Lakebase Postgres Autoscaling

FIRST: Use the parent databricks-core skill for CLI basics, authentication, and profile selection.

Lakebase is Databricks' serverless Postgres-compatible database, available on both AWS and Azure (GA). It provides fully managed OLTP storage with autoscaling, branching, and scale-to-zero.

Provisioned Lakebase no longer exists — never create it. The old Provisioned tier (static, non-scalable Postgres; databricks database CLI / w.database SDK) is being migrated to Autoscaling over summer 2026, with no customer action required. Lakebase today is Autoscaling-only (databricks postgres / w.postgres). Do not attempt to create Provisioned instances; if a user asks for one, tell them Provisioned instances don't exist anymore and point them to Autoscaling. Treat the legacy database resource key in app configs as retired — migrate to the postgres resource key. For the legacy DAB synced_database_tables resource, use the databricks postgres create-synced-table CLI flow instead (see the Common Issues row and references/synced-tables.md).

For context when reading older configs or docs, here is how the retired Provisioned tier maps to Autoscaling:

Compliance: Supports HIPAA, C5, TISAX, or None.

Capabilities

• Project lifecycle -- create, update, delete Lakebase Postgres Autoscaling projects
• Branching -- copy-on-write branches with TTL, point-in-time recovery, and reset
• Compute scaling -- autoscale 0.5--32 CU, fixed 36--112 CU, scale-to-zero
• High availability -- 1 primary + 1--3 secondaries, automatic failover
• PostgreSQL connectivity -- OAuth token refresh, connection pooling, SSL
• Data API -- PostgREST-compatible HTTP CRUD (Autoscaling only)
• Lakebase synced tables -- sync Unity Catalog Delta tables into Postgres (previously known as Reverse ETL)
• Databricks App integration -- scaffold apps with Lakebase feature, deploy-first workflow
• Cloud support -- AWS and Azure (GA)

Reference docs:

• computes-and-scaling.md — Sizing, endpoint management, scale-to-zero, HA
• connectivity.md — Connection patterns, token refresh, Data API
• synced-tables.md — Lakebase synced tables, data type mapping, capacity planning
• lakehouse-sync.md — CDC from Lakebase Postgres to Unity Catalog Delta tables (UI-only — cannot be configured via CLI or API)
• pgvector.md — Vector similarity search with pgvector extension
• off-platform.md — Off-platform Lakebase (NOT Databricks Apps): external Node.js apps connecting via @databricks/lakebase, env management, token refresh, Drizzle ORM

Resource Hierarchy

Project (top-level container)
  └── Branch (isolated database environment, copy-on-write)
        ├── Endpoint (read-write or read-only)
        ├── Database (standard Postgres DB)
        └── Role (Postgres role)

• Project: Top-level container. Creating one auto-provisions a production branch and a primary read-write endpoint.
• Branch: Isolated database environment sharing storage with parent (copy-on-write). States: READY, ARCHIVED.
• Endpoint (called Compute in UI): Compute resource powering a branch. Types: ENDPOINT_TYPE_READ_WRITE, ENDPOINT_TYPE_READ_ONLY.
• Database: Standard Postgres database within a branch. Default: databricks_postgres.
• Role: Postgres role within a branch.

Resource Name Formats

All IDs: 1-63 characters, start with lowercase letter, lowercase letters/numbers/hyphens only (RFC 1123).

CLI Discovery -- ALWAYS Do This First

Note: "Lakebase" is the product name; the CLI command group is postgres. All commands use databricks postgres ....

Do NOT guess command syntax. Discover available commands dynamically:

databricks postgres -h                    # List all subcommands
databricks postgres <subcommand> -h       # Flags, args, JSON fields

Create a Project

First decide: reuse or create. When building or attaching to an app, ask the user whether to reuse an existing project/branch/database — list them with databricks postgres list-projects (then list-branches / list-databases), let the user pick, and confirm which schema the app will own — or create a new project. Only skip listing and create directly when the user explicitly asked for a brand-new project.

databricks postgres create-project <PROJECT_ID> \
  --json '{"spec": {"display_name": "<DISPLAY_NAME>"}}' \
  --profile <PROFILE>

Auto-creates: production branch + primary read-write endpoint (1 CU min/max, scale-to-zero). Long-running operation; CLI waits by default. Use --no-wait to return immediately.

