Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Aws querterly update-tech news #2

23 views

Published on

The 2nd presentation

Published in: Data & Analytics
  • Be the first to comment

  • Be the first to like this

Aws querterly update-tech news #2

  1. 1. Rotem Gabay| rotem@midlink.ca | 050-6958625
  2. 2. Technology Product Fast Data VoltDB – 1 million ACID transaction per second BigData Datameer – spreadsheet over Hadoop Traditional database EnterpriseDB – Affordable enterprise grade database Search Lucidworks – Enterprise search out of the box Visualization Zoomdata – Visualization tailored for BigData platforms I work for MidLink by EMET www.midlink.co.il
  3. 3. D a t a b a s e r e i m a g i n e d f o r t h e c l o u d  Speed and availability of high-end commercial databases  Simplicity and cost-effectiveness of open source databases  Drop-in compatibility with MySQL and PostgreSQL  Simple pay as you go pricing Delivered as a managed service
  4. 4. Re- i m a g i n i n g r e l a t i o n a l d a t a b a s e Automate administrative tasks – fully managed service 1 2 3 Scale-out and distributed design Service-oriented architecture leveraging AWS services
  5. 5. S c a l e - o u t , d i s t r i b u t e d a r c h i t e c t u r e 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
  6. 6. L e v e r a g i n g c l o u d e c o s y s t e m Lambda S3 IAM CloudWatch Invoke Lambda events from stored procedures/triggers. Load data from S3, store snapshots and backups in S 3. Use IAM roles to manage database access control. Upload systems metrics and audit logs to CloudWatch.
  7. 7. A u t o m a t e a d m i n i s t r a t i v e t a s k s 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
  8. 8. Aurora is used by ¾ of the top 100 AWS customers A u r o r a c u s t o m e r a d o p t i o n Fastest growing service in AWS history
  9. 9. W h o a r e m o v i n g t o A u r o r a a n d w h y? Customers using commercial engines Customers using MySQL engines Higher performance – up to 5x Better availability and durability Reduces cost – up to 60% Easy migration; no application change One tenth of the cost; no licenses Integration with cloud ecosystem Comparable performance and availability Migration tooling and services
  10. 10. Cassandra (>100 nodes( Aurora (~10 clusters( RM DNA analysis and matching (millions of reads and writes) Large genealogy company achieved <10ms read latency and an order of magnitude reduction in projected costs by migrating to Aurora Data sharded across ~10 Aurora R3.XLarge clusters - sufficient room for vertical scaling OLAP + OLTP: DNA matching algorithms require millions of reads and batch updates RM RM D a t a s t o r e f o r h i g h - p e r f o r m a n c e a p p l i c a t i o n s
  11. 11. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. An on demand video streaming service stores metadata for millions of videos in in- memory data structures. Regular backups required; high and ever-increasing cost due to years worth of video metadata. Aurora used as the persistent data-store with Redis front-end cache to provide HA and optimal performance at lower costs, and eliminating the need for costly backups. … Amazon Aurora In-memory store Backups $$$ H i g h l y - a v a i l a b l e p e r s i s t e n t d a t a s t o r e
  12. 12. ©2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. S3 Amazon Aurora Transform and insert EMR Redshift • Transaction streams from various sources comes to different S3 buckets. • S3 event invoke a Lambda function, which reads the file and upload to Aurora • Aurora is used for data processing, validation and operational reporting • Processed data is loaded into Redshift and EMR for deep-analytics D a t a p r o c e s s i n g p i p e l i n e
  13. 13. Amazon Aurora is fast… 5x faster than MySQL 3x faster than PostegreSQL
