapEntityName : Sets the name to use when wrapping JSON using wrapInEntityArray. The jsonpQuery should reference this name.
    • A field called timestamp is expected to exist and, if it does not, then current time is inserted.

Parser Error Routing

Currently, we have a few mechanisms for either deferring processing of messages or marking messages as invalid.

Filtered

One can also filter a message by specifying a filterClassName in the parser config. Filtered messages are just dropped rather than passed through.

Parser Architecture

Data flows through the parser bolt via kafka and into the enrichments topology in kafka. Errors are collected with the context of the error (e.g. stacktrace) and original message causing the error and sent to an error queue. Invalid messages as determined by global validation functions are also treated as errors and sent to an error queue.

Multiple sensors can be aggregated into a single Storm topology. When this is done, there will be multiple Kafka spouts, but only a single parser bolt which will handle delegating to the correct parser as needed. There are some constraints around this, in particular regarding some configuration. Additionally, all sensors must flow to the same error topic. The Kafka topic is retrieved from the input Tuple itself.

A worked example of this can be found in the Parser Chaining use case.

Message Format

All Metron messages follow a specific format in order to ingest a message. If a message does not conform to this format it will be dropped and put onto an error queue for further examination. The message must be of a JSON format and must have a JSON tag message like so:

{"message" : message content}

Where appropriate there is also a standardization around the 5-tuple JSON fields. This is done so the topology correlation engine further down stream can correlate messages from different topologies by these fields. We are currently working on expanding the message standardization beyond these fields, but this feature is not yet availabe. The standard field names are as follows:

• ip_src_addr: layer 3 source IP
• ip_dst_addr: layer 3 dest IP
• ip_src_port: layer 4 source port
• ip_dst_port: layer 4 dest port
• protocol: layer 4 protocol
• timestamp (epoch)
• original_string: A human friendly string representation of the message

The timestamp and original_string fields are mandatory. The remaining standard fields are optional. If any of the optional fields are not applicable then the field should be left out of the JSON.

So putting it all together a typical Metron message with all 5-tuple fields present would look like the following:

{
"message": 
{"ip_src_addr": xxxx, 
"ip_dst_addr": xxxx, 
"ip_src_port": xxxx, 
"ip_dst_port": xxxx, 
"protocol": xxxx, 
"original_string": xxx,
"additional-field 1": xxx,
}
}

Global Configuration

There are a few properties which can be managed in the global configuration that have pertinence to parsers and parsing in general.

Parser Configuration

The configuration for the various parser topologies is defined by JSON documents stored in zookeeper.

The document is structured in the following way

• parserClassName : The fully qualified classname for the parser to be used.
• filterClassName : The filter to use. This may be a fully qualified classname of a Class that implements the org.apache.metron.parsers.interfaces.MessageFilter<JSONObject> interface. Message Filters are intended to allow the user to ignore a set of messages via custom logic. The existing implementations are:
  • STELLAR : Allows you to apply a stellar statement which returns a boolean, which will pass every message for which the statement returns true. The Stellar statement that is to be applied is specified by the filter.query property in the parserConfig. Example Stellar Filter which includes messages which contain a the field1 field:

   {
    "filterClassName" : "STELLAR"
   ,"parserConfig" : {
    "filter.query" : "exists(field1)"
    }
   }

