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What’s New in Amazon Aurora for MySQL and PostgreSQL

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It’s been an exciting year for Amazon Aurora, the MySQL-compatible relational database engine that combines the speed and availability of high-end commercial databases with the simplicity and cost-effectiveness of open source databases. In this deep dive session, we’ll discuss best practices and explore new features, include high availability options and new integrations with AWS services. We’ll also discuss the recently-announced Aurora with PostgreSQL compatibility.

Veröffentlicht in: Präsentationen & Vorträge

What’s New in Amazon Aurora for MySQL and PostgreSQL

  1. 1. What’s New in Amazon Aurora Amazon Web Services Kevin Jernigan – Senior Product Manager March 16, 2017
  2. 2. Agenda  What is Aurora?  Review of Aurora performance  New performance enhancements  Review of Aurora availability  New availability enhancements  Other recent and upcoming feature enhancements  Aurora with PostgreSQL compatibility
  3. 3. Open source compatible relational database Performance and availability of commercial databases Simplicity and cost-effectiveness of open source databases What is Amazon Aurora?
  4. 4. A bit of history … Re-imagining relational databases for the cloud era
  5. 5. Relational databases were not designed for the cloud Multiple layers of functionality all in a monolithic stack SQL Transactions Caching Logging
  6. 6. Not much has changed in last 20 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. Re-imagining relational database Automate administrative tasks – fully managed service 1 2 3 Scale-out and distributed design Service-oriented architecture leveraging AWS services
  8. 8. Scale-out, distributed, multi-tenant architecture Master Replica Replica Replica Availability Zone 1 Shared storage volume Availability Zone 2 Availability Zone 3 Storage nodes with SSDs  Purpose-built log-structured distributed storage system designed for databases  Storage volume is striped across hundreds of storage nodes distributed over 3 different availability zones  Six copies of data, two copies in each availability zone to protect against AZ+1 failures  Plan to apply same principles to other layers of the stack SQL Transactions Caching SQL Transactions Caching SQL Transactions Caching
  9. 9. Leveraging cloud ecosystem Lambda S3 IAM CloudWatch Invoke Lambda events from stored procedures/triggers. Load data from S3, store snapshots and backups in S3. Use IAM roles to manage database access control. Upload systems metrics and audit logs to CloudWatch.
  10. 10. Amazon RDS: Automate administrative tasks Schema design Query construction Query optimization Automatic fail-over Backup & recovery Isolation & security Industry compliance Push-button scaling Automated patching Advanced monitoring Routine maintenance Takes care of your time-consuming database management tasks, freeing you to focus on your applications and business You AWS
  11. 11. Meet Amazon Aurora …… The relational database reimagined for the cloud  Speed and availability of high-end commercial databases  Simplicity and cost-effectiveness of open source databases  Drop-in compatibility with MySQL (and soon PostgreSQL)  Simple pay as you go pricing Delivered as a managed service
  12. 12. Performance
  13. 13. WRITE PERFORMANCE READ PERFORMANCE Scaling with instance sizes Aurora scales with instance size for both read and write Aurora MySQL 5.6 MySQL 5.7
  14. 14. Real-life data – gaming workload Aurora vs. RDS MySQL – r3.4XL, MAZ Aurora 3X faster on r3.4xlarge
  15. 15. Do fewer IOs 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 did we achieve this? DATABASES ARE ALL ABOUT I/O NETWORK-ATTACHED STORAGE IS ALL ABOUT PACKETS/SECOND HIGH-THROUGHPUT PROCESSING IS ALL ABOUT CONTEXT SWITCHES
