分布式图算法Pregel

最近看了Google的Pregel论文，图算法有一些经典且不可被替代的应用场景，如社交网络，相互引用等。但是在单个结点上的运算量往往过少，注重的是消息传播和逻辑处理，而不是单纯的大规模计算。虽然已经过去了十几年，但是其中的messsage passing，combiner，aggregator，group partition，状态机等机制还是设计分布式图算法的有效方法。这里针对设计和实现部分做一点点笔记。

Pregel: A System for Large-Scale Graph Processing

key point:

distributed computer clusters -> graph task

graph algorithm

introduction

Target:

Many practical computing problems concern large graphs. e.g. the Web graph and various social networks.

Parallelism for graph challenging:

1. Graph algorithms often exhibit poor locality of memory access,
2. very little work per vertex,
3. and a changing degree of parallelism over the course of execution.

We want a scalable general-purpose system for graph! Compared with existed options:

• efficient
• scalable
• fault-tolerant

inspired by: Valiant’s Bulk Synchronous Parallel model

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Model

data structure

input: directed graph(both have value):

• vertex: vertex identifier!
• edge: associated with source vertices

process

superstep: working unit with state machine -> sync!

vertex: first class citizens

output: may not as same as input(the graph structure can change)

terminal: voting to halt

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API

Compute()

GetValue()

MutableValue()

combiner:

aggregator: global monitor (in my opinion, aggregator is global combiner + global coordinator(more logical than combiner))

aggregator: get global information and do a calculation, not only the combination of data.

e.g. do a particular operation when all the vertices meet a particualr condition

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Implementation

Architecture

group partition: default(hash(ID) mod N)

self-define partition allocator: use locality

hierarchy(up to bottom):

• user program(copies of app) = 1 master + N-1 workers
• physical machine
• group partition
• vertex & outer-edge

Fault Tolerance

checkpoint

worker: partition state -> persistant storage

master: aggregator

regular "ping" message