Distributed Programming and Data Consistency w/ Notes
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Distributed Programming and Data Consistency w/ Notes

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Presentation with NOTES. ...

Presentation with NOTES.

Tuning Data Consistency to obtain efficient Distributed Computing solutions.

The solutions that the academic world and the new NoSQL trend is making available to the IT industry in general.

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  • Slightly updated slideware now at my slideshare page.
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    Obvious copy+paste error. Will fix it later (when I have the source presentation at hand).

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Distributed Programming and Data Consistency w/ Notes Document Transcript

  • 1. Distributed Programming and Data Consistency by Paulo Gaspar @paulogaspar7 1 Twitter: @paulogaspar7 - http://twitter.com/paulogaspar7 Blog: http://paulogaspar7.blogspot.com/
  • 2. Consistency Perception 2
  • 3. What is Consistency? 3 Our perception of consistency is related with what we know about the system and its state. That is how we figure what might fit...
  • 4. What isn’t? 4 ...and what does not fit. Obviously a person will have a different degree of precision and tolerance than an automated system.
  • 5. Consistency across time 5 Consistency also has a time axis, with state sequences that make sense... 1 of 3=> Expected event sequence (3 slide animation which SlideShare won’t handle)
  • 6. Consistency across time 6 2 of 3=> Expected event sequence (3 slide animation which SlideShare won’t handle)
  • 7. Consistency across time 7 3 of 3=> Expected event sequence (3 slide animation which SlideShare won’t handle)
  • 8. Inconsistency across time 8 ...and state sequences that do NOT make sense. 1 of 3=> UNexpected event sequence (3 slide animation which SlideShare won’t handle)
  • 9. Inconsistency across time 9 2 of 3=> UNexpected event sequence (3 slide animation which SlideShare won’t handle)
  • 10. Inconsistency across time 10 3 of 3=> UNexpected event sequence (3 slide animation which SlideShare won’t handle)
  • 11. Consistency is perception ...and time matters... 11 Again, each (type of) observer will have a different degree of evaluation precision and tolerance to inconsistencies.
  • 12. Caching Consistency (The Lower Latency - read performance) 12
  • 13. Data Caching Consistency Multi-layer caching The 3 second cache for a “LIVE” site (e.g.: BBC News live soccer reports) User changing cached data Schrodinger’s Cache? 13 Even on a “live” site you can use a short lived cache. If the user can NOT observe the exact time of each server state changes, are any server to client delays (due to caching) really there? Moreover, it is often a matter of having small update-until-view delays due to caching or really big ones (or the site down) due to overload.
  • 14. Memcached at FB: You HAVE TO Replicate to Scale-Out 14 An example of how you still might have to replicate in order to scale, even with a very high performance store. The reason for FB’s issue (might lack some detail): http://highscalability.com/blog/2009/10/26/facebooks-memcached-multiget-hole-more-machines-more- capacit.html
  • 15. So, now it “Loadbalances”... 15 ...and with LB inconsistencies along the time axis can happen (eg. by reading from alternate out-of-synch backends)
  • 16. ...but then you can have... 16 With the possibility of state sequences that do NOT make sense. 1 of 3=> UNexpected event sequence (3 slide animation which SlideShare won’t handle)
  • 17. Inconsistency across time 17 2 of 3=> UNexpected event sequence (3 slide animation which SlideShare won’t handle)
  • 18. Inconsistency across time 18 3 of 3=> UNexpected event sequence (3 slide animation which SlideShare won’t handle)
  • 19. ...now it can pick >1 versions! 19 Why you can have inconsistencies along the time axis.
  • 20. Slow and Big Consistency (The Higher Latency - BigData) 20
  • 21. MapReduce is for embarrassingly parallel problems with some time... 21 Consistency scenarios, starting from the most “sexy” (Web, Peta Bytes of Data): * MapReduce works like vote counting - vote mapped to voting tables, counted, “reduced” to stats; * MR is appropriate for "embarrassingly parallel" tasks, like indexing the Internet and other huge processing tasks; * We should use it whenever possible; * There is a lot to be learned about Map Reduce: - Evaluation and expression of candidate problems; - Build and manage an its infrastructure; - etc. * Even MR has coordination needs; * Even MR should have SLAs (Service Level Agreements).
