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Bringing Your Applications to the Fast Lane (CPN203) | AWS re:Invent 2013
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Transcript of Bringing Your Applications to the Fast Lane (CPN203) | AWS re:Invent 2013
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© 2013 Amazon.com, Inc. and its affiliates. All rights reserved. May not be copied, modified, or distributed in whole or in part without the express consent of Amazon.com, Inc.
Deepak Singh - Amazon Web ServicesChristos Kalantzis - NetflixSteven Jones - Amazon Web Services
November 15, 2013
Bringing Your Applications to the Fast Lane
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Hello!
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Instance Types
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Performance
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Generations
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Many instance types
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Building Blocks
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General PurposeCompute-optimizedMemory-optimizedStorage-optimizedGPUMicro
M1, M3C1, CC2M2, CR1HI1, HS1CG1, G2Micro
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Workload optimized
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Compute-optimized
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CC2
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2.6 GHz Intel Xeon E5-2670
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16 cores/32 vCPU
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10 Gbps cluster networking
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High Performance Linpack
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Cores 17024
Rmax 240.09
Rpeak 354.12
cc2.8xlarge
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I/O
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HI1 instances
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High random I/O performance
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SSDs
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Netflix and HI1
Christos Kalantzis
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Advantages of using hi1.4xlarge with C*
• Higher application speed• Efficient operations & better data quality• Cost savings
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Higher Application Speeds
• Compared m2.4xlarge & hi1.xlarge• 6 node C* cluster with 250 GB data on each node• Used following Cassandra Stress tool & Options
from three m2.4xlarge clients:–cassandra-stress -d [server-list] -t 25 -r -p 7102 -n 1000000000 -k -o
READ
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Higher Application Speeds
Cassandra-stress C*
C*
C*
C*
C*
C*Cassandra-stress
Cassandra-stress
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m2.4xlarge results
Throughput Speed
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hi1.4xlarge results
Throughput Speed
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Higher Application Speeds
• 40X throughput• Better Latencies
–~37X AVG–~109X 95th %ile–~110X 99th %ile
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Efficient C* Operations & Better Data Quality
• Cassandra => “benefit now, pay later”–Immutable SSTABLES need to be compacted–Can compact faster & with less impact
• Eventual Consistency–Repairs run more often–Increased Consistency without higher CL calls
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Cost Savings
• Can now have multi-tenant clusters–Savings immediate on the second application–m2.4xlarge is $1.640 per hour–hi1.4xlarge is $3.100 per hour
C*App A&B
C*App A&B
C*App A&B
C*App A&B
C*App A&B
C*App A&B
C*App A
C*App A
C*App A
C*App A
C*App A
C*App A
C*App B
C*App B
C*App B
C*App B
C*App B
C*App B
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Cost Savings
• Use Less Nodes–Data : Memory ratio can now be bigger–Cluster size doesn’t need to be doubled as often
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Cost Savings
• Smaller Operational Footprint–Less clusters–Smaller operational teams–Less fire-fighting–Team can focus on automation
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hi1.4xlarge caveats
• 10 Gbps not attained (in our model)–Need to be part of the same Cluster Placement Group–Netflix cross-zone availability model excludes CPG
• Not same as bare metal SSD running on Linux–Virtualization obfuscates some SSD advantages
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Memory-optimized
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244 GiB of RAM
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2.6 GHz Intel Xeon E5 2670
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10 Gbps cluster networking
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In-memory distributed analytics
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e.g. SAP HANA
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Steven JonesAmazon Web Services
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SAP ERP
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SAP ERP
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SAP ERP
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SAP ERP BI
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SAP ERP BI
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SAP ERP BI
Friday, November 15, 13
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SAP ERP BI
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SAP ERP BI
Aggregates
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SAP ERP BI
AggregatesPre-Defined
Queries
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SAP ERP BITREXBWA
AggregatesPre-Defined
Queries
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SAP ERP BITREXBWA