  16. 16. IO traffic in MySQL BINLOG DATA DOUBLE-WRITELOG FRM FILES T Y P E O F W R IT E MYSQL WITH REPLICA EBS mirrorEBS mirror AZ 1 AZ 2 Amazon S3 EBS Amazon Elastic Block Store (EBS) Primary Instance Replica Instance 1 2 3 4 5 Issue write to EBS – EBS issues to mirror, ack when both done Stage write to standby instance through DRBD Issue write to EBS on standby instance IO FLOW Steps 1, 3, 4 are sequential and synchronous This amplifies both latency and jitter Many types of writes for each user operation Have to write data blocks twice to avoid torn writes OBSERVATIONS 780K transactions 7,388K I/Os per million txns (excludes mirroring, standby) Average 7.4 I/Os per transaction PERFORMANCE 30 minute SysBench writeonly workload, 100GB dataset, RDS MultiAZ, 30K PIOPS
  17. 17. IO traffic in Aurora 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 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 27,378K transactions 35X MORE 950K I/Os per 1M txns (6X amplification) 7.7X LESS PERFORMANCE Boxcar redo log records – fully ordered by LSN Shuffle to appropriate segments – partially ordered Boxcar to storage nodes and issue writesReplica Instance
  18. 18. IO 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 foreground latency path Input queue is 46X less than MySQL (unamplified, per node) Favor latency-sensitive operations Use disk space to buffer against spikes in activity OBSERVATIONS IO FLOW ① Receive record and add to in-memory queue ② Persist record and ACK 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. IO traffic in Aurora Replicas 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
  20. 20. “In MySQL, we saw replica lag spike to almost 12 minutes which is almost absurd from an application’s perspective. With Aurora, the maximum read replica lag across 4 replicas never exceeded 20 ms.” Real-life data - read replica latency
  21. 21. 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 when buffer full or time out waiting for writes 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 ACK Advance DB Durable point up to earliest pending ACK
  22. 22. Re-entrant connections multiplexed to active threads Kernel-space epoll() inserts into latch-free event queue Dynamically sizes 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
  23. 23. Scan Delete Aurora lock management Scan Delete Insert Scan Scan Insert Delete Scan Insert Insert MySQL lock manager Aurora lock manager  Same locking semantics as MySQL  Concurrent access to lock chains  Multiple scanners allowed in an individual lock chains  Lock-free deadlock detection Needed to support many concurrent sessions, high update throughput
  24. 24. New Performance Enhancements
  25. 25. Cached read performance Catalog concurrency: Improved data dictionary synchronization and cache eviction. NUMA aware scheduler: Aurora scheduler is now NUMA aware. Helps scale on multi-socket instances. Read views: Aurora now uses a latch-free concurrent read-view algorithm to construct read views. 0 100 200 300 400 500 600 700 MySQL 5.6 MySQL 5.7 Aurora 2015 Aurora 2016 In thousands of read requests/sec * R3.8xlarge instance, <1GB dataset using Sysbench 25% Throughput gain
  26. 26. Smart scheduler: Aurora scheduler now dynamically assigns threads between IO heavy and CPU heavy workloads. Smart selector: Aurora reduces read latency by selecting the copy of data on a storage node with best performance Logical read ahead (LRA): We avoid read IO waits by prefetching pages based on their order in the btree. Non-cached read performance 0 20 40 60 80 100 120 MySQL 5.6 MySQL 5.7 Aurora 2015 Aurora 2016 In thousands of requests/sec * R3.8xlarge instance, 1TB dataset using Sysbench 10% Throughput gain
  27. 27. Scan Delete Hot row contention Scan Delete Insert Scan Scan Insert Delete Scan Insert Insert MySQL lock manager Aurora lock manager Highly contended workloads had high memory and CPU  1.9 (Nov) – lock compression (bitmap for hot locks)  1.9 – replace spinlocks with blocking futex – up to 12x reduction in CPU, 3x improvement in throughput  December – use dynamic programming to release locks: from O(totalLocks * waitLocks) to O(totalLocks) Throughput on Percona TPC-C 100 improved 29x (from 1,452 txns/min to 42,181 txns/min)
