- Filebeat Reference: other versions:
- Overview
- Get started
- Set up and run
- Upgrade
- How Filebeat works
- Configure
- Inputs
- General settings
- Project paths
- Config file loading
- Output
- SSL
- Index lifecycle management (ILM)
- Elasticsearch index template
- Kibana endpoint
- Kibana dashboards
- Processors
- Define processors
- add_cloud_metadata
- add_docker_metadata
- add_fields
- add_host_metadata
- add_id
- add_kubernetes_metadata
- add_labels
- add_locale
- add_observer_metadata
- add_process_metadata
- add_tags
- community_id
- convert
- copy_fields
- decode_base64_field
- decode_cef
- decode_csv_fields
- decode_json_fields
- decompress_gzip_field
- dissect
- dns
- drop_event
- drop_fields
- extract_array
- fingerprint
- include_fields
- registered_domain
- rename
- script
- timestamp
- truncate_fields
- Autodiscover
- Internal queue
- Load balancing
- Logging
- HTTP endpoint
- Regular expression support
- filebeat.reference.yml
- How to guides
- Beats central management
- Modules
- Modules overview
- ActiveMQ module
- Apache module
- Auditd module
- AWS module
- Azure module
- CEF module
- Cisco module
- CoreDNS module
- Elasticsearch module
- Envoyproxy Module
- Google Cloud module
- haproxy module
- IBM MQ module
- Icinga module
- IIS module
- Iptables module
- Kafka module
- Kibana module
- Logstash module
- MISP module
- MongoDB module
- MSSQL module
- MySQL module
- nats module
- NetFlow module
- Nginx module
- Osquery module
- Palo Alto Networks module
- PostgreSQL module
- RabbitMQ module
- Redis module
- Santa module
- Suricata module
- System module
- Traefik module
- Zeek (Bro) Module
- Exported fields
- activemq fields
- Apache fields
- Auditd fields
- AWS fields
- Azure fields
- Beat fields
- Decode CEF processor fields fields
- CEF fields
- Cisco fields
- Cloud provider metadata fields
- Coredns fields
- Docker fields
- ECS fields
- elasticsearch fields
- Envoyproxy fields
- Google Cloud fields
- haproxy fields
- Host fields
- ibmmq fields
- Icinga fields
- IIS fields
- iptables fields
- Jolokia Discovery autodiscover provider fields
- Kafka fields
- kibana fields
- Kubernetes fields
- Log file content fields
- logstash fields
- MISP fields
- mongodb fields
- mssql fields
- MySQL fields
- nats fields
- NetFlow fields
- NetFlow fields
- Nginx fields
- Osquery fields
- panw fields
- PostgreSQL fields
- Process fields
- RabbitMQ fields
- Redis fields
- s3 fields
- Google Santa fields
- Suricata fields
- System fields
- Traefik fields
- Zeek fields
- Monitor
- Secure
- Troubleshoot
- Get help
- Debug
- Common problems
- Can’t read log files from network volumes
- Filebeat isn’t collecting lines from a file
- Too many open file handlers
- Registry file is too large
- Inode reuse causes Filebeat to skip lines
- Log rotation results in lost or duplicate events
- Open file handlers cause issues with Windows file rotation
- Filebeat is using too much CPU
- Dashboard in Kibana is breaking up data fields incorrectly
- Fields are not indexed or usable in Kibana visualizations
- Filebeat isn’t shipping the last line of a file
- Filebeat keeps open file handlers of deleted files for a long time
- Filebeat uses too much bandwidth
- Error loading config file
- Found unexpected or unknown characters
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- @metadata is missing in Logstash
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- SSL client fails to connect to Logstash
- Monitoring UI shows fewer Beats than expected
- Contribute to Beats
Dissect strings
editDissect strings
editThe dissect
processor tokenizes incoming strings using defined patterns.
processors: - dissect: tokenizer: "%{key1} %{key2}" field: "message" target_prefix: "dissect"
The dissect
processor has the following configuration settings:
-
tokenizer
- The field used to define the dissection pattern.
-
field
-
(Optional) The event field to tokenize. Default is
message
. -
target_prefix
-
(Optional) The name of the field where the values will be extracted. When an empty
string is defined, the processor will create the keys at the root of the event. Default is
dissect
. When the target key already exists in the event, the processor won’t replace it and log an error; you need to either drop or rename the key before using dissect.
For tokenization to be successful, all keys must be found and extracted, if one of them cannot be found an error will be logged and no modification is done on the original event.
A key can contain any characters except reserved suffix or prefix modifiers: /
,&
, +
and ?
.
See Conditions for a list of supported conditions.
Dissect example
editFor this example, imagine that an application generates the following messages:
"App01 - WebServer is starting" "App01 - WebServer is up and running" "App01 - WebServer is scaling 2 pods" "App02 - Database is will be restarted in 5 minutes" "App02 - Database is up and running" "App02 - Database is refreshing tables"
Use the dissect
processor to split each message into two fields, for example,
service.name
and service.status
:
processors: - dissect: tokenizer: '"%{service.name} - %{service.status}"' field: "message" target_prefix: ""
This configuration produces fields like:
"service": { "name": "App01", "status": "WebServer is up and running" },
service.name
is an ECS keyword field, which means that you
can use it in Elasticsearch for filtering, sorting, and aggregations.
When possible, use ECS-compatible field names. For more information, see the Elastic Common Schema documentation.
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