dbt-plan 0.3.4

Static analysis tool that warns about risky DDL changes before dbt run

dbt-plan

Static analysis tool that warns about risky DDL changes before dbt run.

Like terraform plan for dbt. No warehouse connection needed. Works with any warehouse (Snowflake, BigQuery, Redshift, Postgres, etc.).

What It Does

dbt-plan analyzes compiled SQL diffs to catch dangerous schema changes at PR time:

• Column changes: detects ADD/DROP COLUMN from SQL diff
• Risk assessment: judges safety based on materialization x on_schema_change rules
• Cascade analysis: finds downstream models that reference dropped columns
• Config changes: detects materialization or on_schema_change policy changes

It does NOT execute anything, connect to any warehouse, or simulate dbt run. It reads files, compares them, and warns you.

Quick Start

pip install dbt-plan

# In your dbt project directory:
dbt-plan run               # One command: compile baseline → compile current → check

That's it. dbt-plan run handles dbt compile, snapshotting, and checking automatically.

More commands

dbt-plan init              # Generate .dbt-plan.yml config + update .gitignore
dbt-plan stats             # Analyze project readiness
dbt-plan ci-setup          # Generate GitHub Actions workflow
dbt-plan check --format github   # GitHub markdown output
dbt-plan check --format json     # JSON for CI pipelines
dbt-plan check --select model1   # Check specific model only

Output Example

$ dbt-plan check

dbt-plan -- 2 model(s) changed

DESTRUCTIVE  int_unified (incremental, sync_all_columns)
  DROP COLUMN  data__device
  DROP COLUMN  data__user
  ADD COLUMN   data__device__uuid
  Downstream: dim_device, fct_events (2 model(s))
  >> BROKEN_REF  fct_events: references dropped column(s): data__device

SAFE  dim_device (table)
  CREATE OR REPLACE TABLE

dbt-plan: 2 checked, 1 safe, 0 warning, 1 destructive, 1 cascade risk(s)

What Works (v0.3.3)

Feature	Status	Details
Column extraction (SQLGlot)	Done	Multi-dialect (Snowflake, BigQuery, Postgres, etc.)
DDL prediction	Done	All materialization x on_schema_change combinations
Downstream impact	Done	Memoized batch BFS, cycle protection
Cascade impact analysis	Done	Broken column refs, build failures in downstream models
Config change detection	Done	Materialization and on_schema_change policy changes
Removed model detection	Done	Always DESTRUCTIVE (ephemeral = SAFE)
Parse failure safety	Done	Never returns SAFE when columns unknown
Duplicate column safety	Done	Ambiguous columns trigger REVIEW REQUIRED
SELECT * fallback	Done	Manifest column definitions as fallback
Output formats	Done	--format text (color) / github / json
Configuration	Done	.dbt-plan.yml + env vars (DBT_PLAN_*) + compile_command
Commands	Done	snapshot, check, init, stats, run, ci-setup
One-command check	Done	dbt-plan run — compile + snapshot + check in one step
CI setup	Done	dbt-plan ci-setup — generates GitHub Actions workflow
Model filtering	Done	--select model1,model2 / ignore_models in config
Package filtering	Done	Auto-excludes dbt package models
CI integration	Done	209 tests, 93% coverage, CI workflow template
Verbose mode	Done	--verbose / -v for debugging

Scope

dbt-plan is a static analysis warning tool, not a runtime simulator.

In scope	Out of scope
Column ADD/DROP detection from compiled SQL	dbt run simulation
materialization × on_schema_change risk rules	Warehouse connection
Cascade broken ref / build failure analysis	seed / source change detection
Config change detection (materialization, osc)	pre_hook / post_hook DDL analysis
CI exit codes + structured output	full_refresh mode judgment

Design principle: false warnings are OK, false safe is never OK.

Future Improvements

Feature	Why It Matters
ddl-reviewed label override	Escape hatch for intentional destructive changes
INFORMATION_SCHEMA query	For SELECT * models without manifest column definitions
Column type detection	ALTER TYPE predictions

DDL Prediction Rules

Materialization	on_schema_change	Predicted DDL	Safety
table	any	CREATE OR REPLACE TABLE	SAFE
view	any	CREATE OR REPLACE VIEW	SAFE
incremental	ignore	no DDL	SAFE
incremental	fail	build failure	WARNING
incremental	append_new_columns	ADD COLUMN only	SAFE
incremental	sync_all_columns	ADD + DROP COLUMN	DESTRUCTIVE if columns removed
any	(model removed)	MODEL REMOVED	DESTRUCTIVE

CI Integration (GitHub Actions)

name: dbt-plan
on:
  pull_request:
    paths: ['models/**', 'macros/**']

jobs:
  plan:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
        with: { fetch-depth: 0 }

      - run: pip install uv && uv sync
      - run: pip install dbt-plan  # or: pip install git+https://github.com/ab180/dbt-plan@v0.2.0

      # Compile and snapshot base branch
      - run: |
          git checkout ${{ github.event.pull_request.base.sha }}
          dbt compile
          dbt-plan snapshot

      # Compile current and check
      - run: |
          git checkout ${{ github.event.pull_request.head.sha }}
          dbt compile
          dbt-plan check --format github >> $GITHUB_STEP_SUMMARY

      # Block destructive changes (exit 1)
      - run: dbt-plan check

How It Works

flowchart TD
    A[dbt-plan snapshot] --> B[Save compiled SQL + manifest.json]

    C[dbt-plan check] --> D[diff_compiled_dirs]
    D --> E[base compiled SQL]
    D --> F[current compiled SQL]
    E --> G[extract_columns]
    F --> H[extract_columns]
    G --> I[base columns]
    H --> J[current columns]
    I --> K[column diff]
    J --> K
    K --> L[predict_ddl + manifest config]
    L --> M{Safety?}
    M -->|SAFE| N[exit 0]
    M -->|WARNING| O[exit 2]
    M -->|DESTRUCTIVE| P[exit 1 — block merge]
    L --> Q[find_downstream]
    Q --> R[format_text / format_github]

Contributing

See CONTRIBUTING.md for development setup, TDD workflow, and coding rules.

Architecture

src/dbt_plan/
├── columns.py      # SQLGlot column extraction (multi-dialect)
├── config.py       # .dbt-plan.yml + env var configuration
├── predictor.py    # DDL risk assessment rules + cascade analysis
├── manifest.py     # manifest.json parsing + downstream BFS
├── diff.py         # compiled SQL directory comparison
├── formatter.py    # text / GitHub markdown / JSON output
└── cli.py          # CLI: snapshot, check, init, stats, run, ci-setup

How to Contribute

Good first issues:

• Add compiled SQL fixtures in tests/fixtures/ for edge cases (UNION, subqueries, etc.)
• Improve error messages for common mistakes

Medium issues:

• ddl-reviewed label override — escape hatch for intentional destructive changes
• INFORMATION_SCHEMA integration — query warehouse for SELECT * resolution

Design decisions: See docs/architecture-decisions.md.

Supported

• dbt-core 1.7+
• Any warehouse: Snowflake, BigQuery, Redshift, Postgres, DuckDB, etc. (--dialect)
• Python 3.10+
• CTE, UNION ALL, QUALIFY, window functions, VARIANT access

License

Apache-2.0