  • 22. MapReduce Implementations (& Cia.) Google, coordination by Chubby using Paxos. Used only at Google; Google BigTable is a Wide Column Store which works on top of GoogleFS. Used only at Google; Hadoop, used at Amazon, Facebook, Rackspace, Twitter, Yahoo!, etc.; Hadoop ZooKeeper implements a Paxos variation and is used at Rackspace, Yahoo!, etc.; Hadoop HBase is a Wide Column Store, on top of HDFS and now uses ZooKeeper. Used at Yahoo! etc. 22 Parallel between Google’s internally developed systems and their Hadoop counterparts. http://hadoop.apache.org/ http://labs.google.com/papers/ The very interesting “coordinators”: http://labs.google.com/papers/chubby.html http://hadoop.apache.org/zookeeper/ Zookeeper sure looks like a very interesting and reusable piece of software. Curiosity: HBase is faster since using ZooKeeper... is it also because of Zookeeper??? http://hadoop.apache.org/hbase/
  • 23. Consistency w/ Interaction (Low Latency - read/write - harder stuff) 23
  • 24. Two “High”/Sexy reasons for Distributing Data Storage (not just cache) High Performance Data Access (Read / Write) High Availability (HA) 24
  • 25. Why care about HA? 1.7% HDDs fail in the 1st year, 8.6% in the 3rd (Google) Unrecoverable RAM errors/year: 1.3% machines, 0.22% DIMM (Google) Router, Rack, PDU, misc. network failures Over 4 nines only through redundancy, best hardware never good enough (James Hamilton-MS and Amazon) 25 Sources: For Google’s numbers check the slideware at: http://videolectures.net/wsdm09_dean_cblirs/ For the James Hamilton quote: http://mvdirona.com/jrh/TalksAndPapers/JamesRH_Ladis2008.pdf Another very quoted paper with Google’s DRAM failure stats and patterns: http://research.google.com/pubs/pub35162.html You can find other HA and Systems related papers from Google and James Hamilton at: http://mvdirona.com/jrh/work/ http://research.google.com/pubs/DistributedSystemsandParallelComputing.html
  • 26. Why care about Latency? Google: Half a second delay caused a 20% drop in traffic (30 results instead of 10, via Marissa Mayer); Amazon found every 100ms of latency costs 1% sales (via Greg Linden); A broker could lose $4 million in revenues per millisecond if their electronic trading platform is 5 ms behind the competition (via NYT). 26 You can find all this references trough this page (if you follow the links): http://highscalability.com/latency-everywhere-and-it-costs-you-sales-how-crush-it Including these: http://glinden.blogspot.com/2006/11/marissa-mayer-at-web-20.html http://perspectives.mvdirona.com/2009/10/31/TheCostOfLatency.aspx http://www.nytimes.com/2009/07/24/business/24trading.html?_r=2&hp
  • 27. Other Distributed Data Contexts (the less sexy daily stuff) EAI / B2B / Systems Integration Geographic Distribution (e.g.:Health System+Hospitals) Systems with n-tier / SOA Architectures 27 The daily jobs of so many IT professionals have much more relation with this type of common distributed systems than with the sexier kind we talked about before. But these fields too would benefit from the learning the lessons and using the technologies we are talking about.
