Version 1.0 of the documentation is no longer actively maintained. The site that you are currently viewing is an archived snapshot. For up-to-date documentation, see the latest version.

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.

Features

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 Get Started

2.1 Run PageRank algorithm locally

To run algorithm with HugeGraph-Computer, you need to install 64-bit JRE/JDK 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

2.1 Download the compiled archive

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

wget https://github.com/apache/hugegraph-computer/releases/download/v${version}/hugegraph-loader-${version}.tar.gz
tar zxvf hugegraph-computer-${version}.tar.gz

2.2 Clone source code to compile and package

Clone the latest version of HugeGraph-Computer source package:

$ git clone https://github.com/apache/hugegraph-computer.git

Compile and generate tar package:

cd hugegraph-computer
mvn clean package -DskipTests

2.3 Start master node

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

cd hugegraph-computer-${version}
bin/start-computer.sh -d local -r master

2.4 Start worker node

bin/start-computer.sh -d local -r worker

2.5 Query algorithm results

2.5.1 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"

2.5.2 Query page_rank property value:

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

2.2 Run PageRank algorithm in Kubernetes

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

2.2.1 Install HugeGraph-Computer CRD

# Kubernetes version >= v1.16
kubectl apply -f https://raw.githubusercontent.com/apache/hugegraph-computer/master/computer-k8s-operator/manifest/hugegraph-computer-crd.v1.yaml

# Kubernetes version < v1.16
kubectl apply -f https://raw.githubusercontent.com/apache/hugegraph-computer/master/computer-k8s-operator/manifest/hugegraph-computer-crd.v1beta1.yaml

2.2.2 Show CRD

kubectl get crd

NAME                                        CREATED AT
hugegraphcomputerjobs.hugegraph.apache.org   2021-09-16T08:01:08Z

2.2.3 Install hugegraph-computer-operator&etcd-server

kubectl apply -f https://raw.githubusercontent.com/apache/hugegraph-computer/master/computer-k8s-operator/manifest/hugegraph-computer-operator.yaml

2.2.4 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

2.2.5 Submit job

More computer crd please see: Computer CRD
More computer config please see: Computer Config Options

cat <<EOF | kubectl apply --filename -
apiVersion: hugegraph.apache.org/v1
kind: HugeGraphComputerJob
metadata:
  namespace: hugegraph-computer-system
  name: &jobName pagerank-sample
spec:
  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
  computerConf:
    job.partitions_count: "20"
    algorithm.params_class: org.apache.hugegraph.computer.algorithm.centrality.pagerank.PageRankParams
    hugegraph.url: http://${hugegraph-server-host}:${hugegraph-server-port} # hugegraph server url
    hugegraph.name: hugegraph # hugegraph graph name
EOF

2.2.6 Show job

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

NAME               JOBID              JOBSTATUS
pagerank-sample    pagerank-sample    RUNNING

2.2.7 Show log of nodes

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

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

2.2.8 Show success event of a job

NOTE: it will only be saved for one hour

kubectl get event --field-selector reason=ComputerJobSucceed --field-selector involvedObject.name=pagerank-sample -n hugegraph-computer-system

2.2.9 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.

3 Built-In algorithms document

3.1 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

3.2 Algorithm describe

TODO

4 Algorithm development guide

TODO