• sensorTopic : The kafka topic to send the parsed messages to. If the topic is prefixed and suffixed by / then it is assumed to be a regex and will match any topic matching the pattern (e.g. /bro.*/ would match bro_cust0, bro_cust1 and bro_cust2)
• readMetadata : Boolean indicating whether to read metadata or not (The default is raw message strategy dependent). See below for a discussion about metadata.
• mergeMetadata : Boolean indicating whether to merge metadata with the message or not (The default is raw message strategy dependent). See below for a discussion about metadata.
• rawMessageStrategy : The strategy to use when reading the raw data and metadata. See below for a discussion about message reading strategies.
• rawMessageStrategyConfig : The raw message strategy configuration map. See below for a discussion about message reading strategies.
• parserConfig : A JSON Map representing the parser implementation specific configuration. Also include batch sizing and timeout for writer configuration here.
  • batchSize : Integer indicating number of records to batch together before sending to the writer. (default to 15)
  • batchTimeout : The timeout after which a batch will be flushed even if batchSize has not been met. Optional. If unspecified, or set to 0, it defaults to a system-determined duration which is a fraction of the Storm parameter topology.message.timeout.secs. Ignored if batchSize is 1, since this disables batching.
  • The kafka writer can be configured within the parser config as well. (This is all configured a priori, but this is convenient for overriding the settings). See here
• fieldTransformations : An array of complex objects representing the transformations to be done on the message generated from the parser before writing out to the kafka topic.
• spoutParallelism : The kafka spout parallelism (default to 1). This can be overridden on the command line, and if there are multiple sensors should be in a comma separated list in the same order as the sensors.
• spoutNumTasks : The number of tasks for the spout (default to 1). This can be overridden on the command line, and if there are multiple sensors should be in a comma separated list in the same order as the sensors.
• parserParallelism : The parser bolt parallelism (default to 1). If there are multiple sensors, the last one’s configuration will be used. This can be overridden on the command line.
• parserNumTasks : The number of tasks for the parser bolt (default to 1). If there are multiple sensors, the last one’s configuration will be used. This can be overridden on the command line.
• errorWriterParallelism : The error writer bolt parallelism (default to 1). This can be overridden on the command line.
• errorWriterNumTasks : The number of tasks for the error writer bolt (default to 1). This can be overridden on the command line.
• numWorkers : The number of workers to use in the topology (default is the storm default of 1).
• numAckers : The number of acker executors to use in the topology (default is the storm default of 1).
• spoutConfig : A map representing a custom spout config (this is a map). If there are multiple sensors, the configs will be merged with the last specified taking precedence. This can be overridden on the command line.
• securityProtocol : 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 config via the security.protocol key. If both are specified, then they are merged and the CLI will take precedence. If multiple sensors are used, any non “PLAINTEXT” value will be used.
• stormConfig : The storm config 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.
• cacheConfig : Cache config for stellar field transformations. This configures a least frequently used cache. This is a map with the following keys. If not explicitly configured (the default), then no cache will be used.
  • stellar.cache.maxSize - The maximum number of elements in the cache. Default is to not use a cache.
  • stellar.cache.maxTimeRetain - The maximum amount of time an element is kept in the cache (in minutes). Default is to not use a cache.

  Example of a cache config to contain at max 20000 stellar expressions for at most 20 minutes.:

{
  "cacheConfig" : {
    "stellar.cache.maxSize" : 20000,
    "stellar.cache.maxTimeRetain" : 20
  }
}

The fieldTransformations is a complex object which defines a transformation which can be done to a message. This transformation can

• Modify existing fields to a message
• Add new fields given the values of existing fields of a message
• Remove existing fields of a message

{
"customer_id" : "my_customer_id"
}

{
...
    "fieldTransformations" : [
          {
            "input" : "field1"
          , "transformation" : "REMOVE"
          }
                      ]
}

{
...
  "fieldTransformations" : [
          {
            "input" : "field1"
          , "transformation" : "REMOVE"
          , "config" : {
              "condition" : "exists(field2) and field2 == 'foo'"
                       }
          }
                      ]
}

{
...
    "fieldTransformations" : [
          {
            "output" : ["field1", "field2" ] 
          , "transformation" : "SELECT"
          }
                      ]
}

{
...
    "fieldTransformations" : [
          {
            "input" : "protocol"
          , "transformation" : "IP_PROTOCOL"
          }
                      ]
}

{
...
    "fieldTransformations" : [
          {
            "transformation" : "RENAME",
          , "config" : {
            "old_field" : "new_field",
            "different_old_field" : "different_new_field"
                       }
          }
                      ]
}

{
...
  "fieldTransformations" : [
    {
     "transformation" : "REGEX_ROUTING"
    ,"input" :  "pix_type"
    ,"output" :  "logical_source_type"
    ,"config" : {
      "cisco-6-302" : [ "^6-302.*", "^06-302.*"]
      "cisco-5-304" : "^5-304.*"
                }
    }
                           ]
...  
}

 "fieldTransformations" : [
         { "transformation" : "STELLAR"
         ,"output" : [ "new_field", "old_field"]
         ,"config" : {
           "new_field" : "old_field"
          ,"old_field" : "null"
                     }
         }
 ]