  • 28. Fallacies of Distributed Computing 1. The network is reliable; 2. Latency is zero; 3. Bandwidth is infinite; 4. The network is secure; 5. Topology doesn't change; 6. There is one administrator; 7. Transport cost is zero; 8. The network is homogeneous. 28 Just to remember this classic on the HA challenges. A few more details at: http://en.wikipedia.org/wiki/Fallacies_of_Distributed_Computing
  • 29. CAP Theorem History 1999: 1st mention on the “Harvest, Yield and Scalable Tolerant Systems” paper by Eric A. Brewer (Berkley/Inktomi) and Armando Fox (Stanford/Berkley) 2000-07-19: Brewer’s CAP Conjecture part of Brewer’s keynote to the PODC Conference 2002-06: Brewer’s CAP Theorem proof published by Seth Gilbert (MIT) and Nancy Lynch (MIT) 2007-10-02: “Amazon's Dynamo” post by Werner Vogels (Amazon’s CTO) quoting the paper: Giuseppe DeCandia, Deniz Hastorun, Madan Jampani, Gunavardhan Kakulapati, Avinash Lakshman, Alex Pilchin, Swami Sivasubramanian, Peter Vosshall and Werner Vogels, “Dynamo: Amazon's Highly Available Key-Value Store”, in the Proceedings of the 21st ACM Symposium on Operating Systems Principles, Stevenson, WA, October 2007. 2007-12-19: “Eventually Consistent” post by Werner Vogels (Amazon’s CTO) 29 The online book “CouchDB: The Definitive Guide” has an interesting introduction to these concepts - the “Eventual Consistency” chapter: http://books.couchdb.org/relax/intro/eventual-consistency Really essential and truly amazing is the Dynamo paper by Werner Vogels et al, proof that BASE really works in truly industrial sites, even with stats describing real life behavior: http://www.allthingsdistributed.com/2007/10/amazons_dynamo.html ...and the now famous Eventually Consistent post by Werner Vogels: http://www.allthingsdistributed.com/2007/12/eventually_consistent.html If you dislike the introductory (justifiable) drama, just jump to the next part because this article, by Julian Browne, is the best I found about the Brewer’s CAP Theorem and its history: http://www.julianbrowne.com/article/viewer/brewers-cap-theorem You should still take a look at: * The 1997 “Cluster-Based Scalable Network Services” paper (Brewer et al.) where the BASE vs ACID dilemma is already mentioned: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.1.2034&rep=rep1&type=pdf * The 1999 “Harvest, Yeld and Scalable Tolerant Systems” paper (Brewer et al.) where the CAP conjecture is already mentioned: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.24.3690&rep=rep1&type=pdf * The PODC 2000 keynote, by Brewer, that made the CAP conjecture and the BASE concept “popular”: http://www.cs.berkeley.edu/~brewer/cs262b-2004/PODC-keynote.pdf * You might also see with your own eyes how CAP became a proved Theorem: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.20.1495&rep=rep1&type=pdf Definition of ACID: http://en.wikipedia.org/wiki/ACID
  • 30. The CAP Theorem strong Consistency, high Availability, Partition-resilience: pick at most 2 30 I simply had to put The Diagram, of course.
  • 31. Eventual Consistency for Availability BASE ACID (Basically Available Soft-state Eventual consistency) (Atomicity, Consistency, Isolation, Durability) Weak Consistency Strong consistency (stale data ok) (NO stale data) Availability first Isolation Best effort Focus on “commit” Approximate answers OK Availability? Aggressive (optimistic) Conservative (pessimistic) Faster Safer 31 You can find a variation of this slide at Brewer’s 2000’s PODC keynote at: http://www.cs.berkeley.edu/~brewer/cs262b-2004/PODC-keynote.pdf I skipped these rather controversial bits: ACID: * Nested transactions; * Difficult evolution (e.g. schema) BASE: * Simpler! * Easier evolution I already tried both ways (data stores with and without schema) and I rather have some schema mechanism for the most complex stuff. ACID: A)tomicity Either all of the tasks of a transaction are performed or none of them are. C)onsistency A database remains in a consistent state before the start of the transaction and after the transaction is over (whether successful or not). I)solation Other operations cannot access or see the data in an intermediate state during a transaction. D)urability Once the user has been notified of success, the transaction will persist. This means it will survive system failure, and that the database system has checked the integrity constraints and won't need to abort the transaction.
