dlin-cli 0.1.1a1

A fast CLI tool for dbt model lineage analysis

dlin

dbt lineage analysis CLI that parses SQL files directly. No dbt compile, no Python, no manifest.json.

Builds a dependency graph from ref() and source() calls in SQL. Designed for AI agents and CI pipelines.

Motivation

When I edited dbt models in VS Code, dbt Power User was my go-to companion for navigating lineage. AI agents have no such companion. I watched them grep through dbt projects to find model dependencies. It works, but they end up calling grep repeatedly and relying on fragile string matching to piece together ref() and source() relationships.

dlin is designed to fill that gap: a CLI tool that lets AI agents understand a dbt project's structure without falling back to grep. It is equally useful for humans, and its stdin/stdout interface makes it easy to combine with jq, git diff, and other CLI tools.

To replace grep, speed and size matter. dlin is a small, self-contained binary with no runtime dependencies. It parses SQL directly, evaluates common Jinja patterns without Python, parallelizes file I/O, and caches aggressively.

The key idea behind dlin is that finding the right models fast is what matters most. AI agents can read SQL and trace column-level relationships on their own; the hard part is knowing which models to look at in the first place. So dlin focuses on model-level lineage and makes that as fast as possible.

Install

Cargo (Rust)

cargo install dlin

pip / uv (Python)

For convenience, dlin is also available as a Python package. The installed binary is native and does not require Python at runtime.

pip install dlin-cli   # or: uv tool install dlin-cli

GitHub Releases

Pre-built binaries for Linux, macOS, and Windows are available on the Releases page. You can also use the installer scripts:

macOS / Linux:

curl --proto '=https' --tlsv1.2 -LsSf https://github.com/eitsupi/dlin/releases/latest/download/dlin-installer.sh | sh

Windows (PowerShell):

powershell -ExecutionPolicy Bypass -c "irm https://github.com/eitsupi/dlin/releases/latest/download/dlin-installer.ps1 | iex"

Quick start

# Full lineage graph
dlin graph -p path/to/dbt/project

# Downstream impact analysis
dlin impact orders

# List models as JSON
dlin list -o json --json-fields unique_id,file_path

# Pipe changed files into lineage
git diff --name-only main | dlin graph -o json

Features

• No dependencies: single binary, no Python, no manifest.json
• Recursive upstream / downstream: -u N / -d N to control traversal depth
• Impact analysis with severity: dlin impact scores downstream nodes and flags exposure reachability
• Composable: stdin accepts model names or file paths; pipe with jq, dlin list, git diff, etc.
• Agent-friendly: --error-format json emits structured {"level","what","why","hint"} on stderr; --help is designed for tool discovery

Mermaid diagrams

dlin outputs Mermaid flowcharts that render natively on GitHub, GitLab, Notion, and other Markdown environments.

Simplified graphs with --collapse

Automatically remove intermediate nodes to see just the endpoints (nodes with no predecessors or no successors); everything in between becomes transitive "(via N)" edges:

# Collapse intermediate models — only endpoints remain
dlin graph --collapse -o mermaid

# Focal mode: keep only sources, exposures, and specified focus models
# (ignores BFS window pseudo-endpoints — ideal with -u/-d limits)
dlin graph orders --collapse=focal -u 3 -o mermaid

flowchart LR
    exposure_weekly_report>"weekly_report"]
    model_combined_orders["combined_orders"]
    model_order_summary["order_summary"]
    source_raw_customers(["raw.customers"])
    source_raw_orders(["raw.orders"])
    source_raw_payments(["raw.payments"])

    source_raw_customers ==>|"exposure (via 2)"| exposure_weekly_report
    source_raw_orders ==>|"exposure (via 3)"| exposure_weekly_report
    source_raw_orders -.->|"source (via 1)"| model_combined_orders
    source_raw_orders -.->|"source (via 1)"| model_order_summary
    source_raw_payments ==>|"exposure (via 3)"| exposure_weekly_report
    source_raw_payments -.->|"source (via 1)"| model_order_summary

    classDef model fill:#4A90D9,stroke:#333,color:#fff
    classDef source fill:#27AE60,stroke:#333,color:#fff
    classDef exposure fill:#E74C3C,stroke:#333,color:#fff
    class exposure_weekly_report exposure
    class model_combined_orders model
    class model_order_summary model
    class source_raw_customers source
    class source_raw_orders source
    class source_raw_payments source

Positional focus models are always preserved during collapse, so dlin graph orders --collapse keeps orders even if it would otherwise be intermediate.

