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Deep Dive on Amazon Aurora

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Deep Dive on Amazon Aurora

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Deep Dive on Amazon Aurora

  1. 1. © 2016, Amazon Web Services, Inc. or its Affiliates. All rights reserved. July 7th, 2016 Amazon Aurora Deep Dive Ronan Guilfoyle, AWS Solution Architect Mario Kostelac, Product Engineer, Intercom
  2. 2. MySQL-compatible relational database Performance and availability of commercial databases Simplicity and cost-effectiveness of open-source databases What is Amazon Aurora?
  3. 3. Expedia: Online travel marketplace  Real-time business intelligence and analytics on a growing corpus of online travel marketplace data.  Current Microsoft SQL Server–based architecture is too expensive. Performance degrades as data volume grows.  Cassandra with Solr index requires large memory footprint and hundreds of nodes, adding cost. Aurora benefits:  Aurora meets scale and performance requirements with much lower cost.  25,000 inserts/sec with peak up to 70,000. 30 millisecond average response time for write and 17 millisecond for read, with 1 month of data. World’s leading online travel company, with a portfolio that includes 150+ travel sites in 70 countries. For more detail see: re:Invent 2015 | (DAT312) Using Amazon Aurora for Enterprise Workloads
  4. 4. Alfresco: Enterprise content management  Scaling Alfresco document repositories to billions of documents.  Support user applications that require sub- second response times. Aurora benefits:  Scaled to 1 billion documents with a throughput of 3 million per hour, which is 10 times faster than their current environment.  Moving from large data centers to cost-effective management with AWS and Aurora. Leading the convergence of enterprise content management and business process management. More than 1,800 organizations in 195 countries rely on Alfresco, including leaders in financial services, healthcare, and the public sector. For more see: re:Invent 2015 | (DAT309) Scaling Massive Content Stores with Amazon Aurora
  5. 5. Relational databases were not designed for cloud Multiple layers of functionality all in a monolithic stack SQL Transactions Caching Logging
  6. 6. Not much has changed in the last 30 years Even when you scale it out, you’re still replicating the same stack SQL Transactions Caching Logging SQL Transactions Caching Logging Application SQL Transactions Caching Logging SQL Transactions Caching Logging Application SQL Transactions Caching Logging SQL Transactions Caching Logging Storage Application
  7. 7. Reimagining the relational database What if you were inventing the database today? You wouldn’t design it the way we did in 1970 You’d build something  that can scale out …  that is self-healing …  that leverages existing AWS services …
  8. 8. A service-oriented architecture applied to databases Moved the logging and storage layer into a multitenant, scale-out database-optimized storage service. Integrated with other AWS services like Amazon EC2, Amazon VPC, Amazon DynamoDB, Amazon SWF, and Amazon Route 53 for control plane operations. Integrated with Amazon S3 for continuous backup with 99.999999999% durability. Control PlaneData Plane Amazon DynamoDB Amazon SWF Amazon Route 53 Logging + Storage SQL Transactions Caching Amazon S3 1 2 3
  9. 9. SQL benchmark results 4 client machines with 1,000 connections each WRITE PERFORMANCE READ PERFORMANCE Single client machine with 1,600 connections Using MySQL SysBench with Amazon Aurora R3.8XL with 32 cores and 244 GB RAM https ://d0.a wsstat ic . com /product -m ark eting/Aurora /R DS_ Auro ra_Perf orm ance_Assessm ent_Benchm ark ing_v 1-2 .pdf
  10. 10. Beyond benchmarks If only real-world applications saw benchmark performance. POSSIBLE DISTORTIONS Real-world requests contend with each other. Real-world metadata rarely fits in the data dictionary cache. Real-world data rarely fits in the buffer cache. Real-world production databases need to run at high availability.
