airflow-provider-yandex-realty 0.1.0

Apache Airflow provider for Yandex Realty Partner API — collect call statistics

airflow-provider-yandex-realty

Apache Airflow provider for the Yandex Realty Partner API — collect call statistics from Яндекс Недвижимость.

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Installation

pip install airflow-provider-yandex-realty

Requires Python 3.10+ and apache-airflow>=2.9.1.

Connection

Create an Airflow connection of type HTTP (conn_id = yandex_realty_default by default).

• Password — the OAuth token, stored without the OAuth prefix (the code adds it when building the Authorization: OAuth {token} header).
• Extra — partner key, agency id, and account(s).

Multiple accounts

{
  "x_authorization": "Vertis public-partner-...",
  "agency_id": "417938",
  "accounts": [
    {"client_id": "103575674"},
    {"client_id": "67890"}
  ]
}

Single account

{
  "x_authorization": "Vertis public-partner-...",
  "agency_id": "417938",
  "client_id": "103575674"
}

Which account a task processes is decided by the operator's account_id parameter — it is a selector into the connection, not a credential:

• single-account — omit account_id (leave it None); the client_id is read from the connection's top-level client_id.
• multi-account — you do not hardcode it. The DAG enumerates accounts from the connection with list_accounts() and passes each account_id automatically (it equals the sanitized client_id).

Usage

YandexRealtyCallsOperator collects calls for a date range, groups them by day, and writes one file per day. One task processes one account.

Single account

Point the operator at the connection — nothing else is needed; the account is read from the connection's top-level client_id.

from airflow_provider_yandex_realty.operators.calls import YandexRealtyCallsOperator

collect = YandexRealtyCallsOperator(
    task_id="collect_calls",
    conn_id="yandex_realty_default",
    date_from="{{ ds }}",
    date_to="{{ ds }}",
    base_dir="/tmp/yandex_realty",
    output_format="json",      # "json" (NDJSON) or "csv"
    add_snapshot_ts=True,      # inject the DAG-run start timestamp (JSON only)
)

Multiple accounts

Don't hardcode account_id. Read the accounts from the connection with list_accounts() and fan out one task per account — account_id comes from the connection, not by hand:

from airflow_provider_yandex_realty.accounts import list_accounts
from airflow_provider_yandex_realty.operators.calls import YandexRealtyCallsOperator

for account in list_accounts("yandex_realty_default"):
    YandexRealtyCallsOperator(
        task_id=f"collect_{account.id}",
        conn_id="yandex_realty_default",
        account_id=account.id,     # selector into extra.accounts, taken from the connection
        date_from="{{ ds }}",
        date_to="{{ ds }}",
    )

date_from, date_to and conn_id are templated. execute returns a list of {date, path, snapshot_ts} entries (one per day written).

For a full multi-account example (collect → GCS → BigQuery + S3), see examples/bq_and_s3_multi_account_dag.py.

Output

• Layout — {base_dir}/{account_id}/{safe_run_id}/{date}.{ext}. The account_id segment is omitted when account_id is None (single-account). Each path component is sanitized ([^\w-] → _) so untrusted values cannot escape base_dir. ext is json or csv.
• JSON — newline-delimited JSON (NDJSON): one JSON object per line.
• CSV — a header row plus one row per call, all fields quoted.
• Columns — the 12 canonical CALL_FIELDS: call_datetime, date, object_name, incoming_phone, internal_phone, wait_duration, call_duration, revenue, object_type, campaign_tariff, client_tariff, is_targeted.
• snapshot_ts — added to each record only when add_snapshot_ts=True, and only in JSON output; the CSV schema never gains a snapshot_ts column.

License

MIT