Metadata-Version: 2.4
Name: dbt-ci
Version: 1.5.2
Summary: CI/CD tool for dbt projects with intelligent change detection and selective execution
Author-email: Patrick Tannoury <patrick.tannoury@datablock.dev>
License: MIT License
        
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Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Build Tools
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.12
Requires-Python: >=3.12
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: click<9,>=8.1
Requires-Dist: pyyaml<7,>=6.0
Requires-Dist: dbt-core<2.0.0,>=1.10.13
Requires-Dist: slack-sdk<4,>=3.27
Provides-Extra: gcp
Requires-Dist: google-cloud-bigquery<4,>=3.20; extra == "gcp"
Requires-Dist: google-cloud-storage<4,>=2.14; extra == "gcp"
Provides-Extra: aws
Requires-Dist: boto3<2,>=1.34; extra == "aws"
Provides-Extra: docker
Requires-Dist: docker<8,>=7.0; extra == "docker"
Provides-Extra: all
Requires-Dist: dbt-ci[gcp]; extra == "all"
Requires-Dist: dbt-ci[aws]; extra == "all"
Requires-Dist: dbt-ci[docker]; extra == "all"
Provides-Extra: postgres
Requires-Dist: psycopg2-binary>=2.9.0; extra == "postgres"
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Provides-Extra: dev
Requires-Dist: dbt-ci[all]; extra == "dev"
Requires-Dist: pytest==9.1.1; extra == "dev"
Requires-Dist: black==26.5.1; extra == "dev"
Requires-Dist: mypy==1.14.1; extra == "dev"
Requires-Dist: flake8==7.1.2; extra == "dev"
Requires-Dist: duckdb==1.2.0; extra == "dev"
Requires-Dist: pytest-cov==7.1.0; extra == "dev"
Requires-Dist: pytest-mock==3.14.0; extra == "dev"
Dynamic: license-file

# dbt-ci

[![Tests](https://github.com/datablock-dev/dbt-ci/actions/workflows/tests.yml/badge.svg)](https://github.com/datablock-dev/dbt-ci/actions/workflows/tests.yml)
[![dbt-core](https://img.shields.io/badge/dbt--core-1.10%20%7C%201.11-orange?logo=dbt&logoColor=white)](https://github.com/datablock-dev/dbt-ci/actions/workflows/tests.yml)

A CI tool for dbt (data build tool) projects that intelligently runs only modified models based on state comparison, supporting multiple execution environments including local, Docker, and dbt runners.

## How It Works

dbt-ci uses a **cache-based workflow**:

1. **`init`** - Downloads reference state from cloud storage (or uses local), compares with current code, and creates a cache of changes
2. **`run/delete/ephemeral`** - Use the cached state automatically (no need to re-specify state paths)

This design ensures:
- ✅ **Consistent state** across all commands in a CI run
- ✅ **Better performance** (no redundant state downloads)
- ✅ **Simpler CLI** (specify state once in init, reuse everywhere)

## Installation

### From PyPI (Recommended)

```bash
pip install dbt-ci
```

#### Extras

The base install deliberately carries only dbt-core and the CLI plumbing. The cloud SDKs
and the Docker client are large and most projects need at most one of them, so they are
installed on demand:

| Extra | Installs | Needed for |
|-------|----------|------------|
| `gcp` | `google-cloud-bigquery`, `google-cloud-storage` | `gs://` state/artifact URIs, the BigQuery connector used by `delete` and `migration` |
| `aws` | `boto3` | `s3://` state/artifact URIs |
| `docker` | `docker` | `--runner docker` |
| `all` | all of the above | |

```bash
pip install 'dbt-ci[gcp]'            # BigQuery + GCS
pip install 'dbt-ci[aws,docker]'     # S3 state, Docker runner
pip install 'dbt-ci[all]'            # everything
```

If a feature needs an extra you haven't installed, dbt-ci says which one and how to
install it rather than failing with an import traceback.

### From GitHub

```bash
# Install from main branch
pip install git+https://github.com/datablock-dev/dbt-ci.git@main

# Install a specific version
pip install git+https://github.com/datablock-dev/dbt-ci.git@v1.0.0
```

### Local Development

```bash
git clone https://github.com/datablock-dev/dbt-ci.git
cd dbt-ci
pip install -e ".[dev]"
```

Or with pipenv:

```bash
pipenv install --dev
```

`pyproject.toml` is the single source of truth for dependencies — the `Pipfile` installs
the project itself rather than restating them, so the two cannot drift apart. Add or
change a dependency in `pyproject.toml`, then run `pipenv lock`.