  11. 11. Scaling user connections SysBench OLTP workload 250 tables Connections Amazon Aurora Amazon RDS MySQL 30 K IOPS (single AZ) 50 40,000 10,000 500 71,000 21,000 5,000 110,000 13,000 8x UP TO FA STER
  12. 12. Scaling table count SysBench write-only workload 1,000 connections, default settings Tables Amazon Aurora MySQL I2.8XL local SSD MySQL I2.8XL RAM disk RDS MySQL 30 K IOPS (single AZ) 10 60,000 18,000 22,000 25,000 100 66,000 19,000 24,000 23,000 1,000 64,000 7,000 18,000 8,000 10,000 54,000 4,000 8,000 5,000 11x UP TO FA STER Number of write operations per second
  13. 13. Scaling dataset size SYSBENCH WRITE-ONLY (Statements) DB Size Amazon Aurora RDS MySQL 30 K IOPS (single AZ) 1GB 107,000 8,400 10GB 107,000 2,400 100GB 101,000 1,500 1TB 26,000 1,200 67x U P TO FASTER DB Size Amazon Aurora RDS MySQL 30K IOPS (single AZ) 80GB 12,582 585 800GB 9,406 69 CLOUDHARMONY TPC-C (Transactions) 136x U P TO FASTER
  14. 14. Running with read replicas SysBench write-only workload 250 tables Updates per second Amazon Aurora RDS MySQL 30 K IOPS (single AZ) 1,000 2.62 ms 0 s 2,000 3.42 ms 1 s 5,000 3.94 ms 60 s 10,000 5.38 ms 300 s 500x UP TO LOW ER LA G
  15. 15. Do fewer I/Os Minimize network packets Cache prior results Offload the database engine DO LESS WORK Process asynchronously Reduce latency path Use lock-free data structures Batch operations together BE MORE EFFICIENT How do we achieve these results? DATABASES ARE ALL ABOUT I/O NETWORK-ATTACHED STORAGE IS ALL ABOUT PACKETS/SECOND HIGH-THROUGHPUT PROCESSING DOES NOT ALLOW CONTEXT SWITCHES
  16. 16. I/O traffic in RDS MySQL BINLOG DATA DOUBLE-WRITELOG FRM FILES T Y P E O F W R IT E MYSQL WITH STANDBY EBS mirrorEBS mirror AZ 1 AZ 2 Amazon S3 EBS Amazon Elastic Block Store (EBS) Primary Instance Standby Instance 1 2 3 4 5 Issue write to Amazon EBS—EBS issues to mirror, acknowledge when both done. Stages write to standby instance using storage level replication. Issues write to EBS on standby instance. I/O FLOW Steps 1, 3, 4 are sequential and synchronous. This amplifies both latency and jitter. Many types of write operations for each user operation. Have to write data blocks twice to avoid torn write operations. OBSERVATIONS 780 K write transactions. 7,388 K I/Os per million transactions (excludes mirroring, standby). Average 7.4 I/Os per transaction. PERFORMANCE 30 minute SysBench write-only workload, 100 GB dataset, RDS Single AZ, 30 K PIOPS
  17. 17. I/O traffic in Aurora (database) AZ 1 AZ 3 Primary Instance Amazon S3 AZ 2 Replica Instance AMAZON AURORA ASYNC 4/6 QUORUM DISTRIBUTED WRITES BINLOG DATA DOUBLE-WRITELOG FRM FILES T Y P E O F W R IT E S 30 minute SysBench writeonly workload, 100GB dataset IO FLOW Only write redo log records; all steps asynchronous. No data block writes (checkpoint, cache replacement). 6x more log writes, but 9x less network traffic. Tolerant of network and storage outlier latency. OBSERVATIONS 27,378 K transactions 35x MORE 950K I/Os per 1M transactions (6x amplification) 7.7x LESS PERFORMANCE Gather redo log records—fully ordered by LSN. Shuffle to appropriate segments—partially ordered. Batch witre to storage nodes and issue write operations.
  18. 18. I/O traffic in Aurora (storage node) LOG RECORDS Primary Instance INCOMING QUEUE STORAGE NODE S3 BACKUP 1 2 3 4 5 6 7 8 UPDATE QUEUE ACK HOT LOG DATA BLOCKS POINT IN TIME SNAPSHOT GC SCRUB COALESCE SORT GROUP PEER TO PEER GOSSIPPeer Storage Nodes All steps are asynchronous. Only steps 1 and 2 are in the foreground latency path. Input queue is 46x less than MySQL (unamplified, per node). Favors latency-sensitive operations. Use disk space to buffer against spikes in activity. OBSERVATIONS I/O FLOW ① Receive record and add to in-memory queue. ② Persist record and acknowledge. ③ Organize records and identify gaps in log. ④ Gossip with peers to fill in holes. ⑤ Coalesce log records into new data block versions. ⑥ Periodically stage log and new block versions to S3. ⑦ Periodically garbage-collect old versions. ⑧ Periodically validate CRC codes on blocks.
  19. 19. Asynchronous group commits Read Write Commit Read Read T1 Commit (T1) Commit (T2) Commit (T3) LSN 10 LSN 12 LSN 22 LSN 50 LSN 30 LSN 34 LSN 41 LSN 47 LSN 20 LSN 49 Commit (T4) Commit (T5) Commit (T6) Commit (T7) Commit (T8) LSN GROWTH Durable LSN at head node COMMIT QUEUE Pending commits in LSN order TIME GROUP COMMIT TRANSACTIONS Read Write Commit Read Read T1 Read Write Commit Read Read Tn TRADITIONAL APPROACH AMAZON AURORA Maintain a buffer of log records to write out to disk. Issue write operations when buffer is full, or time out waiting for write operations. First writer has latency penalty when write rate is low. Request I/O with first write, fill buffer till write picked up. Individual write durable when 4 of 6 storage nodes acknowledge. Advance DB durable point up to earliest pending acknowledgement.
