### Install and Start DataJunction UI

Source: https://github.com/datajunction/dj/blob/main/datajunction-ui/README.md

Install project dependencies and start the DataJunction UI development server.

```bash
yarn install
yarn start
```

--------------------------------

### Start JupyterLab Server

Source: https://github.com/datajunction/dj/blob/main/docs/content/0.1.0/docs/developers/running-locally.md

Navigate to the notebooks directory and start the JupyterLab server to access example notebooks.

```sh
cd notebooks
jupyter lab
```

--------------------------------

### Install JupyterLab

Source: https://github.com/datajunction/dj/blob/main/docs/content/0.1.0/docs/developers/running-locally.md

Install JupyterLab using pip, which is required to run the example notebooks.

```sh
pip install jupyterlab
```

--------------------------------

### Clone Repository and Start Docker Compose

Source: https://github.com/datajunction/dj/blob/main/datajunction-query/README.rst

Clone the DJQS repository and start the Docker Compose environment to get started.

```sh
git clone https://github.com/DataJunction/djqs
cd djqs
docker compose up
```

--------------------------------

### Install DJ from Source

Source: https://github.com/datajunction/dj/blob/main/docs/content/0.1.0/docs/developers/docs-development.md

Install the DataJunction library from its source code.

```python
pip install .
```

--------------------------------

### Launch Default Docker Compose Environment

Source: https://github.com/datajunction/dj/blob/main/datajunction-server/README.md

Starts the DataJunction UI with a minimal backend. Use this for basic setup.

```sh
docker compose up
```

--------------------------------

### Start Docker Compose Demo Environment

Source: https://github.com/datajunction/dj/blob/main/docs/content/0.1.0/docs/developers/running-locally.md

Start the full DataJunction demo environment, including the UI, API, reflection service, query service, and JupyterLab.

```sh
cd dj/
docker compose --profile demo up
```

--------------------------------

### GraphQL GET Endpoint Example Response

Source: https://github.com/datajunction/dj/blob/main/docs/content/0.1.0/docs/developers/the-datajunction-api-specification.md

An example of a successful response from the GET /graphql endpoint, which serves the GraphiQL IDE.

```json
null
```

--------------------------------

### Get Node Example Response

Source: https://github.com/datajunction/dj/blob/main/docs/content/0.1.0/docs/developers/the-datajunction-api-specification.md

This is an example of a successful response when retrieving node information using the GET /nodes/{name} endpoint. It details the structure and fields returned for a node.

```json
{
  "namespace": "string",
  "node_revision_id": 0,
  "node_id": 0,
  "type": "source",
  "name": "string",
  "display_name": "string",
  "version": "string",
  "status": "valid",
  "mode": "published",
  "catalog": {
    "name": "string",
    "engines": []
  },
  "schema_": "string",
  "table": "string",
  "description": "",
  "query": "string",
  "availability": {
    "min_temporal_partition": [],
    "max_temporal_partition": [],
    "catalog": "string",
    "schema_": "string",
    "table": "string",
    "valid_through_ts": 0,
    "url": "string",
    "categorical_partitions": [],
    "temporal_partitions": [],
    "partitions": []
  },
  "columns": [
    {
      "name": "string",
      "display_name": "string",
      "type": "string",
      "attributes": [
        {
          "attribute_type": {
            "namespace": "string",
            "name": "string"
          }
        }
      ],
      "dimension": {
        "name": "string"
      },
      "partition": {
        "type_": "temporal",
        "format": "string",
        "granularity": "string",
        "expression": "string"
      }
    }
  ],
  "updated_at": "2019-08-24T14:15:22Z",
  "materializations": [
    {
      "name": "string",
      "config": {},
      "schedule": "string",
      "job": "string",
      "backfills": [
        {
          "spec": {
            "column_name": "string",
            "values": [
              null
            ],
            "range": [
              null
            ]
          },
          "urls": [
            "string"
          ]
        }
      ],
      "strategy": "string"
    }
  ],
  "parents": [
    {
      "name": "string"
    }
  ],
  "metric_metadata": {
    "direction": "higher_is_better",
    "unit": {
      "name": "string",
      "label": "string",
      "category": "string",
      "abbreviation": "string",
      "description": "string"
    }
  },
  "dimension_links": [
    {
      "dimension": {
        "name": "string"
      },
      "join_type": "left",
      "join_sql": "string",
      "join_cardinality": "one_to_one",
      "role": "string",
      "foreign_keys": {
        "property1": "string",
        "property2": "string"
      }
    }
  ],
  "created_at": "2019-08-24T14:15:22Z",
  "tags": [],
  "current_version": "string",
  "missing_table": false
}
```

--------------------------------

### DJ Server Environment Variables Example

Source: https://github.com/datajunction/dj/blob/main/docs/content/0.1.0/docs/deploying-dj/running-a-dj-server.md

