Configuration
Plugin maintains the configuration in the conf/base/airflow-k8s.yaml file.
# Base url of the Apache Airflow, should include the schema (http/https)
host: https://airflow.example.com
# Directory from where Apache Airflow is reading DAGs definitions
output: gs://airflow-bucket-example-com
# Configuration used to run the pipeline
run_config:
# Name of the image to run as the pipeline steps
image: airflow-k8s-plugin-demo
# Pull policy to be used for the steps. Use Always if you push the images
# on the same tag, or Never if you use only local images
image_pull_policy: IfNotPresent
# Pod startup timeout in seconds - if timeout passes the pipeline fails, default to 600
startup_time: 600
# Namespace for Airflow pods to be created
namespace: airflow
# Name of the Airflow experiment to be created
experiment_name: Airflow K8S Plugin Demo
# Name of the dag as it's presented in Airflow
run_name: airflow-k8s-plugin-demo
# Apache Airflow cron expression for scheduled runs
cron_expression: "@daily"
# Optional start date in format YYYYMMDD, if not provided `days_ago(2)` is used instead
start_date: "20210721"
# Optional pipeline description
description: "Very Important Pipeline"
# Comma separated list of image pull secret names
image_pull_secrets: my-registry-credentials
# Service account name to execute nodes with
service_account_name: airflow
# List of handlers executed after task failure
failure_handlers:
# type of integration, currently only slack is available
- type: slack
# airflow connection id with following parameters:
# host - webhook url
# password - webhook password
# login - username
connection_id: slack
# message template that will be send. It can contains following parameters that will be replaced:
# task
# dag
# execution_time
message_template: |
:red_circle: Task Failed.
*Task*: {task}
*Dag*: {dag}
*Execution Time*: {execution_time}
*Log Url*: {url}
# Optional volume specification
volume:
# Storage class - use null (or no value) to use the default storage
# class deployed on the Kubernetes cluster
storageclass: # default
# The size of the volume that is created. Applicable for some storage
# classes
size: 1Gi
# Access mode of the volume used to exchange data. ReadWriteMany is
# preferred, but it is not supported on some environements (like GKE)
# Default value: ReadWriteOnce
#access_modes: [ReadWriteMany]
# Flag indicating if the data-volume-init step (copying raw data to the
# fresh volume) should be skipped
skip_init: False
# Allows to specify fsGroup executing pipelines within containers
# Default: root user group (to avoid issues with volumes in GKE)
owner: 0
# If set to True, shared persistent volume will not be created at all and all other parameters under
# `volume` are discarded
disabled: False
# List of optional secrets specification
secrets:
# deploy_type: (Optional - default: 'env`) The type of secret deploy in Kubernetes, either `env` or `volume`
- deploy_type: "env"
# deploy_target: (Optional) The environment variable when `deploy_type` `env` or file path when `deploy_type` `volume` where expose secret. If `key` is not provided deploy target should be None.
deploy_target: "SQL_CONN"
# secret: Name of the secrets object in Kubernetes
secret: "airflow-secrets"
# key: (Optional) Key of the secret within the Kubernetes Secret if not provided in `deploy_type` `env` it will mount all secrets in object
key: "sql_alchemy_conn"
# Apache Airflow macros to be exposed for the parameters
# List of macros can be found here:
# https://airflow.apache.org/docs/apache-airflow/stable/macros-ref.html
macro_params: [ds, prev_ds]
# Apache Airflow variables to be exposed for the parameters
variables_params: [env]
# Optional resources specification
resources:
# Default configuration used by all nodes that do not declare the
# resource configuration. It's optional. If node does not declare the resource
# configuration, __default__ is assigned by default, otherwise cluster defaults
# will be used.
__default__:
# Optional labels to be put into pod node selector
node_selectors:
#Labels are user provided key value pairs
node_pool_label/k8s.io: example_value
# Optional labels to apply on pods
labels:
running: airflow
# Optional annotations to apply on pods
annotations:
iam.amazonaws.com/role: airflow
# Optional list of kubernetes tolerations
tolerations:
- key: "group"
value: "data-processing"
effect: "NoExecute"
- key: "group"
operator: "Equal",
value: "data-processing",
effect: "NoSchedule"
requests:
#Optional amount of cpu resources requested from k8s
cpu: "1"
Optional amount of memory resource requested from k8s
memory: "1Gi"
limits:
#Optional amount of cpu resources limit on k8s
cpu: "1"
#Optional amount of memory resource limit on k8s
memory: "1Gi"
# Other arbitrary configurations to use, for example to indicate some exception resources
huge_machines:
node_selectors:
big_node_pool: huge.10x
requests:
cpu: "16"
memory: "128Gi"
limits:
cpu: "32"
memory: "256Gi"
# Optional external dependencies configuration
external_dependencies:
# Can just select dag as a whole
- dag_id: upstream-dag
# or detailed
- dag_id: another-upstream-dag
# with specific task to wait on
task_id: with-precise-task
# Maximum time (minute) to wait for the external dag to finish before this
# pipeline fails, the default is 1440 == 1 day
timeout: 2
# Checks if the external dag exists before waiting for it to finish. If it
# does not exists, fail this pipeline. By default is set to true.
check_existence: False
# Time difference with the previous execution to look at (minutes),
# the default is 0 meaning no difference
execution_delta: 10
# Optional authentication to MLflow API
authentication:
# Strategy that generates the tokens, supported values are:
# - Null
# - GoogleOAuth2 (generating OAuth2 tokens for service account provided by GOOGLE_APPLICATION_CREDENTIALS)
# - Vars (credentials fetched from airflow Variable.get - specify variable keys,
# matching MLflow authentication env variable names, in `params`,
# e.g. ["MLFLOW_TRACKING_USERNAME", "MLFLOW_TRACKING_PASSWORD"])
type: GoogleOAuth2
#params: []
# Optional custom kubermentes pod templates applied on nodes basis
kubernetes_pod_templates:
