Metadata-Version: 2.4
Name: bruin-sdk
Version: 0.3.0
Summary: Python SDK for Bruin CLI — query databases, parse context, and access connections with zero boilerplate.
Project-URL: Homepage, https://github.com/bruin-data/bruin
Project-URL: Repository, https://github.com/bruin-data/bruin-python
Project-URL: Documentation, https://docs.getbruin.com
Author: Bruin Team
License-Expression: Apache-2.0
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Requires-Python: >=3.9
Requires-Dist: pandas
Provides-Extra: all
Requires-Dist: clickhouse-connect; extra == 'all'
Requires-Dist: databricks-sql-connector; extra == 'all'
Requires-Dist: db-dtypes; extra == 'all'
Requires-Dist: duckdb; extra == 'all'
Requires-Dist: google-auth; extra == 'all'
Requires-Dist: google-cloud-bigquery; extra == 'all'
Requires-Dist: google-cloud-spanner; extra == 'all'
Requires-Dist: hdbcli; extra == 'all'
Requires-Dist: ibm-db; extra == 'all'
Requires-Dist: mysql-connector-python; extra == 'all'
Requires-Dist: oracledb; extra == 'all'
Requires-Dist: psycopg2-binary; extra == 'all'
Requires-Dist: pyathena; extra == 'all'
Requires-Dist: pygsheets; extra == 'all'
Requires-Dist: pymssql; extra == 'all'
Requires-Dist: snowflake-connector-python; extra == 'all'
Requires-Dist: trino; extra == 'all'
Requires-Dist: vertica-python; extra == 'all'
Provides-Extra: athena
Requires-Dist: pyathena; extra == 'athena'
Provides-Extra: bigquery
Requires-Dist: db-dtypes; extra == 'bigquery'
Requires-Dist: google-auth; extra == 'bigquery'
Requires-Dist: google-cloud-bigquery; extra == 'bigquery'
Provides-Extra: clickhouse
Requires-Dist: clickhouse-connect; extra == 'clickhouse'
Provides-Extra: databricks
Requires-Dist: databricks-sql-connector; extra == 'databricks'
Provides-Extra: db2
Requires-Dist: ibm-db; extra == 'db2'
Provides-Extra: dev
Requires-Dist: pytest; extra == 'dev'
Requires-Dist: pytest-cov; extra == 'dev'
Provides-Extra: duckdb
Requires-Dist: duckdb; extra == 'duckdb'
Provides-Extra: fabric
Requires-Dist: pymssql; extra == 'fabric'
Provides-Extra: hana
Requires-Dist: hdbcli; extra == 'hana'
Provides-Extra: motherduck
Requires-Dist: duckdb; extra == 'motherduck'
Provides-Extra: mssql
Requires-Dist: pymssql; extra == 'mssql'
Provides-Extra: mysql
Requires-Dist: mysql-connector-python; extra == 'mysql'
Provides-Extra: oracle
Requires-Dist: oracledb; extra == 'oracle'
Provides-Extra: postgres
Requires-Dist: psycopg2-binary; extra == 'postgres'
Provides-Extra: redshift
Requires-Dist: psycopg2-binary; extra == 'redshift'
Provides-Extra: sheets
Requires-Dist: google-auth; extra == 'sheets'
Requires-Dist: pygsheets; extra == 'sheets'
Provides-Extra: snowflake
Requires-Dist: snowflake-connector-python; extra == 'snowflake'
Provides-Extra: spanner
Requires-Dist: google-auth; extra == 'spanner'
Requires-Dist: google-cloud-spanner; extra == 'spanner'
Provides-Extra: synapse
Requires-Dist: pymssql; extra == 'synapse'
Provides-Extra: trino
Requires-Dist: trino; extra == 'trino'
Provides-Extra: vertica
Requires-Dist: vertica-python; extra == 'vertica'
Description-Content-Type: text/markdown