  14. 14. WRITE PERFORMANCE READ PERFORMANCE MySQL SysBench results R3.8XL: 32 cores / 244 GB RAM 5 X f a s t e r t h a n R D S M y S Q L 5 . 6 & 5 . 7 Five times higher throughput than stock MySQL based on industry standard benchmarks. 0 25,000 50,000 75,000 100,000 125,000 150,000 0 100,000 200,000 300,000 400,000 500,000 600,000 700,000 Aurora MySQL 5.6 MySQL 5.7
  15. 15. 5X f a s t e r t h a n R D S M y S Q L 5 . 6 & 5 . 7
  16. 16. Aurora PostgreSQL performance 0 5000 10000 15000 20000 25000 30000 35000 40000 45000 10 15 20 25 30 35 40 45 50 55 60 Throughput,tps Minutes pgbench throughput over time, 150 GiB, 1024 clients PostgreSQL (Single AZ( Amazon Aurora (Three Azs( While running pgbench at load, throughput is 3x more consistent than PostgreSQL
  17. 17. A u r o r a S c a l i n g With user connection With number of tables With database size - SYSBENCH With database size - TPCC Connections Amazon Aurora RDS MySQL w/ 30K IOPS 50 500 5,000 40,000 71,000 110,000 10,000 21,000 13,000 Tables Amazon Aurora MySQL I2.8XL local SSD RDS MySQL w/ 30K IOPS (single AZ) 10 60,000 18,000 25,000 100 66,000 19,000 23,000 1,000 64,000 7,000 8,000 10,000 54,000 4,000 5,000 8x U P T O F A S T E R 11x U P T O F A S T E R DB Size Amazon Aurora RDS MySQL w/ 30K IOPS 1GB 10GB 100GB 1TB 107,000 107,000 101,000 26,000 8,400 2,400 1,500 1,200 DB Size Amazon Aurora RDS MySQL w/ 30K IOPS 80GB 12,582 585 800GB 9,406 69 21 U P T O F A S T E R 136x U P T O F A S T E R
  18. 18. 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 H o w d i d w e a c h i e v e t h i s? DATABASES ARE ALL ABOUT I/O NETWORK-ATTACHED STORAGE IS ALL ABOUT PACKETS/SECOND HIGH-THROUGHPUT PROCESSING IS ALL ABOUT CONTEXT SWITCHES
  19. 19. BINLOG DATA DOUBLE-WRITELOG FRM FILES T Y P E O F W R I T 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 AZ 1 AZ 3 Primary Instance Amazon S3 AZ 2 Replica Instance ASYNC 4/6QUORUM DISTRIBUT ED WRITES Replica Instance AMAZON AURORA 780K transactions 7,388K I/Os per million txns (excludes mirroring, standby) Average 7.4 I/Os per transaction MySQL I/O profile for 30 min Sysbench run 27,378K transactions 35X MORE 0.95I/Os per transaction (6X amplification) 7.7X LESS Aurora IO profile for 30 min Sysbench run A u r o r a I / O p r o f i l e
  20. 20. I O t r a f f i c i n A u r o r a ( s t o r a g e n o d e) 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 SCRUB POINT IN TIME SNAPSHOT GC 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 ①Receive record and add to in-memory queue ②Persist record and ACK ③Organize records and identify gaps in log ④Gossip with peers to fill in holes ⑤Coalesce log records into new data block versions ⑥Periodically stage log and new block versions to S3 ⑦Periodically garbage collect old versions ⑧Periodically validate CRC codes on blocks
  21. 21. Scan Delete A u r o r a l o c k m a n a g e m e n t 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 Needed to support many concurrent sessions, high update throughput Multiple scanners in individual lock chains Lock-free deadlock detection
  22. 22. O n l i n e D D L : A u r o r a v s . M y S Q L Full Table copy; rebuilds all indexes Needs temporary space forDML operations DDL operation impacts DML throughput Table lock applied to apply DML changes Index LeafLeafLeaf Leaf Index Root table name operation column-name time-stamp Table 1 Table 2 Table 3 add-col add-col add-col column-abc column-qpr column-xyz t1 t2 t3 Use schema versioning to decode the block. Modify-on-write primitive to upgrade to latest schema Currently support add NULLable column at end of table Add column anywhere and with default coming soon. MySQL Amazon Aurora
  23. 23. O n l i n e D D L p e r f o r m a n c e On r3.large On r3.8xlarge Aurora MySQL 5.6 MySQL 5.7 10GB table 0.27 sec 3,960sec 1,600sec 50GB table 0.25sec 23,400sec 5,040sec 100GB table 0.26sec 53,460sec 9,720sec Aurora MySQL 5.6 MySQL 5.7 10GB table 0.06sec 900sec 1,080sec 50GB table 0.08sec 4,680sec 5,040sec 100GB table 0.15sec 14,400sec 9,720sec
  24. 24. What about availability “Performance only matters if your database is up”