  • 32. CAP Trade-offs CA without P: Databases providing distributed transactions can only do it while their network is ok; CP without A: While there is a partition, transactions to an ACID database may be blocked until the partition heals (to avoid merge conflicts -> inconsistency); AP without C: Caching provides client-server partition resilience by replicating data, even if the partition prevents verifying if a replica is fresh. In general, any distributed DB problem can be solved with either: expiration-based caching to get AP; or replicas and majority voting to get PC (minority is unavailable). 32 Concept introduced at the 1999 “Harvest, Yeld and Scalable Tolerant Systems” paper (Brewer et al.): http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.24.3690&rep=rep1&type=pdf I should probably skip this slide during a life presentation. This is stuff you have to read about.
  • 33. Living with CAP All systems are probabilistic, wether they realize it or not And so are Distributed Transactions (2 Generals Problem) Weak CAP Principle: The stronger the guarantees made about any two of C, A and P, the weaker the guarantees that can be made about the third Systems should degrade gracefully, instead of all or nothing (e.g.: displaying data from available partitions) Life is Eventually Consistent Aim for Eventual Consistency 33 Steve Yen clearly illustrates the “Life is Eventually Consistent” idea on the slideware (slides 40 to 45) he used for his “No SQL is a Horseless Carriage” talk at NoSQL Oakland 2009: http://dl.dropbox.com/u/2075876/nosql-steve-yen.pdf The Weak CAP Principle was introduced at the 1999 “Harvest, Yeld and Scalable Tolerant Systems” paper (Brewer et al.): http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.24.3690&rep=rep1&type=pdf To understand how hard (ACID) Distributed Transactions are, you have an excellent history of the concepts related to this problem here: http://betathoughts.blogspot.com/2007/06/brief-history-of-consensus-2pc-and.html The difficulties of (ACID) Distributed Transactions are well illustrated by the classic Two Generals’ Problem: http://en.wikipedia.org/wiki/Two_Generals'_Problem Leslie Lamport et al further explore the problem (and its solutions) on the classic “The Byzantine Generals Problem” paper: http://research.microsoft.com/en-us/um/people/lamport/pubs/byz.pdf And if you think that Two Phase Commit is a 100% reliable mechanism... think again: http://www.cs.cornell.edu/courses/cs614/2004sp/papers/Ske81.pdf This is just to illustrate the difficulty of the problem. There are more reliable mechanisms, like Three Phase Commit: http://en.wikipedia.org/wiki/Three-phase_commit_protocol http://ei.cs.vt.edu/~cs5204/fall99/distributedDBMS/sreenu/3pc.html ...or the so called Paxos Commit: http://research.microsoft.com/pubs/64636/tr-2003-96.pdf
  • 34. CAP Theorem History 1999: 1st mention on the “Harvest, Yield, and Scalable Tolerant Systems” paper by Eric A. Brewer (Berkley/Inktomi) and Armando Fox (Stanford/Berkley) 2000-07-19: Brewer’s CAP Conjecture part of Brewer’s keynote to the PODC Conference 2002-06: Brewer’s CAP Theorem proof published by Seth Gilbert (MIT) and Nancy Lynch (MIT) 2007-10-02: “Amazon's Dynamo” post by Werner Vogels (Amazon’s CTO) quoting the paper: Giuseppe DeCandia, Deniz Hastorun, Madan Jampani, Gunavardhan Kakulapati, Avinash Lakshman, Alex Pilchin, Swami Sivasubramanian, Peter Vosshall and Werner Vogels, “Dynamo: Amazon's Highly Available Key-Value Store”, in the Proceedings of the 21st ACM Symposium on Operating Systems Principles, Stevenson, WA, October 2007. 