Contents

• pgGraph
  • Why pgGraph?
  • Quickstart
  • PGXN Source Installation
    • Prerequisites
    • Install with pgxn-client
    • Manual source install
  • Documentation
  • pgGraph: High-Speed Graph Execution Inside PostgreSQL
    • The Tech: Why It’s So Fast
    • PostgreSQL Remains Authoritative
    • How pgGraph Compares
  • Community
  • License

pgGraph

Graph database superpowers for your existing Postgres data.

pgGraph is a PostgreSQL extension for running graph search, traversal, shortest path, and relationship queries directly against ordinary PostgreSQL tables.

Your tables stay the source of truth. pgGraph builds a derived graph index and lets you query it from SQL using functions in the graph schema.

[!IMPORTANT] pgGraph is in early alpha. Even though we have tested it to be stable, please avoid production use for now; try it in Docker or a dedicated development database and share feedback to help the project grow.

Why pgGraph?

PostgreSQL is great at relational queries, but graph-style questions often require custom recursive SQL for each schema:

• “Find records related to Alice within 2 hops.”
• “Find the shortest path between this person and this company.”
• “Search nodes across registered tables.”

pgGraph adds graph queries on top of your existing PostgreSQL tables, without requiring a separate graph database, graph-specific storage system, or a new query language.

Quickstart

The fastest way to try pgGraph is the included quickstart script.

It starts a disposable Docker-backed PostgreSQL database, installs pgGraph, creates two normal PostgreSQL tables, discovers the foreign key relationship, builds the graph, and runs example queries.

You need Docker or Docker Desktop installed and running:

• macOS: install Docker Desktop.
• Windows: install Docker Desktop with WSL2 enabled, then run the script from WSL2 or Git Bash.
• Linux: install Docker Engine and the Docker Compose plugin.

git clone https://github.com/evokoa/pggraph.git
cd pggraph

# run the full quickstart demo
scripts/quickstart.sh

# install into existing Postgres Docker container
scripts/quickstart.sh docker my-postgres 17 appdb postgres

# source build/install with pgrx into local PostgreSQL
scripts/quickstart.sh pgrx

# start Streamlit playground with a preset dataset (panama|ldbc)
scripts/quickstart.sh playground panama

Supported modes:

• quickstart / demo: build and start the Docker Postgres service, load demo data, and run example graph queries. This is the default mode.
• setup: build and start Postgres with pgGraph installed, but do not load the sample graph.
• psql: build and start Postgres, prepare demo data, then open psql.
• docker CONTAINER [PG_MAJOR] [DB_NAME] [DB_USER]: install pgGraph into an existing running Postgres Docker container via scripts/install_into_docker_postgres.sh.
• pgrx [PG_MAJOR]: build and install pgGraph into a local PostgreSQL using cargo pgrx install.
• playground [panama|ldbc]: start the Streamlit playground using a preset dataset.
• clean: stop the Compose database and remove its volume.

The script works on macOS and Linux from a normal terminal, and on Windows from WSL2 or Git Bash with Docker Desktop. It is not a native PowerShell or Command Prompt script.

The root Docker image currently runs PostgreSQL 17. Package scripts can build extension artifacts for officially supported PostgreSQL 14 through 18 targets. PostgreSQL 13 is no longer an official support target after upstream EOL, though the legacy pg13 pgrx feature remains available on a best-effort basis. The PostgreSQL major version of the extension package must match the target server.

PGXN Source Installation

pgGraph is available on PGXN as a source distribution. Because pgGraph is a Rust/pgrx extension, building from source requires the Rust toolchain.

Prerequisites

• PostgreSQL with development files and pg_config on your PATH
  • macOS: brew install postgresql@17
  • Debian/Ubuntu: sudo apt install postgresql-server-dev-17
  • RHEL/Fedora: sudo dnf install postgresql-devel
• Rust toolchain (1.95+ recommended, pinned in graph/rust-toolchain.toml)
• cargo-pgrx 0.18.0

Install with pgxn-client

cargo install cargo-pgrx --version 0.18.0 --locked
# Register the installed PostgreSQL with pgrx (auto-detects the major):
PG_MAJOR=$(pg_config --version | sed -E 's/[^0-9]*([0-9]+).*/\1/')
cargo pgrx init --pg${PG_MAJOR}="$(which pg_config)"
pgxn install pgGraph

Manual source install

git clone https://github.com/evokoa/pggraph.git
cd pggraph
make install # may need sudo
psql -d postgres -c "CREATE EXTENSION graph;"

If you have multiple PostgreSQL installations, set PG_CONFIG to the target server’s pg_config, then re-run the installation:

export PG_CONFIG=/usr/lib/postgresql/17/bin/pg_config
make install

If sudo is needed for make install, preserve PG_CONFIG:

sudo --preserve-env=PG_CONFIG make install

If compilation fails with fatal error: postgres.h: No such file or directory, install the PostgreSQL server development package for the target PostgreSQL major, such as postgresql-server-dev-17 on Ubuntu or Debian.

