$30 off During Our Annual Pro Sale. View Details »
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
Large scale graph processing with apache giraph
Search
André Kelpe
May 23, 2012
Programming
2
5.5k
Large scale graph processing with apache giraph
André Kelpe
May 23, 2012
Tweet
Share
More Decks by André Kelpe
See All by André Kelpe
Cascading 3 and beyond
fs111
0
180
The Cascading (big) data application framework
fs111
1
240
SELECT ALL THE THINGS - Cascading Lingual, ANSI SQL for Apache Hadoop
fs111
0
210
A whirlwind tour through Lingual: ANSI SQL for Apache Hadoop
fs111
1
190
Tor for everyone!
fs111
0
200
Other Decks in Programming
See All in Programming
AI時代を生き抜く 新卒エンジニアの生きる道
coconala_engineer
1
120
関数実行の裏側では何が起きているのか?
minop1205
1
700
まだ間に合う!Claude Code元年をふりかえる
nogu66
5
830
AIコーディングエージェント(Gemini)
kondai24
0
220
sbt 2
xuwei_k
0
300
ViewファーストなRailsアプリ開発のたのしさ
sugiwe
0
470
AIコードレビューがチームの"文脈"を 読めるようになるまで
marutaku
0
350
Cap'n Webについて
yusukebe
0
130
これだけで丸わかり!LangChain v1.0 アップデートまとめ
os1ma
6
1.8k
認証・認可の基本を学ぼう前編
kouyuume
0
240
Microservices rules: What good looks like
cer
PRO
0
1.4k
ID管理機能開発の裏側 高速にSaaS連携を実現したチームのAI活用編
atzzcokek
0
230
Featured
See All Featured
Optimising Largest Contentful Paint
csswizardry
37
3.5k
Cheating the UX When There Is Nothing More to Optimize - PixelPioneers
stephaniewalter
285
14k
Save Time (by Creating Custom Rails Generators)
garrettdimon
PRO
32
1.8k
No one is an island. Learnings from fostering a developers community.
thoeni
21
3.6k
The Art of Programming - Codeland 2020
erikaheidi
56
14k
Context Engineering - Making Every Token Count
addyosmani
9
510
RailsConf & Balkan Ruby 2019: The Past, Present, and Future of Rails at GitHub
eileencodes
141
34k
GraphQLとの向き合い方2022年版
quramy
50
14k
Let's Do A Bunch of Simple Stuff to Make Websites Faster
chriscoyier
508
140k
Optimizing for Happiness
mojombo
379
70k
A Modern Web Designer's Workflow
chriscoyier
698
190k
ReactJS: Keep Simple. Everything can be a component!
pedronauck
666
130k
Transcript
Large scale graph processing with apache giraph André Kelpe @fs111
http://kel.pe
graphs 101
vertices and edges
v2 v5 v4 v7 v3 v8 v6 v1 v9 v8
v10 simple graph
graphs are everywhere road network, the www, social graphs etc.
graphs can be huge
google knows!
Pregel
Pregel by google Describes graph processing approach based on BSP
(Bulk Synchronous Parallel)
pro-tip: search for „pregel_paper.pdf“ on github ;-)
Properties of Pregel batch-oriented, scalable, fault tolerant processing of graphs
It is not a graph database It is a processing
framework
BSP vertex centric processing in so called supersteps
BSP vertices send messages to each other
BSP synchronization points between supersteps
execution of superstep S Each vertex processes messages generated in
S-1 and send messages to be processed in S+1 and determines to halt.
None
apache giraph
giraph Loose implementation of Pregel ideas on top of Hadoop
M/R coming from yahoo
apache giraph http://incubator.apache.org/giraph/
giraph avoid overhead of classic M/R process but reuse existing
infrastructure
giraph simple map jobs in master worker setup. coordination via
zookeeper. messaging via own RPC protocol. in memory processing. custom input and output formats.
current status version 0.1 released compatible with a multitude of
hadoop versions (we use CDH3 at work) still lots of things to do, join the fun!
the APIs the APIs
Vertex-API /** *@param <I> vertex id * @param <V> vertex
data * @param <E> edge data * @param <M> message data */ class BasicVertex<I extends WritableComparable, V extends Writable, E extends Writable, M extends Writable> void compute(Iterator<M> msgIterator); void sendMsg(I id, M msg); void voteToHalt();
Shortest path example https://cwiki.apache.org/confl uence/display/GIRAPH/Shorte st+Paths+Example
v2 v5 v4 v7 v3 v8 v6 v1 v9 v8
v10 simple graph
private boolean isSource() { return (getVertexId().get() == getContext().getConfiguration().getLong(SOURCE_ID, SOURCE_ID_DEFAULT)); }
@Override public void compute(Iterator<DoubleWritable> msgIterator) { if (getSuperstep() == 0) { setVertexValue(new DoubleWritable(Double.MAX_VALUE)); } double minDist = isSource() ? 0d : Double.MAX_VALUE; while (msgIterator.hasNext()) { minDist = Math.min(minDist, msgIterator.next().get()); } if (minDist < getVertexValue().get()) { setVertexValue(new DoubleWritable(minDist)); for (Edge<LongWritable, FloatWritable> edge : getOutEdgeMap().values()) { sendMsg(edge.getDestVertexId(), new DoubleWritable(minDist + edge.getEdgeValue().get())); } } voteToHalt(); }
GiraphJob job = new GiraphJob(getConf(), getClass().getName()); job.setVertexClass(SimpleShortestPathVertex.class); job.setVertexInputFormatClass(SimpleShortestPathsVertexInputFormat.class); job.setVertexOutputFormatClass( SimpleShortestPathsVertexOutputFormat.class);
FileInputFormat.addInputPath(job, new Path(„/foo/bar/baz“)); FileOutputFormat.setOutputPath(job, new Path(„/foo/bar/quux“)); job.getConfiguration().setLong(SimpleShortestPathsVertex.SOURCE_ID, Long.parseLong(argArray[2])); job.setWorkerConfiguration(minWorkers, maxWorkers), 100.0f); GiraphJob
see also http://incubator.apache.org/giraph/ https://cwiki.apache.org/confluence/displ ay/GIRAPH/Shortest+Paths+Example http://googleresearch.blogspot.com/2009/ 06/large-scale-graph-computing-at- google.html
Thanks! Questions?