capacity planning

Solutions Architect, 10gen Asya Kamsky

#MongoDBDays

Capacity Planning: Deploying MongoDB

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

Capacity Planning, Asya Kamsky, 10gen

What are the consequences of not planning?



Why?

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! ;)




Why? What are the consequences of not planning?

Why

•  We want to keep our jobs J

•  and get big raises! ;)

•  so we should stay within reasonable budget




Requirements

What? Why?

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?


•  Availability •  Throughput •  Responsiveness

What?



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?


•  Availability •  Throughput •  Responsiveness

What?



Before it's too late!

When?



Start Launch Version 2

Capacity Planning: Why?

•  Capacity –  Under –  Over –  Just right?

•  Prediction Models –  User/Load –  System(s) Behavior

•  Change Velocity (reaction time) –  Data/Resource-Allocation/Provisioning


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)

Resource Usage

•  Storage –  IOPS –  Size –  Data & Loading Patterns

•  Memory

–  Working Set

•  CPU –  Speed –  Cores

•  Network –  Latency –  Throughput


Storage

• Active • Archival • Loading Patterns •  Integration (BI/DW)


Storage

• Active • Archival • Loading Patterns •  Integration (BI/DW)


Example IOPS

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


Storage Capability

Intel X25-E (SLC) ~ 5,000 IOPS

Fusion IO ~ 135,000 IOPS

Violin Memory 6000 ~ 1,000,000 IOPS

Example IOPS 7,200 rpm SATA ~ 75-100 IOPS

15,000 rpm SAS ~ 175-210 IOPS




Storage Capability

Intel X25-E (SLC) ~ 5,000 IOPS

Fusion IO ~ 135,000 IOPS

Violin Memory 6000 ~ 1,000,000 IOPS

Cost of IOPS 7,200 rpm SATA ~ 75-100 IOPS

15,000 rpm SAS ~ 175-210 IOPS




Storage Costs

Storage


Measuring and Monitoring

Memory

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


Memory

•  Work: – Sorting – Aggregation – Connections


SORTS

Connections

Aggregations

Memory

•  New in 2.4 (just out) – workingSet option on db.serverStatus() db.serverStatus( { workingSet: 1 } )


Measuring and Monitoring

Memory & Storage


> < ?

Memory & Storage


MOPS: MongoDB Ops/sec

Memory & Storage


MOPs

PFs

MOPS: MongoDB Ops/sec

Memory & Storage


% Disk Util

MOPS

CPU

•  Non-indexed Data

•  Sorting

•  Aggregation –  Map/Reduce –  Framework

•  Data –  Fields –  Nesting –  Arrays/Embedded-Docs


CPU


MOPs

CPU


MOPs

CPU %

Memory & Storage


% Disk Util

MOPS

Network

•  Latency –  WriteConcern –  ReadPreference –  Batching –  Documents (and Collections)

•  Throughput –  Update/Write Patterns –  Reads/Queries


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?


Deployment Types

All of these use the same resources:

•  Single Instance

•  Multiple Instances (Replica Set)

•  Cluster (Sharding)

•  Data Centers


Capacity Planning: When?

Monitoring §  Storage

§ Memory

§ CPU

§ Network

§ Application Metrics


Tools

•  MMS (MongoDB Monitoring Service)

•  MongoDB: mongotop, mongostat

•  Linux: iostat, vmstat, sar, etc

•  Windows: Perfmon

Measure realistic loads (generated by Load testing)


Models

•  Load/Users –  Response Time/TTFB

•  System Performance –  Peak Usage –  Min/avg Usage


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


Repeat (continuously)

•  Repeat Testing

•  Repeat Evaluations

•  Repeat Deployment


What if I skip capacity planning?

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Capacity Planning: What If...


Senior Solutions Architect, 10gen

Asya Kamsky

#MongoDBDays

Thank You

capacity planning

Technology

mongodbdayscapacity

requirementscapacity

future planning data

acceptable latency

jobs j

users expectations

big raises

storage capability example