Lakebase Autoscaling

Project (top-level container)
  └── Branch(es) (isolated database environments)
        ├── Compute (primary R/W endpoint)
        ├── Read Replica(s) (optional, read-only)
        ├── Role(s) (Postgres roles)
        └── Database(s) (Postgres databases)
              └── Schema(s)

from databricks.sdk import WorkspaceClient
from databricks.sdk.service.postgres import Project, ProjectSpec

w = WorkspaceClient()

# Create a project (long-running operation)
operation = w.postgres.create_project(
    project=Project(
        spec=ProjectSpec(
            display_name="My Application",
            pg_version="17"
        )
    ),
    project_id="my-app"
)
result = operation.wait()
print(f"Created project: {result.name}")

from databricks.sdk import WorkspaceClient

w = WorkspaceClient()

# Generate database credential for connecting (optionally scoped to an endpoint)
cred = w.postgres.generate_database_credential(
    endpoint="projects/my-app/branches/production/endpoints/ep-primary"
)
token = cred.token  # Use as password in connection string
# Token expires after 1 hour

import psycopg
from databricks.sdk import WorkspaceClient

w = WorkspaceClient()

# Get endpoint details
endpoint = w.postgres.get_endpoint(
    name="projects/my-app/branches/production/endpoints/ep-primary"
)
host = endpoint.status.hosts.host

# Generate token (scoped to endpoint)
cred = w.postgres.generate_database_credential(
    endpoint="projects/my-app/branches/production/endpoints/ep-primary"
)

# Connect using psycopg3
conn_string = (
    f"host={host} "
    f"dbname=databricks_postgres "
    f"user={w.current_user.me().user_name} "
    f"password={cred.token} "
    f"sslmode=require"
)
with psycopg.connect(conn_string) as conn:
    with conn.cursor() as cur:
        cur.execute("SELECT version()")
        print(cur.fetchone())

from databricks.sdk.service.postgres import Branch, BranchSpec, Duration

# Create a dev branch with 7-day expiration
branch = w.postgres.create_branch(
    parent="projects/my-app",
    branch=Branch(
        spec=BranchSpec(
            source_branch="projects/my-app/branches/production",
            ttl=Duration(seconds=604800)  # 7 days
        )
    ),
    branch_id="development"
).wait()
print(f"Branch created: {branch.name}")

from databricks.sdk.service.postgres import Endpoint, EndpointSpec, FieldMask

# Update compute to autoscale between 2-8 CU
w.postgres.update_endpoint(
    name="projects/my-app/branches/production/endpoints/ep-primary",
    endpoint=Endpoint(
        name="projects/my-app/branches/production/endpoints/ep-primary",
        spec=EndpointSpec(
            autoscaling_limit_min_cu=2.0,
            autoscaling_limit_max_cu=8.0
        )
    ),
    update_mask=FieldMask(field_mask=[
        "spec.autoscaling_limit_min_cu",
        "spec.autoscaling_limit_max_cu"
    ])
).wait()

# Create a project
databricks postgres create-project \
    --project-id my-app \
    --json '{"spec": {"display_name": "My App", "pg_version": "17"}}'

# List projects
databricks postgres list-projects

# Get project details
databricks postgres get-project projects/my-app

# Create a branch
databricks postgres create-branch projects/my-app development \
    --json '{"spec": {"source_branch": "projects/my-app/branches/production", "no_expiry": true}}'

# List branches
databricks postgres list-branches projects/my-app

# Get endpoint details
databricks postgres get-endpoint projects/my-app/branches/production/endpoints/ep-primary

# Delete a project
databricks postgres delete-project projects/my-app

%pip install -U "databricks-sdk>=0.81.0" "psycopg[binary]>=3.0" sqlalchemy