wey-gu

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Translated Report (Full Report Below)
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Process:               ServerCat [71437]
Path:                  /Applications/ServerCat.app/Contents/MacOS/ServerCat
Identifier:            tech.baye.servercat
Version:               1.0.1 (276)
App Item ID:           1501532023

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Compile your nebula-java client with modifications

cd nebula-java
mvn -B package -Dmaven.test.skip  -Dmaven.javadoc.skip=true
ls client/target/client-*-SNAPSHOT.jar

Compile your nebula-spark-connector.

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for Docker compose deployment, see https://gist.github.com/wey-gu/950e4f4c673badae375e59007d80d372

a. create services per metad and storaged pod with LoadBalancer type to expose outside the cluster

b. Use TCP Proxy(b.1) or/and DNS(b.2) to resolve endpoint as their : inside the cluster

In this example, in b. I give a demo where both TCP Proxy and DNS are used, actually only DNS is enough if the exposed port remains same of src and target.

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Reference:

• https://stackoverflow.com/questions/13305290/is-there-a-pure-python-implementation-of-murmurhash
• https://github.com/vesoft-inc/nebula/blob/master/src/common/base/MurmurHash2.h
• https://github.com/jievince/rdf-converter/blob/master/murmur2.go

def bytes_to_long(bytes):
    assert len(bytes) == 8
    return sum((b << (k * 8) for k, b in enumerate(bytes)))

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Let's setup Nebula Graph with a graph, Spark, Hadoop, run Nebula Algoritm Node2vec to read Graph from Nebula and Sink result data into a file.

• Bootstrap a test nebula graph cluster
• Run a spark in container
• Setup a hdfs in container
• The result
• appendix
  • The Nebula Algorithm Conf
  • The graph data

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• Fraud Detection https://siwei.io/fraud-detection-with-nebulagraph/
  • 思路是把我这个文章的方式藉由 GraphXR 的能力呈现出来，未来甚至可以用 iframe 来 embed 结果，像这个文章这种 https://distill.pub/2021/gnn-intro/
  • Graph Pattern based 方法，章节 1.2.1
    • 数据集 https://github.com/wey-gu/fraud-detection-datagen
    • 介绍风控专家提供的图模式，展示可视化
      • 

      • -- 针对一笔交易申请关联查询

MATCH (n) WHERE id(n) == "200000010265"

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前言

像一般的 Web 应用之于数据库一样，一个典型的基于 NebulaGraph 的传统线上应用可以又三部分组成：

• 前端，作为人机交互界面，调用后端 API
• 后端，处理基于 Web 的 API，生成 NebulaGraph 图数据库的查询语句，请求 NebulaGraph，处理结果返回 API 调用方
• 图数据库：NebulaGraph

今天，我们给大家简单演示如何开发一个基于 NebulaGraph 的智能机器人应用。本节课旨在快速将我们在 NebulaGraph 入门课程所学与实践开发落地场景连接起来，不会拘泥所有的开发细节， 然而所有的细节在这个示例项目作者的博客中（地址 https://www.siwei.io/siwi/ ）都有更详细介绍，如果同学们感兴趣，欢迎访问博客和它的 Github 代码仓库了解。

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For k8s deployment, see https://gist.github.com/wey-gu/699b9a2ef5dff5f0fb5f288d692ddfd5

Create extral interfaces for host

If not leveraging multiple interfaces, we have to use TLS instead to leverage SNI routing

ip address add 10.1.1.157/24 dev eth0
ip address add 10.1.1.156/24 dev eth0
ip address add 10.1.1.155/24 dev eth0

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Create a docker-compose.yaml

mkdir nebulagraph_docker
cd nebulagraph_docker
vim docker-compose.yaml

It could be like this:

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Add gh action:

name: release

on:
  release:
    types:
      - published