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Architecture by Accident

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Presentation for 2nd Sao Paulo Perl Workshop on May 7th 2011 - notes and comments about architecture evolution on legacy systems

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Architecture by Accident

  1. 1. Architecture by accident Gleicon  Moraes  
  2. 2. Required Listening
  3. 3. Agenda •  Architecture for data - even if you don’t want it •  Databases •  Message Queues •  Cache
  4. 4. Architecture “Everyone  has  a  plan  un4l  they  get  punched  in   the  mouth”  –  Mike  Tyson    
  5. 5. Even if you dont want it ... •  There is an innate architecture on everything •  You may end up with more data than you had planned to •  You may get away from your quick and dirty CRUD •  You probably are querying more than one Database •  At some point you laugh when your boss asks you about 'Integrating Systems' •  Code turns into legacy - and so architectures •  'Scattered' is not the same that 'Distributed'
  6. 6. It  usually  starts  like  this   App Server Database
  7. 7. then App Servers Database
  8. 8. it App Servers Master DB Slave DB
  9. 9. goes App Servers Master DB Slave DB Cache
  10. 10. like App Servers Master DB Slave DB Cache Indexing Service
  11. 11. this App Servers Master DB Slave DB Cache Indexing Service API Servers
  12. 12. and App Servers Master DB Load Balancer/Reverse Proxy Slave DB Cache Indexing Service API Servers
  13. 13. beyond App Servers Master DB Load Balancer/Reverse Proxy Slave DB Cache Indexing Service API Servers Auth Service
  14. 14. Problem is... An architect s first work is apt to be spare and clean. He knows he doesn t know what he s doing, so he does it carefully and with great restraint.   As he designs the first work, frill after frill and embellishment after embellishment occur to him. These get stored away to be used next time. Sooner or later the first system is finished, and the architect, with firm confidence and a demonstrated mastery of that class of systems, is ready to build a second system.   This second is the most dangerous system a man ever designs. When he does his third and later ones, his prior experiences will confirm each other as to the general characteristics of such systems, and their differences will identify those parts of his experience that are particular and not generalizable.   The general tendency is to over-design the second system, using all the ideas and frills that were cautiously sidetracked on the first one. The result, as Ovid says, is a big pile.   — Frederick P. Brooks, Jr. The Mythical Man-Month
  15. 15. Databases
  16. 16. Databases   •  Not  an  off-­‐the-­‐shelf  architectural  duct  tape   •  Not  only  rela4onal,  other  paradigms   •  Usually  the  last  place  sought  for  op4miza4on   •  Usually  the  first  place  to  accomodate  last  minute   changes   •  Good  ideas  to  try  out:  Sharding  and   Denormaliza4on   •  Some  of  your  problems  may  require  something   other  than  a  Rela4onal  Database  
  17. 17. Relevant RDBMS Anti-Patterns –  Dynamic table creation –  Table as cache –  Table as queue –  Table as log file –  Distributed Global Locking –  Stoned Procedures –  Row Alignment –  Extreme JOINs –  Your ORM issue full queries for Dataset iterations –  Throttle Control
  18. 18. Dynamic table creation Problem: To avoid huge tables, "dynamic schema” is created. For example, lets think about a document management company, which is adding new facilities over the country. For each storage facility, a new table is created: item_id - row - column - stuff 1 - 10 - 20 - cat food 2 - 12 - 32 - trout Side Effect: "dynamic queries", which will probably query a "central storage" table and issue a huge join to check if you have enough cat food over the country. It’s different from Sharding. Alternative: - Document storage, modeling a facility as a document -  Key/Value, modeling each facility as a SET -  Sharding properly
  19. 19. Table as cache Problem: Complex queries demand that a result be stored in a separated table, so it can be queried quickly. Worst than views Alternative: - Really ? - Memcached - Redis + AOF + EXPIRE - Denormalization
  20. 20. Table as queue Problem: A table which holds messages to be completed. Worse, they must be sorted by date. Alternative: - RestMQ, Resque - Any other message broker - Redis (LISTS - LPUSH + RPOP) - Use the right tool
  21. 21. Table as log file Problem: A table in which data gets written as a log file. From time to time it needs to be purged. Truncating this table once a day usually is the first task assigned to trainee DBAs. Alternative: - MongoDB capped collection - Redis, and a RRD pattern -  RIAK
  22. 22. Distributed Global Locking Problem: Someone learns java and synchronize. A bit later genius thinks that a distributed synchronize would be awesome. The proper place to do that would be the database of course. Start with a reference counter in a table and end up with this: > select COALESCE(GET_LOCK('my_lock',0 ),0 ) Plain and simple, you might find it embedded in a magic class called DistributedSynchronize or ClusterSemaphore. Locks, transactions and reference counters (which may act as soft locks) doesn't belongs to the database.
  23. 23. Stoned procedures Problem: Stored procedures hold most of your applications logic. Also, some triggers are used to - well - trigger important data events. SP and triggers has the magic property of vanishing of our mind instantly, being impossible to keep versioned. Alternative: - Careful so you don’t use map/reduce as stoned procedures. - Use your preferred language for business stuff, and let event handling to pub/sub or message queues.
  24. 24. Row Alignment Problem: Extra rows are created but not used, just in case. Usually they are named as a1, a2, a3, a4 and called padding. There's good will behind that, specially when version 1 of the software needed an extra column in a 150M lines database and it took 2 days to run an ALTER TABLE. Alternative: - Document based databases as MongoDB and CouchDB, where new atributes are local to the document. Also, having no schema helps - Column based databases may be not the best choice if column creation need restart/migrations