After installation, the tool is available as `dbt-ci`.

## Quick Start

**The Workflow:** Initialize once with `init`, then run commands that use the cached state.

### 1. Initialize State

First, initialize the dbt-ci state. This downloads/reads reference state and creates a cache:

```bash
dbt-ci init \
  --dbt-project-dir dbt \
  --profiles-dir dbt \
  --reference-target production \
  --state dbt/.dbtstate
```

**With Cloud Storage (GCS/S3):**
```bash
dbt-ci init \
  --dbt-project-dir dbt \
  --state-uri gs://my-bucket/dbt-state/manifest.json \
  --reference-target production \
  --state dbt/.dbtstate
```

### 2. Run Modified Models

After initialization, run commands use the cached state automatically:

```bash
# No need to specify --state again!
dbt-ci run \
  --dbt-project-dir dbt \
  --profiles-dir dbt
```

**With Docker:**
```bash
dbt-ci run \
  --runner docker \
  --docker-image ghcr.io/dbt-labs/dbt-bigquery:latest
```

## Commands

All commands share a set of **common options** (listed in the [Common Options](#common-options) section below). Command-specific flags are listed under each command.

---

### `init` - Initialize State

Creates initial state from your dbt project. **Always run this first.** Downloads reference manifest from cloud storage (if specified) and creates a local cache for subsequent commands.

```bash
dbt-ci init \
  --dbt-project-dir dbt \
  --profiles-dir dbt \
  --state-uri gs://my-bucket/manifest.json \
  --reference-target production \
  --state dbt/.dbtstate
```

**Flags:**

| Flag | Aliases | Env Var(s) | Default | Description |
|------|---------|-----------|---------|-------------|
| `--reference-target` | `--ref-target` | `DBT_REFERENCE_TARGET` | `None` | dbt target for the production/reference manifest |
| `--reference-path` | | `DBT_REFERENCE_PATH` | `reference` | **Deprecated — no effect.** The reference compile writes to dbt's own target path |
| `--reference-vars` | `--ref-vars` | `DBT_REFERENCE_VARS` | `None` | Variables to pass to dbt when compiling the reference manifest (YAML string or file path) |
| `--state-uri` | | `DBT_STATE_URI`, `STATE_URI` | `None` | Remote URI for the state manifest (e.g. `gs://bucket/manifest.json`, `s3://bucket/manifest.json`) |
| `--target-compile` | | `DBT_TARGET_COMPILE` | `false` | Run the second compile pass against the actual target |
| `--skip-reference-compile` | | `DBT_SKIP_REFERENCE_COMPILE` | `false` | Skip the compile pass against the reference/production state |
| `--comparison-strategy` | `--comparison` | `DBT_COMPARISON_STRATEGY` | `hybrid` | Strategy for detecting changed nodes: `dbt`, `git`, or `hybrid` |
| `--base-ref` | | `DBT_CI_BASE_REF` | Auto-detected | Base branch to diff against (e.g. `main`). Auto-detected from `GITHUB_BASE_REF` or git if not set |

> All [common options](#common-options) also apply.

**Git comparison and clone depth:** the `git` and `hybrid` strategies diff `origin/<base-ref>...HEAD` — the merge base — so only the commits on your branch count as changes. Shallow clones often don't contain the merge base; dbt-ci falls back to a direct diff and logs a debug message when that happens. For an accurate change set, fetch full history (`actions/checkout` with `fetch-depth: 0`).

Renamed files are reported by git as a rename of one path to another; dbt-ci treats them as the old node being deleted and a new node being added, since dbt identifies nodes by their file path.

---

### `run` - Run Modified Models

Detects and runs models that have changed. Uses cached state from `init`.

```bash
dbt-ci run --dbt-project-dir dbt --mode models
```

**Flags:**

| Flag | Aliases | Env Var(s) | Default | Description |
|------|---------|-----------|---------|-------------|
| `--mode` | `-m`, `--nodes`, `-n` | `DBT_NODES` | `all` | What to run: `all`, `models`, `seeds`, `snapshots`, `tests` |
| `--downstream-depth` | | `DBT_DOWNSTREAM_DEPTH` | Full graph | How many levels of downstream dependencies to include (dbt's `model+N`) |
| `--filters` | `-f` | | `None` | Extra resource-type filter (repeatable, choices: `models`, `seeds`, `snapshots`, `tests`). E.g. `--mode tests -f snapshots` to run only tests that have a snapshot dependency |