AggregatesPre-Defined
Queries
Friday, November 15, 13
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SAP ERP BITREXBWA
AggregatesPre-Defined
Queries
Friday, November 15, 13
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SAP ERP BITREXBWA
AggregatesPre-Defined
Queries
Friday, November 15, 13
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SAP ERP BITREXBWA
AggregatesPre-Defined
Queries
Friday, November 15, 13
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SAP ERP BITREXBWA
AggregatesPre-Defined
Queries
Friday, November 15, 13
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SAP ERP BITREXBWA
AggregatesPre-Defined
Queries
Friday, November 15, 13
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SAP ERP BITREXBWA
AggregatesPre-Defined
Queries
Friday, November 15, 13
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SAP ERP BITREXBWA
AggregatesPre-Defined
Queries
Friday, November 15, 13
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SAP ERP BITREXBWA
AggregatesPre-Defined
Queries
Friday, November 15, 13
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SAP ERP BITREXBWA
AggregatesPre-Defined
Queries
Friday, November 15, 13
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SAP ERP BITREXBWA
AggregatesPre-Defined
Queries
Friday, November 15, 13
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SAP ERP BITREXBWA
AggregatesPre-Defined
Queries
Friday, November 15, 13
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SAP ERP BITREXBWA
AggregatesPre-Defined
Queries
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Magnetic Disk
SSD/Flash
RAM
CPU Cache
CPU Register
Latency
Storage Type
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Magnetic Disk
SSD/Flash
RAM
CPU Cache
CPU Register
Latency
Storage Type
RAM is up to 100,000 x Faster
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http://tinyurl.com/hana-perf
SAP HANAHigh Performance Analytic Appliance
aka In-Memory Database Platform
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SAP ERP BIHANA
Friday, November 15, 13
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SAP ERPHANA
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EC2 Cluster Compute instances
2 x Intel Xeon E5-2670 processors32 vCPUs with hyperthreading
64-bit60.5 GB RAM
10 Gigabit Network
2 x Intel Xeon E5-2670 processors
32 vCPUs with hyperthreading
64-bit
244 GB RAM
10 Gigabit Network
NUMA and Turbo Support
cc2.8xlarge cr1.8xlarge
New
All c* instances support high-performance (10 gigabit) networking to Elastic Block Storage volumes (EBS)
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SAP HANA on AWS
1,776 Core HANA Cluster on AWS
60B Rows | 8M Rows/s | 3M Queries/h | 330ms
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Where to go for HANASAP Developer Center http://bit.ly/aws-hana-devFree SAP license AWS Marketplacehttp://bit.ly/
aws-hana-one99¢ / hr SAP License
SAP HANA Marketplacehttp://bit.ly/aws-hana-byolBYOL
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New generations
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Latest Generation Hardware
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Hardware features, e.g. AVX
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SSDs
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Enhanced Networking
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Better price-performance
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C3
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Best raw compute capacity
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2.8 GHz Intel Xeon E5-2680v2(Ivy Bridge)
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Clustering as a “feature”
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High Performance Linpack
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Cores 26496
Rmax 484.18
Rpeak 593.87
c3.8xlarge
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0"
100"
200"
300"
400"
500"
600"
CC1" CC2" C3"
Rmax%
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Cores 8192
Rmax 163.9
Rpeak 183.5
c3.8xlarge
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I2
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Coming Soon
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Best Price for IOPS
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More sizes
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More SSD
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More IOPS
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More memory
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Friday, November 15, 13
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350,000+ random read IOPS320,000+ random write IOPS
i2.8xlarge
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175,000+ random read IOPS160,000+ random write IOPS
i2.4xlarge
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Enhanced Networking
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Enhanced Networking
Low latencyLow jitter
Very high PPS performance
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cc2.8xlarge+ c3.8xlarge+ c3.8xlarge+(enhanced)+
cc2.8xlarge+ c3.8xlarge+ c3.8xlarge+(enhanced)+
Latency
PPS
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p50$
p90$
p999$
p100$
Low network jitter
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General PurposeCompute-optimizedMemory-optimizedStorage-optimizedGPUMicro
M1, M3C1, CC2, C3M2, CR1HI1, I2, HS1CG1, G2Micro
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General PurposeCompute-optimizedMemory-optimizedStorage-optimizedGPUMicro
M1, M3C1, CC2, C3M2, CR1HI1, I2, HS1CG1, G2Micro
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More to come
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Friday, November 15, 13