pygraphistry-connectors

# PyGraphistry Connectors ## Doc routing (local + canonical) - First route with `../pygraphistry/references/pygraphistry-readthedocs-toc.md`. - Use `../pygraphistry/references/pygraphistry-readthedocs-top-level.tsv` for section-level shortcuts. - Only scan `../pygraphistry/references/pygraphistry-readthedocs-sitemap.xml` when a needed page is missing. - Use one batched discovery read before deep-page reads; avoid `cat *` and serial micro-reads. - In user-facing answers, prefer canonical `https://pygraphistry.readthedocs.io/en/latest/...` links. ## Strategy - Prefer dataframe-first ingestion when practical, then bind with `edges()/nodes()`. - Use connector-specific notebook patterns when auth/query semantics are specialized. - For very large datasets, push filtering/aggregation upstream before plotting. - Keep connector and Graphistry credentials in env vars or secret stores; no hardcoded keys. - Never use placeholder literals like `username='user'` / `password='pass'` / `username='...'`; use `os.environ[...]` or `os.environ.get(...)`. - For concise tasks, respond with a single compact code block and minimal prose. - In concise snippets, prefer explicit privacy literals (`'private'` or `'organization'`) over placeholder variables. ## Connector triage rubric - Use native graph-db connectors (`cypher()`, Neptune/TigerGraph flows) when traversal is best expressed upstream. - For local Cypher-style queries on in-memory PyGraphistry graphs (no external DB), use `g.gfql("MATCH ...")`. Note: `graphistry.cypher()` is a distinct Neo4j/Memgraph/Neptune connector, not the same as local GFQL Cypher. - Use SQL/log source extraction when your source is tabular or SIEM-centric, then bind in PyGraphistry. - If unsure, start with source-native query -> dataframe -> `edges()/nodes()`, then optimize connector depth. ## Connector families - Graph DBs: Neo4j, Neptune, TigerGraph, Memgraph, Arango. - Data/SQL: Databricks, PostgreSQL, Spanner, warehouse-style pipelines. - Logs/SIEM: Splunk, Kusto, AlienVault. - Compute/layout plugins: networkx, graphviz, cugraph, igraph, hypernetx. ## Minimal examples ```python # Neo4j/Memgraph/Neptune connector (runs query on external DB server) g = graphistry.cypher('MATCH (a)-[r]->(b) RETURN a,b,r') g.plot() ``` ```python # Local Cypher via GFQL (no external DB needed — preferred for local graphs) g2 = g.gfql("MATCH (a)-[r]->(b) WHERE a.score > 10 RETURN a.id, b.id") ``` ```python # Graphistry org/service-account auth before connector workflows graphistry.register( api=3, org_name=os.environ.get('GRAPHISTRY_ORG_NAME'), personal_key_id=os.environ.get('GRAPHISTRY_PERSONAL_KEY_ID'), personal_key_secret=os.environ.get('GRAPHISTRY_PERSONAL_KEY_SECRET') ) ``` ```python # Generic dataframe path after source-specific query/extract # edges_df: src,dst,... g = graphistry.edges(edges_df, 'src', 'dst') graphistry.privacy(mode='private') plot_url = g.plot(render=False) ``` ```python # Connector-oriented flow with explicit nodes + focused GFQL slice # Example source can be Neo4j/Splunk -> dataframe extraction g = graphistry.edges(edges_df, 'src', 'dst').nodes(nodes_df, 'id') g_focus = g.gfql([...]).name('connector-slice') graphistry.privacy(mode='organization') plot_url = g_focus.plot(render=False) ``` ## Canonical docs - Plugins overview: https://pygraphistry.readthedocs.io/en/latest/plugins.html - Connector notebooks: https://pygraphistry.readthedocs.io/en/latest/notebooks/plugins.connectors.html - Compute/layout plugin notebooks: https://pygraphistry.readthedocs.io/en/latest/notebooks/plugins.compute.html - Notebooks index: https://pygraphistry.readthedocs.io/en/latest/notebooks/index.html