Pipe to build focused diagrams

Combine dlin list, jq, and dlin graph to extract exactly the nodes you want:

# Staging models → 1 hop downstream, models only, grouped by directory
dlin list -s 'path:models/staging' -o json | jq -r '.[].label' |
  dlin graph -d 1 --node-type model --group-by directory -o mermaid

flowchart LR
    subgraph models_marts["models/marts"]
        model_combined_orders["combined_orders"]
        model_customers["customers"]
        model_order_summary["order_summary"]
        model_orders["orders"]
    end
    subgraph models_staging["models/staging"]
        model_stg_customers["stg_customers"]
        model_stg_online_orders["stg_online_orders"]
        model_stg_orders["stg_orders"]
        model_stg_payments["stg_payments"]
        model_stg_retail_orders["stg_retail_orders"]
    end

    model_orders -->|ref| model_customers
    model_stg_customers -->|ref| model_customers
    model_stg_online_orders -->|ref| model_combined_orders
    model_stg_orders -->|ref| model_order_summary
    model_stg_orders -->|ref| model_orders
    model_stg_payments -->|ref| model_order_summary
    model_stg_payments -->|ref| model_orders
    model_stg_retail_orders -->|ref| model_combined_orders

    classDef model fill:#4A90D9,stroke:#333,color:#fff
    class model_combined_orders model
    class model_customers model
    class model_order_summary model
    class model_orders model
    class model_stg_customers model
    class model_stg_online_orders model
    class model_stg_orders model
    class model_stg_payments model
    class model_stg_retail_orders model

Column names in nodes with --show-columns

Add --show-columns to include column names inside Mermaid node labels — useful for understanding what each model produces at a glance:

dlin graph orders -u 1 -d 0 --show-columns --node-type model,source -o mermaid

flowchart LR
    model_orders["orders<br/>---<br/>order_id, customer_id, order_date, status, total_amount, payment_method"]
    model_stg_orders["stg_orders<br/>---<br/>order_id, customer_id, order_date, status"]
    model_stg_payments["stg_payments<br/>---<br/>payment_id, order_id, amount, payment_method"]

    model_stg_orders -->|ref| model_orders
    model_stg_payments -->|ref| model_orders

    classDef model fill:#4A90D9,stroke:#333,color:#fff
    class model_orders model
    class model_stg_orders model
    class model_stg_payments model

Combines well with --collapse to show rich detail on fewer endpoint nodes.

Other graph options

dlin graph orders -u 2 -d 1                            # focus on specific model
dlin graph -o mermaid --collapse --show-columns        # columns in collapsed nodes
dlin graph orders --collapse=focal -u 3 -o mermaid    # focal: sources + exposures + orders
dlin graph -o mermaid --group-by directory             # group by directory
dlin graph -o mermaid --direction tb                   # top-to-bottom layout
dlin graph --node-type source,exposure                 # filter by node type
dlin graph -o dot | dot -Tsvg > out.svg                # Graphviz rendering

Output formats: ASCII (default), JSON, Mermaid, Graphviz DOT, Plain, SVG, HTML.

Key subcommands

list

dlin list                                                   # all models and sources
dlin list orders -o json --json-fields unique_id,file_path  # specific model as JSON
dlin list --node-type source                                # sources only

impact

$ dlin impact orders
Impact Analysis: orders
==================================================
Overall Severity: CRITICAL

Summary:
  Affected models:    1
  Affected tests:     1
  Affected exposures: 1

Impacted Nodes:
  [critical] weekly_report (exposure, distance: 1)
  [high    ] customers (model, distance: 1) [models/marts/customers.sql]
  [low     ] assert_orders_positive_amount (test, distance: 1)

Filtering

dlin graph -s tag:finance,path:marts  # selector expressions (union)
dlin graph --node-type model,source   # filter by node type

Data sources

dlin aims to work without dbt compile. By default it parses SQL files directly, but it can also leverage a pre-compiled manifest.json for additional accuracy when one is available.

SQL parsing (default): extracts ref() and source() from SQL via regex + Jinja template evaluation. No Python or dbt needed.

Manifest mode (--source manifest): reads a pre-compiled manifest.json for full accuracy with complex Jinja logic.

Limitations of SQL parse mode

• var() resolves from dbt_project.yml only (--vars CLI overrides not supported)
• Runtime context (target.type, env_var()) is not evaluated
• Conditional Jinja branches use default values; non-default paths may be missed

When these limitations matter, use --source manifest.

Credits

Hard fork of dbt-lineage-viewer by Simon Muller (MIT license). The original focused on TUI-based exploration; dlin removes the TUI and targets non-interactive use: scripting, CI, and AI agents.

License

MIT