  25. 25. Six copies across three availability zones 4 out 6 write quorum; 3 out of 6 read quorum Peer-to-peer replication for repairs Volume striped across hundreds of storage nodes SQL Transaction AZ 1 AZ 2 AZ 3 Caching SQL Transaction AZ 1 AZ 2 AZ 3 Caching Read and write availabilityRead availability 6 way replicated storage Survives catastrophic failures
  26. 26. Up to 15 promotable read replicas Master Read Replica Read Replica Read Replica Shared distributed storage volume Reader end-point ► Up to 15 promotable read replicas across multiple availability zones ►Re-do log based replication leads to low replica lag – typically < 10ms ►Reader end-point with load balancing and auto-scaling * NEW*
  27. 27. Database fail - over time 0.00% 5.00% 10.00% 15.00% 20.00% 25.00% 30.00% 35.00% 1 3 5 7 911131517192123252729313335 0-5 s – 30% of fail-overs 50.00% 45.00% 40.00% 35.00% 30.00% 25.00% 20.00% 15.00% 10.00% 5.00% 0.00% 1 3 5 7 911131517192123252729313335 5-10 s – 40% of fail-overs 0% 10% 20% 30% 40% 50% 60% 1 3 5 7 911131517192123252729313335 10-20 s – 25% of fail-overs 0% 5% 10% 15% 20% 1 3 5 7 911131517192123252729313335 20-30 s – 5% of fail-overs
  28. 28. C r o s s - r e g i o n r e a d r e p l i c a s F a s t e r d i s a s t e r r e c o v e r y a n d e n h a n c e d d a t a l o c a l i t y Promote read-replica to a master for faster recovery in the event of disaster Bring data close to your customer’s applications in different regions Promote to a master for easy migration
  29. 29. A v a i l a b i l i t y i s a b o u t m o r e t h a n H W f a i l u r e s You also incur availability disruptions when you 1. Patch your database software – Zero Down Time Patch 2. Perform large scale database reorganizations – Fast Cloning 3. DBA errors requiring database restores – Online Point-in-time Restore
  30. 30. Z e r o d o w n t i m e p a t c h i n g 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
  31. 31. D a t a b a s e b a c k t r a c k Backtrack brings the database to a point in time without requiring restore from backups • Backtracking from an unintentional DML or DDL operation • Backtrack is not destructive. You can backtrack multiple times to find the right point in time t0 t1 t2 t0 t1 t2 t3 t4 t3 t4 Rewind to t1 Rewind to t3 Invisible Invisible
  32. 32. Security and Monitoring
  33. 33. S e c u r i t y a n d c o m p l i a n c e  Encryption to secure data at rest using customer managed keys • AES-256; hardware accelerated • All blocks on disk and in Amazon S3 are encrypted • Key management via AWS KMS  Encrypted cross-region replication, snapshot copy - SSL to secure data in transit  Advanced auditing and logging without any performance impact  Database activity monitoring Data Key 1 Data Key 2 Data Key 3 Data Key 4 Customer Master Key(s) Storage Node Storage Node Storage Node Storage Node Database Engine *NEW*
  34. 34. A u r o r a A u d i t i n g 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
  35. 35. D a t a b a s e a c t i v i t y m o n i t o r i n g Search: Look for specific events across log files. Metrics: Measure activity in your Aurora DB cluster. Continuously monitor activity in your DB clusters by sending these audit logs to CloudWatch Logs. Export to S3 for long term archival; analyze logs using Athena; visualize logs with QuickSight. Visualizations: Create activity dashboards Alarms: Get notified or take actions Amazon Aurora Amazon CloudWatch Amazon Athena Amazon QuickSight S3
  36. 36. I n d u s t r y c e r t i f i c a t i o n s 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* *New*
  37. 37. P e r f o r m a n c e I n s i g h t s Dashboard showing Load on Database • Easy •Powerful Identifies source of bottlenecks • Top SQL Adjustable time frame • Hour, day, week , month • Up to 35 days of data Max CPU
  38. 38. Amazon Aurora is easy to use Automated storage management, security and compliance, advanced monitoring, database migration.