2007-12-19: “Eventually Consistent” post by Werner Vogels (Amazon’s CTO) 34 Repeated slide, repeated notes (to pass focus from CAP to Dynamo and Eventual Consistency): The online book “CouchDB: The Definitive Guide” has an interesting introduction to these concepts - the “Eventual Consistency” chapter: http://books.couchdb.org/relax/intro/eventual-consistency Really essential and truly amazing is the Dynamo paper by Werner Vogels et al, proof that BASE really works in truly industrial sites, even with stats describing real life behavior: http://www.allthingsdistributed.com/2007/10/amazons_dynamo.html ...and the now famous Eventually Consistent post by Werner Vogels: http://www.allthingsdistributed.com/2007/12/eventually_consistent.html If you dislike the introductory (justifiable) drama, just jump to the next part because this article, by Julian Browne, is the best I found about the Brewer’s CAP Theorem and its history: http://www.julianbrowne.com/article/viewer/brewers-cap-theorem You should still take a look at: * The 1997 “Cluster-Based Scalable Network Services” paper (Brewer et al.) where the BASE vs ACID dilemma is already mentioned: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.1.2034&rep=rep1&type=pdf * The 1999 “Harvest, Yeld and Scalable Tolerant Systems” paper (Brewer et al.) where the CAP conjecture is already mentioned: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.24.3690&rep=rep1&type=pdf * The PODC 2000 keynote, by Brewer, that made the CAP conjecture and the BASE concept “popular”: http://www.cs.berkeley.edu/~brewer/cs262b-2004/PODC-keynote.pdf * You might also see with your own eyes how CAP became a proved Theorem: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.20.1495&rep=rep1&type=pdf Definition of ACID: http://en.wikipedia.org/wiki/ACID
  • 35. Amazon’s Dynamo DB Also a “Wide Column Store” Problem Technique Partitioning Consistent Hashing High Availability for writes Vector clocks with reconciliation during reads Handling temporary failures Sloppy Quorum and hinted handoff (NRW) Recovering from permanent failures Anti-entropy using Merkle trees Membership and failure detection Gossip-based membership protocol and failure detection. 35 The source here is the already mentioned Dynamo paper: http://www.allthingsdistributed.com/2007/10/amazons_dynamo.html Strict distributed DBs, rather than dealing with the uncertainty of the correctness of an answer, make data is made unavailable until it is absolutely certain that it is correct. At Amazon, SLAs are expressed and measured at the 99.9th percentile of the distribution - avg or median not good enough to provide a good experience for all. The choice for 99.9% over an even higher percentile has been made based on a cost-benefit analysis which demonstrated a significant increase in cost to improve performance that much. Experiences with Amazon’s production systems have shown that this approach provides a better overall experience compared to those systems that meet SLAs defined based on the mean or median.
  • 36. N: number of nodes to replicate each item to; W: number of required nodes for write success; R: number of required nodes for write success. W < N = remaining nodes will receive the write later. R < N = remaining nodes ignored. 36 Also based in the already mentioned Dynamo paper: http://www.allthingsdistributed.com/2007/10/amazons_dynamo.html ...but you can find a similar diagram and similar mechanisms described about several (NoSQL) databases that partially clone Dynamo.
