Sizing a database cluster makes or breaks your application. Too small and you could sustain spikes in usage and recover from a node loss or an operational slowdown. Too big and your cluster will cost more and waste valuable human resources. Since different workloads have different requirements, successful sizing of your application should be optimized for both throughput and latency performance. However, in many cases, the requirements for each contradicts each other. In this webinar, we explain how to remediate the contradicting forces and build a sustainable cluster to meet both performance and resiliency requirements.

1. Eyal Gutkind - VP of Solutions, ScyllaDB
Sizing your Scylla Cluster
A walk through the process

2. Presenter
2
Eyal Gutkind
Eyal Gutkind is VP of Solutions at ScyllaDB. Prior to joining
ScyllaDB, Eyal held product management roles at Mirantis
and DataStax, and spent 12 years with Mellanox
Technologies in various engineering management and
product marketing roles. Eyal holds a BSc. degree in
Electrical and Computer Engineering from Ben Gurion
University in Israel and an MBA from Fuqua School of
Business at Duke University.

3. 3
+ The Real-Time Big Data Database
+ Drop-in replacement for Cassandra
+ 10X the performance & low tail latency
+ New: Scylla Cloud, DBaaS
+ Open source and enterprise editions
+ Founded by the creators of KVM hypervisor
+ HQs: Palo Alto, CA; Herzelia, Israel
About ScyllaDB

4. Agenda
+ Understand your workload
+ The machines
+ Let’s build a system
+ How it will look at different IaaS
+ Our sizing process

5. Understand
Your Workload

6. Thinking about database cluster sizing?!
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7. Make sure you have all requirements set
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8. Make sure you have all requirements set
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+ Business

9. Make sure you have all requirements set
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+ Business
+ Application

10. Make sure you have all requirements set
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+ Business
+ Application
+ Infrastructure

11. Make sure you have all requirements set
11
+ Business
+ Application
+ Infrastructure
+ Resiliency

12. Make sure you have all requirements set
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+ Business
+ Application
+ Infrastructure
+ Resiliency
+ Developer and Operator Friendliness

13. The obvious...
+ Data volume ingested per second/hour/day/year
+ Data attrition policy
+ Data format : Text, Binary blob
+ Required replication factor
+ What’s in your storage system?
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14. 14
The shape of your workload

15. The shape of your workload
https://www.scylladb.com/2019/05/23/workload-prioritization-running-oltp-and-olap-traffic-on-t
he-same-superhighway/

16. The Machines

17. Someone needs to do the job!
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18. Memory and data volume
+ Keep disk space to memory at a reasonable ratio
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19. Connectivity
+ Enable enough bandwidth for client-server and server-server communication
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20. Connectivity
+ Enable enough bandwidth for
client-server and server-server
communication
+ Geo-replication benefits
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21. Let’s build a system

22. Let’s build a system
Business requirements:
+ 1st year customers: 30M profiles
+ Number of records per profile: 12
+ Avg. size of each record: 3KB
+ Data types: text
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23. Let’s build a system
Business requirements:
+ 1st year customers: 30M profiles
+ Number of records per profile: 12
+ Avg. size of each record: 3KB
+ Data types: text
Application requirements:
+ 99% write response time: 15ms
+ 99% read response time: 10ms
+ Peak throughput: 150,000 operations/sec
+ Read:Write ratio: 70:30
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24. Let’s build a system
Business requirements:
+ 1st year customers: 30M profiles
+ Number of records per profile: 12
+ Avg. size of each record: 3KB
+ Data types: text
Application requirements:
+ 99% write response time: 15ms
+ 99% read response time: 10ms
+ Peak throughput: 150,000 operations/sec
+ Read:Write ratio: 70:30
Infrastructure:
+ AWS
+ Available instance types: all
+ Multi-DC: Oregon and N.Virgina
+ OS: CentOS 7.6
+ Replication Factor: 3
24