#### Limiting blast radius

By default a change selects its **entire** downstream graph. On a large project a change
to a core staging model therefore rebuilds almost everything, which is the opposite of
what change-based CI is for. `--downstream-depth` caps how far a change propagates:

```bash
dbt-ci run --downstream-depth 0   # only what changed
dbt-ci run --downstream-depth 1   # changed models and their direct children
dbt-ci run --downstream-depth 2   # two levels out
dbt-ci run                        # unlimited (default)
```

For a chain `customers → l1 → l2 → l3` where only `customers` changed:

| Flag | Models run |
|------|------------|
| `--downstream-depth 0` | `customers` |
| `--downstream-depth 1` | `customers`, `l1` |
| `--downstream-depth 2` | `customers`, `l1`, `l2` |
| *(omitted)* | `customers`, `l1`, `l2`, `l3` |

New and deleted nodes are always included regardless of depth — a new model has to run
whether or not anything depends on it yet.

> All [common options](#common-options) also apply.

**Examples:**
```bash
# Run only modified models
dbt-ci run --mode models

# Run modified models with defer to production
dbt-ci run --mode models --defer

# Run all modified resources (models, tests, seeds, etc.)
dbt-ci run --mode all

# With Docker
dbt-ci run --runner docker --mode models
```

---

### `ephemeral` - Ephemeral Environment

Clones changed models and their downstream dependencies into an isolated target schema using **`dbt clone`**, allowing integration testing without affecting production. Uses cached state from `init`.

> **Important:** `--target` and `--vars` must match the environment you want to clone into. The clone operation reads your `profiles.yml` to determine the target database/schema — if these are wrong, models will be cloned to the wrong location or the command will fail.

```bash
dbt-ci ephemeral \
  --target my-pr-env \
  --vars '{"use_production_data":"false"}'
```

**How it works:**
1. Reads the cached change set from `init`
2. Builds a selection of all affected models and their downstream dependencies
3. Runs `dbt clone --select <nodes>` targeting the specified environment
4. The cloned tables/views can then be used as the base for subsequent `dbt run` commands in the PR environment

**Flags:**

| Flag | Aliases | Env Var(s) | Default | Description |
|------|---------|-----------|---------|-------------|
| `--keep-env` | | `DBT_KEEP_ENV` | `false` | **Deprecated — no effect.** `ephemeral` never destroys the environment it creates; use [`finalize --clean-ephemeral`](#finalize---finalize-state) to tear it down |

> All [common options](#common-options) also apply.

---

### `delete` - Delete Removed Models

Detects and deletes models that have been removed from the project. Uses cached state from `init`.

```bash
dbt-ci delete --dry-run  # preview what will be deleted
dbt-ci delete            # execute deletions
```

**Flags:**

> Only [common options](#common-options) apply — no command-specific flags.

---

### `migration` - Migrate Partitioning Changes

Detects models whose partitioning configuration changed between the reference and target
state, and rebuilds the affected tables with the new partitioning spec. Uses cached state
from `init`.

BigQuery cannot change a table's partitioning in place, so each affected table is copied
into a temporary table with the new spec, the original is dropped, and the copy is
renamed back. Only **incremental** models are considered — other materializations are
rebuilt by dbt anyway.

```bash
dbt-ci migration --dry-run  # list the tables that would be rebuilt
dbt-ci migration            # apply the changes
```

> **Warning:** this rewrites tables in place. Always review the `--dry-run` output first.
> Requires a connector implementing a migration strategy — currently BigQuery only.

**Flags:**

> Only [common options](#common-options) apply — no command-specific flags.

---

### `report` - Summarise the Run

Renders the change set detected by `init` and the status of every command that has run
so far. In GitHub Actions the report is appended to the job summary automatically, so no
workflow wiring is needed beyond calling it.

```bash
dbt-ci report                       # → $GITHUB_STEP_SUMMARY, or stdout locally
dbt-ci report --output report.md    # → a file, e.g. to post as a PR comment
dbt-ci report --format json         # → machine-readable
```

The report covers:

- **Change counts and node names**, grouped as modified / new / deleted. Long lists fold
  into a collapsible block.
- **Exposure impact** — the exposures downstream of the change set, including those
  downstream of *deleted* nodes, which is usually the case worth catching.
- **Command status and duration** for each of `init`, `run`, `delete`, `ephemeral` and
  `migration` that has run.

**Flags:**

| Flag | Aliases | Env Var(s) | Default | Description |
|------|---------|-----------|---------|-------------|
| `--output` | `-o` | `DBT_REPORT_OUTPUT` | `$GITHUB_STEP_SUMMARY` or stdout | Where to write the report |
| `--format` | `-F` | `DBT_REPORT_FORMAT` | `markdown` | `markdown` or `json` |

> All [common options](#common-options) also apply.