# Name of the node you want to apply the custom template to.
# if you specify __default__, this template will be applied to all nodes.
# Otherwise it will be only applied to nodes tagged with `k8s_template:<node_name>`
spark:
# Kubernetes pod template.
# It's the full content of the pod-template file (as a string)
# `run_config.volume` and `MLFLOW_RUN_ID` env are disabled when this is set.
# Note: python F-string formatting is applied to this string, so
# you can also use some dynamic values, e.g. to calculate pod name.
template: |-
type: Pod
metadata:
name: {PodGenerator.make_unique_pod_id('{{ task_instance.task_id }}')}
labels:
spark_driver: {'{{ task_instance.task_id }}'}
# Optionally, you can also override the image
# image:
# Optional spark configuration
spark:
# Type of spark clusters to use, supported values: dataproc and custom
type: dataproc
# Optional factory of spark operators class
operator_factory: my_project.factories.OperatorFactory
# Region indicates location of cluster for public cloud configurations, for example region in GCP
region: europe-west1
# Project indicates logical placement inside public cloud configuration, for example project in GCP
project_id: target-project
# Name of the cluster to be created
cluster_name: ephemeral
# Location where the spark artifacts are uploaded
artifacts_path: gs://dataproc-staging-europe-west2-546213781-jabcdefp4/packages
# Optional path in the project to the script portion preprended to generated init script
user_init_path: relative_location_to_src/init_script.sh
# Optional path in the project to the script portion appended to generated init script
user_post_init_path: relative_location_to_src/post_init_script.sh
# Optional configuration of the cluster, used during cluster creation, depends on type of the cluster
cluster_config: # example dataproc configuration
master_config:
disk_config:
boot_disk_size_gb: 35
worker_config:
disk_config:
boot_disk_size_gb: 35
Indicate resources in pipeline nodes
Every node declared in kedro pipelines is executed inside pod. Pod definition declares resources to be used based
on provided plugin configuration and presence of the tag resources in kedro node definition.
If no such tag is present, plugin will assign __default__ from plugin resources configuration.
If no __default__ is given in plugin resources configuration or no resources configuration is given, pod
definition will not be given any information on how to allocate resources to pod, thus default k8s cluster values
will be used.
# train_model node is assigned resources from `huge_machines` configuration, if no such configuration exists,
# `__default__` is used, and if __default__ does not exist, k8s cluster default values are used
node(func=train_model, inputs=["X_train", "y_train"], outputs="regressor", name='train_model', tags=['resources:huge_machines'])
# evaluate_model node is assigned resources `__default__` configuration and if it does not exist,
# k8s cluster default values are used
node(func=evaluate_model, inputs=["X_train", "y_train"], outputs="regressor", name='evaluate_model')
Custom kubernetes pod templates
You can provide custom kubernetes pod templates using kubernetes_pod_templates. Pod template to be used is based on
the provided plugin configuration and presence of the tag k8s_template in kedro node definition.
If no such tag is present, plugin will assign __default__.template from plugin kubernetes_pod_templates configuration, if exists.
If no __default__ is given in plugin kubernetes_pod_templates configuration or no kubernetes_pod_templates configuration is provided at all,
the following plugin’s default minimal pod template will be used.
type: Pod
metadata:
name: {PodGenerator.make_unique_pod_id('{{ task_instance.task_id }}')}
spec:
containers:
- name: base
env:
- name: MLFLOW_RUN_ID
value: {{ task_instance.xcom_pull(key="mlflow_run_id") }}
volumes:
- name: storage
persistentVolumeClaim:
claimName: {self._pvc_name}
where environment and volumes sections are present only if kedro mflow is used in the project and/or
run_config.volume is not disabled.
Note, that claimName is calculated the following way
pvc_name = '{{ project_name | safe | slugify }}.{% raw %}{{ ts_nodash | lower }}{% endraw %}'
If you do use a custom pod template and you want to keep the built-in mlflow/volume support you need to include these sections in your template as well.
# train_model node is assigned a custom pod template from `spark` configuration, if no such configuration exists,
# `__default__` is used, and if __default__ does not exist, the plugin's minimal pod template is used
node(func=train_model, inputs=["X_train", "y_train"], outputs="regressor", name='train_model', tags=['k8s_template:spark'])
# evaluate_model node is assigned a custom pod template `__default__` configuration and if it does not exist,
# the plugin's default minimal pod template
node(func=evaluate_model, inputs=["X_train", "y_train"], outputs="regressor", name='evaluate_model')
When using custom kubernetes pod templates the resulting pod configuration is a merge between
properties provided via plugin settings, e.g. resources.__default__.annotations, and those specified in a
template. In case of a conflict, plugin settings precede that of the template.
Dynamic configuration support
kedro-airflow-k8s contains hook that enables TemplatedConfigLoader. It allows passing environment variables to
configuration files. It reads all environment variables following KEDRO_CONFIG_
There are two special variables KEDRO_CONFIG_COMMIT_ID, KEDRO_CONFIG_BRANCH_NAME with support specifying default when variable is not set, e.g. ${commit_id|dirty}