Deploying MongoDB can be a challenge if you don't understand how resources are used nor how to plan for the capacity of your systems. If you need to deploy, or grow, a MongoDB single instance, replica set, or tens of sharded clusters then you probably share the same challenges in trying to size that deployment. This talk will cover what resources MongoDB uses, and how to plan for their use in your deployment. Topics covered will include understanding how to model and plan capacity needs from the perspective of a new deployment, growing an existing one, and defining where the steps along scalability on your path to the top. The goal of this presentation will be to provide you with the tools needed to be successful in managing your MongoDB capacity planning tasks.

1. Capacity Planning:
Deploying MongoDB

2. This deck is a work in progress!
• Written by Asya (and presented by her a few
times)
• …but it’s still new, so you may want to rehearse it a
few extra times before presenting

3. Capacity Planning: Why, What, When
• Don't be the "goat" – spent too much $ or caused
failure of site, etc.
• Users frequently ask about HW they need for their
application. What does 10gen "recommend"?
• No right answer in a vacuum.
• Why we need to plan to meet expectations, etc.
– future planning
• data increases, don't want performance drop-off

4. Capacity Planning: Why, What, When
Why?
What are the consequences of not planning?

5. Why
• Once we launch, we don't want to have avoidable
down time due to poorly selected HW
• As our success grows we want to stay in front of
the demand curve
• We want to meet business' and users' expectations
• We want to keep our jobs J
• and get big raises! ;)

6. Capacity Planning: Why, What, When
Why?
What are the consequences of not planning?

7. Why
• We want to keep our jobs J
• and get big raises! ;)
• so we should stay within reasonable budget

8. Capacity Planning: Why, What, When
What?
Why?
Requirements

9. What
• There is one thing that is absolutely mandatory to
have in order to succeed in capacity planning
• Without it, you will not be successful
• We must have REQUIREMENTS from business
– without requirements, we're building a roadmap without
knowing the desired destination
Imagine building a car without knowing what its top speed
should be, acceleration, MPH, and cost?

10. Capacity Planning: Why, What, When
What?
• Availability
• Throughput
• Responsiveness

11. What
• Availability: what is uptime requirement?
• Throughput
– average read/write/users
– peak throughput?
– OPS (operations per second)? per hour? per day?
• Responsiveness
– what is acceptable latency?
– is higher during peak times acceptable?

12. Capacity Planning: Why, What, When
What?
• Availability
• Throughput
• Responsiveness

13. Capacity Planning: Why, What, When
When?
Before it's too late!
Start Launch Version 2

15. Capacity Planning: Why?
• Capacity
– Under
– Over
– Just right?
• Prediction Models
– User/Load
– System(s) Behavior
• Change Velocity (reaction time)
– Data/Resource-Allocation/Provisioning

16. Capacity Planning: What?
• Understand Resources
– Storage
– Memory
– CPU
– Network
• Understand Your Application
– Monitor and Collect Metrics
– Model to Predict Change
– Allocate and Deploy
– (repeat process)

17. Resource Usage
• Storage • CPU
– IOPS – Speed
– Size – Cores
– Data & Loading Patterns
• Memory • Network
– Latency
– Working Set – Throughput

18. Storage
• Active
• Archival
• Loading Patterns
• Integration (BI/DW)

19. Storage
Example IOPS
• Active
• Archival
• Loading Patterns
• Integration (BI/DW)

20. Storage Capability
Example IOPS
7,200 rpm SATA ~ 75-100 IOPS
15,000 rpm SAS ~ 175-210 IOPS
Amazon EBS/Provisioned ~ 100 IOPS "up to" 2,000 IOPS
Amazon SSD 9,000 – 120,000 IOPS

21. Storage Capability
Example IOPS
7,200 rpm SATA ~ 75-100 IOPS
15,000 rpm SAS ~ 175-210 IOPS
Amazon EBS/Provisioned ~ 100 IOPS "up to" 2,000 IOPS
Amazon SSD 9,000 – 120,000 IOPS
Intel X25-E (SLC) ~ 5,000 IOPS
Fusion IO ~ 135,000 IOPS
Violin Memory 6000 ~ 1,000,000 IOPS

22. Storage Costs
Cost of IOPS
7,200 rpm SATA ~ 75-100 IOPS
15,000 rpm SAS ~ 175-210 IOPS
Amazon EBS/Provisioned ~ 100 IOPS "up to" 2,000 IOPS
Amazon SSD 9,000 – 120,000 IOPS
Intel X25-E (SLC) ~ 5,000 IOPS
Fusion IO ~ 135,000 IOPS
Violin Memory 6000 ~ 1,000,000 IOPS

23. Memory
• Working Set
– Active Data in Memory
– Measured Over Periods

24. Memory
• Work: SORTS
– Sorting Connections
– Aggregation
– Connections Aggregations

25. Memory & Storage
?
>
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26. Memory & Storage
MOPs
PFs

27. Memory & Storage
% Disk Util
MOPS: MongoDB Ops/sec
MOPS

28. Memory & Storage
% Disk Util
MOPS

29. Memory & Storage
% Disk Util
MOPS

30. CPU
• Non-indexed Data
• Sorting
• Aggregation
– Map/Reduce
– Framework
• Data
– Fields
– Nesting
– Arrays/Embedded-Docs

31. Network
• Latency
– WriteConcern
– ReadPreference
– Batching
– Documents (and Collections)
• Throughput
– Update/Write Patterns
– Reads/Queries

32. Starter Questions
• What is the working set?
– How does that equate to memory
– How much disk access will that require
• How efficient are the queries?
• What is the rate of data change?
• How big are the highs and lows?

33. Deployment Types
All of these use the same resources:
• Single Instance
• Multiple Instances (Replica Set)
• Cluster (Sharding)
• Data Centers

34. Capacity Planning: When?
Monitoring
§ Storage
§ Memory
§ CPU
§ Network
§ Application Metrics

35. Tools
• MMS (MongoDB Monitoring Service)
• MongoDB: mongotop, mongostat
• Linux: iostat, vmstat, sar, etc
• Windows: Perfmon
Measure realistic loads (generated by Load testing)

36. Models
• Load/Users
– Response Time/TTFB
• System Performance
– Peak Usage
– Min Usage

37. Velocity of Change
• Limitations -> takes time
– Data Movement
– Allocation/Provisioning (servers/mem/disk)
• Improvement
– Limit Size of Change (if you can)
– Increase Frequency
– MEASURE its effect
– Practice

38. Repeat (continuously)
• Repeat Testing
• Repeat Evaluations
• Repeat Deployment

39. Capacity Planning: What If...
What if I skip capacity planning?
You will be featured ...

40. Thank You