  • 37. Wikipedia image Merkle Tree / Hash Tree Used to verify / compare a set of data blocks and efficiently find where the mismatches are. 37 Also based in the already mentioned Dynamo paper: http://www.allthingsdistributed.com/2007/10/amazons_dynamo.html ...and on the Wikipedia article about this algorithm: http://en.wikipedia.org/wiki/Hash_tree
  • 38. Wikipedia image Vector Clocks On each internal even a process increments its logical clock; Before sending a message, it increments its own clock in the vector and sends it with the message; On receiving a message, it increments its clock and updates each element on its own vector to max.(own, msg). 38 Also based in the already mentioned Dynamo paper: http://www.allthingsdistributed.com/2007/10/amazons_dynamo.html ...and on the Wikipedia article about this algorithm: http://en.wikipedia.org/wiki/Vector_clock Vector Clocks (and other similar algorithms) have a predecessor in Lamport timestamps: http://en.wikipedia.org/wiki/Lamport_timestamps Introduced in the classic paper “Time, Clocks, and the Ordering of Events in a Distributed System” by Leslie Lamport: http://en.wikipedia.org/wiki/Lamport_timestamps
  • 39. Amazon Dynamo Lessons (according to the paper) Data returned to Shopping Cart 24h profiling: 0.00057% of requests saw 2 versions; 0.00047% of requests saw 3 versions and 0.00009% of requests saw 4 versions. In two years applications have received successful responses (without timing out) for 99.9995% of its requests and no data loss event has occurred to date; With coordination via Gossip protocol it is harder to scale further than a few hundred nodes. (Could be better w/ Chubby / ZK like coordinators?) 39 Also based in the already mentioned Dynamo paper: http://www.allthingsdistributed.com/2007/10/amazons_dynamo.html Wikipedia has an article on Gossip Protocols (although, at the data I write this, not as precise as other Wikipedia articles I just quoted): http://en.wikipedia.org/wiki/Gossip_protocol The solution I mention as a possibly more scalable alternative to Gossip Protocols for consensus is the use of Paxos (or derivates) Coordinators, like the proprietary Google’s Chubby or the open source Apache Hadoop Zookeeper. When I first wrote and used (at my SAPO Codebits 2009 talk) these slides, the only support I still had to my (then intuitive) belief that these more directed approaches should be more efficient than Gossip Protocols was the 6.6 part from the Dynamo paper - the paper even mentions the possibility of “introducing hierarchical extensions to Dynamo”. Thanks to my SAPO Codebits talk I met Henrique Moniz, then a Ph.D. student at the University of Lisbon. After I discussed this issue (consensus scalability) with him he pointed me to a couple of interesting papers, one of which immediately captured my attention: * Gossip-based broadcast protocols by João Leitão http://www.gsd.inesc-id.pt/~jleitao/pdf/masterthesis-leitao.pdf This paper offers a more complete description of gossip protocols overhead and, to my surprise, also pointed a few reliability weak spots on known Gossip Protocols. The paper goes on to present a more robust and efficient Gossip Protocol called “HyParView” using a more “directed” approach. HyParView sure looks like an interesting solution in terms of robustness for environments with an high incidence of system/network failures but I still believe that using coordinators will be more efficient in a well controlled data center. Not that using coordinators and making them scale out BIG is exactly trivial, as you can read here: -On the “Vertical Paxos and Primary-Backup Replication” paper, by Leslie Lamport et al, that Henrique Moniz pointed me to: http://research.microsoft.com/pubs/80907/podc09v6.pdf -Or on this interesting article from the Cloudera’s blog about the (now upcoming) Observers feature of Apache
  • 40. Eventually Consistent Systems Banks EAI Integrations Many messaging based (SOA) systems Google Amazon Etc. 40 Unlike what many examples say, Banks often use Eventual Consistency on many (limited value/risk) transactions - or use “large” periodic transaction / compensation fixed windows to process large numbers of larger value movements. So much for those ACID transaction examples...
