arcgis-item-graph 0.1.9

CLI tool for building and querying ArcGIS item dependency graphs

ArcGIS Item Dependency Management

Overview

This tool builds and maintains an organization-wide ArcGIS item dependency graph, showing what Web Maps, Dashboards, Feature Services, and other items depend on each other. You can query the graph by item ID or portal search string and receive CSV, Excel, interactive HTML, and GML outputs — making it safe to audit, migrate, and clean up portal content without breaking downstream items.

---

Quick Start

1. Install

Standard Python / Mac / Linux:

pip install arcgis-item-graph

ArcGIS Pro (Windows) — uses Pro's bundled Python:

"%PROGRAMFILES%\ArcGIS\Pro\bin\Python\Scripts\pip.exe" install arcgis-item-graph

Windows one-click installer: Download install.bat from the Releases page and double-click it.

2. Configure

arcgis-graph setup

The wizard prompts for your portal URL, authentication method (named profile or username/password), and output preferences. Your credentials are never stored in config.yaml — they go to a gitignored .env file.

3. Build the graph (run once)

arcgis-graph create

This crawls your portal and saves a dependency graph locally. For large organizations (5,000+ items) it can take 30–90 minutes.

4. Query

arcgis-graph query --item-id abc123
arcgis-graph query --search "owner:jsmith type:Dashboard"

---

Prerequisites

• Python 3.9 or later
• ArcGIS API for Python 2.4.0 or later (arcgis>=2.4.0)

---

Setup

See Quick Start above for installation and configuration.

For development setup, see For Contributors below.

---

Configuration

config/config.yaml controls authentication and all run-time settings. Two auth options are available:

Option 1 — Named ArcGIS profile (recommended for GIS admins)

Set the auth.profile key to the name of a saved ArcGIS credential profile:

auth:
  profile: "my_portal_profile"   # created via arcgis.gis.GIS(profile=...)
  verify_cert: true

Run python -c "from arcgis.gis import GIS; GIS(profile='my_portal_profile')" to verify the profile name is correct.

Option 2 — Environment variables

Leave auth.profile blank and create a .env file in the project root:

ARCGIS_URL=https://your-portal/portal
ARCGIS_USER=your_username
ARCGIS_PASSWORD=your_password

The CLI loads .env automatically when a profile is not set.

Other settings

Key	Default	Description
paths.output_dir	outputs/	Where all output files are written
paths.gml_file	outputs/graph.gml	Persistent graph file
create.max_items	10000	Upper limit on items indexed
update.max_retries	5	Retries on transient API errors
query.output_formats	excel, html, gml	Default outputs for each query (excel, csv, html, gml)

---

Usage

All commands are run via the unified CLI entry point:

python -m cli [--config /path/to/config.yaml] {create,update,query} [options]

Build the graph (run once)

Crawls the entire portal and saves a GML snapshot. For large organizations (5,000+ items) this can take 30–90 minutes.

python -m cli create

Keep the graph current (run on a schedule)

Finds items modified since the last run and merges changes into the existing GML. Designed for a daily cron job.

python -m cli update

Query the graph

# Query by item ID
python -m cli query --item-id abc123

# Query by portal search string
python -m cli query --search "owner:jsmith type:Dashboard"

# Request specific output formats for a single run
python -m cli query --item-id abc123 --format excel
python -m cli query --item-id abc123 --format csv --format html

# Use a different config file
python -m cli --config /path/to/other/config.yaml query --item-id abc123

Interactive dashboard (live server mode)

Add --serve to any query command to start a local HTTP server and open the dashboard in your browser automatically:

arcgis-graph query --item-id abc123 --serve
arcgis-graph query --search "owner:jsmith" --serve

# Use a different port if 8765 is taken
arcgis-graph query --item-id abc123 --serve --port 9000

The server runs at http://localhost:8765/ by default. It exposes:

• GET / — the interactive HTML dashboard
• GET /query?id=<item_id> — live re-query from inside the dashboard (click any node)
• GET /export/excel?ids=<id1>,<id2> — download an Excel report for selected items

Press Ctrl+C in the terminal to stop the server.