Example of a .env file for configuring a DJ server. Demonstrates setting nested configuration options using double underscores.

```bash
QUERY_SERVICE=http://djqs:8001
SECRET=a-fake-secretkey
NODE_LIST_MAX=10000

# Writer DB (required)
WRITER_DB__URI=postgresql+psycopg://dj:dj@postgres_metadata:5432/dj
WRITER_DB__POOL_SIZE=20
WRITER_DB__MAX_OVERFLOW=20
WRITER_DB__POOL_TIMEOUT=10
WRITER_DB__CONNECT_TIMEOUT=5
WRITER_DB__POOL_PRE_PING=true
WRITER_DB__ECHO=false
WRITER_DB__KEEPALIVES=1
WRITER_DB__KEEPALIVES_IDLE=30
WRITER_DB__KEEPALIVES_INTERVAL=10
WRITER_DB__KEEPALIVES_COUNT=5

# Reader DB (optional)
READER_DB__URI=postgresql+psycopg://dj:dj@postgres_metadata:5432/dj
READER_DB__POOL_SIZE=10
READER_DB__MAX_OVERFLOW=10
READER_DB__POOL_TIMEOUT=5
READER_DB__CONNECT_TIMEOUT=5
READER_DB__POOL_PRE_PING=true
READER_DB__ECHO=false
READER_DB__KEEPALIVES=1
READER_DB__KEEPALIVES_IDLE=30
READER_DB__KEEPALIVES_INTERVAL=10
READER_DB__KEEPALIVES_COUNT=5
```

--------------------------------

### Real-Time Deployment Output Example

Source: https://github.com/datajunction/dj/blob/main/docs/content/0.1.0/docs/data-modeling/yaml.md

This example shows the typical real-time output observed in the terminal when running the `dj push` command. It includes a status table with UUID, Status, and Progress.

```text
Pushing project from: ./my-project

┌─────────────────────────────────────────────────────────────┐
│                      Deployment Status                      │
├──────────────────────┬─────────────┬────────────────────────┤
│ UUID                 │ Status      │ Progress               │
├──────────────────────┼─────────────┼────────────────────────┤
│ abc123-def456-...    │ RUNNING     │ Deploying nodes...     │
└──────────────────────┴─────────────┴────────────────────────┘

Deployment finished: SUCCESS
```

--------------------------------

### Install DataJunction with MCP Extra

Source: https://github.com/datajunction/dj/blob/main/docs/content/0.1.0/docs/getting-started/ai-assistants.md

Install the DataJunction Python client with the MCP extra to enable integration with AI assistants.

```bash
pip install datajunction[mcp]
```

--------------------------------

### Build Specific Documentation Version

Source: https://github.com/datajunction/dj/blob/main/docs/content/0.1.0/docs/developers/docs-development.md

Example of building the documentation for version 0.1.2 and setting it as the latest.

```sh
./build-docs.sh 0.1.2 true

Start building sites …
Total in 2496 ms
```

--------------------------------

### GenericMaterializationConfigInput Schema Example

Source: https://github.com/datajunction/dj/blob/main/docs/content/0.1.0/docs/developers/the-datajunction-api-specification.md

An example of the GenericMaterializationConfigInput schema, used for materialization configurations.

```json
{
  "spark": {},
  "lookback_window": "string"
}
```

--------------------------------

### Get Help with DataJunction CLI Commands

Source: https://github.com/datajunction/dj/blob/main/docs/content/0.1.0/docs/getting-started/using-the-cli.md

Display all available CLI commands or get detailed help for a specific command.

```bash
# Show all available commands
dj --help

# Show help for specific command
dj push --help
dj delete-node --help
```

--------------------------------

### Example Dev Release Output (Python Client)

Source: https://github.com/datajunction/dj/blob/main/docs/content/0.1.0/docs/developers/releasing-new-version.md

This example shows the output of `make dev-release` for the Python client, including versioning, build artifacts, and publish success messages.

```shell
% make dev-release
hatch version dev
Old: 0.0.1a20
New: 0.0.1a20.dev0
hatch build
[sdist]
dist/datajunction-0.0.1a20.dev0.tar.gz

[wheel]
dist/datajunction-0.0.1a20.dev0-py3-none-any.whl
hatch publish
dist/datajunction-0.0.1a20.dev0.tar.gz ... success
dist/datajunction-0.0.1a20.dev0-py3-none-any.whl ... success

[datajunction]
https://pypi.org/project/datajunction/0.0.1a20.dev0/
```

--------------------------------

### GenericCubeConfigInput Schema Example

Source: https://github.com/datajunction/dj/blob/main/docs/content/0.1.0/docs/developers/the-datajunction-api-specification.md

An example of the GenericCubeConfigInput schema, used for configuring cubes.

```json
{
  "spark": {},
  "lookback_window": "string",
  "dimensions": [
    "string"
  ],
  "measures": {
    "property1": {
      "metric": "string",
      "measures": [
        {
          "name": "string",
          "field_name": "string",
          "agg": "string",
          "type": "string"
        }
      ],
      "combiner": "string"
    },
    "property2": {
      "metric": "string",
      "measures": [
        {
          "name": "string",
          "field_name": "string",
          "agg": "string",
          "type": "string"
        }
      ],
      "combiner": "string"
    }
  },
  "metrics": [
    {
      "name": "string",
      "type": "string",
      "column": "string",
      "node": "string",
      "semantic_entity": "string",
      "semantic_type": "string"
    }
  ]
}
```