  20. 20. Re-entrant connections multiplexed to active threads. Kernel-space epoll() inserts into latch-free event queue. Dynamically size threads pool. Gracefully handles 5000+ concurrent client sessions on r3.8xl. Standard MySQL—one thread per connection. Doesn’t scale with connection count. MySQL EE—connections assigned to thread group. Requires careful stall threshold tuning. CLIENTCONNECTION CLIENTCONNECTION LATCH FREE TASK QUEUE epoll() MYSQL THREAD MODEL AURORA THREAD MODEL Adaptive thread pool
  21. 21. I/O Traffic in Aurora (read replica) PAGE CACHE UPDATE Aurora Master 30% Read 70% Write Aurora Replica 100% New Reads Shared Multi-AZ Storage MySQL Master 30% Read 70% Write MySQL Replica 30% New Reads 70% Write SINGLE-THREADED BINLOG APPLY Data Volume Data Volume Logical: Ship SQL statements to replica. Write workload similar on both instances. Independent storage. Can result in data drift between master and replica. Physical: Ship redo from master to replica. Replica shares storage. No writes performed. Cached pages have redo applied. Advance read view when all commits seen. MYSQL READ SCALING AMAZON AURORA READ SCALING
  22. 22. Availability “Performance only matters if your database is up”
  23. 23. Storage node availability Quorum system for read/write; latency tolerant. Peer-to-peer gossip replication to fill in holes. Continuous backup to S3 (designed for 11 9s durability). Continuous scrubbing of data blocks. Continuous monitoring of nodes and disks for repair. 10 GB segments as unit of repair or hotspot rebalance to quickly rebalance load. Quorum membership changes do not stall write operations. AZ 1 AZ 2 AZ 3 Amazon S3
  24. 24. Traditional databases Have to replay logs since the last checkpoint. Typically 5 minutes between checkpoints. Single-threaded in MySQL; requires a large number of disk accesses. Amazon Aurora Underlying storage replays redo records on demand as part of a disk read. Parallel, distributed, asynchronous. No replay for startup. Checkpointed Data Redo Log Crash at T0 requires a re-application of the SQL in the redo log since last checkpoint. T0 T0 Crash at T0 will result in redo logs being applied to each segment on demand, in parallel, asynchronously. Instant crash recovery
  25. 25. Survivable caches We moved the cache out of the database process. Cache remains warm in the event of a database restart. Lets you resume fully loaded operations much faster. Instant crash recovery + survivable cache = quick and easy recovery from DB failures. SQL Transactions Caching SQL Transactions Caching SQL Transactions Caching Caching process is outside the DB process and remains warm across a database restart.
  26. 26. Faster, more predictable failover App RunningFailure Detection DNS Propagation Recovery Recovery DB Failure MYSQL App Running Failure Detection DNS Propagation Recovery DB Failure AURORA WITH MARIADB DRIVER 1 5 – 2 0 s e c . 3 – 2 0 s e c .
  27. 27. High availability with Read Replicas Amazon S3 AZ 1 AZ 2 AZ 3 Aurora Primary instance Cluster volume spans 3 AZs Aurora Replica Aurora Replica db.r3.8xlarge db.r3.2xlarge Priority: tier-1 db.r3.8xlarge Priority: tier-0
  28. 28. High availability with Read Replicas Amazon S3 AZ 1 AZ 2 AZ 3 Aurora Primary instance Cluster volume spans 3 AZs Aurora Replica Aurora Primary Instance db.r3.8xlarge db.r3.2xlarge Priority: tier-1 db.r3.8xlarge
  29. 29. ALTER SYSTEM CRASH [{INSTANCE | DISPATCHER | NODE}] ALTER SYSTEM SIMULATE percent_failure DISK failure_type IN [DISK index | NODE index] FOR INTERVAL interval ALTER SYSTEM SIMULATE percent_failure NETWORK failure_type [TO {ALL | read_replica | availability_zone}] FOR INTERVAL interval Simulate failures using SQL To cause the failure of a component at the database node: To simulate the failure of disks: To simulate the failure of networking:
  30. 30. Thank you! http://aws.amazon.com/rds/aurora
  31. 31. Please remember to rate this session under My Agenda on awssummit.london
  32. 32. Mario Kostelac Product engineer @mariokostelac
  33. 33. Intercom and Aurora
  34. 34. !=
  35. 35. 😭
  36. 36. main DB conversations DB
  37. 37. SELECT MAX(id) FROM conversations;
  38. 38. >2B
  39. 39. What does 2B rows in MySQL table mean? - your table can’t fit in RAM (not cool) - unpredictable performance (not cool) - you have a fixed schema (cool?)
  40. 40. that’s SQL, I want fixed schema
  41. 41. NO! that’s SQL, I want fixed schema
  42. 42. Options for migrations • ALTER TABLE statement? • Percona migrations?
  43. 43. ⏰ I !
  44. 44. My Intermission ?
  45. 45. Testing time • synthetic testing • production-like testing
  46. 46. sysbench
  47. 47. Aurora > sysbench
  48. 48. Migration plan
  49. 49. Migration plan MySQL prod MySQL rollbackAurora
  50. 50. Where are we now? • aurora is more forgiving • uptime is great • predictable performance • schema changes are slow, but safe operation • fast failovers • read replicas
  51. 51. We think about product again! we are product company!
  52. 52. Mario Kostelac @mariokostelac mario@intercom.io all used icons are official AWS icons or downloaded from freepik.com

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