After creation, verify:

databricks postgres list-branches projects/<PROJECT_ID> --profile <PROFILE>
databricks postgres list-endpoints projects/<PROJECT_ID>/branches/<BRANCH_ID> --profile <PROFILE>
databricks postgres list-databases projects/<PROJECT_ID>/branches/<BRANCH_ID> --profile <PROFILE>

Extract connection values from JSON output:

Updating a Project

databricks postgres update-project projects/<PROJECT_ID> spec.display_name \
  --json '{"spec": {"display_name": "My Updated Application"}}' \
  --profile <PROFILE>

Deleting a Project

WARNING: Permanent -- deletes all branches, computes, databases, roles, and data. Do not delete without explicit user permission.

databricks postgres delete-project projects/<PROJECT_ID> --profile <PROFILE>

Autoscaling

Endpoints use compute units (CU) (~2 GB RAM per CU). Range: 0.5--32 CU (dynamic), 36--112 CU (fixed). Scale-to-zero enabled by default (5 min timeout).

See computes-and-scaling.md for sizing tables, endpoint CRUD, and configuration details.

Branches

Branches are copy-on-write snapshots. Use for testing schema migrations, trying queries, or previewing data changes without affecting production.

databricks postgres create-branch projects/<PROJECT_ID> <BRANCH_ID> \
  --json '{"spec": {"source_branch": "projects/<PROJECT_ID>/branches/<SOURCE>", "no_expiry": true}}' \
  --profile <PROFILE>

Branches require an expiration policy: "no_expiry": true for permanent, or "ttl": "<seconds>s" (max 30 days).

Limits: 10 unarchived branches per project. 8 TB logical data per branch. 1,000 projects per workspace.

Point-in-time branching: Create from a past state (within restore window) for recovery. Run databricks postgres create-branch -h for time specification fields.

Reset: Replaces branch data with latest from parent. Local changes are lost. Root branches and branches with children cannot be reset.

databricks postgres reset-branch projects/<PROJECT_ID>/branches/<BRANCH_ID> --profile <PROFILE>

Delete: Protected branches must be unprotected first (update-branch to set spec.is_protected to false). Cannot delete branches with children. Never delete the production branch.

What's Next

Build a Databricks App

After creating a project, scaffold a connected Databricks App:

# 1. Get branch name
databricks postgres list-branches projects/<PROJECT_ID> --profile <PROFILE>

# 2. Get database name
databricks postgres list-databases projects/<PROJECT_ID>/branches/<BRANCH_ID> --profile <PROFILE>

# 3. Scaffold with lakebase feature
databricks apps init --name <APP_NAME> --features lakebase \
  --set "lakebase.postgres.branch=<BRANCH_NAME>" \
  --set "lakebase.postgres.database=<DATABASE_NAME>" \
  --run none --profile <PROFILE>

For the full app workflow, use the databricks-apps skill.

Attach Lakebase to an existing app

apps init --features lakebase (above) wires the database at scaffold time. To attach a project to an existing app, update its resources.

Use the postgres resource key — its fields are branch + database (full resource paths from the table above). The legacy database key (instance_name + database_name) is deprecated; using it fails with Database instance <name> does not exist. Get the exact paths from list-branches / list-databases (the DB name is often hyphenated, e.g. databricks-postgres).

Update the app's resources with databricks apps create-update — the method to use for any app (the older databricks apps update is legacy and can't change resources for an app in a space). update_mask=resources replaces the whole resources array, so read the app's current resources and merge the new one in (or you'll detach the rest). Pass everything in --json; only APP_NAME is positional:

databricks apps create-update <APP_NAME> --json @update.json --profile <PROFILE>   # waits for completion; --no-wait to return early

{
  "update_mask": "resources",
  "app": {
    "resources": [
      {
        "name": "postgres",
        "postgres": {
          "branch": "projects/<PROJECT_ID>/branches/<BRANCH_ID>",
          "database": "projects/<PROJECT_ID>/branches/<BRANCH_ID>/databases/<DATABASE_ID>",
          "permission": "CAN_CONNECT_AND_CREATE"
        }
      }
    ]
  }
}

Confirm the branch/database with the user — don't default to production silently. The app's service principal must be able to create and own the schema(s) it uses there, so avoid a branch/database where those schema names are already owned by a user (the SP will hit permission denied … 42501) — a fresh/dedicated branch, or a new app-owned schema, is cleanest. See Schema Permissions for Deployed Apps below for the full ownership model.

Schema Permissions for Deployed Apps

The app's Service Principal has CAN_CONNECT_AND_CREATE -- it can create new objects but cannot access existing schemas. The SP must create the schema to become its owner.

ALWAYS deploy the app before running it locally. This is the #1 source of Lakebase permission errors.