  39. 39. S i m p l i f y s t o r a g e m a n a g e m e n t Continuous, incremental backups to Amazon S3 Instantly create user snapshots—no performance impact Automatic storage scaling up to 64 TB—no performance impact Automatic restriping, mirror repair, hot spot management, encryption Up to 64TB of storage – auto-incremented in 10GB units up to 64 TB
  40. 40. F a s t d a t a b a s e c l o n i n g Create a copy of a database without duplicate storage costs • Creation of a clone is nearly instantaneous – we don’t copy data • Data copy happens only on write – when original and cloned volume data differ Typical use cases: • Clone a production DB to run tests • Reorganize a database • Save a point in time snapshot for analysis without impacting production system. Production database Clone Clone Clone Dev/test applications Benchmarks Production applications Production applications
  41. 41. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. D e v T e s t e n v i r o n m e n t s Master Replica Shared storage volume Clone Shared storage volume Master Replica Production Account Development Account Cross-account snapshot Test on prod data • Create a clone of multi-terabyte DB clusters in minutes. Use for diagnosis and pre- prod testing • Restore a production DB cluster in a development accounts for development, staging, or partnering with other teams
  42. 42. F a s t d a t a b a s e c l o n i n g
  43. 43. L e v e r a g e M y S Q L a n d A W S e c o s y s t e m s Query and Monitoring Business Intelligence Source: Amazon Data Integration “We ran our compatibility test suites against Amazon Aurora and everything just worked." - Dan Jewett, Vice President of Product Management at Tableau Lambda IAM CloudWatch S3 Route53 KMS AWS Ecosystem VPC SWF
  44. 44. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. H y b r i d o p e r a t i o n s a n d m i g r a t i o n Amazon Aurora Corporate Data Center Replication Analytics Prod apps Provider of an online marketplace for short-term rentals replicates on-premise production data into Aurora for read-only analytics workloads. Seamless replicate to Aurora: Full compatibility with MySQL and PostgreSQL Data replicated in minutes – run analytics on near real-time data Provides a clean waypoint towards full write/read migration MySQL
  45. 45. Amazon Aurora saves you money 1/10th of the cost of commercial databases Cheaper than even MySQL
  46. 46. C o s t o f o w n e r s h i p : A u r o r a v s . M y S Q L M y S Q L c o n f i g u r a t i o n h o u r l y c o s t Primary r3.8XL Standby r3.8XL Replica r3.8XL Replica R3.8XL Storage 6 TB / 10 K PIOP Storage 6 TB / 10 K PIOP Storage 6TB / 5 K PIOP Storage 6TB / 5 K PIOP $1.33/ hr $1.33/ hr $1.33/ hr $1.33/ hr $2,42/ hr $2,42/ hr $2,42/ hr Instance cost: Storage cost: $5.32/ hr $8.30/ hr Total cost: $13.62/ hr $2,42/ hr
  47. 47. C o s t o f o w n e r s h i p : A u r o r a v s . M y S Q L A u r o r a c o n f i g u r a t i o n h o u r l y c o s t Instance cost: Storage cost: $4.86 / hr $4.43 / hr Total cost: $9.29 / hr Primary r3.8XL Replica r3.8XL Replica R3.8XL Storage / 6 TB $1.62/ hr $1.62/ hr $1.62/ hr $4.43/ hr *At a macro level Aurora saves over 50% in storage cost compared to RDS MySQL. 31.8% Savings No idle standby instance Single shared storage volume No PIOPs – pay for use I/O Reduction in overall IOP
  48. 48. C o s t o f o w n e r s h i p : A u r o r a v s . M y S Q L F u r t h e r o p p o r t u n i t y f o r s a v i n g Instance cost: Storage cost: $2.43 / hr $4.43 / hr Total cost: $6.86 / hrStorage IOPs assumptions: 1. 2. Average IOPs is 50% of Max IOPs 50%savings from shipping logs vs. full pages 49.6% Savings Primary r3.8XL Replica r3.8XL Replica r3.8XL Storage / 6TB $0.81/ hr $0.81/ hr $0.81/ hr $4.43/ hr r3.4XL r3.4XL r3.4XL Use smaller instance size Pay-as-you-go storage
