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hugegraph-pd

README.md

HugeGraph PD

License Version

Overview

HugeGraph PD (Placement Driver) is a meta server that provides cluster management and coordination services for HugeGraph distributed deployments. It serves as the central control plane responsible for:

  • Service Discovery: Automatic registration and discovery of Store and Server nodes
  • Partition Management: Dynamic partition allocation, balancing, and rebalancing across Store nodes
  • Metadata Storage: Centralized storage of cluster metadata, configuration, and state information
  • Node Scheduling: Intelligent scheduling and load balancing of graph operations
  • Health Monitoring: Continuous health checks and failure detection via heartbeat mechanism

PD uses SOFA-jraft for Raft consensus and RocksDB for persistent metadata storage, ensuring high availability and consistency in distributed environments.

Architecture

HugeGraph PD is a Maven multi-module project consisting of 8 modules:

Module Description
hg-pd-grpc gRPC protocol definitions (.proto files) and generated Java stubs for inter-service communication
hg-pd-common Shared utilities, constants, and helper classes used across PD modules
hg-pd-core Core PD logic: Raft integration, metadata stores, partition allocation, store monitoring, task scheduling
hg-pd-service gRPC service implementations and REST API (Spring Boot) for management and metrics
hg-pd-client Java client library for applications to communicate with PD cluster
hg-pd-cli Command-line utilities for PD administration and debugging
hg-pd-test Unit and integration tests for all PD components
hg-pd-dist Distribution assembly: packaging, configuration templates, startup scripts

For detailed architecture and design, see Architecture Documentation.

Quick Start

Prerequisites

  • Java: 11 or higher
  • Maven: 3.5 or higher
  • Disk Space: At least 1GB for PD data directory

Build

From the project root (build PD and all dependencies):

mvn clean package -pl hugegraph-pd -am -DskipTests

Or build from the hugegraph-pd directory:

cd hugegraph-pd
mvn clean install -DskipTests

The assembled distribution will be available at:

hugegraph-pd/hg-pd-dist/target/hugegraph-pd-<version>.tar.gz

Run

Extract the distribution package and start PD:

tar -xzf hugegraph-pd-<version>.tar.gz
cd hugegraph-pd-<version>

# Start PD server
bin/start-hugegraph-pd.sh

# Stop PD server
bin/stop-hugegraph-pd.sh

Startup Options

bin/start-hugegraph-pd.sh [-g GC_TYPE] [-j "JVM_OPTIONS"] [-y ENABLE_OTEL]
  • -g: GC type (g1 or ZGC, default: g1)
  • -j: Custom JVM options (e.g., -j "-Xmx4g -Xms4g")
  • -y: Enable OpenTelemetry tracing (true or false, default: false)

Configuration

Key configuration file: conf/application.yml

Core Settings

Parameter Default Description
grpc.host 127.0.0.1 gRPC server bind address (use actual IP for production)
grpc.port 8686 gRPC server port
server.port 8620 REST API port for management and metrics
raft.address 127.0.0.1:8610 Raft service address for this PD node
raft.peers-list 127.0.0.1:8610 Comma-separated list of all PD nodes in the Raft cluster
pd.data-path ./pd_data Directory for storing PD metadata and Raft logs

Single-Node Example

grpc:
  host: 127.0.0.1
  port: 8686

server:
  port: 8620

raft:
  address: 127.0.0.1:8610
  peers-list: 127.0.0.1:8610

pd:
  data-path: ./pd_data

3-Node Cluster Example

For a production 3-node PD cluster, configure each node:

Node 1 (192.168.1.10):

grpc:
  host: 192.168.1.10
  port: 8686
raft:
  address: 192.168.1.10:8610
  peers-list: 192.168.1.10:8610,192.168.1.11:8610,192.168.1.12:8610

Node 2 (192.168.1.11):

grpc:
  host: 192.168.1.11
  port: 8686
raft:
  address: 192.168.1.11:8610
  peers-list: 192.168.1.10:8610,192.168.1.11:8610,192.168.1.12:8610

Node 3 (192.168.1.12):

grpc:
  host: 192.168.1.12
  port: 8686
raft:
  address: 192.168.1.12:8610
  peers-list: 192.168.1.10:8610,192.168.1.11:8610,192.168.1.12:8610

For detailed configuration options and production tuning, see Configuration Guide.

Verify Deployment

Check if PD is running:

# Check process
ps aux | grep hugegraph-pd

# Test REST API
curl http://localhost:8620/actuator/health

# Check logs
tail -f logs/hugegraph-pd.log

gRPC API

PD exposes several gRPC services for cluster management. Key services include:

  • PD Service (PDGrpc): Store registration, partition queries, leader election
  • KV Service (KvServiceGrpc): Distributed key-value operations for metadata
  • Watch Service (HgPdWatchGrpc): Watch for partition and store changes
  • Pulse Service (HgPdPulseGrpc): Heartbeat and health monitoring

Proto definitions are located in:

hugegraph-pd/hg-pd-grpc/src/main/proto/

For API reference and usage examples, see API Reference.

Testing

Run PD tests:

# All PD tests
mvn test -pl hugegraph-pd/hg-pd-test -am

# Specific test class
mvn test -pl hugegraph-pd/hg-pd-test -am -Dtest=YourTestClass

Docker

Build PD Docker image:

# From project root
docker build -f hugegraph-pd/Dockerfile -t hugegraph-pd:latest .

# Run container
docker run -d \
  -p 8620:8620 \
  -p 8686:8686 \
  -p 8610:8610 \
  -v /path/to/conf:/hugegraph-pd/conf \
  -v /path/to/data:/hugegraph-pd/pd_data \
  --name hugegraph-pd \
  hugegraph-pd:latest

For Docker Compose examples with HugeGraph Store and Server, see:

hugegraph-server/hugegraph-dist/docker/example/

Documentation

Production Deployment Notes

Cluster Size

  • Minimum: 3 nodes (Raft quorum requirement)
  • Recommended: 3 or 5 nodes for production (odd numbers for Raft election)

High Availability

  • PD uses Raft consensus for leader election and data replication
  • Cluster can tolerate up to (N-1)/2 node failures (e.g., 1 failure in 3-node cluster)
  • Leader handles all write operations; followers handle read operations

Network Requirements

  • Ensure low latency (<5ms) between PD nodes for Raft consensus
  • Open required ports: 8620 (REST), 8686 (gRPC), 8610 (Raft)

Monitoring

PD exposes metrics via REST API at:

  • Health check: http://<pd-host>:8620/actuator/health
  • Metrics: http://<pd-host>:8620/actuator/metrics

Community

Contributing

Contributions are welcome! Please read our Development Guide and follow the Apache HugeGraph contribution guidelines.