# Bruin Python SDK

The official Python SDK for [Bruin CLI](https://github.com/bruin-data/bruin). Query databases, access connections, and read pipeline context — all with zero boilerplate.

```python
from bruin import query, get_connection, context

# One-liner: query any database Bruin manages
df = query("SELECT * FROM users WHERE created_at > '{{start_date}}'")

# Access pipeline context
print(context.start_date)    # datetime.date(2024, 6, 1)
print(context.pipeline)      # "my_pipeline"
print(context.asset_name)    # "my_asset"

# Get a typed database client
conn = get_connection("my_bigquery")
client = conn.client  # google.cloud.bigquery.Client, ready to use
```

## Installation

Add `bruin-sdk` to the `requirements.txt` that sits next to your Python assets:

```
bruin-sdk
pandas
```

For specific database connections, install the corresponding extras:

```
bruin-sdk[bigquery]     # Google BigQuery
bruin-sdk[snowflake]    # Snowflake
bruin-sdk[postgres]     # PostgreSQL / Redshift
bruin-sdk[redshift]     # Redshift (alias for postgres extra)
bruin-sdk[mssql]        # Microsoft SQL Server
bruin-sdk[mysql]        # MySQL
bruin-sdk[duckdb]       # DuckDB
bruin-sdk[sheets]       # Google Sheets (for GCP connections)
bruin-sdk[all]          # Everything
```

## Quick Start

### Before (manual boilerplate)

```python
""" @bruin
name: my_asset
connection: bigquery_conn
secrets:
    - key: bigquery_conn
@bruin """

import os
import json
from google.cloud import bigquery

# Parse connection JSON from env var
raw = json.loads(os.environ["bigquery_conn"])
sa_info = json.loads(raw["service_account_json"])

# Create client manually
client = bigquery.Client.from_service_account_info(
    sa_info, project=raw["project_id"]
)

# Execute query
start = os.environ["BRUIN_START_DATE"]
df = client.query(f"SELECT * FROM users WHERE dt >= '{start}'").to_dataframe()
```

### After (with SDK)

```python
""" @bruin
name: my_asset
connection: bigquery_conn
@bruin """

from bruin import query, context

df = query(f"SELECT * FROM users WHERE dt >= '{context.start_date}'")
```

---

## API Reference

### `context`

A module-level object that provides access to all `BRUIN_*` environment variables as properly typed Python values. Each property reads the env var fresh on every access — no caching, no stale values.

```python
from bruin import context
```

| Property | Type | Env Var | Description |
|----------|------|---------|-------------|
| `context.start_date` | `date \| None` | `BRUIN_START_DATE` | Pipeline run start date |
| `context.end_date` | `date \| None` | `BRUIN_END_DATE` | Pipeline run end date |
| `context.start_datetime` | `datetime \| None` | `BRUIN_START_DATETIME` | Start date with time |
| `context.end_datetime` | `datetime \| None` | `BRUIN_END_DATETIME` | End date with time |
| `context.execution_date` | `date \| None` | `BRUIN_EXECUTION_DATE` | Execution date |
| `context.run_id` | `str \| None` | `BRUIN_RUN_ID` | Unique run identifier |
| `context.pipeline` | `str \| None` | `BRUIN_PIPELINE` | Pipeline name |
| `context.asset_name` | `str \| None` | `BRUIN_ASSET` | Current asset name |
| `context.connection` | `str \| None` | `BRUIN_CONNECTION` | Asset's default connection |
| `context.is_full_refresh` | `bool` | `BRUIN_FULL_REFRESH` | `True` when `--full-refresh` flag is set |
| `context.vars` | `dict` | `BRUIN_VARS` | Pipeline variables (types preserved from JSON Schema) |

All properties return `None` when the corresponding env var is missing (except `is_full_refresh` which returns `False`, and `vars` which returns `{}`).

```python
from bruin import context

# Dates
print(context.start_date)       # datetime.date(2024, 6, 1)
print(context.end_date)         # datetime.date(2024, 6, 2)

# Pipeline variables (types preserved from pipeline.yml JSON Schema)
segment = context.vars["segment"]     # str: "enterprise"
horizon = context.vars["horizon"]     # int: 30
cohorts = context.vars["cohorts"]     # list[dict]

# Conditional logic
if context.is_full_refresh:
    df = query("SELECT * FROM users")
else:
    df = query(f"SELECT * FROM users WHERE dt >= '{context.start_date}'")
```