--------------------------------

### List Catalogs Response Example

Source: https://github.com/datajunction/dj/blob/main/docs/content/0.1.0/docs/developers/the-datajunction-api-specification.md

This is an example of a successful response when listing available catalogs. It shows the expected structure for catalog information.

```json
[
  {
    "name": "string",
    "engines": []
  }
]
```

--------------------------------

### Install DataJunction Javascript Client (CommonJS)

Source: https://github.com/datajunction/dj/blob/main/docs/content/0.1.0/docs/developers/clients.md

Install the DataJunction Javascript client using npm for use in Node.js projects. This command installs the necessary package.

```sh
npm install datajunction
```

--------------------------------

### Build Documentation Version (Not Latest)

Source: https://github.com/datajunction/dj/blob/main/docs/content/0.1.0/docs/developers/docs-development.md

Example of building the documentation for version 0.1.1 without setting it as the latest.

```sh
./build-docs.sh 0.1.1 false

Start building sites …
Total in 2472 ms
```

--------------------------------

### Install DataJunction Python Client

Source: https://github.com/datajunction/dj/blob/main/docs/content/0.1.0/docs/developers/clients.md

Install the DataJunction Python client using pip. This is the first step to using the client library in your Python projects.

```sh
pip install datajunction
```

--------------------------------

### Custom Cache Implementation Example

Source: https://github.com/datajunction/dj/blob/main/docs/content/0.1.0/docs/deploying-dj/caching.md

An example of a custom cache class `MyCustomCache` that extends `CacheInterface`. It includes placeholders for custom initialization and caching logic.

```python
from fastapi import Request
from datajunction_server.internal.caching.noop_cache import noop_cache

class MyCustomCache(CacheInterface):
    """A custom cache implementation"""

    def __init__(self):
        # Initialize your custom cache here
        ...

    def get(self, key: str) -> Optional[Any]:
        # Implement the logic to retrieve a cached value
        ...

    def set(self, key: str, value: Any, timeout: int = 300) -> None:
        # Implement the logic to cache a value
        ...

    def delete(self, key: str) -> None:
        # Implement the logic to delete a cache key
        ...

def get_cache(request: Request) -> Optional[CacheInterface]:
    """Dependency for retrieving a custom cache implementation"""
    cache_control = request.headers.get("Cache-Control", "")
    skip_cache = "no-cache" in cache_control
    return noop_cache if skip_cache else MyCustomCache()
```

--------------------------------

### Build Older Documentation Version

Source: https://github.com/datajunction/dj/blob/main/docs/content/0.1.0/docs/developers/docs-development.md

Example of building the documentation for an older version, 0.1.0, without setting it as the latest.

```sh
./build-docs.sh 0.1.0 false

Start building sites …
Total in 2549 ms
```

--------------------------------

### Dimension Link YAML Example

Source: https://github.com/datajunction/dj/blob/main/docs/content/0.1.0/docs/data-modeling/yaml.md

Example demonstrating both join and reference type dimension links within a node's YAML configuration. Includes optional fields like default_value for join links.

```yaml
description: Hard hat dimension
display_name: Local Hard Hats
query: ...
primary_key: ...
dimension_links:
  - type: join
    node_column: state_id
    dimension_node: ${prefix}roads.us_state
    default_value: Unknown  # Optional: fallback for NULL values from LEFT JOIN
  - type: reference
    node_column: birth_date
    dimension: ${prefix}roads.date_dim.dateint
    role: birth_date
```

--------------------------------

### Add Engines To Catalog Request Body Example

Source: https://github.com/datajunction/dj/blob/main/docs/content/0.1.0/docs/developers/the-datajunction-api-specification.md

Example of the request body format for adding one or more engines to a catalog. Each engine object requires a name, version, URI, and dialect.

```json
[
  {
    "name": "string",
    "version": "string",
    "uri": "string",
    "dialect": "spark"
  }
]
```

--------------------------------

### Clone DataJunction Demo Notebooks Repository

Source: https://github.com/datajunction/dj/blob/main/docs/content/0.1.0/docs/developers/running-locally.md

Clone the DataJunction demo repository to access example notebooks.

```sh
git clone git@github.com:DataJunction/dj-demo.git
```

--------------------------------

### Column Partition Configuration

Source: https://github.com/datajunction/dj/blob/main/docs/content/0.1.0/docs/data-modeling/yaml.md

Example of configuring partition settings for a column, including format, granularity, and type.

```yaml
columns:
- name: utc_date
  partition:
    format: yyyyMMdd
    granularity: day
    type_: temporal
```

--------------------------------

### Install DataJunction Claude Integration

Source: https://github.com/datajunction/dj/blob/main/datajunction-clients/python/README.md

