Taming Graph Dynamics at Scale

talk by Felix Cuadrado, Queen Mary University at @ds_ldn Data Science London meetup

Transcript

1. Taming Graph Dynamics at Scale Felix Cuadrado (@felixcuadrado) Queen Mary University of London Joint work with Luis Vaquero, Dyonisios Logothetis, Claudio Martella Data Science London Meetup 10th December 2014
2. Graphs model real world sources 2
3. Large-scale graph processing distributed partitions: parallel computation communication between partitions cut edges partitioning quality α performance 3
4. Pregel’s node-centric processing model BSP SYNC BARRIER Compute, Send Messages Receive Messages BSP SYNC BARRIER GraphX [JPDC 94] [SIGMOD 10] [VLDB 12] [OSDI 14] 4
5. Example: PageRank in Apache Giraph public void compute(Vertex <LongWritable, DoubleWritable, FloatWritable> vertex, Iterable messages) throws IOException { if (getSuperstep() >=1 ) { double sum = 0; for (DoubleWritable message : messages) { sum += message.get(); } DoubleWritable vertexValue = new DoubleWritable((0.15f / getTotalNumVertices()) + 0.85f * sum); vertex.setValue(vertexValue); } if (getSuperstep() < MAX_SUPERSTEPS) { long edges = vertex.getNumEdges(); sendMessageToAllEdges(vertex, new DoubleWritable(vertex.getValue().get() / edges)); } else { vertex.voteToHalt(); } } 5
6. • Slowly growing • Long-life structures • Friendship graphs • The Web graph • Road Network Real-world graphs are dynamic • Rapidly evolving • Streams of events/messages • Calls, interactions, mobile proximity • Relationships decay/become stale over time 6
7. Dynamic distributed graph processing BSP Graph Processing Dynamic Graph API 7
8. Dynamic graph processing model BSP SYNC BARRIER Update Graph BSP SYNC BARRIER Compute, Send Messages Receive Messages 8
9. Partitioning dynamic graphs partition quality α execution performance 9
10. Partitioning dynamic graphs Hash Partitioning Deterministic Greedy Stream Partitioning [KDD12] 0 5 10 15 20 25 30 time elapsed (days) 0.4 0.5 0.6 0.7 0.8 ratio of cuts Interaction graph from 1 month of mobile calls data (CDR) Inactive links expire in 1 week partition quality: ratio of edges cut between partitions 10
11. Migrate where your neighbors are Iterative algorithm Migration Quotas Approach: Adaptive graph partitioning [ICDCS 14] 11
12. Adaptive migration integration BSP SYNC BARRIER Compute, Messaging Update Graph BSP SYNC BARRIER Migrate Decide Migrations 12
13. Adapting mobile call graphs adaptive hash wk1 wk2 wk3 wk4 0 2 4 6 8 10 throughput (queries per hour) Maximum clique performance week Hash partitioning Adaptive partitioning 0 5 10 15 20 25 30 time elapsed (days) 0.4 0.5 0.6 0.7 0.8 ratio of cuts Quality of partitioning Dataset: 1 month of mobile calls 21 million unique nodes 7% Addition 4% Deletion each week Sliding window of one week Algorithm: Maximum clique computation 13
14. Adapting real-time social graphs 0 10 20 30 40 50 Average Tweets per sec 0 1 2 3 4 5 Superstep time (s) 0 2 4 6 8 10 12 14 16 18 20 22 24 Time (h) Tweets per second Hash superstep time Adaptive superstep time Dataset: one week of tweets published from London in 2012 Algorithm: TunkRank (User influence metric) 14
15. • Initial partitioning is not that important with dynamic graphs • Adaptive partitioning / repartitioning might be needed • >50% performance improvement on dynamic graphs • Partitioning overhead should be considered • Smarter partition strategies might not be practical • Migrations /repartitions might not be worth it • BSP aids system optimisations • Message aggregation (migration & computation) Lessons learnt 15