Configuration

Configure py-gdelt using environment variables, TOML files, or programmatic settings.

Environment Variables

Set environment variables to configure default behavior:

# Timeouts and retries
export GDELT_TIMEOUT=60
export GDELT_MAX_RETRIES=5

# Caching
export GDELT_CACHE_DIR=/path/to/cache
export GDELT_CACHE_TTL=3600

# BigQuery
export GDELT_BIGQUERY_PROJECT=my-project
export GDELT_BIGQUERY_CREDENTIALS=/path/to/credentials.json

# Behavior
export GDELT_FALLBACK_TO_BIGQUERY=true
export GDELT_VALIDATE_CODES=true
export GDELT_MAX_CONCURRENT_DOWNLOADS=10

TOML Configuration

Create a gdelt.toml file:

[gdelt]
timeout = 60
max_retries = 5
cache_dir = "/path/to/cache"
cache_ttl = 3600
fallback_to_bigquery = true
validate_codes = true
max_concurrent_downloads = 10

[gdelt.bigquery]
project = "my-project"
credentials = "/path/to/credentials.json"

Load it with:

from pathlib import Path
from py_gdelt import GDELTClient

config_path = Path("gdelt.toml")
async with GDELTClient(config_path=config_path) as client:
    ...

Programmatic Settings

Use GDELTSettings for full control:

from pathlib import Path
from py_gdelt import GDELTClient, GDELTSettings

settings = GDELTSettings(
    # Timeouts and retries
    timeout=60,
    max_retries=5,

    # Caching
    cache_dir=Path.home() / ".cache" / "gdelt",
    cache_ttl=3600,

    # BigQuery
    bigquery_project="my-project",
    bigquery_credentials=Path("/path/to/credentials.json"),

    # Behavior
    fallback_to_bigquery=True,
    validate_codes=True,
    max_concurrent_downloads=10,
)

async with GDELTClient(settings=settings) as client:
    ...

Configuration Options

Timeouts

• timeout (int): HTTP request timeout in seconds. Default: 30
• connect_timeout (int): Connection timeout in seconds. Default: 10

Retries

• max_retries (int): Maximum retry attempts. Default: 3
• retry_backoff (float): Backoff multiplier for retries. Default: 2.0

Caching

• cache_dir (Path): Directory for cached files. Default: ~/.cache/gdelt
• cache_ttl (int): Cache time-to-live in seconds. Default: 3600 (1 hour)
• use_cache (bool): Enable/disable caching. Default: True

BigQuery

• bigquery_project (str): Google Cloud project ID. Default: None
• bigquery_credentials (Path): Path to credentials JSON. Default: None
• fallback_to_bigquery (bool): Use BigQuery when file sources fail. Default: False

Performance

• max_concurrent_downloads (int): Max parallel file downloads. Default: 5
• chunk_size (int): Download chunk size in bytes. Default: 8192

Validation

• validate_codes (bool): Validate CAMEO/country codes. Default: False
• strict_mode (bool): Raise errors on invalid codes. Default: False

Priority Order

Configuration is loaded in this order (later overrides earlier):

1. Default values
2. Environment variables
3. TOML configuration file
4. Programmatic GDELTSettings

BigQuery Setup

For BigQuery access:

1. Install dependencies: pip install py-gdelt[bigquery]
2. Create Google Cloud project
3. Enable BigQuery API
4. Create service account and download credentials
5. Set credentials path:

export GOOGLE_APPLICATION_CREDENTIALS="/path/to/credentials.json"

Or in code:

settings = GDELTSettings(
    bigquery_project="my-project",
    bigquery_credentials=Path("/path/to/credentials.json"),
    fallback_to_bigquery=True,
)

Best Practices

• Use environment variables for sensitive data (credentials)
• Use TOML for team-shared configuration
• Use programmatic settings for runtime customization
• Enable caching in development, configure carefully in production
• Set appropriate timeouts for your network conditions
• Enable BigQuery fallback for production reliability