---

### `config` - Generate a Config File

Writes a commented `dbt-ci.config.yaml` skeleton with the common options pre-filled, so
you don't have to memorise every flag. See [Configuration File](#configuration-file).

```bash
dbt-ci config                          # create ./dbt-ci.config.yaml
dbt-ci config --output dbt/dbt-ci.config.yaml
dbt-ci config --force                  # overwrite an existing file
```

**Flags:**

| Flag | Aliases | Default | Description |
|------|---------|---------|-------------|
| `--output` | `-o` | `dbt-ci.config.yaml` | Destination path for the generated file |
| `--force` | `-f` | `false` | Overwrite the file if it already exists |

> This command does not take the [common options](#common-options).

---

### `finalize` - Finalize State

Run after `run`, `delete`, or `ephemeral` to upload artifacts and clean up the local cache for the next CI run.

```bash
dbt-ci finalize
dbt-ci finalize --artifacts-uri s3://my-bucket/dbt-artifacts/
```

**Flags:**

| Flag | Aliases | Env Var(s) | Default | Description |
|------|---------|-----------|---------|-------------|
| `--artifacts-uri` | | `DBT_ARTIFACTS_URI`, `ARTIFACTS_URI` | `None` | Object storage URI for uploading run artifacts such as the updated `manifest.json` (e.g. `s3://bucket/dbt-artifacts/`) |
| `--files` | | `DBT_FINALIZE_FILES` | `manifest` | Which artifacts to upload (repeatable): `manifest`, `cache`, `log` |
| `--clean-ephemeral` | `--destroy-ephemeral` | `DBT_CLEAN_EPHEMERAL`, `DBT_DESTROY_EPHEMERAL` | `false` | Clean up the ephemeral environment as part of finalization |

Uploaded artifacts land at `<artifacts-uri>/manifest.json`, `<artifacts-uri>/cache.json`
and `<artifacts-uri>/logs.txt` respectively. `log` uploads the run log that dbt-ci writes
to `<cache dir>/dbt-ci.log`.

> **Note:** the log file always records at `DEBUG` level, including the resolved
> configuration for each command. Values whose names look sensitive (webhook, token,
> password, secret, credential, api_key) are masked before being written, so uploading
> the log does not publish your Slack webhook or credentials passed via `--docker-env`.
> This is a name-based heuristic — review your artifact bucket's access controls before
> enabling `--files log`.

> All [common options](#common-options) also apply.

## Runners

dbt-ci supports multiple execution environments:

### Local Runner

Execute dbt commands directly on your machine:

```bash
# After init
dbt-ci run \
  --runner local \
  --dbt-project-dir dbt
```

### dbt Runner (Python API)

Uses dbt's Python API (fastest, default):

```bash
# After init - uses dbt Python API
dbt-ci run \
  --runner dbt \
  --dbt-project-dir dbt
```

### Docker Runner

Run dbt commands inside a Docker container. Requires `pip install 'dbt-ci[docker]'`:

```bash
dbt-ci run \
  --runner docker \
  --docker-image ghcr.io/dbt-labs/dbt-duckdb:latest \
  --docker-volumes "$(pwd)/dbt:/dbt:rw"
```

When using the Docker runner, `--project-dir`, `--profiles-dir`, and `--state` are derived from the volume map — if the host path is covered by a mounted volume, the corresponding container path is passed to dbt inside the container. If no matching volume is found, the flag is omitted and dbt falls back to its own defaults (typically the container's `WORKDIR`).

**For Apple Silicon Macs:**

```bash
dbt-ci run \
  --runner docker \
  --docker-platform linux/amd64 \
  --docker-image ghcr.io/dbt-labs/dbt-postgres:latest \
  --docker-volumes "$(pwd)/dbt:/dbt:rw"
```

#### Docker Advanced Options

**Platform (for Apple Silicon compatibility):**
```bash
--docker-platform linux/amd64  # or linux/arm64
```

**Custom Volumes:**
```bash
--docker-volumes "/host/path:/container/path" --docker-volumes "/another:/path:ro"
```

**Environment Variables:**
```bash
--docker-env "DBT_ENV=prod" --docker-env "MY_API_KEY=secret"
```

**Network Mode:**
```bash
--docker-network bridge  # or host, none, container:name
```

**User:**
```bash
--docker-user "1000:1000"  # or leave empty for auto-detect
```

**Additional Docker Args:**
```bash
--docker-args "--memory=2g --cpus=2"
```

dbt-ci drives Docker through the Python SDK rather than the `docker` CLI, so `--docker-args` supports the subset of `docker run` flags that map onto SDK options. Both `--flag value` and `--flag=value` spellings work:

| Supported in `--docker-args` | Effect |
|------------------------------|--------|
| `--memory` / `-m` | Container memory limit |
| `--cpus` | CPU quota |
| `--shm-size` | Size of `/dev/shm` |
| `--env` / `-e` | Extra environment variables (merged with `--docker-env`) |
| `--add-host` | Extra host-to-IP mappings |
| `--workdir` / `-w` | Working directory inside the container |
| `--hostname` | Container hostname |
| `--privileged` | Extended privileges |
| `--platform`, `--network` | Override `--docker-platform` / `--docker-network` |

Anything else is logged as ignored rather than dropped silently.

> **Note:** containers are removed once the command finishes, so repeated CI runs don't accumulate stopped containers.

**Complete Docker Example:**
```bash
dbt-ci run \
  --runner docker \
  --docker-image ghcr.io/dbt-labs/dbt-postgres:1.7.0 \
  --docker-platform linux/amd64 \
  --docker-env "POSTGRES_HOST=host.docker.internal" \
  --docker-network host \
  --docker-volumes "$(pwd):/workspace" \
  --docker-volumes "$HOME/.aws:/root/.aws:ro" \
  --target prod
```

## Common Options

These flags are available on **every** command.

### Configuration File

dbt-ci supports a `dbt-ci.config.yaml` file as an alternative to passing every flag on the command line. It is loaded before any other options, and a flag passed on the command line always wins over it.

**Default location:** `dbt-ci.config.yaml` in the current working directory (override with `--config` / `DBT_CONFIG`).

If the config file is not found in the current directory, dbt-ci will automatically look for it inside `--dbt-project-dir` (or `DBT_PROJECT_DIR`). This means if your dbt project lives in a subdirectory (e.g. `dbt/`), placing `dbt-ci.config.yaml` there and setting `DBT_PROJECT_DIR=dbt` is enough — no `--config` flag needed.

The file uses a **nested style** where top-level keys map to common options, and command-specific options live under their command's key (`init`, `run`, `finalize`, `ephemeral`, `docker`):

Add the following comment to the top of your `dbt-ci.config.yaml` to get autocompletion and validation in editors that support [yaml-language-server](https://github.com/redhat-developer/yaml-language-server) (e.g. VS Code with the [YAML extension](https://marketplace.visualstudio.com/items?itemName=redhat.vscode-yaml)):

```yaml
# yaml-language-server: $schema=https://raw.githubusercontent.com/datablock-dev/dbt-ci/main/dbt-ci.config.schema.json
```

#### dbt-ci.config.yaml
```yaml
# yaml-language-server: $schema=https://raw.githubusercontent.com/datablock-dev/dbt-ci/main/dbt-ci.config.schema.json
project_dir: dbt
profiles_dir: dbt
state: dbt/.dbtstate
runner: docker

init:
  state-uri: gs://my-bucket/dbt-state/manifest.json
  reference-target: production
  comparison-strategy: hybrid
  base-ref: main

run:
  nodes: models
  downstream-depth: 2

finalize:
  artifacts-uri: s3://my-bucket/dbt-artifacts/
  files:
    - manifest
    - cache

docker:
  image: docker.pkg.dev/my-project/dbt:latest
  volumes:
    - "${PWD}/dbt:/dbt:rw"
    - "${GOOGLE_APPLICATION_CREDENTIALS}:${GOOGLE_APPLICATION_CREDENTIALS}:ro"
  env:
    - "DBT_PROFILES_DIR=/dbt"
    - "GOOGLE_APPLICATION_CREDENTIALS=${GOOGLE_APPLICATION_CREDENTIALS}"
  network: host
```

The legacy flat `DBT_*` key style is also supported:

```yaml
DBT_RUNNER: docker
DBT_PROJECT_DIR: dbt
DBT_STATE: dbt/.dbtstate
```

**Precedence (highest → lowest):**
1. CLI flags
2. `dbt-ci.config.yaml`
3. Shell environment variables
4. Built-in defaults

The config file sits **above** shell environment variables: a value you commit to
`dbt-ci.config.yaml` is deliberate, whereas the environment a CI runner happens to
export is not. To override a config value per-run, pass the flag rather than setting the
environment variable.

`--filters` on `run` is command-line only — it has no environment variable or config
file key.

The config file is validated on load. dbt-ci will exit with a clear error message if it contains unknown keys, invalid enum values (e.g. `runner: kubernetes`), or wrong types (e.g. `defer: "yes"` instead of a boolean).