Note: The PGXN distribution name is pgGraph but the PostgreSQL extension name is graph. Use CREATE EXTENSION graph; after installation.

Documentation

More information is available in the pgGraph docs:

Overview · Quickstart · Installation · Playground · Querying · SQL API

pgGraph: High-Speed Graph Execution Inside PostgreSQL

pgGraph is not “Postgres plus graph syntax.” It is a cache-friendly graph execution layer for data that already lives in your ordinary relational tables.

The core idea is simple but powerful: keep PostgreSQL as your system of record, but build a highly optimized, read-heavy graph runtime from that relational metadata. The result is closer to a rebuildable graph index than a graph database: it is built from Postgres tables, operated with Postgres controls, and optimized for repeated bounded traversal over known topology.

The Tech: Why It’s So Fast

Graph traversals usually die on recursive SQL queries or endless joins. pgGraph bypasses this by compiling your relational data into a specialized memory structure.

• O(1) adjacency via CSR. graph.build() compiles your relationships into forward and reverse compressed sparse row (CSR) edge stores. A node’s neighbors are stored as a contiguous array slice. Instead of rediscovering relationships via SQL, traversals are executed as raw, graph-native memory scans.
• A tight traversal loop. SQL-facing calls resolve coordinates, labels, filters, and tenant scopes before entering the traversal loop. Once inside, the engine streams CSR neighbors, checking compact u8 edge-label IDs, typed FilterIndex values, tenant bitmaps, active bits, and sync overlays.
• Read-only artifact mapping. Persisted .pggraph artifacts are written atomically. When a new Postgres backend spins up, it validates the artifact and maps immutable forward graph arrays and the resolution index read-only. The operating system page cache can then share those physical pages across isolated PostgreSQL backends without copying the base graph into each backend’s Rust heap. This is not a replacement for PostgreSQL’s buffer pool: PostgreSQL remains responsible for table storage, WAL, MVCC, durability, and crash recovery, while pgGraph’s artifact is derived state that can be rebuilt from source tables.
• Predictable and safe. Unbounded graph expansion can crash a database. pgGraph includes explicit circuit breakers: depth limits, visited-node tracking, frontier limits, pagination, and strict OOM/memory safeguards.

PostgreSQL Remains Authoritative

Your application data does not move. Source tables, constraints, indexes, ACLs, RLS, backups, and app writes remain 100% standard PostgreSQL concerns.

pgGraph is strictly derived state. You run the algorithms over internal node indexes, and the engine returns source table coordinates or hydrates the raw PostgreSQL rows on the fly. Build, sync, vacuum, and maintenance operations are fully visible and SQL-callable.

How pgGraph Compares

vs. Apache AGE: Execution Layer vs. Storage Layer

Apache AGE is a property graph database inside Postgres. It uses graph namespaces, vertex and edge tables, agtype, and openCypher.

pgGraph does not ask you to move your data or learn Cypher. You keep your existing schema and accelerate it with SQL functions like graph.search() and graph.shortest_path(). Use AGE for a dedicated property graph model; use pgGraph to add bounded, high-speed graph traversal to an existing relational schema.

vs. PostgreSQL 19 SQL/PGQ

SQL:2023 and PostgreSQL 19 introduce CREATE PROPERTY GRAPH, GRAPH_TABLE, and standard graph pattern matching backed by PostgreSQL’s planner and optimizer — the same engine that makes PostgreSQL’s relational queries strong.

pgGraph operates at a different layer. SQL/PGQ expresses graph patterns and lets the optimizer choose how to execute them. pgGraph precomputes CSR adjacency stores and rebuildable artifacts for workloads that repeatedly traverse the same topology with bounded depth, path limits, filters, tenants, and application pagination. The two can be complementary: future adapters could map eligible SQL/PGQ patterns onto pgGraph’s precomputed runtime, while general graph queries continue to use PostgreSQL’s relational execution path.

Community

pgGraph is built by Evokoa. Follow the project through the links at the top of this README.

License

Apache-2.0. See LICENSE.