Installs the DataJunction skill and configures the Claude Code integration, including the DJ MCP server.

```bash
dj setup-claude
```

--------------------------------

### Add Catalog Request Body Example

Source: https://github.com/datajunction/dj/blob/main/docs/content/0.1.0/docs/developers/the-datajunction-api-specification.md

This is an example of the request body structure required to add a new catalog. It includes the name and engines for the catalog.

```json
{
  "name": "string",
  "engines": []
}
```

--------------------------------

### Launch Full Docker Compose Environment with Demo Profile

Source: https://github.com/datajunction/dj/blob/main/datajunction-server/README.md

Starts the full suite of DataJunction services, including query and reflection services. Use the 'demo' profile for comprehensive testing.

```sh
docker compose --profile demo up
```

--------------------------------

### Install Only DataJunction Skill

Source: https://github.com/datajunction/dj/blob/main/datajunction-clients/python/README.md

Installs only the DataJunction skill for Claude Code, skipping the MCP server setup.

```bash
dj setup-claude --no-mcp
```

--------------------------------

### Push with Real-Time Output Example

Source: https://github.com/datajunction/dj/blob/main/docs/content/0.1.0/docs/data-modeling/yaml.md

This command pushes project files and demonstrates the real-time output you can expect during the deployment process. It helps in monitoring progress and identifying issues.

```sh
dj push ./example_project --namespace my.namespace
```

--------------------------------

### List Tags API Response

Source: https://github.com/datajunction/dj/blob/main/docs/content/0.1.0/docs/developers/the-datajunction-api-specification.md

Example response for the GET /tags endpoint, detailing the structure of a successful tag list.

```json
[
  {
    "description": "string",
    "display_name": "string",
    "tag_metadata": {},
    "name": "string",
    "tag_type": "string"
  }
]
```

--------------------------------

### Example 200 Response for Get Data

Source: https://github.com/datajunction/dj/blob/main/docs/content/0.1.0/docs/developers/the-datajunction-api-specification.md

Illustrates the structure of a successful response when retrieving data via a DJ SQL query. Includes query details, execution times, results, and pagination links.

```json
{
  "id": "string",
  "engine_name": "string",
  "engine_version": "string",
  "submitted_query": "string",
  "executed_query": "string",
  "scheduled": "2019-08-24T14:15:22Z",
  "started": "2019-08-24T14:15:22Z",
  "finished": "2019-08-24T14:15:22Z",
  "state": "UNKNOWN",
  "progress": 0,
  "output_table": {
    "catalog": "string",
    "schema": "string",
    "table": "string"
  },
  "results": [
    {
      "sql": "string",
      "columns": [
        {
          "name": "string",
          "type": "string",
          "column": "string",
          "node": "string",
          "semantic_entity": "string",
          "semantic_type": "string"
        }
      ],
      "rows": [
        [
          null
        ]
      ],
      "row_count": 0
    }
  ],
  "next": "http://example.com",
  "previous": "http://example.com",
  "errors": [
    "string"
  ],
  "links": [
    "http://example.com"
  ]
}
```

--------------------------------

### Get Cube Information (JSON Response)

Source: https://github.com/datajunction/dj/blob/main/docs/content/0.1.0/docs/developers/the-datajunction-api-specification.md

This is an example of a successful response when retrieving information about a specific cube using the DataJunction API. It details the cube's metadata, elements, dimensions, metrics, and materialization configurations.

```json
{
  "node_revision_id": 0,
  "node_id": 0,
  "type": "source",
  "name": "string",
  "display_name": "string",
  "version": "string",
  "status": "valid",
  "mode": "published",
  "description": "",
  "availability": {
    "min_temporal_partition": [],
    "max_temporal_partition": [],
    "catalog": "string",
    "schema_": "string",
    "table": "string",
    "valid_through_ts": 0,
    "url": "string",
    "categorical_partitions": [],
    "temporal_partitions": [],
    "partitions": []
  },
  "cube_elements": [
    {
      "name": "string",
      "display_name": "string",
      "node_name": "string",
      "type": "string",
      "partition": {
        "type_": "temporal",
        "format": "string",
        "granularity": "string",
        "expression": "string"
      }
    }
  ],
  "cube_node_metrics": [
    "string"
  ],
  "cube_node_dimensions": [
    "string"
  ],
  "query": "string",
  "columns": [
    {
      "name": "string",
      "display_name": "string",
      "type": "string",
      "attributes": [
        {
          "attribute_type": {
            "namespace": "string",
            "name": "string"
          }
        }
      ],
      "dimension": {
        "name": "string"
      },
      "partition": {
        "type_": "temporal",
        "format": "string",
        "granularity": "string",
        "expression": "string"
      }
    }
  ],
  "updated_at": "2019-08-24T14:15:22Z",
  "materializations": [
    {
      "name": "string",
      "config": {},
      "schedule": "string",
      "job": "string",
      "backfills": [
        {
          "spec": {
            "column_name": "string",
            "values": [
              null
            ],
            "range": [
              null
            ]
          },
          "urls": [
            "string"
          ]
        }
      ],
      "strategy": "string"
    }
  ],
  "tags": [
    {
      "description": "string",
      "display_name": "string",
      "tag_metadata": {},
      "name": "string",
      "tag_type": "string"
    }
  ]
}
```