  49. 49. ©2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Aurora Serverless use cases • Infrequently used applications (e.g. a low-volume blog site( • Applications with intermittent or unpredictable load (e.g. a news site) • Development or test databases not needed on nights or weekends • Consolidated fleets of multi-tenant SaaS applications
  50. 50. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. A u r o r a S e r v e r l e s s • Starts up on demand, shuts down when not in use • Scales up & down automatically • No application impact when scaling • Pay per second, 1 minute minimum WARM POOL OF INSTANCES APPLICATION DATABASE STORAGE SCALABLE DB CAPACITY REQUEST ROUTER DATABASE END-POINT
  51. 51. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Instance provisioning and scaling • First request triggers instance provisioning. Usually 1-3 seconds • Instance auto-scales up and down as workload changes. Usually 1-3 seconds • Instances hibernate after user-defined period of inactivity • Scaling operations are transparent to the application – user sessions are not terminated • Database storage is persisted until explicitly deleted by user DATABASE STORAGE WARM POOL APPLICATION REQUEST ROUTER CURRENT INSTANCE NEW INSTANCE
  52. 52. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. S c a l i n g t o a n d f r o m z e r o • If desired, instances are removed after user-defined period of inactivity • While database is stopped, you only pay for storage • A database request triggers instance provisioning (usually under 5 seconds( • Scaling to zero is more expensive as there is no running server from which to copy the buffer cache WARM POOL INSTANCE DATABASE STORAGE REQUEST ROUTER APPLICATION PROXY ENDPOINT
  53. 53. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. S c a l i n g t o a n d f r o m z e r o • If desired, instances are removed after user-defined period of inactivity • While database is stopped, you only pay for storage • A database request triggers instance provisioning (usually under 5 seconds( • Scaling to zero is more expensive as there is no running server from which to copy the buffer cache WARM POOL INSTANCE DATABASE STORAGE REQUEST ROUTER APPLICATION PROXY ENDPOINT
  54. 54. Aurora serverless limitations
  55. 55. m1.small
  56. 56. m1.small m1.large m1.xlarge
  57. 57. Guinness World Record for fastest analysis of 1,000 Genomes, running on Amazon EC2 FPGA instances
  58. 58. 3 5 7 11 12 13 23 42 52 60 70 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Launches in the last 18 months: • Compute Optimized: C5, M5, C5d, M5d • General Purpose: T2 Unlimited • Accelerated Computing: G3, P3 • Memory Optimized: x1e • I/O Optimized: I3, H1 • Programmable/FPGAs: F1 • Bare Metal: I3.metal NEW :T3 instances
  59. 59. Low cost & flexible Develop and test Short-term, spiky and/or unpredictable
  60. 60. Discount up to 75% off the On-Demand price Steady state and committed usage 1and 3 year terms • No upfront • Pratial upfront • Full upfront
  61. 61. Low Cost Faster Results Fault tolerant Instance FlexibleLow Cost Faster Results
  62. 62. simplifies the provisioning across different EC2 instance types and purchase models. Fast Access to Capacity Optimize Price & Availability Customize Based on Application
  63. 63. To turbo boost an application using Spot Instances
  64. 64. Are Spot Instances actually cheaper? • If used intelligently, they can save you a lot of money • Be careful! Naive usage may end up costing more than on-demand!