`${VAR_NAME}` references inside the config file are resolved from the shell environment at load time.

> **Note:** `dbt-ci.config.yaml` is ignored by git by default (it is listed in `.gitignore`). Use it for local developer overrides and commit a `.example` variant for your team.

### Core

| Flag | Aliases | Env Var(s) | Default | Description |
|------|---------|-----------|---------|-------------|
| `--dbt-project-dir` | | `DBT_PROJECT_DIR` | `.` | Path to the dbt project directory |
| `--profiles-dir` | | `DBT_PROFILES_DIR` | Auto-detect | Path to the directory containing `profiles.yml` |
| `--reference-state` | `--state` | `DBT_STATE` | `None` | Local path to the reference state directory (where `manifest.json` is stored) |
| `--target` | `-t` | `DBT_TARGET` | From `profiles.yml` | dbt target to use |
| `--vars` | `-v` | `DBT_VARS` | `""` | YAML string or path to a YAML file with dbt variables |
| `--defer` | | `DBT_DEFER` | `false` | Pass dbt's `--defer` flag (defers unmodified nodes to the production state) |
| `--runner` | `-r` | `DBT_RUNNER` | `dbt` | Runner to use: `dbt`, `local`, `docker`, `bash` |
| `--entrypoint` | | `DBT_ENTRYPOINT` | `dbt` | Command entrypoint for dbt |
| `--dbt-version` | | `DBT_VERSION` | Current | Pin a specific dbt version (e.g. `1.10.13`). **Requires `--runner local`** |
| `--adapter` | `-a` | `DBT_ADAPTER` | `None` | dbt adapter to install (e.g. `dbt-bigquery`, `dbt-duckdb=1.10.0`). **Requires `--runner local`** |
| `--config` | `-c` | `DBT_CONFIG` | `dbt-ci.config.yaml` | Path to a dbt-ci YAML configuration file |
| `--dry-run` | | `DBT_DRY_RUN` | `false` | Print commands without executing them |
| `--quiet` | `-q` | `DBT_QUIET` | `false` | Run in quiet mode with minimal output |
| `--log-level` | | `DBT_LOG_LEVEL` | `INFO` | Logging verbosity: `DEBUG`, `INFO`, `WARNING`, `ERROR`, `CRITICAL` |
| `--slack-webhook` | `--slack-webhook-url` | `SLACK_WEBHOOK`, `SLACK_WEBHOOK_URL` | `None` | Slack webhook URL for CI notifications |

### Docker Runner

Only used when `--runner docker` is set.

| Flag | Env Var(s) | Default | Description |
|------|-----------|---------|-------------|
| `--docker-image` | `DBT_DOCKER_IMAGE` | `ghcr.io/dbt-labs/dbt-core:latest` | Docker image to use |
| `--docker-platform` | `DBT_DOCKER_PLATFORM` | Auto-detect | Platform override, e.g. `linux/amd64` or `linux/arm64` |
| `--docker-volumes` | `DBT_DOCKER_VOLUMES` | `[]` | Volume mounts (repeatable): `host:container[:mode]` |
| `--docker-env` | `DBT_DOCKER_ENV` | `[]` | Environment variables (repeatable): `KEY=VALUE` |
| `--docker-network` | `DBT_DOCKER_NETWORK` | `host` | Docker network mode |
| `--docker-user` | `DBT_DOCKER_USER` | Invoking user (`uid:gid`) | User to run as inside the container (`UID:GID`). Defaults to the UID and GID of the process running dbt-ci so container-written files are owned by the invoking user. |
| `--docker-args` | `DBT_DOCKER_ARGS` | `""` | Extra arguments appended to `docker run` |

### Pinning a dbt version or adapter

`--dbt-version` and `--adapter` install the requested packages into a cached virtual
environment under `~/.cache/dbt-ci/venvs/` and run dbt from it. Only the **`local`**
runner can execute an arbitrary dbt binary, so the pin applies there:

```bash
dbt-ci run --runner local --dbt-version 1.10.13 --adapter dbt-duckdb=1.10.0
```

Adapters may be given with or without a version (`dbt-bigquery`, `dbt-bigquery=1.10.0`,
`dbt-bigquery==1.10.0`). Each version/adapter combination gets its own cached
environment, and an interrupted install is rebuilt on the next run rather than reused.

