Developing polyglot persistence applications (SpringOne India 2012)

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Developing polyglot persistence applications (SpringOne India 2012)

  1. Developing polyglot persistence applicationsChris RichardsonAuthor of POJOs in Action, Founder of the original CloudFoundry.com @crichardson chris.richardson@springsource.com http://plainoldobjects.com/
  2. Presentation goalThe benefits and drawbacks of polyglot persistence andHow to design applications that use this approach
  3. About Chris
  4. (About Chris)
  5. About Chris()
  6. About Chris
  7. About Chrishttp://www.theregister.co.uk/2009/08/19/springsource_cloud_foundry/
  8. vmc push About-Chris Developer Advocate for CloudFoundry.comSignup at http://cloudfoundry.com
  9. Agenda• Why polyglot persistence?• Using Redis as a cache• Optimizing queries using Redis materialized views• Synchronizing MySQL and Redis• Tracking changes to entities
  10. Food to Go• Take-out food delivery service• “Launched” in 2006
  11. Food To Go Architecture RESTAURANT CONSUMER OWNER Order Restaurant taking Management MySQL Database
  12. Success Growth challenges• Increasing traffic• Increasing data volume• Distribute across a few data centers• Increasing domain model complexity
  13. Limitations of relational databases• Scalability• Distribution• Schema updates• O/R impedance mismatch• Handling semi-structured data
  14. Solution: Spend Moneyhttp://upload.wikimedia.org/wikipedia/commons/e/e5/Rising_Sun_Yacht.JPG OR http://www.trekbikes.com/us/en/bikes/road/race_performance/madone_5_series/madone_5_2/#
  15. Solution: Use NoSQL Benefits Drawbacks• Higher performance • Limited transactions• Higher scalability • Limited querying• Richer data-model • Relaxed consistency• Schema-less • Unconstrained data
  16. Example NoSQL DatabasesDatabase Key featuresCassandra Extensible column store, very scalable, distributedNeo4j Graph database Document-oriented, fast, scalableMongoDBRedis Key-value store, very fast http://nosql-database.org/ lists 122+ NoSQL databases
  17. Redis• Advanced key-value store K1 V1• C-based server K2 V2• Very fast, e.g. 100K reqs/sec ... ...• Optional persistence• Transactions with optimistic locking• Master-slave replication• Sharding using client-side consistent hashing
  18. Sorted sets Value Key a b myset 5.0 10. Members are Scoresorted by score
  19. Adding members to a sorted set Redis Server Key Score Value a zadd myset 5.0 a myset 5.0
  20. Adding members to a sorted set Redis Server a b zadd myset 10.0 b myset 5.0 10.
  21. Adding members to a sorted set Redis Server c a b zadd myset 1.0 c myset 1.0 5.0 10.
  22. Retrieving members by index range Start End Key Index Index Redis Server zrange myset 0 1 c a b myset 1.0 5.0 10. c a
  23. Retrieving members by score Min Max Key value value Redis Serverzrangebyscore myset 1 6 c a b myset 1.0 5.0 10. c a
  24. Redis use cases• Replacement for Memcached • Handling tasks that overload an RDBMS • Session state • Hit counts - INCR • Cache of data retrieved from • Most recent N items - LPUSH and system of record (SOR) LTRIM• Replica of SOR for queries • Randomly selecting an item – needing high-performance SRANDMEMBER • Queuing – Lists with LPOP, RPUSH, …. • High score tables – Sorted sets and ZINCRBY • …
  25. Redis is great but there are tradeoffs• Low-level query language: PK-based access only• Limited transaction model: • Read first and then execute updates as batch • Difficult to compose code• Data must fit in memory• Single-threaded server: run multiple with client-side sharding• Missing features such as access control, ...