Note: Opening the saved .html file directly (file://...) will not work for node re-queries or Excel exports because those features require the live server. Always use --serve for the full interactive experience.

Run python -m cli --help or python -m cli <command> --help for the full list of options and overrides.

---

Shared Deployment (Team Use)

For team environments, point paths.gml_file and paths.output_dir at a UNC share so all users read from the same graph without running create individually.

1. Admin: initial setup

# On the admin machine, configure config.yaml to point at the share:
#   paths.gml_file: "\\\\server\\share\\arcgis-graph\\graph.gml"
#   paths.output_dir: "\\\\server\\share\\arcgis-graph\\outputs"

arcgis-graph create   # one-time full crawl (~30-60 min for large orgs)

2. Automation: scheduled updates

Windows Task Scheduler (hourly):

arcgis-graph update --config \\server\share\arcgis-graph\config.yaml --skip-if-fresh

Linux/macOS cron (hourly):

0 * * * *  arcgis-graph update --config /mnt/share/arcgis-graph/config.yaml --skip-if-fresh

--skip-if-fresh prevents double-runs if automation fires while a manual update is in progress.

3. Users: query with cached results

Users point their local config.yaml at the share paths and run:

arcgis-graph query --item-id <id>

If the same item was queried within 24 hours, the cached outputs are returned instantly. Use --force-refresh to bypass the cache and re-run the query.

Freshness thresholds (configurable)

cache:
  update_warn_hours: 24    # Warn in query if graph is older than this (24 = daily, the default)
  query_cache_hours: 24    # Reuse cached query outputs within this window

---

Output files

All output files land in the directory set by paths.output_dir (default: outputs/).

Command	Output files
create	graph.gml, graph.timestamp
update	Updates graph.gml in place
query	dependency_report_<timestamp>.csv — tabular summary; dependency_report_<timestamp>.xlsx — 3-sheet Excel workbook (All Items, Dependency Edges, Broken Dependencies); dependency_graph_<timestamp>.html — interactive visualization; query_subgraph_<timestamp>.gml — sub-graph for further analysis

---

Project structure

arcgis_item_graph/   Core library: creator, updater, query, reporter, visualizer, utils
cli/                 Unified CLI entry point (python -m cli ...)
config/              config.example.yaml template — copy to config.yaml and fill in credentials
docs/                Documentation and design plans
lib/                 Vendored frontend assets (cytoscape.js, dagre, cytoscape-dagre) for offline HTML output
outputs/             Generated output files (gitignored)
tests/               Unit and integration tests (pytest)

---

For Contributors

1. Clone the repository

git clone https://github.com/your-org/ArcGIS-Item-Dependency-Management.git
cd ArcGIS-Item-Dependency-Management

2. Install in editable mode with dev dependencies

pip install -e ".[dev]"

3. Activate the commit-message hook

git config core.hooksPath .githooks

4. Create your configuration file

cp config/config.example.yaml config/config.yaml
# or just run: arcgis-graph setup

---

Running tests

pytest tests/ -v

---

Performance & Architecture Notes

Graph Traversal

The query BFS uses collections.deque for O(1) popleft (O(V+E) total). Seed items not found in the cached GML file are fetched live in parallel via ThreadPoolExecutor (default 10 workers, configurable via fetch_workers on ItemGraphQuery).

Update Hydration

ItemGraphUpdater hydrates all cached graph nodes concurrently using ThreadPoolExecutor (default 10 workers, configurable via hydration_workers). Graph mutations (node removal) happen serially on the main thread after all fetches complete. The modified-items search enforces a max_items cap (defaults to create.max_items from config) and warns when results may be truncated.

Timestamps

All timestamps are stored in milliseconds with sub-second precision (int(t.timestamp() * 1000)).

Excel Reports

ItemGraphReporter.to_excel() builds all three sheets from a single pass through to_dataframe() — node.contains() is called once per node.