--------------------------------

### Complete Development Workflow Example

Source: https://github.com/datajunction/dj/blob/main/docs/content/0.1.0/docs/data-modeling/yaml.md

This sequence demonstrates a typical development workflow: pulling existing data, making changes, performing a dry run validation, pushing to a development namespace, and finally deploying to production.

```sh
# 1. Export existing namespace to get started
dj pull production.analytics ./my-project

# 2. Make changes to YAML files
# ... edit files ...

# 3. Validate changes with dry run
dj deploy ./my-project --dryrun

# 4. Deploy to development namespace first
dj push ./my-project --namespace development.analytics

# 5. After testing, deploy to production
dj push ./my-project --namespace production.analytics
```

--------------------------------

### Install Only MCP Server

Source: https://github.com/datajunction/dj/blob/main/datajunction-clients/python/README.md

Installs only the DJ MCP server for Claude Code integration, skipping the skill installation.

```bash
dj setup-claude --no-skills
```

--------------------------------

### ErrorCode Schema Example

Source: https://github.com/datajunction/dj/blob/main/docs/content/0.1.0/docs/developers/the-datajunction-api-specification.md

An example of the ErrorCode schema, which is an integer.

```json
0
```

--------------------------------

### Launch Hugo Server Locally

Source: https://github.com/datajunction/dj/blob/main/docs/content/0.1.0/docs/developers/docs-development.md

Navigate to the docs directory and launch the Hugo server to preview the DataJunction specification page.

```sh
cd docs
hugo serve --contentDir=content/0.1.0
```

--------------------------------

### MaterializationJobTypeEnum Example

Source: https://github.com/datajunction/dj/blob/main/docs/content/0.1.0/docs/developers/the-datajunction-api-specification.md

An example of a MaterializationJobTypeEnum, detailing a Spark SQL materialization job.

```json
{
  "name": "spark_sql",
  "label": "Spark SQL",
  "description": "Spark SQL materialization job",
  "allowed_node_types": [
    "transform",
    "dimension",
    "cube"
  ],
  "job_class": "SparkSqlMaterializationJob"
}
```

--------------------------------

### HealthCheck Schema Example

Source: https://github.com/datajunction/dj/blob/main/docs/content/0.1.0/docs/developers/the-datajunction-api-specification.md

An example of the HealthCheck schema, indicating the status of a service.

```json
{
  "name": "string",
  "status": "ok"
}
```

--------------------------------

### Start Docker Environment with Trino

Source: https://github.com/datajunction/dj/blob/main/docs/content/0.1.0/docs/getting-started/dj-and-trino.md

Use Docker Compose to launch the DataJunction query service and Trino containers. The `--profile demo` flag is required for the query service, and `--profile trino` is required for Trino.

```bash
docker compose --profile demo --profile trino up
```

--------------------------------

### HTTPValidationError Schema Example

Source: https://github.com/datajunction/dj/blob/main/docs/content/0.1.0/docs/developers/the-datajunction-api-specification.md

An example of the HTTPValidationError schema, used for validation errors.

```json
{
  "detail": [
    {
      "loc": [
        "string"
      ],
      "msg": "string",
      "type": "string"
    }
  ]
}
```

--------------------------------

### Initialize DJ Client and Login

Source: https://github.com/datajunction/dj/blob/main/datajunction-server/tests/api/files/client_test/include_client_setup.txt

Initializes the DataJunction client with a given URL and performs basic authentication.

```python
from datajunction import (
    DJBuilder, Source, Dimension, Transform, Metric,
    Namespace, MetricUnit, MetricDirection, ColumnAttribute,
)

DJ_URL = "http://test"

dj = DJBuilder(DJ_URL)
dj.basic_login("dj", "dj")
```

--------------------------------

### AttributeOutput Schema Example

Source: https://github.com/datajunction/dj/blob/main/docs/content/0.1.0/docs/developers/the-datajunction-api-specification.md

An example of the AttributeOutput schema, specifying an attribute type.

```json
{
  "attribute_type": {
    "namespace": "string",
    "name": "string"
  }
}

```

--------------------------------

### Install ANTLR4 Tools

Source: https://github.com/datajunction/dj/blob/main/CONTRIBUTING.rst

Installs the ANTLR generator tool required for generating parsers.

```sh
pip install antlr4-tools
```

--------------------------------

### Import an Existing Project

Source: https://github.com/datajunction/dj/blob/main/notebooks/DJ Projects Tutorial.ipynb

Import a DataJunction project from a specified namespace ('default') into a local directory ('./example_project'). Existing files will be ignored if 'ignore_existing_files' is set to True.

```python
Project.pull(client=dj, namespace="default", target_path="./example_project", ignore_existing_files=True)
```

