泰坦的"超级节目"

Question

我正在开发一个可以很好地处理图形数据库(Titan)的应用程序,除了它有很多边缘的顶点问题,即超节点.

上面的超节点链接指向Titan作者的博客文章,解释了解决问题的方法.解决方案似乎是通过边缘过滤来减少顶点的数量.

Unfortunately I want to groupCount attributes of edges or vertices. For example I have 1 million users and each user belongs to a country. How can I do a fast groupCount to work out the number of users in each country?

What I've tried so far can be shown in this elaborate groovy script:

g = TitanFactory.open('titan.properties')  // Cassandra
r = new Random(100)
people = 1e6

def newKey(g, name, type) {
    return g
        .makeType()
        .name(name)
        .simple()
        .functional()
        .indexed()
        .dataType(type)
        .makePropertyKey()
}

def newLabel(g, name, key) {
    return g
        .makeType()
        .name(name)
        .primaryKey(key)
        .makeEdgeLabel()
}

country = newKey(g, 'country', String.class)
newLabel(g, 'lives', country)

g.stopTransaction(SUCCESS)

root = g.addVertex()
countries = ['AU', 'US', 'CN', 'NZ', 'UK', 'PL', 'RU', 'NL', 'FR', 'SP', 'IT']

(1..people).each {
    country = countries[(r.nextFloat() * countries.size()).toInteger()]
    g.startTransaction()
    person = g.addVertex([name: 'John the #' + it])
    g.addEdge(g.getVertex(root.id), person, 'lives', [country: country])
    g.stopTransaction(SUCCESS)
}

t0 = new Date().time

m = [:]    
root = g.getVertex(root.id)
root.outE('lives').country.groupCount(m).iterate()

t1 = new Date().time

println "groupCount seconds: " + ((t1 - t0) / 1000)

Basically one root node (for the sake of Titan not having an "all" nodes lookup), linked to many person via edges that have the country property. When I run the groupCount() on 1 million vertices, it takes over a minute.

I realise Titan is probably iterating over each edge and collecting counts, but is there a way to make this run faster in Titan, or any other graph database? Can the index itself be counted so it doesn't have to traverse? Are my indexes correct?

Answer 1

如果您将"国家/地区"作为"生活"标签的主键,那么您可以更快地检索特定国家/地区的所有人.但是,在您的情况下,您感兴趣的是一个组计数,该组计数需要检索该根节点的所有边缘,以便迭代它们并对这些国家进行抢占.

因此,这种分析查询更适合图形分析框架Faunus.它不需要根顶点,因为它通过完整的数据库扫描执行groupcount,从而避免了超级节点问题.Faunus还使用Gremlin作为查询语言,因此您只需稍微修改您的查询:

g.V.country.groupCount.cap...

HTH,马蒂亚斯