  65. 65. Available Spot Instances User A Bid: $10 User B Bid: $5 User C Bid: $1 Spot Bid Price $1
  66. 66. Available Spot Instances User A Bid: $10 User B Bid: $5 User C Bid: $1 Spot Bid Price 1$
  67. 67. Available Spot Instances User A Bid: $10 User B Bid: $5 User C Bid: $1 Spot Bid Price 5$
  68. 68. On-Demand Price
  69. 69. Scheduled Tasks ExecutionTime Instances lost due to outbid events
  70. 70. Scheduled Tasks ExecutionTime Lost tasks rescheduled
  71. 71. Spot Fleet: 9 Instances, 3 Markets us-west-2b $ us-west-2a $ us-west-2c $ Amazon EC2 Spot Fleet
  72. 72. Spot Fleet: 9 Instances, 3 Markets us-west-2b $ us-west-2c $ us-west-2a $$$$$
  73. 73. Spot Fleet: 9 Instances, 3 Markets us-west-2b $$$$$ us-west-2c $ us-west-2a $
  74. 74. Spot Fleet: 9 Instances, 3 Markets us-west-2a $$$$$ us-west-2b $$$$$ us-west-2c $
  75. 75. On-Demand Price Challenges: • Availability • Reliability
  76. 76. Option 1: Move back to On-Demand and wait for fluctuation to stop SeagullInfrastructureCost Timeline (June 2016-Sept 2016) YELP costs spiked by 250% when transitioning back to On-Demand Instances for a few days
  77. 77. Getting outbid in three markets doesn’t impact the cluster Option 2: Diversify! Add more Spot markets to reduce impact Number of units in cluster, grouped by Spot market
  78. 78. Diversification isn’t always easy Is your application compatible with other instance sizes and types (e.g., EBS instances, GPU instances)?
  79. 79. How does your application perform on different instance types? ExecutionTime Scheduled Tasks (color-coded by instance id)
  80. 80. Be simple and don’t bid too high Diversify your Spot markets
  81. 81. Scale to 1 million vCPUs with On- Demand backup
  82. 82. AZa AZb US-West-2 Capacity Example D2 C4 M4 I2 R3 R4
  83. 83. To scale on vCPUs, memory or containers 4 2 1 1 1 2 2 8
  84. 84. Flexible Capacity Allocation Select an instance allocation strategy that works for your app and scale in App-aware units (vCPU, GB RAM etc.) Massive Scale Launch 1000’s of instances with a single API call & scale to 1000s of cores, TBs of memory etc. Simplified Provisioning Select instances, assign weights, specify target capacity for On-Demand and Spot Instances, and build a fleet within seconds
  85. 85. Spot strategies Diversified Lowest Price across N capacity pools On-Demand Strategies Lowest Price On-Demand Prioritized List
  86. 86. Automatically finds the multiple cheapest options per purchase model Balance your cost and availability Lowest Price * N Works well with larger EC2 Fleets Fraction of fleet subject to possible interruption and subsequent replenishment. Diversified
  87. 87. Automatically finds the single cheapest option If you just want cheap cores or memory this will automate if for you Lowest Price You specifically control the order in which capacity is fulfilled If you have RIs or very specific instance requirements On- Demand Prioritized List
  88. 88. Fast Access to Capacity Optimize Price & Availability Customize Based on Application
  89. 89. Consistency across services Easier to manage permissions Easier to propagate updates Quicker to learn new EC2 services
  90. 90. What is the difference between Spot Fleet and EC2 Fleet? Does this work with EC2 Auto Scaling groups? Does EC2 Fleet use my RIs? How much does EC2 Fleet cost?
  91. 91. Developer has idea Download & Install 3D Engine Install device SDK Register on platform Install platform SDK Develop application Submit for approval Publish application Customer finds app on store Customer hits purchase button Customer waits for download Customer Uses application Application Pipeline
  92. 92. Simplify
  93. 93. Focus
  94. 94. Let’s change how we communicate Real Environment Mixed Reality Augmented Virtuality Virtual Environment Augmented Reality
  95. 95. ©2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  96. 96. ©2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  97. 97. ©2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.

×