--------------------------------

### Granularity Schema Example

Source: https://github.com/datajunction/dj/blob/main/docs/content/0.1.0/docs/developers/the-datajunction-api-specification.md

An example of the Granularity schema, representing a time dimension granularity.

```json
"second"
```

--------------------------------

### AttributeTypeIdentifier Schema Example

Source: https://github.com/datajunction/dj/blob/main/docs/content/0.1.0/docs/developers/the-datajunction-api-specification.md

An example of the AttributeTypeIdentifier schema, used for identifying attribute types.

```json
{
  "namespace": "system",
  "name": "string"
}

```

--------------------------------

### AttributeTypeBase Schema Example

Source: https://github.com/datajunction/dj/blob/main/docs/content/0.1.0/docs/developers/the-datajunction-api-specification.md

An example of the AttributeTypeBase schema, defining properties for an attribute type.

```json
{
  "namespace": "system",
  "name": "string",
  "description": "string",
  "allowed_node_types": [
    "source"
  ],
  "uniqueness_scope": [
    "node"
  ],
  "id": 0
}

```

--------------------------------

### Build Documentation Script Syntax

Source: https://github.com/datajunction/dj/blob/main/docs/content/0.1.0/docs/developers/docs-development.md

The build script requires a version and an optional flag to set it as the latest. Run without arguments for help.

```sh
./build-docs.sh
```

--------------------------------

### Navigate to Docs Directory

Source: https://github.com/datajunction/dj/blob/main/docs/content/0.1.0/docs/developers/docs-development.md

Change your current directory to the 'docs' folder within the cloned DJ repository.

```sh
cd dj/docs
```

--------------------------------

### Star Schema Dimensional Modeling Example

Source: https://github.com/datajunction/dj/blob/main/datajunction-clients/python/datajunction/skills/datajunction.md

Illustrates a star schema with a fact table, dimension links to user, product, and date dimensions, and how a metric inherits these links.

```text
[Fact Table: transactions]
    ├─ dimension_link: user_id → [Dimension: users]
    │   ├─ Attributes: country, signup_date, tier
    │   │  └─ dimension_link: signup_date → [Dimension: date]
    │   │      └─ Attributes: month, quarter, year
    │   └─ Attributes: country, signup_date, tier
    ├─ dimension_link: product_id → [Dimension: products]
    │   └─ Attributes: category, price, brand
    └─ dimension_link: date → [Dimension: dates]
        └─ Attributes: month, quarter, year

[Metric: revenue]
    ├─ Defined on: transactions
    └─ Auto-inherits dimension links from transactions
        ├─ users.country (via user_id)
        ├─ products.category (via product_id)
        └─ dates.month (via date)
```

--------------------------------

### Example JSON Response for Linking Dimension

Source: https://github.com/datajunction/dj/blob/main/docs/content/0.1.0/docs/developers/the-datajunction-api-specification.md

This is an example of a successful JSON response when linking a dimension to a node. It indicates the operation was successful.

```json
"string"
```

--------------------------------

### Get Data for a Specific Metric

Source: https://github.com/datajunction/dj/blob/main/CONTRIBUTING.rst

Fetch data for a particular metric by its name. This is a basic GET request to the /data/{metric_name}/ endpoint.

```bash
$ curl http://localhost:8000/data/avg_repair_price/ | jq
```

--------------------------------

### Create Base Metrics for Ratio Calculation

Source: https://github.com/datajunction/dj/blob/main/datajunction-clients/python/datajunction/skills/datajunction-api.md

Step 1 of creating a ratio metric: create the individual base metrics (e.g., clicks, impressions) using separate POST requests to the API.

```bash
curl -X POST $DJ_URL/nodes/metric/ -d '{
  "name": "finance.clicks",
  "query": "SELECT COUNT_IF(event = '\''click'\'') FROM finance.events",
  "owners": ["marketing@company.com"],
  "mode": "published"
}'
```

```bash
curl -X POST $DJ_URL/nodes/metric/ -d '{
  "name": "finance.impressions",
  "query": "SELECT COUNT_IF(event = '\''impression'\'') FROM finance.events",
  "owners": ["marketing@company.com"],
  "mode": "published"
}'
```

--------------------------------

### Initialize DJAdmin Client

Source: https://github.com/datajunction/dj/blob/main/datajunction-clients/python/README.md

Instantiate the DJAdmin client to interact with the DataJunction server. Ensure the URL is correct for your environment.

```python
from datajunction import DJAdmin

djadmin = DJAdmin("http://localhost:8000")
```

--------------------------------

### Load and Compile Project

Source: https://github.com/datajunction/dj/blob/main/notebooks/DJ Projects Tutorial.ipynb

Load a DataJunction project from a local directory and compile it. Compilation prepares the project for validation and deployment.

```python
project = Project.load("./example_project")
compiled_project = project.compile()
```

--------------------------------

### DataJunction Node Type Examples

Source: https://github.com/datajunction/dj/blob/main/datajunction-clients/python/datajunction/skills/datajunction.md

