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env.template
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env.template
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# ,-. ;-. ,--. . . . , ,--. ,-. ,-. ,--. ,-. ,-. . . ,--. , ,-. #
# / \ | ) | |\ | | / | | ) ( ` | / / \ |\ | | | / #
# | | |-' |- | \| | / |- |-< `-. |- | | | | \| |- | | -. #
# \ / | | | | |/ | | \ . ) | \ \ / | | | | \ | #
# `-' ' `--' ' ' ' `--' ' ' `-' `--' `-' `-' ' ' ' ' `-' #
########################################################################################
# Airflow Settings
########################################################################################
# Some brand-based suggestions: #C52B9B (pink), #FFE033 (yellow)
AIRFLOW__WEBSERVER__NAVBAR_COLOR="#FFF"
# Disabled by default to make development easier
# (enabled on prod for security)
AIRFLOW__CORE__HIDE_SENSITIVE_VAR_CONN_FIELDS=False
# Use the following python code to generate a fernet key for production
# python -c "import base64, os; print(base64.urlsafe_b64encode(os.urandom(32)).decode())"
# AIRFLOW__CORE__FERNET_KEY=
# CSRF key https://airflow.apache.org/docs/apache-airflow/stable/configurations-ref.html#secret-key
AIRFLOW__WEBSERVER__SECRET_KEY=sample-secret-key=
# Executor to use
AIRFLOW__CORE__EXECUTOR=LocalExecutor
# Environment this instance is being run in
AIRFLOW_VAR_ENVIRONMENT=local
########################################################################################
# API Keys
########################################################################################
# See: https://airflow.apache.org/docs/apache-airflow/stable/howto/variable.html#storing-variables-in-environment-variables
# Example:
# AIRFLOW_VAR_API_KEY_BROOKLYN_MUSEUM=apikey
########################################################################################
# Connection/Variable info
########################################################################################
# Airflow primary metadata database
# Change the following line in production to use the appropriate DB
AIRFLOW__DATABASE__SQL_ALCHEMY_CONN=postgresql+psycopg2://airflow:airflow@upstream_db:5432/airflow
# Remote logging connection ID
# Replace "access_key" and "secret+key" with the real values. Secret key must be URL-encoded
AIRFLOW_CONN_AWS_DEFAULT=aws://test_key:test_secret@?region_name=us-east-1&endpoint_url=http%3A%2F%2Fs3%3A5000
# Catalog DB connection. Change the following line in production to use the appropriate DB
AIRFLOW_CONN_POSTGRES_OPENLEDGER_UPSTREAM=postgres://deploy:deploy@upstream_db:5432/openledger
AIRFLOW_CONN_POSTGRES_OPENLEDGER_TESTING=postgres://deploy:deploy@upstream_db:5432/openledger
AIRFLOW_CONN_POSTGRES_OPENLEDGER_API_STAGING=postgres://deploy:deploy@db:5432/openledger
