Developing polyglot persistence applications (svcc, svcc2013)
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Developing polyglot persistence applications (svcc, svcc2013)

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NoSQL databases such as Redis, MongoDB and Cassandra are emerging as a compelling choice for many applications. They can simplify the persistence of complex data models and offer significantly better ...

NoSQL databases such as Redis, MongoDB and Cassandra are emerging as a compelling choice for many applications. They can simplify the persistence of complex data models and offer significantly better scalability and performance. However, using a NoSQL database means giving up the benefits of the relational model such as SQL, constraints and ACID transactions. For some applications, the solution is polyglot persistence: using SQL and NoSQL databases together. In this talk, you will learn about the benefits and drawbacks of polyglot persistence and how to design applications that use this approach. We will explore the architecture and implementation of an example application that uses MySQL as the system of record and Redis as a very high-performance database that handles queries from the front-end. You will learn about mechanisms for maintaining consistency across the various databases.

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    Developing polyglot persistence applications (svcc, svcc2013) Developing polyglot persistence applications (svcc, svcc2013) Presentation Transcript

    • DEVELOPING POLYGLOT PERSISTENCE APPLICATIONS Chris Richardson Author of POJOs in Action Founder of the original CloudFoundry.com @crichardson chris@chrisrichardson.net http://plainoldobjects.com
    • @crichardson Presentation goal The benefits and drawbacks of polyglot persistence and How to design applications that use this approach
    • @crichardson About Chris
    • @crichardson (About Chris)
    • @crichardson About Chris()
    • @crichardson About Chris
    • @crichardson About Chris http://www.theregister.co.uk/2009/08/19/springsource_cloud_foundry/
    • @crichardson About Chris
    • @crichardson Agenda • Why polyglot persistence? • Using Redis as a cache • Optimizing queries using Redis materialized views • Synchronizing MySQL and Redis • Tracking changes to entities
    • @crichardson Food to Go • Take-out food delivery service • “Launched” in 2006
    • @crichardson FoodTo Go Architecture Order taking Restaurant Management MySQL Database CONSUMER RESTAURANT OWNER
    • @crichardson Success Growth challenges • Increasing traffic • Increasing data volume • Distribute across a few data centers • Increasing domain model complexity
    • @crichardson Limitations of relational databases • Scalability • Distribution • Schema updates • O/R impedance mismatch • Handling semi-structured data
    • @crichardson Solution: Spend $$$ - high-end databases and servers http://www.powerandmotoryacht.com/megayachts/megayacht-musashi
    • @crichardson Solution: Spend $$$ - open- source stack + DevOps people http://www.trekbikes.com/us/en/bikes/road/race_performance/madone_5_series/madone_5_2/#
    • @crichardson Solution: Use NoSQL Benefits • Higher performance • Higher scalability • Richer data-model • Schema-less Drawbacks • Limited transactions • Limited querying • Relaxed consistency • Unconstrained data
    • @crichardson Example NoSQL Databases Database Key features Cassandra Extensible column store, very scalable, distributed MongoDB Document-oriented, fast, scalable Redis Key-value store, very fast http://nosql-database.org/ lists 122+ NoSQL databases
    • @crichardson Redis: Fast and Scalable Redis master Redis slave Redis slave File system Client In-memory = FAST Durable Redis master Redis slave Redis slave File system Consistent Hashing
    • @crichardson Redis: advanced key/value store K1 V1 K2 V2 ... ... String SET, GET List LPUSH, LPOP, ... Set SADD, SMEMBERS, .. Sorted Set Hashes ZADD, ZRANGE HSET, HGET
    • @crichardson Sorted sets myset a 5.0 b 10. Key Value ScoreMembers are sorted by score
    • @crichardson Adding members to a sorted set Redis Server zadd myset 5.0 a myset a 5.0 Key Score Value
    • @crichardson Adding members to a sorted set Redis Server zadd myset 10.0 b myset a 5.0 b 10.