Correct workflow:

1. Deploy first: databricks apps deploy <APP_NAME> --profile <PROFILE>
2. Grant local access (if needed): assign databricks_superuser via UI (project creators already have access)
3. Develop locally: your credentials get DML access to SP-owned schemas

If you already ran locally first and hit permission denied: the schema is owned by your credentials, not the SP. Do NOT drop the schema without asking the user -- dropping it deletes all data.

Ask the user to choose:

• (A) Drop and redeploy: databricks psql --project <PROJECT_ID> -- -c "DROP SCHEMA IF EXISTS <SCHEMA_NAME> CASCADE;", then databricks apps deploy from the app directory. The SP recreates the schema on startup.
• (B) Export first, then drop and redeploy: export via pg_dump (use connection details from databricks postgres get-endpoint; see Other Workflows below for HOST and TOKEN) or copy tables to a temp schema using databricks psql --project <PROJECT_ID>, then do option A. After the SP recreates the schema on redeploy, restore with pg_restore or re-INSERT from the temp schema.

Other Workflows

# Connect a Postgres client -- get connection string
databricks postgres get-endpoint projects/<PROJECT_ID>/branches/<BRANCH_ID>/endpoints/<ENDPOINT_ID> --profile <PROFILE>

# Manage roles
databricks postgres create-role -h

# Add a read replica
databricks postgres create-endpoint projects/<PROJECT_ID>/branches/<BRANCH_ID> <ENDPOINT_ID> \
  --json '{"spec": {"type": "ENDPOINT_TYPE_READ_ONLY"}}' --profile <PROFILE>

Run SQL against Lakebase (GRANT, CREATE INDEX, etc.):

# 1. Get endpoint host
databricks postgres get-endpoint projects/<PROJECT_ID>/branches/<BRANCH_ID>/endpoints/<ENDPOINT_ID> --profile <PROFILE>

# 2. Generate OAuth token
databricks postgres generate-database-credential \
  projects/<PROJECT_ID>/branches/<BRANCH_ID>/endpoints/<ENDPOINT_ID> \
  --profile <PROFILE>

# 3. Connect (use token from step 2 as password, host from step 1)
PGPASSWORD='<TOKEN>' psql "host=<HOST> user=<USERNAME> dbname=databricks_postgres sslmode=require"

Note: generate-database-credential requires the endpoint resource path (.../endpoints/<ENDPOINT_ID>), not a database or branch path.

Scriptable version (single copy-paste, useful for agents):

EP=projects/<PROJECT_ID>/branches/<BRANCH_ID>/endpoints/<ENDPOINT_ID>
# get-endpoint JSON shape: {"status": {"hosts": {"host": "<HOSTNAME>"}, ...}, ...}
HOST=$(databricks postgres get-endpoint $EP --profile <PROFILE> -o json \
  | python3 -c "import json,sys; print(json.load(sys.stdin)['status']['hosts']['host'])")
TOKEN=$(databricks postgres generate-database-credential $EP --profile <PROFILE> -o json \
  | python3 -c "import json,sys; print(json.load(sys.stdin)['token'])")
PGPASSWORD="$TOKEN" psql "host=$HOST user=<USERNAME> dbname=databricks_postgres sslmode=require"

Grant app SP access to synced tables (run as project owner after sync is ONLINE and app is deployed):

GRANT USAGE ON SCHEMA public TO "<SP_CLIENT_ID>";
GRANT SELECT ON ALL TABLES IN SCHEMA public TO "<SP_CLIENT_ID>";
ALTER DEFAULT PRIVILEGES IN SCHEMA public GRANT SELECT ON TABLES TO "<SP_CLIENT_ID>";

For least-privilege, consider syncing into a dedicated schema instead of public so the grant is scoped to synced data only.

Get SP client ID: databricks apps get <APP_NAME> --profile <PROFILE> → service_principal_client_id field.

Data API: PostgREST-compatible HTTP CRUD on Postgres tables. See connectivity.md. Synced Tables: Sync Delta tables into Lakebase. See synced-tables.md.

PostgreSQL Extensions

Lakebase supports PostgreSQL extensions (e.g., pgvector for vector embeddings, pg_stat_statements for query statistics). See the full list of supported extensions.

-- List available extensions
SELECT * FROM pg_available_extensions ORDER BY name;

-- Install an extension
CREATE EXTENSION IF NOT EXISTS <extension_name>;

For vector embeddings with pgvector, see pgvector.md.

Troubleshooting

SDK and Version Requirements