  28. 28. Hot row contention MySQL 5.6 MySQL 5.7 Aurora Improvement 500 connections 6,093 25,289 73,955 2.92x 5000 connections 1,671 2,592 42,181 16.3x Percona TPC-C – 10GB * Numbers are in tpmC, measured using release 1.10 on an R3.8xlarge, MySQL numbers using RDS and EBS with 30K PIOPS MySQL 5.6 MySQL 5.7 Aurora Improvement 500 connections 3,231 11,868 70,663 5.95x 5000 connections 5,575 13,005 30,221 2.32x Percona TPC-C – 100GB
  29. 29.  Accelerates batch inserts sorted by primary key – works by caching the cursor position in an index traversal.  Dynamically turns itself on or off based on data pattern  Avoids contention in acquiring latches while navigating down the tree.  Bi-directional, works across all insert statements • LOAD INFILE, INSERT INTO SELECT, INSERT INTO REPLACE and, Multi-value inserts. Insert performance Index R4 R5R2 R3R0 R1 R6 R7 R8 Index Root Index R4 R5R2 R3R0 R1 R6 R7 R8 Index Root MySQL: Traverses B-tree starting from root for all inserts Aurora: Inserts avoids index traversal;
  30. 30. Faster index build  MySQL 5.6 leverages Linux read ahead – but this requires consecutive block addresses in the btree. It inserts entries top down into the new btree, causing splits and lots of logging.  Aurora’s scan pre-fetches blocks based on position in tree, not block address.  Aurora builds the leaf blocks and then the branches of the tree. • No splits during the build. • Each page touched only once. • One log record per page. 2-4X better than MySQL 5.6 or MySQL 5.7 0 2 4 6 8 10 12 r3.large on 10GB dataset r3.8xlarge on 10GB dataset r3.8xlarge on 100GB dataset Hours RDS MySQL 5.6 RDS MySQL 5.7 Aurora 2016
  31. 31. Why Spatial Index Need to store and reason about spatial data • E.g. “Find all people within 1 mile of a hospital” • Spatial data is multi-dimensional • B-Tree indexes are one-dimensional Aurora supports spatial data types (point/polygon) • GEOMETRY data types inherited from MySQL 5.6 • This spatial data cannot be indexed Two possible approaches: • Specialized access method for spatial data (e.g., R-Tree) • Map spatial objects to one-dimensional space & store in B- Tree - space-filling curve using a grid approximation A B A A A A A A A B B B B B A COVERS B B COVEREDBY A A CONTAINS B B INSIDE A A TOUCH B B TOUCH A A OVERLAPBDYINTERSECT B B OVERLAPBDYINTERSECT A A OVERLAPBDYDISJOINT B B OVERLAPBDYDISJOINT A A EQUAL B B EQUAL A A DISJOINT B B DISJOINT A A COVERS B B ON A
  32. 32. Spatial Indexes in Aurora Z-index used in Aurora Challenges with R-Trees Keeping it efficient while balanced Rectangles should not overlap or cover empty space Degenerates over time Re-indexing is expensive R-Tree used in MySQL 5.7 Z-index (dimensionally ordered space filling curve) Uses regular B-Tree for storing and indexing Removes sensitivity to resolution parameter Adapts to granularity of actual data without user declaration Eg GeoWave (National Geospatial-Intelligence Agency)
  33. 33. Spatial Index Benchmarks Sysbench – points and polygons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . * r3.8xlarge using Sysbench on <1GB dataset * Write Only: 4000 clients, Select Only: 2000 clients, ST_EQUALS 0 20000 40000 60000 80000 100000 120000 140000 Select-only (reads/sec) Write-only (writes/sec) Aurora MySQL 5.7
  34. 34. Availability “Performance only matters if your database is up”
  35. 35. Storage Durability Storage volume automatically grows up to 64 TB Quorum system for read/write; latency tolerant Peer to peer gossip replication to fill in holes Continuous backup to S3 (built for 11 9s durability) Continuous monitoring of nodes and disks for repair 10GB segments as unit of repair or hotspot rebalance Quorum membership changes do not stall writes AZ 1 AZ 2 AZ 3 Amazon S3
