geoip

If this filter is successful, add any arbitrary fields to this event. Field names can be dynamic and include parts of the event using the %{field}.

Example:

    filter {
      geoip {
        add_field => { "foo_%{somefield}" => "Hello world, from %{host}" }
      }
    }
[source,ruby]
    # You can also add multiple fields at once:
    filter {
      geoip {
        add_field => {
          "foo_%{somefield}" => "Hello world, from %{host}"
          "new_field" => "new_static_value"
        }
      }
    }

If the event has field "somefield" == "hello" this filter, on success, would add field foo_hello if it is present, with the value above and the %{host} piece replaced with that value from the event. The second example would also add a hardcoded field.

If this filter is successful, add arbitrary tags to the event. Tags can be dynamic and include parts of the event using the %{field} syntax.

Example:

    filter {
      geoip {
        add_tag => [ "foo_%{somefield}" ]
      }
    }
[source,ruby]
    # You can also add multiple tags at once:
    filter {
      geoip {
        add_tag => [ "foo_%{somefield}", "taggedy_tag"]
      }
    }

If the event has field "somefield" == "hello" this filter, on success, would add a tag foo_hello (and the second example would of course add a taggedy_tag tag).

GeoIP lookup is surprisingly expensive. This filter uses an cache to take advantage of the fact that IPs agents are often found adjacent to one another in log files and rarely have a random distribution. The higher you set this the more likely an item is to be in the cache and the faster this filter will run. However, if you set this too high you can use more memory than desired. Since the Geoip API upgraded to v2, there is not any eviction policy so far, if cache is full, no more record can be added. Experiment with different values for this option to find the best performance for your dataset.

This MUST be set to a value > 0. There is really no reason to not want this behavior, the overhead is minimal and the speed gains are large.

It is important to note that this config value is global to the geoip_type. That is to say all instances of the geoip filter of the same geoip_type share the same cache. The last declared cache size will win. The reason for this is that there would be no benefit to having multiple caches for different instances at different points in the pipeline, that would just increase the number of cache misses and waste memory.

The path to the GeoLite2 database file which Logstash should use. Only City database is supported by now.

If not specified, this will default to the GeoLite2 City database that ships with Logstash.

Disable or enable metric logging for this specific plugin instance by default we record all the metrics we can, but you can disable metrics collection for a specific plugin.

An array of geoip fields to be included in the event.

Possible fields depend on the database type. By default, all geoip fields are included in the event.

For the built-in GeoLite2 City database, the following are available: city_name, continent_code, country_code2, country_code3, country_name, dma_code, ip, latitude, longitude, postal_code, region_name and timezone.

Add a unique ID to the plugin configuration. If no ID is specified, Logstash will generate one. It is strongly recommended to set this ID in your configuration. This is particularly useful when you have two or more plugins of the same type, for example, if you have 2 grok filters. Adding a named ID in this case will help in monitoring Logstash when using the monitoring APIs.

output {
 stdout {
   id => "my_plugin_id"
 }
}

GeoIP lookup is surprisingly expensive. This filter uses an LRU cache to take advantage of the fact that IPs agents are often found adjacent to one another in log files and rarely have a random distribution. The higher you set this the more likely an item is to be in the cache and the faster this filter will run. However, if you set this too high you can use more memory than desired.

Experiment with different values for this option to find the best performance for your dataset.

This MUST be set to a value > 0. There is really no reason to not want this behavior, the overhead is minimal and the speed gains are large.

It is important to note that this config value is global to the geoip_type. That is to say all instances of the geoip filter of the same geoip_type share the same cache. The last declared cache size will win. The reason for this is that there would be no benefit to having multiple caches for different instances at different points in the pipeline, that would just increase the number of cache misses and waste memory.

Call the filter flush method at regular interval. Optional.

If this filter is successful, remove arbitrary fields from this event. Fields names can be dynamic and include parts of the event using the %{field} Example:

    filter {
      geoip {
        remove_field => [ "foo_%{somefield}" ]
      }
    }
[source,ruby]
    # You can also remove multiple fields at once:
    filter {
      geoip {
        remove_field => [ "foo_%{somefield}", "my_extraneous_field" ]
      }
    }

If the event has field "somefield" == "hello" this filter, on success, would remove the field with name foo_hello if it is present. The second example would remove an additional, non-dynamic field.

If this filter is successful, remove arbitrary tags from the event. Tags can be dynamic and include parts of the event using the %{field} syntax.

Example:

    filter {
      geoip {
        remove_tag => [ "foo_%{somefield}" ]
      }
    }
[source,ruby]
    # You can also remove multiple tags at once:
    filter {
      geoip {
        remove_tag => [ "foo_%{somefield}", "sad_unwanted_tag"]
      }
    }

If the event has field "somefield" == "hello" this filter, on success, would remove the tag foo_hello if it is present. The second example would remove a sad, unwanted tag as well.

The field containing the IP address or hostname to map via geoip. If this field is an array, only the first value will be used.

Tags the event on failure to look up geo information. This can be used in later analysis.

Specify the field into which Logstash should store the geoip data. This can be useful, for example, if you have src_ip and dst_ip fields and would like the GeoIP information of both IPs.

If you save the data to a target field other than geoip and want to use the geo_point related functions in Elasticsearch, you need to alter the template provided with the Elasticsearch output and configure the output to use the new template.

Even if you don’t use the geo_point mapping, the [target][location] field is still valid GeoJSON.