  26. And don’t forget:An RDBMS is fine for many applications
  27. The future is polyglot e.g. Netflix • RDBMS • SimpleDB • Cassandra • Hadoop/HbaseIEEE Software Sept/October 2010 - Debasish Ghosh / Twitter @debasishg
  28. Agenda• Why polyglot persistence?• Using Redis as a cache• Optimizing queries using Redis materialized views• Synchronizing MySQL and Redis• Tracking changes to entities
  29. Increase scalability by caching RESTAURANT CONSUMER OWNER Order Restaurant taking Management MySQL Cache Database
  30. Caching Options• Where: • Hibernate 2nd level cache • Explicit calls from application code • Caching aspect• Cache technologies: Ehcache, Memcached, Infinispan, ... Redis is also an option
  31. Using Redis as a cache• Spring 3.1 cache abstraction • Annotations specify which methods to cache • CacheManager - pluggable back-end cache• Spring Data for Redis • Simplifies the development of Redis applications • Provides RedisTemplate (analogous to JdbcTemplate) • Provides RedisCacheManager
  32. Using Spring 3.1 Caching@Servicepublic class RestaurantManagementServiceImpl implements RestaurantManagementService { private final RestaurantRepository restaurantRepository; @Autowired public RestaurantManagementServiceImpl(RestaurantRepository restaurantRepository) { this.restaurantRepository = restaurantRepository; } @Override public void add(Restaurant restaurant) { Cache result restaurantRepository.add(restaurant); } @Override @Cacheable(value = "Restaurant") public Restaurant findById(int id) { return restaurantRepository.findRestaurant(id); Evict from } cache @Override @CacheEvict(value = "Restaurant", key="#restaurant.id") public void update(Restaurant restaurant) { restaurantRepository.update(restaurant); }
  33. Configuring the Redis Cache Manager Enables caching <cache:annotation-driven /> <bean id="cacheManager" class="org.springframework.data.redis.cache.RedisCacheManager" > <constructor-arg ref="restaurantTemplate"/> </bean> Specifies CacheManager The RedisTemplate used implementation to access Redis
  34. Domain object to key-value mapping? Restaurant K1 V1TimeRangeTimeRange MenuItem MenuItem K2 V2 ... ... ServiceArea
  35. RedisTemplate• Analogous to JdbcTemplate• Encapsulates boilerplate code, e.g. connection management• Maps Java objects Redis byte[]’s
  36. Serializers: object byte[]• RedisTemplate has multiple serializers• DefaultSerializer - defaults to JdkSerializationRedisSerializer• KeySerializer• ValueSerializer• HashKeySerializer• HashValueSerializer
  37. Serializing a Restaurant as JSON@Configurationpublic class RestaurantManagementRedisConfiguration { @Autowired private RestaurantObjectMapperFactory restaurantObjectMapperFactory; private JacksonJsonRedisSerializer<Restaurant> makeRestaurantJsonSerializer() { JacksonJsonRedisSerializer<Restaurant> serializer = new JacksonJsonRedisSerializer<Restaurant>(Restaurant.class); ... return serializer; } @Bean @Qualifier("Restaurant") public RedisTemplate<String, Restaurant> restaurantTemplate(RedisConnectionFactory factory) { RedisTemplate<String, Restaurant> template = new RedisTemplate<String, Restaurant>(); template.setConnectionFactory(factory); JacksonJsonRedisSerializer<Restaurant> jsonSerializer = makeRestaurantJsonSerializer(); template.setValueSerializer(jsonSerializer); return template; } Serialize restaurants using Jackson} JSON
  38. Caching with Redis RESTAURANT CONSUMER OWNER Order Restaurant taking Management Redis MySQLFirst Second Cache Database
  39. Agenda• Why polyglot persistence?• Using Redis as a cache• Optimizing queries using Redis materialized views• Synchronizing MySQL and Redis• Tracking changes to entities
  40. Finding available restaurantsAvailable restaurants = Serve the zip code of the delivery address AND Are open at the delivery timepublic interface AvailableRestaurantRepository { List<AvailableRestaurant> findAvailableRestaurants(Address deliveryAddress, Date deliveryTime); ...}
  41. Food to Go – Domain model (partial)class Restaurant { class TimeRange { long id; long id; String name; int dayOfWeek; Set<String> serviceArea; int openTime; Set<TimeRange> openingHours; int closeTime; List<MenuItem> menuItems; }} class MenuItem { String name; double price; }
  42. Database schemaID Name … RESTAURANT table1 Ajanta2 Montclair EggshopRestaurant_id zipcode RESTAURANT_ZIPCODE table1 947071 946192 946112 94619 RESTAURANT_TIME_RANGE tableRestaurant_id dayOfWeek openTime closeTime1 Monday 1130 14301 Monday 1730 21302 Tuesday 1130 …
  43. Finding available restaurants on Monday, 6.15pm for 94619 zipcode Straightforward three-way joinselect r.*from restaurant r inner join restaurant_time_range tr on r.id =tr.restaurant_id inner join restaurant_zipcode sa on r.id = sa.restaurant_idwhere ’94619’ = sa.zip_code and tr.day_of_week=’monday’ and tr.openingtime <= 1815 and 1815 <= tr.closingtime