    • @crichardson Adding members to a sorted set Redis Server zadd myset 1.0 c myset a 5.0 b 10. c 1.0
    • @crichardson Retrieving members by index range Redis Server zrange myset 0 1 myset a 5.0 b 10. c 1.0 ac Key Start Index End Index
    • @crichardson Retrieving members by score Redis Server zrangebyscore myset 1 6 myset a 5.0 b 10. c 1.0 ac Key Min value Max value
    • @crichardson Redis use cases • Replacement for Memcached • Session state • Cache of data retrieved from system of record (SOR) • Replica of SOR for queries needing high-performance • Handling tasks that overload an RDBMS • Hit counts - INCR • Most recent N items - LPUSH and LTRIM • Randomly selecting an item – SRANDMEMBER • Queuing – Lists with LPOP, RPUSH, …. • High score tables – Sorted sets and ZINCRBY • …
    • @crichardson The future is polyglot IEEE Software Sept/October 2010 - Debasish Ghosh / Twitter @debasishg e.g. Netflix • RDBMS • SimpleDB • Cassandra • Hadoop/Hbase
    • @crichardson Benefits RDBMS ACID transactions Rich queries ... + NoSQL Performance Scalability ...
    • @crichardson Drawbacks Complexity Development Operational
    • @crichardson Polyglot Pattern: Cache Order taking Restaurant Management MySQL Database CONSUMER RESTAURANT OWNER Redis Cache First Second
    • @crichardson Redis:Timeline Polyglot Pattern: write to SQL and NoSQL Follower 1 Follower 2 Follower 3 ... LPUSHX LTRIM MySQL Tweets Followers INSERT NEW TWEET Follows Avoids expensive MySQL joins
    • @crichardson Polyglot Pattern: replicate from MySQL to NoSQL Query Service Update Service MySQL Database Reader Writer Redis System of RecordMaterialized view query() update() replicate and denormalize
    • @crichardson Agenda • Why polyglot persistence? • Using Redis as a cache • Optimizing queries using Redis materialized views • Synchronizing MySQL and Redis • Tracking changes to entities
    • @crichardson Food to Go – Domain model (partial) class Restaurant { long id; String name; Set<String> serviceArea; Set<TimeRange> openingHours; List<MenuItem> menuItems; } class MenuItem { String name; double price; } class TimeRange { long id; int dayOfWeek; int openTime; int closeTime; }
    • @crichardson Database schema ID Name … 1 Ajanta 2 Montclair Eggshop Restaurant_id zipcode 1 94707 1 94619 2 94611 2 94619 Restaurant_id dayOfWeek openTime closeTime 1 Monday 1130 1430 1 Monday 1730 2130 2 Tuesday 1130 … RESTAURANT table RESTAURANT_ZIPCODE table RESTAURANT_TIME_RANGE table
    • @crichardson RestaurantRepository public interface RestaurantRepository { void addRestaurant(Restaurant restaurant); Restaurant findById(long id); ... } Food To Go will have scaling eventually issues
    • @crichardson Increase scalability by caching Order taking Restaurant Management MySQL Database CONSUMER RESTAURANT OWNER Cache
    • @crichardson Caching Options • Where: • Hibernate 2nd level cache • Explicit calls from application code • Caching aspect • Cache technologies: Ehcache, Memcached, Infinispan, ... Redis is also an option
    • @crichardson 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
    • @crichardson Using Spring 3.1 Caching @Service public class RestaurantManagementServiceImpl implements RestaurantManagementService { private final RestaurantRepository restaurantRepository; @Autowired public RestaurantManagementServiceImpl(RestaurantRepository restaurantRepository) { this.restaurantRepository = restaurantRepository; } @Override public void add(Restaurant restaurant) { restaurantRepository.add(restaurant); } @Override @Cacheable(value = "Restaurant") public Restaurant findById(int id) { return restaurantRepository.findRestaurant(id); } @Override @CacheEvict(value = "Restaurant", key="#restaurant.id") public void update(Restaurant restaurant) { restaurantRepository.update(restaurant); } Cache result Evict from cache
    • @crichardson Configuring the Redis Cache Manager <cache:annotation-driven /> <bean id="cacheManager" class="org.springframework.data.redis.cache.RedisCacheManager" > <constructor-arg ref="restaurantTemplate"/> </bean> Enables caching Specifies CacheManager implementation The RedisTemplate used to access Redis
    • @crichardson TimeRange MenuItem Domain object to key-value mapping? Restaurant TimeRange MenuItem ServiceArea K1 V1 K2 V2 ... ...
    • @crichardson RedisTemplate handles the mapping • Principal API provided by Spring Data to Redis • Analogous to JdbcTemplate • Encapsulates boilerplate code, e.g. connection management • Maps Java objects Redis byte[]’s
    • @crichardson Serializers: object byte[] • RedisTemplate has multiple serializers • DefaultSerializer - defaults to JdkSerializationRedisSerializer • KeySerializer • ValueSerializer • ...