Examples of different node types used in DataJunction, including physical tables, transformations, dimensions, metrics, and cubes.

```text
catalog.finance.transactions_table
```

```text
events.clean.user_events
```

```text
core.users
```

```text
core.dates
```

```text
finance.total_revenue
```

```text
finance.revenue_cube
```

--------------------------------

### GraphQL GET Endpoint

Source: https://github.com/datajunction/dj/blob/main/docs/content/0.1.0/docs/developers/the-datajunction-api-specification.md

Handles HTTP GET requests to the /graphql endpoint, typically used for interacting with the GraphiQL IDE or executing simple queries.

```APIDOC
## GET /graphql

### Description
Handles HTTP GET requests to the /graphql endpoint, providing access to the GraphiQL integrated development environment or executing queries via GET.

### Method
GET

### Endpoint
/graphql

### Responses
#### Success Response (200)
- **Description**: The GraphiQL integrated development environment.

#### Error Response (404)
- **Description**: Not found if GraphiQL or query via GET are not enabled.

### Response Example
```json
null
```
```

--------------------------------

### Serve Versions Page Locally

Source: https://github.com/datajunction/dj/blob/main/docs/content/0.1.0/docs/developers/docs-development.md

Start a local Hugo development server specifically for the 'Versions' page, which acts as a sub-site listing release notes.

```sh
hugo serve --contentDir content/versions/
```

--------------------------------

### List All Metrics (Python)

Source: https://github.com/datajunction/dj/blob/main/docs/content/0.1.0/docs/getting-started/listing-metrics.md

Use the DJClient in Python to list all available metrics. Ensure DJ_URL is set.

```python
from datajunction import DJClient
dj = DJClient(DJ_URL)
metrics = dj.metrics()
```

--------------------------------

### Spark Join Strategy Hint Example

Source: https://github.com/datajunction/dj/blob/main/docs/content/0.1.0/docs/data-modeling/dimension-links.md

Demonstrates how to apply a single Spark join strategy hint to a dimension link. This hint is emitted immediately after SELECT using Spark's optimizer hint syntax.

```text
SELECT /*+ BROADCAST(t2) */
    t1.event_secs,
    t2.name AS user_name
FROM events t1
LEFT JOIN user t2 ON t1.user_id = t2.id
```

--------------------------------

### Get Table Columns for Reflection

Source: https://github.com/datajunction/dj/blob/main/datajunction-query/README.rst

Retrieve column names and types for a specific table using the GET /table/{table}/columns/ endpoint. This is useful for reflection services.

```sh
curl -X 'GET' \
  'http://localhost:8001/table/djdb.roads.repair_orders/columns/?engine=sqlalchemy-postgresql&engine_version=15.2' \
  -H 'accept: application/json'
```

--------------------------------

### Example Dimension Nodes ER Diagram

Source: https://github.com/datajunction/dj/blob/main/docs/content/0.1.0/docs/data-modeling/dimensions.md

This mermaid diagram illustrates the structure of three example dimension nodes: Country, Dispatcher, and Contractor. It shows their primary keys and attributes.

```mermaid
%%{init: {  "theme": "base",
'themeVariables': { 'primaryColor': '#ffefd0'}}}%%
erDiagram
   "default.country | Country" {
        id int PK
        name str
        country_code str
        population long
    }
   "default.dispatcher | Dispatcher" {
        dispatcher_id int PK
        company_name str
        phone str
    }
   "default.contractor | Contractor" {
        contractor_id int PK
        company_name str
        contact_name str
        contact_title str
        address str
        city str
        state str
        postal_code str
        country str
    }
```

--------------------------------

### Initialize DataJunction Full Python Client

Source: https://github.com/datajunction/dj/blob/main/docs/content/0.1.0/docs/developers/clients.md

Initialize the full DataJunction client in Python. This client allows for both querying and building/managing data pipelines.

```py
from datajunction import DJBuilder

dj = DJBuilder("http://localhost:8000")
```

--------------------------------

### Add Dependency with Version Constraint (Specific)

Source: https://github.com/datajunction/dj/blob/main/CONTRIBUTING.rst

When adding a new dependency with a version less than 1.0, pin the exact version in setup.cfg. This ensures stability if the dependency is expected to change frequently.

```config
some-package==0.0.1
```

--------------------------------

### Create Spark Engine

Source: https://github.com/datajunction/dj/blob/main/datajunction-query/README.rst

Configure a 'spark' engine with its version and URI. The URI 'spark://local[*]' specifies a local Spark cluster.

```bash
curl -X 'POST' \
  'http://localhost:8001/engines/' \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -d '{
  "name": "spark",
  "version": "3.3.2",
  "uri": "spark://local[*]"
}'
```

--------------------------------

### Initialize DJ Builder

Source: https://github.com/datajunction/dj/blob/main/datajunction-clients/python/README.md

Initializes the DJBuilder client. Use 'http://dj:8000' if running in the demo docker container.

```python
from datajunction import DJBuilder

djbuilder = DJBuilder("http://localhost:8000")
```

