Here’s a sample io_config file. You can learn more about variable interpolation and referencing secrets via our variables overview here.

version: 0.1.1
default:
  # Default profile created for data IO access.
  # Add your credentials for the source you use, and delete the rest.
  # AWS
  AWS_ACCESS_KEY_ID: "{{ env_var('AWS_ACCESS_KEY_ID') }}"
  AWS_SECRET_ACCESS_KEY: "{{ env_var('AWS_SECRET_ACCESS_KEY') }}"
  AWS_SESSION_TOKEN: session_token (Used to generate Redshift credentials)
  AWS_REGION: region
  # Azure
  AZURE_CLIENT_ID: "{{ env_var('AZURE_CLIENT_ID') }}"
  AZURE_CLIENT_SECRET: "{{ env_var('AZURE_CLIENT_SECRET') }}"
  AZURE_STORAGE_ACCOUNT_NAME: "{{ env_var('AZURE_STORAGE_ACCOUNT_NAME') }}"
  AZURE_TENANT_ID: "{{ env_var('AZURE_TENANT_ID') }}"
  # ClickHouse
  CLICKHOUSE_DATABASE: default
  CLICKHOUSE_HOST: host.docker.internal
  CLICKHOUSE_INTERFACE: http
  CLICKHOUSE_PASSWORD: null
  CLICKHOUSE_PORT: 8123
  CLICKHOUSE_USERNAME: null
  # Druid
  DRUID_HOST: hostname
  DRUID_PASSWORD: password
  DRUID_PATH: /druid/v2/sql/
  DRUID_PORT: 8082
  DRUID_SCHEME: http
  DRUID_USER: user
  # Google
  GOOGLE_SERVICE_ACC_KEY:
    type: service_account
    project_id: project-id
    private_key_id: key-id
    private_key: "-----BEGIN PRIVATE KEY-----\nyour_private_key\n-----END_PRIVATE_KEY"
    client_email: your_service_account_email
    auth_uri: "https://accounts.google.com/o/oauth2/auth"
    token_uri: "https://accounts.google.com/o/oauth2/token"
    auth_provider_x509_cert_url: "https://www.googleapis.com/oauth2/v1/certs"
    client_x509_cert_url: "https://www.googleapis.com/robot/v1/metadata/x509/your_service_account_email"
  GOOGLE_SERVICE_ACC_KEY_FILEPATH: "/path/to/your/service/account/key.json"
  GOOGLE_LOCATION: US # Optional
  # MongoDB
  MONGODB_DATABASE: database
  MONGODB_HOST: host
  MONGODB_PASSWORD: password
  MONGODB_PORT: 27017
  MONGODB_COLLECTION: collection
  MONGODB_USER: user
  # MSSQL
  MSSQL_DATABASE: database
  MSSQL_SCHEMA: schema
  MSSQL_DRIVER: "ODBC Driver 18 for SQL Server"
  MSSQL_HOST: host
  MSSQL_PASSWORD: password
  MSSQL_PORT: 1433
  MSSQL_USER: SA
  # MySQL
  MYSQL_DATABASE: database
  MYSQL_HOST: host
  MYSQL_PASSWORD: password
  MYSQL_PORT: 3306
  MYSQL_USER: root
  # Pinot
  PINOT_HOST: hostname
  PINOT_PASSWORD: password
  PINOT_PATH: /query/sql
  PINOT_PORT: 8000
  PINOT_SCHEME: http
  PINOT_USER: user
  # PostgreSQL
  POSTGRES_CONNECT_TIMEOUT: 10
  POSTGRES_DBNAME: postgres
  POSTGRES_SCHEMA: public # Optional
  POSTGRES_USER: username
  POSTGRES_PASSWORD: password
  POSTGRES_HOST: hostname
  POSTGRES_PORT: 5432
  # Redshift
  REDSHIFT_SCHEMA: public # Optional
  REDSHIFT_DBNAME: redshift_db_name
  REDSHIFT_HOST: redshift_cluster_id.identifier.region.redshift.amazonaws.com
  REDSHIFT_PORT: 5439
  REDSHIFT_TEMP_CRED_USER: temp_username
  REDSHIFT_TEMP_CRED_PASSWORD: temp_password
  REDSHIFT_DBUSER: redshift_db_user
  REDSHIFT_CLUSTER_ID: redshift_cluster_id
  REDSHIFT_IAM_PROFILE: default
  # Snowflake
  SNOWFLAKE_USER: username
  SNOWFLAKE_PASSWORD: password
  SNOWFLAKE_ACCOUNT: account_id.region
  SNOWFLAKE_DEFAULT_WH: null                  # Optional default warehouse
  SNOWFLAKE_DEFAULT_DB: null                  # Optional default database
  SNOWFLAKE_DEFAULT_SCHEMA: null              # Optional default schema
  SNOWFLAKE_PRIVATE_KEY_PASSPHRASE: null      # Optional private key passphrase
  SNOWFLAKE_PRIVATE_KEY_PATH: null            # Optional private key path
  SNOWFLAKE_ROLE: null                        # Optional role name
  SNOWFLAKE_TIMEOUT: null                     # Optional timeout in seconds
  # Trino
  trino:
    catalog: postgresql                       # Change this to the catalog of your choice
    host: 127.0.0.1
    http_headers:
      X-Something: 'mage=power'
    http_scheme: http
    password: mage1337                        # Optional
    port: 8080
    schema: core_data
    session_properties:                       # Optional
      acc01.optimize_locality_enabled: false
      optimize_hash_generation: true
    source: trino-cli                         # Optional
    user: admin
    verify: /path/to/your/ca.crt              # Optional
    data_type_properties:
      timestamp_precision: 3                              # Optional. Set TIMESTAMP(n) precision