The other runners resolve dbt from elsewhere and log a warning if a pin is set:

| Runner | dbt comes from |
|--------|----------------|
| `dbt` | the `dbt-core` installed alongside dbt-ci (runs in-process) |
| `docker` | the configured `--docker-image` |
| `bash` | the script at `--shell-path` |

### Bash Runner

Only used when `--runner bash` is set.

| Flag | Aliases | Env Var(s) | Default | Description |
|------|---------|-----------|---------|-------------|
| `--shell-path` | `--bash-path` | `DBT_SHELL_PATH` | `/bin/bash` | Path to the shell executable |

## Cloud Storage Support

dbt-ci supports storing and retrieving state files from cloud storage (GCS, S3), making it ideal for distributed CI/CD workflows.

> Requires the matching extra: `pip install 'dbt-ci[gcp]'` for `gs://` URIs, `pip install 'dbt-ci[aws]'` for `s3://` URIs.

### GCS/S3 State Storage

Store your dbt reference state in cloud storage for shared access across CI runs:

```bash
# Initialize and download state from GCS
dbt-ci init \
  --dbt-project-dir dbt \
  --state-uri gs://my-bucket/dbt-state/manifest.json \
  --reference-target production \
  --state dbt/.dbtstate

# Run using cached state (no need to specify URI again)
dbt-ci run --dbt-project-dir dbt --mode models
```

**Benefits:**
- 🔄 **Shared State**: Download the same reference state across different CI jobs
- 💾 **Cache-Based**: After init, commands use local cache (no repeated downloads)
- 📦 **No Git Commits**: State files don't need to be committed to version control
- 🚀 **Scalable**: Works seamlessly in containerized and distributed environments
- 🔐 **Secure**: Leverage cloud IAM and bucket policies for access control

**Configuration:**

The tool uses cloud credentials from your environment. Ensure your bucket is accessible:

```bash
# For GCS
export GOOGLE_APPLICATION_CREDENTIALS=/path/to/service-account.json

# For AWS S3
export AWS_ACCESS_KEY_ID=your_key
export AWS_SECRET_ACCESS_KEY=your_secret
export AWS_DEFAULT_REGION=us-east-1

# Or use IAM roles (recommended in CI/CD)
dbt-ci init --state-uri gs://my-bucket/manifest.json
```

**Supported URI Formats:**
- `gs://bucket-name/path/to/manifest.json` (Google Cloud Storage)
- `s3://bucket-name/path/to/manifest.json` (AWS S3)

## Environment Variables

All CLI options can also be set via environment variables:

```bash
export DBT_PROJECT_DIR=./dbt
export DBT_PROFILES_DIR=./dbt
export DBT_TARGET=production
export DBT_RUNNER=local

# After running init, just use:
dbt-ci run
```

**Common Environment Variables:**
- `DBT_PROJECT_DIR` - Path to dbt project
- `DBT_PROFILES_DIR` - Path to profiles.yml location
- `DBT_TARGET` - Target environment to use
- `DBT_RUNNER` - Runner type (local, docker, bash, dbt)
- `DBT_CI_CACHE_DIR` - Where the state cache and log file live (default: `<temp dir>/dbt-ci`)

**Note:** State management is cache-based. Run `init` once, then subsequent commands automatically use the cached state.

### Settings Inherited From `init`

`init` records the `--target` and `--vars` it ran with, and later commands reuse them, so
they only need to be given once:

```bash
dbt-ci init --target ci --vars '{"use_production_data": false}' --state dbt/.dbtstate
dbt-ci run          # runs against target 'ci' with the same vars
dbt-ci delete       # likewise
```

Passing the flag explicitly still wins — the cache only fills in what was left unset.

### Cache Location

`init` writes its cache (state comparison, manifests, run report and log file) to
`<temp dir>/dbt-ci`, and later commands read it from there. Because the location is
fixed, two dbt-ci runs executing at the same time on the same machine — for example two
pull request jobs on a shared self-hosted runner — would overwrite each other's state.

Set `DBT_CI_CACHE_DIR` to give each run its own directory:

```bash
export DBT_CI_CACHE_DIR="/tmp/dbt-ci-${GITHUB_RUN_ID}"
dbt-ci init ...
dbt-ci run ...
```

All commands in the same CI job must see the same value, since that is how `run`,
`delete`, `ephemeral` and `finalize` find the cache written by `init`.