  44. How to scale queries?
  45. Option #1: Query caching• [ZipCode, DeliveryTime] ⇨ list of available restaurants BUT• Long tail queries• Update restaurant ⇨ Flush entire cache Ineffective
  46. Option #2: Master/Slave replication Writes Consistent reads Queries MySQL (Inconsistent reads) Master MySQL MySQL MySQL Slave 1 Slave 2 Slave N
  47. Master/Slave replication• Mostly straightforward BUT• Assumes that SQL query is efficient• Complexity of administration of slaves• Doesn’t scale writes
  48. Option #3: Redis materialized views RESTAURANT CONSUMER OWNER Order Restaurant taking Management System ofCopy update() Record findAvailable() MySQL Redis Cache Database
  49. BUT how to implement findAvailableRestaurants() with Redis?! ?select r.*from restaurant r K1 V1 inner join restaurant_time_range tr on r.id =tr.restaurant_id inner join restaurant_zipcode sa on r.id = sa.restaurant_id K2 V2where ’94619’ = sa.zip_code and tr.day_of_week=’monday’ and tr.openingtime <= 1815 ... ... and 1815 <= tr.closingtime Solution: Build an index using sorted sets and ZRANGEBYSCORE
  50. Where we need to beZRANGEBYSCORE myset 1 6 = sorted_setselect value,score key value scorefrom sorted_setwhere key = ‘myset’ and score >= 1 and score <= 6
  51. We need to denormalizeThink materialized view
  52. Simplification #1: DenormalizationRestaurant_id Day_of_week Open_time Close_time Zip_code1 Monday 1130 1430 947071 Monday 1130 1430 946191 Monday 1730 2130 947071 Monday 1730 2130 946192 Monday 0700 1430 94619… SELECT restaurant_id FROM time_range_zip_code WHERE day_of_week = ‘Monday’ Simpler query: § No joins AND zip_code = 94619 § Two = and two < AND 1815 < close_time AND open_time < 1815
  53. Simplification #2: Application filteringSELECT restaurant_id, open_timeFROM time_range_zip_codeWHERE day_of_week = ‘Monday’ Even simpler query • No joins AND zip_code = 94619 • Two = and one < AND 1815 < close_time AND open_time < 1815
  54. Simplification #3: Eliminate multiple =’s with concatenation Restaurant_id Zip_dow Open_time Close_time 1 94707:Monday 1130 1430 1 94619:Monday 1130 1430 1 94707:Monday 1730 2130 1 94619:Monday 1730 2130 2 94619:Monday 0700 1430 …SELECT restaurant_id, open_timeFROM time_range_zip_codeWHERE zip_code_day_of_week = ‘94619:Monday’ AND 1815 < close_time key range
  55. Simplification #4: Eliminate multiple RETURN VALUES with concatenation zip_dow open_time_restaurant_id close_time 94707:Monday 1130_1 1430 94619:Monday 1130_1 1430 94707:Monday 1730_1 2130 94619:Monday 1730_1 2130 94619:Monday 0700_2 1430 ... SELECT open_time_restaurant_id, FROM time_range_zip_code WHERE zip_code_day_of_week = ‘94619:Monday’ AND 1815 < close_time ✔
  56. Using a Redis sorted set as an index zip_dow open_time_restaurant_id close_time 94707:Monday 1130_1 1430 94619:Monday 1130_1 1430 94707:Monday 1730_1 2130 94619:Monday 1730_1 2130 94619:Monday 0700_2 1430 ... Key Sorted Set [ Entry:Score, …] 94619:Monday [0700_2:1430, 1130_1:1430, 1730_1:2130] 94707:Monday [1130_1:1430, 1730_1:2130]
  57. Querying with ZRANGEBYSCORE Key Sorted Set [ Entry:Score, …] 94619:Monday [0700_2:1430, 1130_1:1430, 1730_1:2130] 94707:Monday [1130_1:1430, 1730_1:2130] Delivery zip and day Delivery time ZRANGEBYSCORE 94619:Monday 1815 2359 è {1730_1} 1730 is before 1815 è Ajanta is open
  58. Adding a Restaurant@Componentpublic class AvailableRestaurantRepositoryImpl implements AvailableRestaurantRepository { @Override public void add(Restaurant restaurant) { addRestaurantDetails(restaurant); Store as addAvailabilityIndexEntries(restaurant); JSON } Text private void addRestaurantDetails(Restaurant restaurant) { restaurantTemplate.opsForValue().set(keyFormatter.key(restaurant.getId()), restaurant); } private void addAvailabilityIndexEntries(Restaurant restaurant) { for (TimeRange tr : restaurant.getOpeningHours()) { String indexValue = formatTrId(restaurant, tr); key member int dayOfWeek = tr.getDayOfWeek(); int closingTime = tr.getClosingTime(); for (String zipCode : restaurant.getServiceArea()) { redisTemplate.opsForZSet().add(closingTimesKey(zipCode, dayOfWeek), indexValue, closingTime); } } } score
