Dead Letter Queues (DLQ)

HTTP request failure. If the HTTP request fails (because Elasticsearch is unreachable or because it returned an HTTP error code), the Elasticsearch output retries the entire request indefinitely. In these scenarios, the dead letter queue has no opportunity to intercept.

HTTP request success. The Elasticsearch Bulk API can perform multiple actions using the same request. If the Bulk API request is successful, it returns 200 OK, even if some documents in the batch have failed. In this situation, the errors flag for the request will be true.

The response body can include metadata indicating that one or more specific actions in the bulk request could not be performed, along with an HTTP-style status code per entry to indicate why the action could not be performed. If the DLQ is configured, individual indexing failures are routed there.

Dead letter queues are disabled by default. To enable dead letter queues, set the dead_letter_queue_enable option in the logstash.yml settings file:

dead_letter_queue.enable: true

Dead letter queues are stored as files in the local directory of the Logstash instance. By default, the dead letter queue files are stored in path.data/dead_letter_queue. Each pipeline has a separate queue. For example, the dead letter queue for the main pipeline is stored in LOGSTASH_HOME/data/dead_letter_queue/main by default. The queue files are numbered sequentially: 1.log, 2.log, and so on.

You can set path.dead_letter_queue in the logstash.yml file to specify a different path for the files:

path.dead_letter_queue: "path/to/data/dead_letter_queue"

When you are ready to process events in the dead letter queue, you create a pipeline that uses the dead_letter_queue input plugin to read from the dead letter queue. The pipeline configuration that you use depends, of course, on what you need to do. For example, if the dead letter queue contains events that resulted from a mapping error in Elasticsearch, you can create a pipeline that reads the "dead" events, removes the field that caused the mapping issue, and re-indexes the clean events into Elasticsearch.

The following example shows a simple pipeline that reads events from the dead letter queue and writes the events, including metadata, to standard output:

input {
  dead_letter_queue {
    path => "/path/to/data/dead_letter_queue" 
    commit_offsets => true 
    pipeline_id => "main" 
  }
}

output {
  stdout {
    codec => rubydebug { metadata => true }
  }
}

For another example, see Example: Processing data that has mapping errors.

When the pipeline has finished processing all the events in the dead letter queue, it will continue to run and process new events as they stream into the queue. This means that you do not need to stop your production system to handle events in the dead letter queue.

When you read from the dead letter queue, you might not want to process all the events in the queue, especially if there are a lot of old events in the queue. You can start processing events at a specific point in the queue by using the start_timestamp option. This option configures the pipeline to start processing events based on the timestamp of when they entered the queue:

input {
  dead_letter_queue {
    path => "/path/to/data/dead_letter_queue"
    start_timestamp => "2017-06-06T23:40:37"
    pipeline_id => "main"
  }
}

For this example, the pipeline starts reading all events that were delivered to the dead letter queue on or after June 6, 2017, at 23:40:37.

In this example, the user attempts to index a document that includes geo_ip data, but the data cannot be processed because it contains a mapping error:

{"geoip":{"location":"home"}}

Indexing fails because the Logstash output plugin expects a geo_point object in the location field, but the value is a string. The failed event is written to the dead letter queue, along with metadata about the error that caused the failure:

{
   "@metadata" => {
    "dead_letter_queue" => {
       "entry_time" => #<Java::OrgLogstash::Timestamp:0x5b5dacd5>,
        "plugin_id" => "fb80f1925088497215b8d037e622dec5819b503e-4",
      "plugin_type" => "elasticsearch",
           "reason" => "Could not index event to Elasticsearch. status: 400, action: [\"index\", {:_id=>nil, :_index=>\"logstash-2017.06.22\", :_type=>\"doc\", :_routing=>nil}, 2017-06-22T01:29:29.804Z My-MacBook-Pro-2.local {\"geoip\":{\"location\":\"home\"}}], response: {\"index\"=>{\"_index\"=>\"logstash-2017.06.22\", \"_type\"=>\"doc\", \"_id\"=>\"AVzNayPze1iR9yDdI2MD\", \"status\"=>400, \"error\"=>{\"type\"=>\"mapper_parsing_exception\", \"reason\"=>\"failed to parse\", \"caused_by\"=>{\"type\"=>\"illegal_argument_exception\", \"reason\"=>\"illegal latitude value [266.30859375] for geoip.location\"}}}}"
    }
  },
  "@timestamp" => 2017-06-22T01:29:29.804Z,
    "@version" => "1",
       "geoip" => {
    "location" => "home"
  },
        "host" => "My-MacBook-Pro-2.local",
     "message" => "{\"geoip\":{\"location\":\"home\"}}"
}

To process the failed event, you create the following pipeline that reads from the dead letter queue and removes the mapping problem:

input {
  dead_letter_queue {
    path => "/path/to/data/dead_letter_queue/" 
  }
}
filter {
  mutate {
    remove_field => "[geoip][location]" 
  }
}
output {
  elasticsearch{
    hosts => [ "localhost:9200" ] 
  }
}