 "fieldTransformations" : [
         { "transformation" : "STELLAR"
         ,"output" : [ "new_field"]
         ,"config" : {
           "new_field" : "TO_UPPER(field1)"
          ,"new_field" : "TO_LOWER(new_field)"
                     }
         }
 ]

{
...
    "fieldTransformations" : [
          {
           "transformation" : "STELLAR"
          ,"output" : [ "utc_timestamp", "url_host", "url_protocol" ]
          ,"config" : {
            "utc_timestamp" : "TO_EPOCH_TIMESTAMP(timestamp, 'yyyy-MM-dd
HH:mm:ss', MAP_GET(dc, dc2tz, 'UTC') )"
           ,"url_host" : "URL_TO_HOST(url)"
           ,"url_protocol" : "URL_TO_PROTOCOL(url)"
                      }
          }
                      ]
   ,"parserConfig" : {
      "dc2tz" : {
                "nyc" : "EST"
               ,"la" : "PST"
               ,"london" : "UTC"
                }
    }
}

{
  "parserClassName":"org.apache.metron.parsers.GrokParser",
  "sensorTopic":"yaf",
  "fieldTransformations" : [
                    {
                      "input" : "protocol"
                     ,"transformation": "IP_PROTOCOL"
                    }
                    ],
  "parserConfig":
  {
    "grokPath":"/patterns/yaf",
    "patternLabel":"YAF_DELIMITED",
    "timestampField":"start_time",
    "timeFields": ["start_time", "end_time"],
    "dateFormat":"yyyy-MM-dd HH:mm:ss.S"
  }
}

Parser Adapters

Parser adapters are loaded dynamically in each Metron topology. They are defined in the Parser Config (defined above) JSON file in Zookeeper.

usage: start_parser_topology.sh
 -e,--extra_topology_options <JSON_FILE>               Extra options in the form
                                                       of a JSON file with a map
                                                       for content.
 -esc,--extra_kafka_spout_config <JSON_FILE>           Extra spout config options
                                                       in the form of a JSON file
                                                       with a map for content.
                                                       Possible keys are:
                                                       retryDelayMaxMs,retryDelay
                                                       Multiplier,retryInitialDel
                                                       ayMs,stateUpdateIntervalMs
                                                       ,bufferSizeBytes,fetchMaxW
                                                       ait,fetchSizeBytes,maxOffs
                                                       etBehind,metricsTimeBucket
                                                       SizeInSecs,socketTimeoutMs
 -ewnt,--error_writer_num_tasks <NUM_TASKS>            Error Writer Num Tasks
 -ewp,--error_writer_p <PARALLELISM_HINT>              Error Writer Parallelism
                                                       Hint
 -h,--help                                             This screen
 -iwnt,--invalid_writer_num_tasks <NUM_TASKS>          Invalid Writer Num Tasks
 -iwp,--invalid_writer_p <PARALLELISM_HINT>            Invalid Message Writer Parallelism Hint
 -k,--kafka <BROKER_URL>                               Kafka Broker URL
 -ksp,--kafka_security_protocol <SECURITY_PROTOCOL>    Kafka Security Protocol
 -mt,--message_timeout <TIMEOUT_IN_SECS>               Message Timeout in Seconds
 -mtp,--max_task_parallelism <MAX_TASK>                Max task parallelism
 -na,--num_ackers <NUM_ACKERS>                         Number of Ackers
 -nw,--num_workers <NUM_WORKERS>                       Number of Workers
 -ot,--output_topic <KAFKA_TOPIC>                      Output Kafka Topic
 -pnt,--parser_num_tasks <NUM_TASKS>                   Parser Num Tasks
 -pp,--parser_p <PARALLELISM_HINT>                     Parser Parallelism Hint
 -s,--sensor <SENSOR_TYPE>                             Sensor Type
 -snt,--spout_num_tasks <NUM_TASKS>                    Spout Num Tasks
 -sp,--spout_p <SPOUT_PARALLELISM_HINT>                Spout Parallelism Hint
 -t,--test <TEST>                                      Run in Test Mode
 -z,--zk <ZK_QUORUM>                                   Zookeeper Quroum URL
                                                       (zk1:2181,zk2:2181,...