​
Unified config

Mage Pro provides a unified framework for configuring connections using io_config.yaml across different types of pipelines:

  • Batch pipeline (Python block, SQL block)
  • Data integration pipeline
  • Streaming pipeline

Each pipeline reads from the same io_config.yaml file, which allows you to centralize connection information and switch between pipeline types with minimal configuration changes.

​
Batch pipeline

​
Example: Python block (Postgres)

from mage_ai.settings.repo import get_repo_path
from mage_ai.io.config import ConfigFileLoader
from mage_ai.io.postgres import Postgres
from os import path

@data_loader
def load_data_from_postgres(**kwargs):
    query = 'SELECT 1'
    config_path = path.join(get_repo_path(), 'io_config.yaml')
    config_profile = 'default'

    with Postgres.with_config(ConfigFileLoader(config_path, config_profile)) as loader:
        return loader.load(query)

​
Example: SQL block

Just select the desired IO Config profile in the UI (under the “Profile” dropdown). The profile must exist in your io_config.yaml and contain valid credentials for the source or destination.

​
Data integration pipeline

Data integration pipelines use source and destination blocks. These blocks can reference connection credentials from io_config.yaml via the io_config_profile key.

The following sources and destinations systems currently support this configuration method:

  • BigQuery
  • Microsolft SQL Server
  • MySQL
  • Postgres
  • Redshift
  • Snowflake

​
Example source config (Postgres)

io_config_profile: default  # profile in the io_config.yaml

​
Example destination config (Postgres)

io_config_profile: default  # profile in the io_config.yaml
schema: test                # Override the schema

With this setup, you don’t need to hard-code connection details in your block configs—just use the profile name.

​
Streaming pipeline

Streaming pipelines also support io_config.yaml for setting up destinations.

​
Example: Destination block config (BigQuery)

connector_type: bigquery
profile: default    # profile in the io_config.yaml
config:
  table_id: table_id
  if_exists: "append"

The profile field points to the profile in your io_config.yaml, and additional block-specific settings can still be defined under config.