## CI/CD Integration

### GitHub Actions Example

```yaml
name: dbt CI

on: [pull_request]

jobs:
  dbt-ci:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      
      - name: Set up Python
        uses: actions/setup-python@v4
        with:
          python-version: '3.11'
      
      - name: Configure AWS Credentials
        uses: aws-actions/configure-aws-credentials@v2
        with:
          role-to-assume: arn:aws:iam::123456789012:role/GitHubActionsRole
          aws-region: us-east-1
      
      - name: Install dbt-ci
        # The gcp extra provides the GCS client needed for the gs:// state URI below
        run: pip install 'dbt-ci[gcp] @ git+https://github.com/datablock-dev/dbt-ci.git@main'
      
      - name: Initialize dbt-ci with cloud state
        run: |
          dbt-ci init \
            --dbt-project-dir dbt \
            --state-uri gs://my-dbt-state/prod/manifest.json \
            --reference-target production \
            --state dbt/.dbtstate
      
      - name: Run modified models
        run: |
          dbt-ci run --mode models
```

### GitLab CI Example

```yaml
dbt-ci:
  image: python:3.11
  script:
    - pip install 'dbt-ci[gcp] @ git+https://github.com/datablock-dev/dbt-ci.git@main'
    - dbt-ci init --dbt-project-dir dbt --state-uri gs://my-dbt-state/prod/manifest.json --reference-target production --state dbt/.dbtstate
    - dbt-ci run --mode models
  only:
    - merge_requests
```

## Features

- **🎯 Smart Detection**: Automatically identifies modified, new, and deleted models
- **📊 Dependency Tracking**: Generates and traverses dependency graphs for lineage analysis
- **🔄 State Comparison**: Compares current state against production for precise CI
- **☁️ Cloud Storage**: S3 integration for shared state across distributed CI/CD workflows
- **🚀 Multiple Runners**: Supports local, Docker, bash, and dbt Python API execution
- **🐳 Docker-First**: Extensive Docker configuration for containerized workflows
- **⚡ Selective Execution**: Run only what changed, saving time and resources
- **🔌 Adapter Support**: Install specific dbt versions and adapters on-demand
- **💬 Notifications**: Slack webhook integration for CI/CD alerts
- **♻️ Ephemeral Environments**: Test changes in isolated environments
- **🧹 Cleanup**: Automatically remove deleted models from target warehouse
- **🎯 Blast-Radius Control**: Cap how far a change propagates with `--downstream-depth`
- **📝 Run Reports**: Markdown summary of the change set, exposure impact and command status
- **🔀 Partition Migrations**: Rebuild tables whose partitioning configuration changed (BigQuery)

## Use Cases

### Pull Request CI
Only build and test models affected by PR changes:
```bash
# Initialize with reference state
dbt-ci init --state-uri gs://bucket/manifest.json --reference-target production --state dbt/.dbtstate

# Run modified models with defer
dbt-ci run --mode models --defer
```

### Distributed CI with Cloud Storage
Share state across multiple CI jobs:
```bash
# Job 1: Initialize state (downloads from cloud)
dbt-ci init --state-uri gs://my-bucket/manifest.json --reference-target production --state dbt/.dbtstate

# Job 2: Run models (uses cached state)
dbt-ci run --mode models

# Job 3: Run tests (uses cached state)
dbt-ci run --mode tests
```

### Selective Testing
Run tests only for modified models:
```bash
# After init
dbt-ci run --mode tests
```

### Schema Migrations
Clean up deleted models from production:
```bash
# After init
dbt-ci delete --target production
```

### Multi-Environment Testing
Create ephemeral test environments:
```bash
dbt-ci ephemeral --keep-env
```

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

### Development Setup

1. Clone the repository
2. Install dependencies: `pip install -e ".[dev]"`
3. Run tests: `pytest tests/`
4. Run linting: `black dbt_ci/ tests/`

### Commit Message Format

This project uses [Conventional Commits](https://www.conventionalcommits.org/) for automated releases:

- `feat:` New feature (minor version bump)
- `fix:` Bug fix (patch version bump)
- `docs:` Documentation changes
- `refactor:` Code refactoring
- `test:` Adding tests
- `chore:` Maintenance tasks

Example:
```bash
git commit -m "feat: add Docker runner support"
git commit -m "fix: resolve path resolution on Windows"
```

See [RELEASING.md](RELEASING.md) for details on the automated release process.

## License

See [LICENSE](LICENSE) file for details.

## Links

- **PyPI**: [https://pypi.org/project/dbt-ci/](https://pypi.org/project/dbt-ci/)
- **Documentation**: [https://datablock.dev](https://datablock.dev)
- **Issues**: [GitHub Issues](https://github.com/datablock-dev/dbt-ci/issues)
- **Discussions**: [GitHub Discussions](https://github.com/datablock-dev/dbt-ci/discussions)
- **Changelog**: [CHANGELOG.md](CHANGELOG.md)
