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Stripe CTF 3.0 - Follow Up

Stripe CTF 3.0 - Follow Up

From BigData Meetup #4 - Timisoara, June 2014

Bogdan Gaza

July 01, 2014
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  1. Big Data #5 Bogdan Gâza Bloom filters N hash functions

    k1 k2 k3 kn probabilistic fals positives / no fals negatives
  2. Big Data #5 Bogdan Gâza SHA1 - ridiculous parallel bash:

    400 Hash/s stripe go miners: 1.9 MHash/s gpu: 1-2 GHash/s
  3. Big Data #5 Bogdan Gâza Load balancing algorithm 4 req

    / ip request requests < 25 ms apart Solution
  4. Big Data #5 Bogdan Gâza LB Indexer Indexer Indexer 4

    minutes to index 4 nodes with 500MB of RAM Scala Latency based scoring To pass the level < 0.15s / query Around 100M words Arbitrary substring
  5. Big Data #5 Bogdan Gâza 1st approach: inverted index vs

    substring search Solution Trie vs substring search marisa / patricia Trie / radix tree / suffix tree vs substring search DAWG vs substring search Sharding
  6. Big Data #5 Bogdan Gâza SQLite SQLite SQLite SQLite SQLite

    SQLite Unreliable network ! octopus octopus simulates: netsplit / lagsplit / SPOF
  7. Big Data #5 Bogdan Gâza Consensus Reliability in the presence

    of faulty processes examples: who can commit to the DB who is the leader state machine replication
  8. Big Data #5 Bogdan Gâza ZAB - High performance broadcast

    in primary backup systems - 2011 RAFT - Understandable consensus algorithm - 2013 Paxos - Part-time parliament - Laslie Lamport ‘90 Consensus
  9. Big Data #5 Bogdan Gâza ZAB - High performance broadcast

    in primary backup systems - 2011 RAFT - Understandable consensus algorithm - 2013 Paxos - Part-time parliament - Laslie Lamport ‘90 Consensus