  25. 25. Extreme JOINs Problem: Business rules modeled as tables. Table inheritance (Product -> SubProduct_A). To find the complete data for a user plan, one must issue gigantic queries with lots of JOINs. Alternative: - Document storage, as MongoDB - Denormalization - Serialized objects
  26. 26. Your ORM ... Problem: Your ORM issue full queries for dataset iterations, your ORM maps and creates tables which mimics your classes, even the inheritance, and the performance is bad because the queries are huge, etc, etc Alternative: Apart from denormalization and good old common sense, ORMs are trying to bridge two things with distinct impedance. There is nothing to relational models which maps cleanly to classes and objects. Not even the basic unit which is the domain(set) of each column. Black Magic ?
  27. 27. Throttle Control Problem: A request tracker to create a throttle control by IP address, login, operation or any other event using a relational database   Ranging from an update … select to a lock/transaction block, no relational database would be the best place to do that. Alternative: use memcached, redis or any other DHT which has expiration by creating a key as THROTLE:<IP>:YYYYMMDDHH and increment it. At first glance sounds the same but the expiration will take care of cleaning up old entries. Also search time is the same as looking for a key.
  28. 28. No silver bullet - Consider alternatives   - Think outside the norm   - Denormalize   - Simplify  
  29. 29. Cycle of changes - Product A 1. There was the database model   2. Then, the cache was needed. Performance was no good.   3. Cache key: query, value: resultset   4. High or inexistent expiration time [w00t]   (Now there's a turning point. Data didn't need to change often. Denormalization was a given with cache)   5. The cache needs to be warmed or the app wont work.   6. Key/Value storage was a natural choice. No data on MySQL anymore.
  30. 30. Cycle of changes - Product B 1. Postgres DB storing crawler results.   2. There was a counter in each row, and updating this counter caused contention errors.   3. Memcache for reads. Performance is better.   4. First MongoDB test, no more deadlocks from counter update.   5. Data model was simplified, the entire crawled doc was stored.  
  31. 31. Stuff to think about Think if the data you use aren't denormalized (cached)   Most of the anti-patterns contain signs that a non-relational route (or at least a partial route) may help.   Are you dependent on cache ? Does your application fails when there is no cache ? Does it just slows down ?   Are you ready to think more about your data ?   Think about the way to put and to get back your data from the database (be it SQL or NoSQL).  
  32. 32. Extra - MongoDB and Redis The next two slides are here to show what is like to use MongoDB and Redis for the same task.   There is more to managing your data than stuffing it inside a database. You gotta plan ahead for searches and migrations.   This example is about storing books and searching between them. MongoDB makes it simpler, just liek using its query language. Redis requires that you keep track of tags and ids to use SET operations to recover which books you want.   Check http://rediscookbook.org and http:// cookbook.mongodb.org/ for recipes on data handling.
  33. 33. MongoDB/Redis recap - Books MongoDB   Redis   {   'id': 1,     'title' : 'Diving into Python',   SET book:1 {'title' : 'Diving into Python',     'author': 'Mark Pilgrim', 'author': 'Mark Pilgrim'}   'tags': ['python','programming', 'computing']   SET book:2 { 'title' : 'Programing Erlang',   }   'author': 'Joe Armstrong'} SET book:3 { 'title' : 'Programing in Haskell',   {   'author': 'Graham Hutton'}   'id':2,   'title' : 'Programing Erlang',     'author': 'Joe Armstrong',   SADD tag:python 1 SADD tag:erlang 2   'tags': ['erlang','programming', 'computing',   SADD tag:haskell 3   'distributedcomputing', 'FP']   SADD tag:programming 1 2 3   } SADD tag computing 1 2 3   {   SADD tag:distributedcomputing 2   'id':3,   SADD tag:FP 2 3   'title' : 'Programing in Haskell',   'author': 'Graham Hutton',   'tags': ['haskell','programming', 'computing', 'FP']   }  
  34. 34. MongoDB/Redis recap - Books MongoDB   Redis   Search tags for erlang or haskell:     SINTER 'tag:erlang' 'tag:haskell'   db.books.find({"tags": { $in: ['erlang', 'haskell']     0 results }       }) SINTER 'tag:programming' 'tag:computing'   3 results: 1, 2, 3 Search tags for erlang AND haskell (no results)   SUNION 'tag:erlang' 'tag:haskell'   db.books.find({"tags":   2 results: 2 and 3   { $all: ['erlang', 'haskell']     }   SDIFF 'tag:programming' 'tag:haskell'     }) 2 results: 1 and 2 (haskell is excluded) This search yields 3 results   db.books.find({"tags":   { $all: ['programming', 'computing']   }     })
  35. 35. Message Queues
  36. 36. Decoupling db writes with Message Queues
  37. 37. Coupled comment
  38. 38. Uncoupled comment - producer
  39. 39. Uncoupled comment - consumer
  40. 40. Async HTML Scrapper Fetch Page 1st parse Fetch Page Message Queue 1st parse Consumer Fetch Page 1st parse Fetch Page 1st parse
  41. 41. M/R   Fetch Data Map(Fun) Fetch Data Message Queue Map(Fun) Reduce Fetch Data Map(Fun) Fetch Data Map(Fun)
  42. 42. M/R  –  Wordcount(Map)  
  43. 43. M/R  –  Wordcount(Reduce)  
  44. 44. Cache
  45. 45. Cache
  46. 46. HTML processing - no cache http://github.com/gleicon/vuvuzelr/proxy_no_cache.rb
  47. 47. HTML processing - Cached http://github.com/gleicon/vuvuzelr/proxy.rb
  48. 48. Conclusion
  49. 49. Thanks   •  @gleicon   •  hQp://www.7co.cc   •  hQp://github.com/gleicon   •  gleicon@gmail.com  
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Presentation for 2nd Sao Paulo Perl Workshop on May 7th 2011 - notes and comments about architecture evolution on legacy systems

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