TEST_CONN_ID=postgres_openledger_testing
# Elasticsearch connections. Change the following line in production to use the appropriate URLs.
AIRFLOW_CONN_ELASTICSEARCH_HTTP_PRODUCTION=http://es:9200
AIRFLOW_CONN_ELASTICSEARCH_HTTP_STAGING=http://es:9200
# AWS CloudWatch connection. Change the following line to toggle alarms during a Data Refresh.
# AIRFLOW_CONN_AWS_CLOUDWATCH=aws://<key>:<secret>@?region_name=us-east-1
# API DB connection. Change the following line in production to use the appropriate DB
AIRFLOW_CONN_POSTGRES_OPENLEDGER_API=postgres://deploy:deploy@db:5432/openledger
# Slack webhook connection info (note that these values are modified by the Docker entrypoint)
# A distinction is made here between "notifications" and "alerts", the former being
# useful updates and the latter being alarms or actionable errors.
AIRFLOW_CONN_SLACK_NOTIFICATIONS=https://slack
AIRFLOW_CONN_SLACK_ALERTS=https://slack
S3_LOCAL_ENDPOINT=http://s3:5000
# Set to a non-default value supported by minio in local development to workaround
# Minio's lack of support for all AWS storage classes, while still using a non-default
# value so that the expected behaviour can be verified (specifically, that the storage
# class is not the default "STANDARD")
# https://github.com/minio/minio/issues/5469
AIRFLOW_VAR_DEFAULT_S3_TSV_STORAGE_CLASS=REDUCED_REDUNDANCY
# Connection to the Ingestion Server, used for managing data refreshes. Default is used to
# connect to your locally running ingestion server.
AIRFLOW_CONN_DATA_REFRESH=http://ingestion_server:8001
# Connection to the Staging Ingestion Server, used for managing indices on the staging
# elasticsearch cluster. Default is used to connect to your locally running ingestion
# server.
AIRFLOW_CONN_STAGING_DATA_REFRESH=http://ingestion_server:8001
# Connect to sensitive terms list used for filtered index creation in the data refresh.
# For local development, the mock terms list is served by the catalog indexer worker. Change to the
# full production URL in order to use the production sensitive terms list.
AIRFLOW_CONN_SENSITIVE_TERMS="http://catalog_indexer_worker:8003/static/mock_sensitive_terms.txt"
# Django Admin url. Change the following line to use the appropriate environment.
DJANGO_ADMIN_URL="https://localhost:8000/admin"
# GitHub - used for maintenance
AIRFLOW_VAR_GITHUB_API_KEY="not_set"
########################################################################################
# Other config
########################################################################################
# Version of the catalog docker image to use. Defaults to `latest` and is not used for
# local development (since the image is always built locally). See available tags at
# https://ghcr.io/wordpress/openverse-catalog
DOCKER_IMAGE_TAG=latest
# External port airflow will be mounted to
AIRFLOW_PORT=9090
# Minutes to wait until processing a file that hasn't been modified
LOADER_FILE_AGE=1
# Contact email for any APIs
# AWS/S3 configuration - does not need to be changed for development
AWS_ACCESS_KEY=test_key
AWS_SECRET_KEY=test_secret
AWS_DEFAULT_REGION=us-east-1
# General bucket used for TSV->DB ingestion and logging
OPENVERSE_BUCKET=openverse-catalog
# Seconds to wait before poking for availability of the data refresh pool when running a data_refresh
# DAG. Used to shorten the time for testing purposes.
DATA_REFRESH_POKE_INTERVAL=5
# The pool to use for the data refresh tasks. Defaults to `data_refresh` in production but
# we override this locally to prevent having to frequently recreate it.
DATA_REFRESH_POOL=default_pool
# Number of Retries if DAG task fails to run
DEFAULT_RETRY_COUNT = 2
# Whether to enable catchup for dated DAGs, allowing automatic backfill.
AIRFLOW_VAR_CATCHUP_ENABLED=false
# Number of records to expect in a healthy ES index. Used during the data refresh to verify that
# a new index is healthy before promoting.
ES_INDEX_READINESS_RECORD_COUNT=1000
# Throttle for elasticsearch reindexing. This is used by DAGs that directly connect
# to Elasticsearch; it is not used for DAGs which reindex via the ingestion server, such as
# the data refreshes.
AIRFLOW_VAR_ES_INDEX_THROTTLING_RATE=20000
# Warning in dependency, nothing we can do
# https://docs.sqlalchemy.org/en/20/errors.html#error-b8d9
SQLALCHEMY_SILENCE_UBER_WARNING=1
# Default size of batches for the data refresh altering step
AIRFLOW_VAR_DATA_REFRESH_ALTER_BATCH_SIZE=1000
# S3 prefix location for the Rekognition dataset
AIRFLOW_VAR_REKOGNITION_DATASET_PREFIX=image_analysis_labels.jsonl
# In-memory buffer size for processing Rekognition data
AIRFLOW_VAR_REKOGNITION_MEMORY_BUFFER_SIZE=25
# File buffer size for reading Rekognition data from S3
AIRFLOW_VAR_REKOGNITION_FILE_BUFFER_SIZE=16384
AIRFLOW_VAR_AIRFLOW_RDS_ARN=unset
AIRFLOW_VAR_AIRFLOW_RDS_SNAPSHOTS_TO_RETAIN=7
# Whether to toggle production CloudWatch alarms when running a data refresh DAG.
# Used to prevent requiring AWS credentials when running locally.
AIRFLOW_VAR_TOGGLE_CLOUDWATCH_ALARMS=false
# Whether to delete source data from airflow and DB once ingestion is complete.
# This is used to support data quality testing in SQL-only DAGs like iNaturalist
AIRFLOW_VAR_SQL_RM_SOURCE_DATA_AFTER_INGESTION=false