25. Let’s build a system
Business requirements:
+ 1st year customers: 30M profiles
+ Number of records per profile: 12
+ Avg. size of each record: 3KB
+ Data types: text
Application requirements:
+ 99% write response time: 15ms
+ 99% read response time: 10ms
+ Peak throughput: 150,000 operations/sec
+ Read:Write ratio: 70:30
Infrastructure:
+ AWS
+ Available instance types: all
+ Multi-DC: Oregon and N.Virgina
+ OS: CentOS 7.6
+ Replication Factor: 3
Auxiliary applications:
+ Spark
25

26. How it looks
at different IaaS

27. Let’s build a system
+ End of year Total raw data size: 2TB, Starting with ~1TB
+ Typical record size read/written by the application: 3KB
+ Data model: 20-30 tables, up to 20 columns per row, 10-50 rows per partition, mainly text
+ Latency requirements: 10ms Write, 15ms Read, for the 99%
+ Read:Write ratio: 70:30
+ Throughput: 150,000 database op/s
+ IaaS: AWS, multi-region, multi-availability-zone, N. Virginia and Oregon
+ Replication Factor: 3
+ Spark for analytics
27

28. Let’s build a system, Amazon Web Services
+ Per Data Center
+ Needed disk space: 12TB
+ Media type: NVMe drives to meet latency SLA
+ Requires 30 threads, 15 physical cores
+ Per Data center instance options
+ 3 x i3.4xlarge → Total disk: 11.5TB
and
+ 3 x i3.2xlarge for the Spark cluster
+ 1x i3.2xlarge for Scyla monitoring and Scylla manager
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29. Let’s build a system, Azure
+ Per Data Center
+ Needed disk space: 12TB
+ Media type: NVMe drives to meet latency SLA
+ Requires 30 threads, 15 physical cores
+ Per Data center instance options
+ 3 x standard L16 v2 → Total disk: 11.5TB
and
+ 3 x standard L8 v2 for the Spark cluster
+ 1x standard L8 v2 for Scyla monitoring and Scylla manager
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30. Let’s build a system, Google Cloud
+ Per Data Center
+ Needed disk space: 12TB
+ Media type: NVMe drives to meet latency SLA
+ Requires 30 threads, 15 physical cores
+ Per Data center instance options
+ 6 x n1-standard-16 + 5x NVMe based direct attached, 375GB drives
and
+ 3 x n1-standard-8 for the Spark cluster
+ 1 x n1-standard-8 for Scyla monitoring and Scylla manager
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31. Let’s build a system, Scylla Cloud
+ Per Data Center
+ Needed disk space: 12TB
+ Media type: NVMe drives to meet latency SLA
+ Requires 30 threads, 15 physical cores
+ Per Data center instance options
+ 3 x i3.4xlarge → Total disk: 11.5TB
and
+ 3 x i3.2xlarge for the Spark cluster
31

32. Let’s build a system, on premise
+ Per Data Center
+ Needed disk space: 12TB
+ Media type: NVMe drives to meet latency SLA
+ Requires 30 threads, 15 physical cores
+ Per Data center instance options
+ 3 machines with 8 physical cores each and at least 4TB of SSD direct attached drives
+ 3 machines with 4 or more physical cores for Spark
+ 1 x machine for Scyla monitoring and Scylla manager
+ Scylla nodes and Spark nodes: 128GB RAM
+ Network: 10GbE
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33. Our sizing process

34. Scylla’s sizing sheet
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35. Summary
+ Do not think only storage!
+ Gather applications and business requirements
+ Throughput and SLAs
+ Growth expectations
+ Security and compliance needs
+ Select the right infrastructure
+ Think about resiliency and high availability
Ask us questions!
35

36. Best Practices for Data Modeling
August 7, 2019 | 10:00 AM PT - 1:00 PM ET
How to Shrink Your
Datacenter Footprint by 50%
August 14, 2019 | 10:00 AM PT - 1:00 PM ET

37. Q&A
Stay in touch
eyal@scylladb.com
@gutkinde

38. United States
1900 Embarcadero Road
Palo Alto, CA 94303
Israel
11 Galgalei Haplada
Herzelia, Israel
www.scylladb.com
@scylladb
Thank you