Writing Logs to Elasticsearch¶
Airflow can be configured to read task logs from Elasticsearch and optionally write logs to stdout in standard or json format. These logs can later be collected and forwarded to the Elasticsearch cluster using tools like fluentd, logstash or others.
You can choose to have all task logs from workers output to the highest parent level process, instead of the standard file locations. This allows for some additional flexibility in container environments like Kubernetes, where container stdout is already being logged to the host nodes. From there a log shipping tool can be used to forward them along to Elasticsearch. To use this feature, set the write_stdout option in airflow.cfg.
You can also choose to have the logs output in a JSON format, using the json_format option. Airflow uses the standard Python logging module and JSON fields are directly extracted from the LogRecord object. To use this feature, set the json_fields option in airflow.cfg. Add the fields to the comma-delimited string that you want collected for the logs. These fields are from the LogRecord object in the logging module. Documentation on different attributes can be found here.
First, to use the handler, airflow.cfg must be configured as follows:
[logging]
# Airflow can store logs remotely in AWS S3, Google Cloud Storage or Elastic Search.
# Users must supply an Airflow connection id that provides access to the storage
# location. If remote_logging is set to true, see UPDATING.md for additional
# configuration requirements.
remote_logging = True
[elasticsearch]
host = <host>:<port>
log_id_template = {{dag_id}}-{{task_id}}-{{execution_date}}-{{try_number}}
end_of_log_mark = end_of_log
write_stdout =
json_fields =
To output task logs to stdout in JSON format, the following config could be used:
[logging]
# Airflow can store logs remotely in AWS S3, Google Cloud Storage or Elastic Search.
# Users must supply an Airflow connection id that provides access to the storage
# location. If remote_logging is set to true, see UPDATING.md for additional
# configuration requirements.
remote_logging = True
[elasticsearch]
host = <host>:<port>
log_id_template = {{dag_id}}-{{task_id}}-{{execution_date}}-{{try_number}}
end_of_log_mark = end_of_log
write_stdout = True
json_format = True
json_fields = asctime, filename, lineno, levelname, message
Writing Logs to Elasticsearch over TLS¶
To add custom configurations to ElasticSearch (e.g. turning on ssl_verify, adding a custom self-signed
cert, etc.) use the elasticsearch_configs setting in your airflow.cfg
[logging]
# Airflow can store logs remotely in AWS S3, Google Cloud Storage or Elastic Search.
# Users must supply an Airflow connection id that provides access to the storage
# location. If remote_logging is set to true, see UPDATING.md for additional
# configuration requirements.
remote_logging = True
[elasticsearch_configs]
use_ssl=True
verify_certs=True
ca_certs=/path/to/CA_certs
Elasticsearch External Link¶
A user can configure Airflow to show a link to an Elasticsearch log viewing system (e.g. Kibana).
To enable it, airflow.cfg must be configured as in the example below. Note the required {log_id} in the URL, when constructing the external link, Airflow replaces this parameter with the same log_id_template used for writing logs (see Writing Logs to Elasticsearch).
[elasticsearch]
# Qualified URL for an elasticsearch frontend (like Kibana) with a template argument for log_id
# Code will construct log_id using the log_id template from the argument above.
# NOTE: The code will prefix the https:// automatically, don't include that here.
frontend = <host_port>/{log_id}