--------------------------------

### Setup Claude Code with Custom DJ Server URL

Source: https://github.com/datajunction/dj/blob/main/docs/content/0.1.0/docs/getting-started/ai-assistants.md

Configure Claude Code with a custom DataJunction server URL by setting the DJ_URL environment variable before running the setup command.

```bash
DJ_URL=https://dj.yourcompany.com dj setup-claude
```

--------------------------------

### Create DuckDB Engine

Source: https://github.com/datajunction/dj/blob/main/datajunction-query/README.rst

Configure a 'duckdb' engine with its version and URI. The URI 'duckdb://local[*]' indicates a local DuckDB instance.

```bash
curl -X 'POST' \
  'http://localhost:8001/engines/' \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -d '{
  "name": "duckdb",
  "version": "0.7.1",
  "uri": "duckdb://local[*]"
}'
```

--------------------------------

### Selective Installation of Claude Components

Source: https://github.com/datajunction/dj/blob/main/docs/content/0.1.0/docs/getting-started/ai-assistants.md

Perform selective installations of DataJunction components for Claude Code using command-line flags to include or exclude specific parts like MCP, skills, or agents.

```bash
dj setup-claude --no-mcp
```

```bash
dj setup-claude --no-skills
```

```bash
dj setup-claude --no-agents
```

--------------------------------

### Serve Specific Docs Version Locally

Source: https://github.com/datajunction/dj/blob/main/docs/content/0.1.0/docs/developers/docs-development.md

Start a local Hugo development server to preview a specific version of the DJ documentation (e.g., 0.1.0). The site will be available at http://localhost:1313/ by default.

```sh
hugo serve --contentDir content/0.1.0/
```

--------------------------------

### Get A Metric

Source: https://github.com/datajunction/dj/blob/main/docs/content/0.1.0/docs/developers/the-datajunction-api-specification.md

Retrieves a specific metric by its name.

```APIDOC
## GET /metrics/{name}

### Description
Return a metric by name.

### Method
GET

### Endpoint
/metrics/{name}

### Parameters
#### Path Parameters
- **name** (string) - Required - The name of the metric to retrieve.

### Response
#### Success Response (200)
- **id** (integer) - The metric's ID.
- **name** (string) - The metric's name.
- **display_name** (string) - The metric's display name.
- **current_version** (string) - The current version of the metric.
- **description** (string) - The metric's description.
- **created_at** (string) - The timestamp when the metric was created.
- **updated_at** (string) - The timestamp when the metric was last updated.
- **query** (string) - The metric's query.
- **upstream_node** (string) - The name of the upstream node.
- **expression** (string) - The metric's expression.
- **dimensions** (array) - A list of dimensions associated with the metric.
- **metric_metadata** (object) - Metadata about the metric's direction and unit.
- **required_dimensions** (array) - A list of dimensions required for the metric.

### Response Example
```json
{
  "id": 0,
  "name": "string",
  "display_name": "string",
  "current_version": "string",
  "description": "",
  "created_at": "2019-08-24T14:15:22Z",
  "updated_at": "2019-08-24T14:15:22Z",
  "query": "string",
  "upstream_node": "string",
  "expression": "string",
  "dimensions": [
    {
      "name": "string",
      "node_name": "string",
      "node_display_name": "string",
      "is_primary_key": true,
      "type": "string",
      "path": [
        "string"
      ]
    }
  ],
  "metric_metadata": {
    "direction": "higher_is_better",
    "unit": {
      "name": "string",
      "label": "string",
      "category": "string",
      "abbreviation": "string",
      "description": "string"
    }
  },
  "required_dimensions": [
    "string"
  ]
}
```
```

--------------------------------

### Get Engine

Source: https://github.com/datajunction/dj/blob/main/datajunction-clients/python/README.md

Retrieves details for a specific engine.

```APIDOC
## Get Engine

### Description
Retrieves details for a specific engine.

### Method
```python
get_engine(name: str)
```

### Endpoint
N/A (Client method)

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
- **name** (str) - Required - The name of the engine to retrieve.

### Request Example
```python
djadmin.get_engine(name="Spark")
```

### Response
#### Success Response (200)
- **engine_details** (dict) - Details of the specified engine.
```

--------------------------------

### Make Dev Release

Source: https://github.com/datajunction/dj/blob/main/docs/content/0.1.0/docs/developers/releasing-new-version.md

Run this command in the component directory to create a development release. It will output the version and target release URL.

```shell
make dev-release
```

--------------------------------

### Get Catalog

Source: https://github.com/datajunction/dj/blob/main/datajunction-clients/python/README.md

Retrieves details for a specific catalog.

```APIDOC
## Get Catalog

### Description
Retrieves details for a specific catalog.

### Method
```python
get_catalog(name: str)
```

### Endpoint
N/A (Client method)

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
- **name** (str) - Required - The name of the catalog to retrieve.

### Request Example
```python
djadmin.get_catalog(name="my-new-catalog")
```

### Response
#### Success Response (200)
- **catalog_details** (dict) - Details of the specified catalog.
```