  • 41. ACID and FAST (Lowest Latency - read/write - hardest stuff) 41
  • 42. Immediately Consistent Systems Data-grids: Coherence Trading Gigaspaces All Data in RAM Online Gambling Can do ACID Very High Speed Max. Scale-out 42 Trading and Online Gambling really need to do large volumes of fast ACID transactions and are the big customers of Data Grids. Why Online Gambling needs ACID transactions has all to do with the type of game and the type of rules/assets (some virtual) it involves. Why Trading really needs ACID is s bit more obvious: you might be able to compensate an overdraft at a bank (more so for limited values) but you really cannot sell shares you do not have for sale. The performance needs are obvious for both too. For Trading there are even some new reasons, like (again): http://www.nytimes.com/2009/07/24/business/24trading.html?_r=2&hp
  • 43. Tools (Most with source code to pick from) 43
  • 44. NoSQL Taxonomy by Steve Yen [PG] key‐value‐cache: memcached, repcached, coherence [?], infinispan, eXtreme scale, jboss cache, velocity, terracota [???] key‐value‐store: keyspace [w/Paxos], flare, schema‐free, RAMCloud [, Mnesia (Erlang), Chordless] eventually‐consistent key‐value‐store: dynamo, Voldemort, Dynomite, SubRecord, MotionDb, Dovetaildb ordered‐key‐value‐store: tokyo tyrant[, BerkleyDB], lightcloud, NMDB, luxio, memcachedb, actord data‐structures server: redis tuple‐store: gigaspaces [?], coord, apache river object database: ZopeDB, db4o, Shoal document store: CouchDB [evC, MVCC], MongoDB [evC], Jackrabbit, XML Databases, ThruDB, CloudKit, Perservere, Riak Basho [evC], Scalaris [Erlang, w/Paxos] wide columnar store: BigTable, Hadoop HBase [w/ Zookeeper], [Amazon Dynamo-evC, ] Cassandra [evC], Hypertable, KAI, OpenNeptune, Qbase, KDI [graph database: Neo4J, Sones, etc.] 44 From Steve Yen’s slideware (slide 54) he used for his “No SQL is a Horseless Carriage” talk at NoSQL Oakland 2009: http://dl.dropbox.com/u/2075876/nosql-steve-yen.pdf I do not completely understand or agree with Steve’s criteria but it sure is a possible starting point on building a database/storage taxonomy. The stuff in square brackets is mine. “evC” means Eventually Consistent and “?” just means I have doubts / don’t understand some specific classification.
  • 45. Opportunities (...to use these toys) 45
  • 46. Cases to talk about Analytics Live soccer game site (like BBC News did) Log like / timeline systems (forums, healthcare, Twitter, etc.) EAI Integrations (Should use Vector Clocks?) Zookeeper at the “Farm” (Config./Coord.) Logistic Planing across EU Trading 46 This is the placeholder slide to exercise the ideas and discuss possible applications of some of the mechanisms which were presented on this talk (had no time at Codebits... still tuning this not-so-easy presentation). Except for the last two scenarios (and the Twitter alternative on the “Log like” one) all others represent quite common types of problems which you can meet without having to work for a Fortune Top 50 company or for a mega web portal / service. Even an “Analytics” with enough data to justify using MapReduce is common enough. Many large (but not necessarily huge) companies often quit doing more with the data they have just because of the trouble of finding a way to do it (“more”). * “Analytics” (high data + easy on consistency as it is) is currently seem to be the playground of Map Reduce, with Hadoop stuff being used “everywhere”. Look at how many times you can find the words “analytics” or “analysis” (and “MapReduce”) on these “Powered by” Hadoop web pages: http://wiki.apache.org/hadoop/PoweredBy http://wiki.apache.org/hadoop/Hbase/PoweredBy * “Live soccer game...” is a nice problem to discuss short live caching and its consistency issues; * “Log like / timeline systems...” are systems where information is mostly “insert only” and most of the effort to keep consistency is related to keeping proper ordering information (with timestamps being usually enough), properly merging the data from different sources and respect the explicit or implicit SLAs on data synchronizations. Obviously, there are different difficulties across the several cases here mentioned, depending on data flow, necessary performance, etc.; * “EAI Integrations” often need better knowledge about ordering and are not as simples as the previous scenario. Due to factors like the use of asynchronous and event driven mechanisms and the possibility of having updates for a given document across multiple steps of a (multiple) process(es), a timestamp is often too limited as ordering information... but is often the most you get. IMO this is a good scenario for using Vector Clocks and company; * “Zookeeper” is a great system even if “just” to configure the simplest web (or webservice) farm, to coordinate the simplest cross farm operations (e.g.: cache related) or just for each server to know which are its peers; * “Logistic Planing” is a complex scenario which demands a mix of solutions. It revolves around a logistics company which transports goods across Europe, with planning offices on different countries. I will probably have to remove it from this slide for any future talk I might give on this topic even if it is the most interesting of them all. So, it does not make much sense to develop it here (maybe a blog post since, to me, this is a >10 year old
  • 47. Q&A 47