  36. 36. More Replicas Aurora cluster contains primary node and up to fifteen replicas Failing database nodes are automatically detected and replaced Failing database processes are automatically detected and recycled Customer application may scale-out read traffic across replicas Replica automatically promoted on persistent outage AZ 1 AZ 3AZ 2 Primary Node Primary Node Primary Node Primary Node Primary Node Secondary Node Primary Node Primary Node Secondary Node
  37. 37. Continuous Backup Segment snapshot Log records Recovery point Segment 1 Segment 2 Segment 3 Time • Take periodic snapshot of each segment in parallel; stream the redo logs to Amazon S3 • Backup happens continuously without performance or availability impact • At restore, retrieve the appropriate segment snapshots and log streams to storage nodes • Apply log streams to segment snapshots in parallel and asynchronously
  38. 38. 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
  39. 39. Survivable Caches We moved the cache out of the database process Cache remains warm in the event of 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
  40. 40. Faster Fail-Over 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
  41. 41. Database fail-over time 0.00% 5.00% 10.00% 15.00% 20.00% 25.00% 30.00% 35.00% 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 0 - 5s – 30% of fail-overs 0.00% 5.00% 10.00% 15.00% 20.00% 25.00% 30.00% 35.00% 40.00% 45.00% 50.00% 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 5 - 10s – 40% of fail-overs 0% 10% 20% 30% 40% 50% 60% 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 10 - 20s – 25% of fail-overs 0% 5% 10% 15% 20% 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 20 - 30s – 5% of fail-overs
  42. 42. New Availability Enhancements
  43. 43. Availability is about more than HW failures You also incur availability disruptions when you 1. Patch your database software 2. Modify your database schema 3. Perform large scale database reorganizations 4. DBA errors requiring database restores
  44. 44. Zero downtime patching Networking state Application state Storage Service App state Net state App state Net state BeforeZDP New DB Engine Old DB Engine New DB Engine Old DB Engine WithZDP User sessions terminate during patching User sessions remain active through patching Storage Service
  45. 45. Zero downtime patching – current constraints We have to go to our current patching model when we can’t park connections: • Long running queries • Open transactions • Bin-log enabled • Parameter changes pending • Temporary tables open • Locked Tables • SSL connections open • Read replicas instances We are working on addressing the above.
  46. 46. Removing Blockers
  47. 47. T2 RI discounts Up to 34% with a 1-year RI Up to 57% with a 3-year RI vCPU Mem Hourly Price db.t2.medium 2 4 $0.082 db.r3.large 2 15.25 $0.29 db.r3.xlarge 4 30.5 $0.58 db.r3.2xlarge 8 61 $1.16 db.r3.4xlarge 16 122 $2.32 db.r3.8xlarge 32 244 $4.64 An R3.Large is too expensive for my use case T2.Small coming in Q1. 2017 *Prices are for Virginia
  48. 48. My databases need to meet certifications  Amazon Aurora gives each database instance IP firewall protection  Aurora offers transparent encryption at rest and SSL protection for data in transit  Amazon VPC lets you isolate and control network configuration and connect securely to your IT infrastructure  AWS Identity and Access Management provides resource-level permission controls *New* *New*
  49. 49. Aurora Auditing MariaDB server_audit plugin Aurora native audit support We can sustain over 500K events/sec Create event string DDL DML Query DCL Connect DDL DML Query DCL Connect Write to File Create event string Create event string Create event string Create event string Create event string Latch-free queue Write to File Write to File Write to File MySQL 5.7 Aurora Audit Off 95K 615K 6.47x Audit On 33K 525K 15.9x Sysbench Select-only Workload on 8xlarge Instance