---

### `query(sql, connection=None)`

Execute SQL and return results.

```python
from bruin import query
```

**Parameters:**

| Parameter | Type | Default | Description |
|-----------|------|---------|-------------|
| `sql` | `str` | *(required)* | SQL statement to execute |
| `connection` | `str \| None` | `None` | Connection name. When `None`, uses the asset's default connection (`BRUIN_CONNECTION`) |

**Returns:** `pandas.DataFrame` for data-returning statements (`SELECT`, `WITH`, `SHOW`, `DESCRIBE`, `EXPLAIN`), `None` for DDL/DML (`CREATE`, `INSERT`, `UPDATE`, `DELETE`, `DROP`, etc.).

```python
# Uses the asset's default connection (from the `connection:` field in asset definition)
df = query("SELECT * FROM users")

# Explicit connection name
df = query("SELECT * FROM users", connection="my_bigquery")

# DDL/DML returns None
query("CREATE TABLE temp_users AS SELECT * FROM users")
query("INSERT INTO audit_log VALUES ('ran_asset', NOW())")

# Works with any supported database
df_bq = query("SELECT * FROM users", connection="my_bigquery")
df_sf = query("SELECT * FROM users", connection="my_snowflake")
df_pg = query("SELECT * FROM users", connection="my_postgres")
```

Every query is automatically annotated with `@bruin.config` metadata for observability and cost tracking.

---

### `get_connection(name)`

Get a typed connection object with a lazy database client.

```python
from bruin import get_connection
```

**Parameters:**

| Parameter | Type | Description |
|-----------|------|-------------|
| `name` | `str` | Connection name as defined in `.bruin.yml` (auto-injected from `connection:` or listed in `secrets`) |

**Returns:** `Connection` or `GCPConnection` depending on the connection type.

```python
conn = get_connection("my_bigquery")
conn.name    # "my_bigquery"
conn.type    # "google_cloud_platform"
conn.raw     # dict — the parsed connection JSON
conn.client  # Lazy-initialized database client
```

#### Connection types

| Type | `.client` returns | Install extra |
|------|-------------------|---------------|
| `google_cloud_platform` | `bigquery.Client` | `bruin-sdk[bigquery]` |
| `snowflake` | `snowflake.connector.Connection` | `bruin-sdk[snowflake]` |
| `postgres` | `psycopg2.connection` | `bruin-sdk[postgres]` |
| `redshift` | `psycopg2.connection` | `bruin-sdk[redshift]` |
| `mssql` | `pymssql.Connection` | `bruin-sdk[mssql]` |
| `mysql` | `mysql.connector.Connection` | `bruin-sdk[mysql]` |
| `duckdb` | `duckdb.DuckDBPyConnection` | `bruin-sdk[duckdb]` |
| `generic` | N/A (raises error) | — |

Client creation is **lazy** — the actual database connection is only established when `.client` is first accessed.

#### GCP connections

GCP connections have extra methods since one connection can access multiple Google services:

```python
conn = get_connection("my_gcp")

# BigQuery (most common — also available as .client)
bq_client = conn.bigquery()
df = bq_client.query("SELECT 1").to_dataframe()

# Google Sheets
sheets_client = conn.sheets()  # requires bruin-sdk[sheets]

# Cloud Storage
gcs_client = conn.storage()  # requires google-cloud-storage

# Raw credentials for any Google API
creds = conn.credentials  # google.oauth2.Credentials
```

#### Generic connections

Generic connections hold a raw string value (like an API key or webhook URL). They don't have a database client:

```python
conn = get_connection("slack_webhook")
conn.type    # "generic"
conn.raw     # "https://hooks.slack.com/services/T00/B00/xxx"
conn.client  # raises ConnectionTypeError
```

---

### `Connection.query(sql)`

Connections also have a `.query()` method — an alternative to the top-level `query()`:

```python
conn = get_connection("my_bigquery")

# These are equivalent:
df = conn.query("SELECT * FROM users")
df = query("SELECT * FROM users", connection="my_bigquery")
```

Same return behavior: `DataFrame` for SELECT, `None` for DDL/DML.

