dlin-cli 0.1.0rc4

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.

cargo install --git https://github.com/eitsupi/dlin.git

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

Why dlin?

dbt's built-in dbt ls and manifest-based tools require dbt compile (and therefore Python) before you can query the graph. dlin parses SQL directly, so it works anywhere Rust runs — in CI, in AI agents, or on a machine without Python installed.

• 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 --node-type

Skip intermediate models to see the big picture. When --node-type removes nodes, transitive edges are preserved automatically:

# Which sources feed into which exposures? (all intermediate models removed)
dlin graph --node-type source,exposure -o mermaid

flowchart LR
    exposure_weekly_report>"weekly_report"]
    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 2)"| exposure_weekly_report
    source_raw_payments ==>|"exposure (via 2)"| exposure_weekly_report

    classDef source fill:#27AE60,stroke:#333,color:#fff
    classDef exposure fill:#E74C3C,stroke:#333,color:#fff
    class exposure_weekly_report exposure
    class source_raw_customers source
    class source_raw_orders source
    class source_raw_payments source

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

Other graph options

dlin graph orders -u 2 -d 1                    # focus on specific model
dlin graph -o mermaid --group-by node-type     # group by source/model/test/...
dlin graph -o mermaid --direction tb           # top-to-bottom layout
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

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

For full accuracy, 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