  50. 50. AWS ecosystem Lambda S3 IAM CloudWatch Generate Lambda event from Aurora stored procedures. Load data from S3, store snapshots and backups in S3. Use IAM roles to manage database access control. Upload systems metrics and audit logs to CloudWatch. *NEW* Q1
  51. 51. MySQL compatibility Business Intelligence Data Integration Query and Monitoring “We ran our compatibility test suites against Amazon Aurora and everything just worked." - Dan Jewett, VP, Product Management at Tableau MySQL 5.6 / InnoDB compatible  No application compatibility issues reported in last 18 months  MySQL ISV applications run pretty much as is Working on 5.7 compatibility  Running a bit slower than expected – hope to make it available soon  Back ported 81 fixes from different MySQL releases;
  52. 52. Amazon Aurora with PostgreSQL compatibility
  53. 53. My applications require PostgreSQL Amazon Aurora with PostgreSQL compatibility now in preview Same underlying scale out, 3 AZ, 6 copy, fault tolerant, self healing, expanding database optimized storage tier Integrated with PostgreSQL 9.6 Logging + Storage SQL Transactions Caching Amazon S3
  54. 54. Open source database In active development for 20 years Owned by a foundation, not a single company Permissive innovation-friendly open source license PostgreSQL Fast Facts Open Source Initiative
  55. 55. High performance out of the box Object-oriented and ANSI-SQL:2008 compatible Most geospatial features of any open-source database Supports stored procedures in 12 languages (Java, Perl, Python, Ruby, Tcl, C/C++, its own Oracle-like PL/pgSQL, etc.) PostgreSQL Fast Facts
  56. 56. Most Oracle-compatible open-source database Highest AWS Schema Conversion Tool automatic conversion rates are from Oracle to PostgreSQL PostgreSQL Fast Facts
  57. 57. What does PostgreSQL compatibility mean? PostgreSQL 9.6 + Amazon Aurora cloud-optimized storage Performance: Up to 2x+ better performance than PostgreSQL alone Availability: failover time of < 30 seconds Durability: 6 copies across 3 Availability Zones Read Replicas: single-digit millisecond lag times on up to 15 replicas Amazon Aurora Storage
  58. 58. What does PostgreSQL compatibility mean? Cloud-native security and encryption AWS Key Management Service (KMS) and AWS Identity and Access Management (IAM) Easy to manage with Amazon RDS Easy to load and unload AWS Database Migration Service and AWS Schema Conversion Tool Fully compatible with PostgreSQL, now and for the foreseeable future Not a compatibility layer – native PostgreSQL implementation AWS DMS Amazon RDS PostgreSQL
  59. 59. Amazon Aurora with PostgreSQL Compatibility Performance By The Numbers Measurement Result PgBench >= 2x faster SysBench 2x-3x faster Data Loading 3x faster Response Time >2x faster Throughput Jitter >3x more consistent Throughput at Scale 3x faster Recovery Speed Up to 85x faster
  60. 60. First Step: Enhanced Monitoring Released 2016 O/S Metrics Process & thread List Up to 1 second granularity
  61. 61. Next Step: Performance Insights Database Engine Performance Tuning
  62. 62. Performance Insights at a glance Automates sampling of data Exposes data via API Provides UI to show Database Load Database Load:
  63. 63. High Performance Easy to Operate & Compatible High Availability Secure by Design Amazon Aurora with PostgreSQL Compatibility – Launch Roadmap  2x or faster than PostgreSQL  Up to 64 TB of storage per instance  Write jitter reduction  Near synchronous replicas  Reader endpoint  Enhanced OS monitoring  Performance Insights  Push button migration  Auto-scaling storage  Continuous backup and PITR  Easy provisioning / patching  All PostgreSQL features  All RDS for PostgreSQL extensions  AWS DMS supported inbound  Failover in less than 30 seconds  Customer specifiable failover order  Up to 15 readable failover targets  Instant crash recovery  Survivable buffer cache  X-region snapshot copy  Encryption at rest (AWS KMS)  Encryption in transit (SSL)  Amazon VPC by default  Row Level Security
  64. 64. Q&A Thank you!