  59. Finding available Restaurants@Componentpublic class AvailableRestaurantRepositoryImpl implements AvailableRestaurantRepository { @Override public List<AvailableRestaurant> findAvailableRestaurants(Address deliveryAddress, Date deliveryTime) { Find those that String zipCode = deliveryAddress.getZip(); close after int dayOfWeek = DateTimeUtil.dayOfWeek(deliveryTime); int timeOfDay = DateTimeUtil.timeOfDay(deliveryTime); String closingTimesKey = closingTimesKey(zipCode, dayOfWeek); Set<String> trsClosingAfter = redisTemplate.opsForZSet().rangeByScore(closingTimesKey, timeOfDay, 2359); Set<String> restaurantIds = new HashSet<String>(); for (String tr : trsClosingAfter) { Filter out those that String[] values = tr.split("_"); open after if (Integer.parseInt(values[0]) <= timeOfDay) restaurantIds.add(values[1]); } Collection<String> keys = keyFormatter.keys(restaurantIds); return availableRestaurantTemplate.opsForValue().multiGet(keys); Retrieve open } restaurants
  60. Sorry Ted!http://en.wikipedia.org/wiki/Edgar_F._Codd
  61. Agenda• Why polyglot persistence?• Using Redis as a cache• Optimizing queries using Redis materialized views• Synchronizing MySQL and Redis• Tracking changes to entities
  62. MySQL & Redisneed to be consistent
  63. Two-Phase commit is not an option• Redis does not support it• Even if it did, 2PC is best avoided http://www.infoq.com/articles/ebay-scalability-best-practices
  64. AtomicConsistent Basically AvailableIsolated Soft stateDurable Eventually consistentBASE: An Acid Alternative http://queue.acm.org/detail.cfm?id=1394128
  65. Updating Redis #FAILbegin MySQL transaction update MySQL Redis has update update Redis MySQL does notrollback MySQL transactionbegin MySQL transaction update MySQL MySQL has updatecommit MySQL transaction Redis does not<<system crashes>> update Redis
  66. Updating Redis reliably Step 1 of 2begin MySQL transaction update MySQL ACID queue CRUD event in MySQLcommit transaction Event Id Operation: Create, Update, Delete New entity state, e.g. JSON
  67. Updating Redis reliably Step 2 of 2for each CRUD event in MySQL queue get next CRUD event from MySQL queue If CRUD event is not duplicate then Update Redis (incl. eventId) end if begin MySQL transaction mark CRUD event as processed commit transaction
  68. Step 1 Step 2 Timer EntityCrudEvent EntityCrudEvent apply(event) Redis Repository Processor UpdaterINSERT INTO ... SELECT ... FROM ... ENTITY_CRUD_EVENT ID JSON processed? Redis
  69. Optimistic locking Updating RedisWATCH restaurant:lastSeenEventId:≪restaurantId≫lastSeenEventId = GET restaurant:lastSeenEventId:≪restaurantId≫ Duplicateif (lastSeenEventId >= eventId) return; detectionMULTI SET restaurant:lastSeenEventId:≪restaurantId≫ eventId Transaction ... update the restaurant data...EXEC
  70. Agenda• Why polyglot persistence?• Using Redis as a cache• Optimizing queries using Redis materialized views• Synchronizing MySQL and Redis• Tracking changes to entities
  71. How do we generate CRUD events?
  72. Change tracking options• Explicit code• Hibernate event listener• Service-layer aspect• CQRS/Event-sourcing
  73. HibernateEvent EntityCrudEvent Listener Repository ENTITY_CRUD_EVENT ID JSON processed?
  74. Hibernate event listenerpublic class ChangeTrackingListener implements PostInsertEventListener, PostDeleteEventListener, PostUpdateEventListener { @Autowired private EntityCrudEventRepository entityCrudEventRepository; private void maybeTrackChange(Object entity, EntityCrudEventType eventType) { if (isTrackedEntity(entity)) { entityCrudEventRepository.add(new EntityCrudEvent(eventType, entity)); } } @Override public void onPostInsert(PostInsertEvent event) { Object entity = event.getEntity(); maybeTrackChange(entity, EntityCrudEventType.CREATE); } @Override public void onPostUpdate(PostUpdateEvent event) { Object entity = event.getEntity(); maybeTrackChange(entity, EntityCrudEventType.UPDATE); } @Override public void onPostDelete(PostDeleteEvent event) { Object entity = event.getEntity(); maybeTrackChange(entity, EntityCrudEventType.DELETE); }
  75. Summary• Each SQL/NoSQL database = set of tradeoffs• Polyglot persistence: leverage the strengths of SQL and NoSQL databases• Use Redis as a distributed cache• Store denormalized data in Redis for fast querying• Reliable database synchronization required
  76. @crichardson chris.richardson@springsource.com http://plainoldobjects.com Questions? Sign up for CloudFoundry.com

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