The --extra_kafka_spout_config Option

These options are intended to configure the Storm Kafka Spout more completely. These options can be specified in a JSON file containing a map associating the kafka spout configuration parameter to a value. The range of values possible to configure are:

• spout.pollTimeoutMs - Specifies the time, in milliseconds, spent waiting in poll if data is not available. Default is 2s
• spout.firstPollOffsetStrategy - Sets the offset used by the Kafka spout in the first poll to Kafka broker upon process start. One of
  • EARLIEST
  • LATEST
  • UNCOMMITTED_EARLIEST - Last uncommitted and if offsets aren’t found, defaults to earliest. NOTE: This is the default.
  • UNCOMMITTED_LATEST - Last uncommitted and if offsets aren’t found, defaults to latest.
• spout.offsetCommitPeriodMs - Specifies the period, in milliseconds, the offset commit task is periodically called. Default is 15s.
• spout.maxUncommittedOffsets - Defines the max number of polled offsets (records) that can be pending commit, before another poll can take place. Once this limit is reached, no more offsets (records) can be polled until the next successful commit(s) sets the number of pending offsets bellow the threshold. The default is 10,000,000.
• spout.maxRetries - Defines the max number of retrials in case of tuple failure. The default is to retry forever, which means that no new records are committed until the previous polled records have been acked. This guarantees at once delivery of all the previously polled records. By specifying a finite value for maxRetries, the user decides to sacrifice guarantee of delivery for the previous polled records in favor of processing more records.
• Any of the configs in the Consumer API for Kafka 0.10.x

For instance, creating a JSON file which will set the offsets to UNCOMMITTED_EARLIEST

{
  "spout.firstPollOffsetStrategy" : "UNCOMMITTED_EARLIEST"
}

This would be loaded by passing the file as argument to --extra_kafka_spout_config

The --extra_topology_options Option

These options are intended to be Storm configuration options and will live in a JSON file which will be loaded into the Storm config. For instance, if you wanted to set a storm property on the config called topology.ticks.tuple.freq.secs to 1000 and storm.local.dir to /opt/my/path you could create a file called custom_config.json containing

{ 
  "topology.ticks.tuple.freq.secs" : 1000,
  "storm.local.dir" : "/opt/my/path"
}

and pass --extra_topology_options custom_config.json to start_parser_topology.sh.

Notes on Performance Tuning

Default installed Metron is untuned for production deployment. There are a few knobs to tune to get the most out of your system.

When using aggregated parsers, it’s highly recommended to aggregate parsers with similar velocity and parser complexity together.

Notes on Adding a New Sensor

In order to allow for meta alerts to be queries alongside regular alerts in Elasticsearch 2.x, it is necessary to add an additional field to the templates and mapping for existing sensors.

Please see a description of the steps necessary to make this change in the metron-elasticsearch Using Metron with Elasticsearch 2.x

If Solr is selected as the real-time store, it is also necessary to add additional fields. See the Solr section in metron-indexing for more details.

Kafka Queue

The kafka queue associated with your parser is a collection point for all of the data sent to your parser. As such, make sure that the number of partitions in the kafka topic is sufficient to handle the throughput that you expect from your parser topology.

Parser Topology

The enrichment topology as started by the $METRON_HOME/bin/start_parser_topology.sh script uses a default of one executor per bolt. In a real production system, this should be customized by modifying the arguments sent to this utility.

• Topology Wide
  • --num_workers : The number of workers for the topology
  • --num_ackers : The number of ackers for the topology
• The Kafka Spout
  • --spout_num_tasks : The number of tasks for the spout
  • --spout_p : The parallelism hint for the spout
  • Ensure that the spout has enough parallelism so that it can dedicate a worker per partition in your kafka topic.
• The Parser Bolt
  • --parser_num_tasks : The number of tasks for the parser bolt
  • --parser_p : The parallelism hint for the spout
  • This is bolt that gets the most processing, so ensure that it is configured with sufficient parallelism to match your throughput expectations.
• The Error Message Writer Bolt
  • --error_writer_num_tasks : The number of tasks for the error writer bolt
  • --error_writer_p : The parallelism hint for the error writer bolt

Finally, if workers and executors are new to you, the following might be of use to you:

• Understanding the Parallelism of a Storm Topology

JSON Path

“JSONPath expressions always refer to a JSON structure in the same way as XPath expression are used in combination with an XML document.”

Stefan Goessner

• JSON Path concept
• Read about JSON Path library Apache Metron uses
• Try JSON Path expressions online