    • @crichardson Serializing a Restaurant as JSON @Configuration public 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
    • @crichardson Caching with Redis Order taking Restaurant Management MySQL Database CONSUMER RESTAURANT OWNER Redis Cache First Second
    • @crichardson Storing restaurants in Redis ...JSON...restaurant:1
    • @crichardson Agenda • Why polyglot persistence? • Using Redis as a cache • Optimizing queries using Redis materialized views • Synchronizing MySQL and Redis • Tracking changes to entities
    • @crichardson Finding available restaurants Available restaurants = Serve the zip code of the delivery address AND Are open at the delivery time public interface AvailableRestaurantRepository { List<AvailableRestaurant> findAvailableRestaurants(Address deliveryAddress, Date deliveryTime); ... }
    • @crichardson Finding available restaurants on Monday, 6.15pm for 94619 zipcode Straightforward three-way join select 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_id where ’94619’ = sa.zip_code and tr.day_of_week=’monday’ and tr.openingtime <= 1815 and 1815 <= tr.closingtime
    • @crichardson How to scale queries?
    • @crichardson Option #1: Query caching • [ZipCode, DeliveryTime] ⇨ list of available restaurants BUT • Long tail queries • Update restaurant ⇨ Flush entire cache Ineffective
    • @crichardson Option #2: Master/Slave replication MySQL Master MySQL Slave 1 MySQL Slave 2 MySQL Slave N Writes Consistent reads Queries (Inconsistent reads)
    • @crichardson Master/Slave replication • Mostly straightforward BUT • Assumes that SQL query is efficient • Complexity of administration of slaves • Doesn’t scale writes
    • @crichardson Option #3: Redis materialized views Order taking Restaurant Management MySQL Database CONSUMER RESTAURANT OWNER CacheRedis System of RecordCopy findAvailable() update()
    • @crichardson BUT how to implement findAvailableRestaurants() with Redis?! ? select 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_id where ’94619’ = sa.zip_code and tr.day_of_week=’monday’ and tr.openingtime <= 1815 and 1815 <= tr.closingtime K1 V1 K2 V2 ... ...
    • @crichardson Solution: Build an index using sorted sets and ZRANGEBYSCORE ZRANGEBYSCORE myset 1 6 select value from sorted_set where key = ‘myset’ and score >= 1 and score <= 6 = key value score sorted_set
    • @crichardson select 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_id where ’94619’ = sa.zip_code and tr.day_of_week=’monday’ and tr.openingtime <= 1815 and 1815 <= tr.closingtime select value from sorted_set where key = ? and score >= ? and score <= ? ? How to transform the SELECT statement?
    • @crichardson We need to denormalize Think materialized view
    • @crichardson Simplification #1: Denormalization Restaurant_id Day_of_week Open_time Close_time Zip_code 1 Monday 1130 1430 94707 1 Monday 1130 1430 94619 1 Monday 1730 2130 94707 1 Monday 1730 2130 94619 2 Monday 0700 1430 94619 … SELECT restaurant_id FROM time_range_zip_code WHERE day_of_week = ‘Monday’ AND zip_code = 94619 AND 1815 < close_time AND open_time < 1815 Simpler query: § No joins § Two = and two <
    • @crichardson Simplification #2:Application filtering SELECT restaurant_id, open_time FROM time_range_zip_code WHERE day_of_week = ‘Monday’ AND zip_code = 94619 AND 1815 < close_time AND open_time < 1815 Even simpler query • No joins • Two = and one <
    • @crichardson Simplification #3: Eliminate multiple =’s with concatenation SELECT restaurant_id, open_time FROM time_range_zip_code WHERE zip_code_day_of_week = ‘94619:Monday’ AND 1815 < close_time 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 … key range
    • @crichardson Simplification #4: Eliminate multiple RETURN VALUES with concatenation SELECT open_time_restaurant_id, FROM time_range_zip_code WHERE zip_code_day_of_week = ‘94619:Monday’ AND 1815 < close_time 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 ... ✔
    • @crichardson 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 ... Sorted Set [ Entry:Score, …] [1130_1:1430, 1730_1:2130] [0700_2:1430, 1130_1:1430, 1730_1:2130] Using a Redis sorted set as an index Key 94619:Monday 94707:Monday 94619:Monday 0700_2 1430
