HugeGraph-Computer Quick Start

1 HugeGraph-Computer Overview

The HugeGraph-Computer is a distributed graph processing system for HugeGraph (OLAP). It is an implementation of Pregel. It runs on Kubernetes framework.


  • Support distributed MPP graph computing, and integrates with HugeGraph as graph input/output storage.
  • Based on BSP(Bulk Synchronous Parallel) model, an algorithm performs computing through multiple parallel iterations, every iteration is a superstep.
  • Auto memory management. The framework will never be OOM(Out of Memory) since it will split some data to disk if it doesn’t have enough memory to hold all the data.
  • The part of edges or the messages of super node can be in memory, so you will never lose it.
  • You can load the data from HDFS or HugeGraph, or any other system.
  • You can output the results to HDFS or HugeGraph, or any other system.
  • Easy to develop a new algorithm. You just need to focus on a vertex only processing just like as in a single server, without worrying about message transfer and memory/storage management.

2 Dependency for Building/Running

2.1 Install Java 11 (JDK 11)

Must use ≥ Java 11 to run Computer, and configure by yourself.

Be sure to execute the java -version command to check the jdk version before reading

3 Get Started

3.1 Run PageRank algorithm locally

To run algorithm with HugeGraph-Computer, you need to install Java 11 or later versions.

You also need to deploy HugeGraph-Server and Etcd.

There are two ways to get HugeGraph-Computer:

  • Download the compiled tarball
  • Clone source code then compile and package

3.1.1 Download the compiled archive

Download the latest version of the HugeGraph-Computer release package:

tar zxvf apache-hugegraph-computer-incubating-${version}.tar.gz -C hugegraph-computer

3.1.2 Clone source code to compile and package

Clone the latest version of HugeGraph-Computer source package:

$ git clone

Compile and generate tar package:

cd hugegraph-computer
mvn clean package -DskipTests

3.1.3 Start master node

You can use -c parameter specify the configuration file, more computer config please see:Computer Config Options

cd hugegraph-computer
bin/ -d local -r master

3.1.4 Start worker node

bin/ -d local -r worker

3.1.5 Query algorithm results Enable OLAP index query for server

If OLAP index is not enabled, it needs to enable, more reference: modify-graphs-read-mode

PUT http://localhost:8080/graphs/hugegraph/graph_read_mode

"ALL" Query page_rank property value:

curl "http://localhost:8080/graphs/hugegraph/graph/vertices?page&limit=3" | gunzip

3.2 Run PageRank algorithm in Kubernetes

To run algorithm with HugeGraph-Computer you need to deploy HugeGraph-Server first

3.2.1 Install HugeGraph-Computer CRD

# Kubernetes version >= v1.16
kubectl apply -f

# Kubernetes version < v1.16
kubectl apply -f

3.2.2 Show CRD

kubectl get crd

NAME                                        CREATED AT   2021-09-16T08:01:08Z

3.2.3 Install hugegraph-computer-operator&etcd-server

kubectl apply -f

3.2.4 Wait for hugegraph-computer-operator&etcd-server deployment to complete

kubectl get pod -n hugegraph-computer-operator-system

NAME                                                              READY   STATUS    RESTARTS   AGE
hugegraph-computer-operator-controller-manager-58c5545949-jqvzl   1/1     Running   0          15h
hugegraph-computer-operator-etcd-28lm67jxk5                       1/1     Running   0          15h

3.2.5 Submit job

More computer crd please see: Computer CRD

More computer config please see: Computer Config Options

cat <<EOF | kubectl apply --filename -
kind: HugeGraphComputerJob
  namespace: hugegraph-computer-operator-system
  name: &jobName pagerank-sample
  jobId: *jobName
  algorithmName: page_rank
  image: hugegraph/hugegraph-computer:latest # algorithm image url
  jarFile: /hugegraph/hugegraph-computer/algorithm/builtin-algorithm.jar # algorithm jar path
  pullPolicy: Always
  workerCpu: "4"
  workerMemory: "4Gi"
  workerInstances: 5
    job.partitions_count: "20"
    hugegraph.url: http://${hugegraph-server-host}:${hugegraph-server-port} # hugegraph server url hugegraph # hugegraph graph name

3.2.6 Show job

kubectl get hcjob/pagerank-sample -n hugegraph-computer-operator-system

NAME               JOBID              JOBSTATUS
pagerank-sample    pagerank-sample    RUNNING

3.2.7 Show log of nodes

# Show the master log
kubectl logs -l component=pagerank-sample-master -n hugegraph-computer-operator-system

# Show the worker log
kubectl logs -l component=pagerank-sample-worker -n hugegraph-computer-operator-system

# Show diagnostic log of a job
# NOTE: diagnostic log exist only when the job fails, and it will only be saved for one hour.
kubectl get event --field-selector reason=ComputerJobFailed --field-selector -n hugegraph-computer-operator-system

3.2.8 Show success event of a job

NOTE: it will only be saved for one hour

kubectl get event --field-selector reason=ComputerJobSucceed --field-selector -n hugegraph-computer-operator-system

3.2.9 Query algorithm results

If the output to Hugegraph-Server is consistent with Locally, if output to HDFS, please check the result file in the directory of /hugegraph-computer/results/{jobId} directory.

4 Built-In algorithms document

4.1 Supported algorithms list:

Centrality Algorithm:
  • PageRank
  • BetweennessCentrality
  • ClosenessCentrality
  • DegreeCentrality
Community Algorithm:
  • ClusteringCoefficient
  • Kcore
  • Lpa
  • TriangleCount
  • Wcc
Path Algorithm:
  • RingsDetection
  • RingsDetectionWithFilter

More algorithms please see: Built-In algorithms

4.2 Algorithm describe


5 Algorithm development guide


6 Note

  • If some classes under computer-k8s cannot be found, you need to execute mvn compile in advance to generate corresponding classes.