---

## Exceptions

All SDK exceptions inherit from `BruinError`:

```python
from bruin.exceptions import (
    BruinError,              # Base class
    ConnectionNotFoundError, # Connection name not found or env var missing
    ConnectionParseError,    # Invalid JSON in connection env var
    ConnectionTypeError,     # Unsupported or generic connection type
    QueryError,              # SQL execution failed
)
```

```python
try:
    df = query("SELECT * FROM users", connection="missing")
except ConnectionNotFoundError as e:
    print(e)
    # Connection 'missing' not found. Available connections: my_bigquery, my_snowflake.
```

Missing optional dependencies give clear install instructions:

```python
conn = get_connection("my_snowflake")
conn.client
# ImportError: Install bruin-sdk[snowflake] to use Snowflake connections:
#   pip install 'bruin-sdk[snowflake]'
```

---

## Asset Setup

When you set the `connection` field in your asset definition, Bruin automatically injects the connection's credentials — no need to list it in `secrets`:

```python
""" @bruin
name: my_asset
connection: my_bigquery
@bruin """

from bruin import query

# Uses my_bigquery automatically
df = query("SELECT * FROM users")
```

If you need additional connections beyond the default, add them to `secrets`:

```python
""" @bruin
name: my_asset
connection: my_bigquery
secrets:
    - key: my_postgres
@bruin """

from bruin import query, get_connection

# Default connection (my_bigquery)
df = query("SELECT * FROM users")

# Additional connection via secrets
pg = get_connection("my_postgres")
```

---

## Examples

### Incremental load with date filtering

```python
""" @bruin
name: analytics.daily_events
connection: my_bigquery
@bruin """

from bruin import query, context

if context.is_full_refresh:
    df = query("SELECT * FROM raw.events")
else:
    df = query(f"""
        SELECT * FROM raw.events
        WHERE event_date BETWEEN '{context.start_date}' AND '{context.end_date}'
    """)

print(f"Loaded {len(df)} events")
```

### Cross-database ETL

```python
""" @bruin
name: sync.postgres_to_bigquery
secrets:
    - key: my_postgres
    - key: my_bigquery
@bruin """

from bruin import query, get_connection

# Read from Postgres
df = query("SELECT * FROM users WHERE active = true", connection="my_postgres")

# Write to BigQuery
bq = get_connection("my_bigquery")
df.to_gbq(
    "staging.active_users",
    project_id=bq.raw["project_id"],
    credentials=bq.credentials,
    if_exists="replace",
)
```

### Using pipeline variables

```yaml
# pipeline.yml
name: marketing
variables:
  segment:
    type: string
    default: "enterprise"
  lookback_days:
    type: integer
    default: 30
```

```python
""" @bruin
name: marketing.segment_report
connection: my_snowflake
@bruin """

from bruin import query, context

segment = context.vars["segment"]
lookback = context.vars["lookback_days"]

df = query(f"""
    SELECT * FROM customers
    WHERE segment = '{segment}'
    AND created_at >= DATEADD(day, -{lookback}, CURRENT_DATE())
""")

print(f"Found {len(df)} {segment} customers in last {lookback} days")
```

### DDL operations

```python
""" @bruin
name: setup.create_tables
connection: my_postgres
@bruin """

from bruin import query

# DDL returns None
query("CREATE TABLE IF NOT EXISTS audit_log (event TEXT, ts TIMESTAMP)")
query("INSERT INTO audit_log VALUES ('setup_complete', NOW())")

# SELECT returns DataFrame
df = query("SELECT COUNT(*) as cnt FROM audit_log")
print(f"Audit log has {df['cnt'][0]} entries")
```