    • @crichardson Querying with ZRANGEBYSCORE ZRANGEBYSCORE 94619:Monday 1815 2359 è {1730_1} 1730 is before 1815 è Ajanta is open Delivery timeDelivery zip and day Sorted Set [ Entry:Score, …] [1130_1:1430, 1730_1:2130] [0700_2:1430, 1130_1:1430, 1730_1:2130] Key 94619:Monday 94707:Monday
    • @crichardson Adding a Restaurant Text @Component public class AvailableRestaurantRepositoryImpl implements AvailableRestaurantRepository { @Override public void add(Restaurant restaurant) { addRestaurantDetails(restaurant); addAvailabilityIndexEntries(restaurant); } 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); int dayOfWeek = tr.getDayOfWeek(); int closingTime = tr.getClosingTime(); for (String zipCode : restaurant.getServiceArea()) { redisTemplate.opsForZSet().add(closingTimesKey(zipCode, dayOfWeek), indexValue, closingTime); } } } key member score Store as JSON
    • @crichardson Finding available Restaurants @Component public class AvailableRestaurantRepositoryImpl implements AvailableRestaurantRepository { @Override public List<AvailableRestaurant> findAvailableRestaurants(Address deliveryAddress, Date deliveryTime) { String zipCode = deliveryAddress.getZip(); 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) { String[] values = tr.split("_"); if (Integer.parseInt(values[0]) <= timeOfDay) restaurantIds.add(values[1]); } Collection<String> keys = keyFormatter.keys(restaurantIds); return availableRestaurantTemplate.opsForValue().multiGet(keys); } Find those that close after Filter out those that open after Retrieve open restaurants
    • @crichardson Representing restaurants in Redis ...JSON...restaurant:1 94619:Monday [0700_2:1430, 1130_1:1430, ...] 94619:Tuesday [0700_2:1430, 1130_1:1430, ...]
    • @crichardson NoSQL Denormalized representation for each query
    • @crichardson SorryTed! http://en.wikipedia.org/wiki/Edgar_F._Codd
    • @crichardson Agenda • Why polyglot persistence? • Using Redis as a cache • Optimizing queries using Redis materialized views • Synchronizing MySQL and Redis • Tracking changes to entities
    • @crichardson MySQL & Redis need to be consistent
    • @crichardson 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
    • @crichardson Atomic Consistent Isolated Durable Basically Available Soft state Eventually consistent BASE:An Acid Alternative http://queue.acm.org/detail.cfm?id=1394128
    • @crichardson Updating Redis #FAIL begin MySQL transaction update MySQL update Redis rollback MySQL transaction Redis has update MySQL does not begin MySQL transaction update MySQL commit MySQL transaction <<system crashes>> update Redis MySQL has update Redis does not
    • @crichardson Updating Redis reliably Step 1 of 2 begin MySQL transaction update MySQL queue CRUD event in MySQL commit transaction ACID Event Id Operation: Create, Update, Delete New entity state, e.g. JSON
    • @crichardson Updating Redis reliably Step 2 of 2 for 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
    • @crichardson ID JSON processed? ENTITY_CRUD_EVENT EntityCrudEvent Processor Redis Updater apply(event) Step 1 Step 2 EntityCrudEvent Repository INSERT INTO ... Timer SELECT ... FROM ... Redis
    • @crichardson Updating Redis WATCH restaurant:lastSeenEventId:≪restaurantId≫ Optimistic locking Duplicate detection lastSeenEventId = GET restaurant:lastSeenEventId:≪restaurantId≫ if (lastSeenEventId >= eventId) return; Transaction MULTI SET restaurant:lastSeenEventId:≪restaurantId≫ eventId ... update the restaurant data... EXEC
    • @crichardson Representing restaurants in Redis restaurant:lastSeenEventId:1 55 duplicate detection ...JSON...restaurant:1 94619:Monday [0700_2:1430, 1130_1:1430, ...] 94619:Tuesday [0700_2:1430, 1130_1:1430, ...]
    • @crichardson Agenda • Why polyglot persistence? • Using Redis as a cache • Optimizing queries using Redis materialized views • Synchronizing MySQL and Redis • Tracking changes to entities
    • @crichardson How do we generate CRUD events?
    • @crichardson Change tracking options • Explicit code • Hibernate event listener • Service-layer aspect • CQRS/Event-sourcing
    • @crichardson ID JSON processed? ENTITY_CRUD_EVENT EntityCrudEvent Repository HibernateEvent Listener
    • @crichardson Hibernate event listener public 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); }
    • @crichardson 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
    • @crichardson Questions? @crichardson chris@chrisrichardson.net http://plainoldobjects.com