This document provides a summary of a presentation on scaling MongoDB on Amazon Web Services. The presentation covered why MongoDB should be scaled on AWS due to its ability to rapidly scale on demand in a cost effective manner. It also discussed planning considerations for MongoDB and AWS topologies as well as selecting appropriate EC2 instances and storage options. The presentation then covered deploying MongoDB on AWS using DevOps tools like CloudFormation, Chef, and Puppet. It also discussed security, monitoring options like MongoDB Monitoring Service and CloudWatch, and backup strategies using snapshots.

1. DAT209 - Scaling MongoDB on Amazon Web
Services
Michael Saffitz, CTO & Co-Founder, Apptentive
November 15, 2013
© 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.

2. Nice to Meet You!
Mike Saffitz
CTO, Co-Founder, Apptentive
Follow at: @msaffitz • Connect at: mike@apptentive.com
Apptentive
The easiest way for anyone with an app to talk with their customers
Follow at: @apptentive • Connect at: info@apptentive.com

3. Apptentive & AWS

4. Apptentive & AWS
Route53
CloudFront
IAM
S3
Web Servers
EC2: 6 x c1.medium
api.apptentive.com
www.apptentive.com
(Elastic Load
Balancer)
apptentive.com/blog
Elastic Beanstalk, RDS
CloudWatch
Elastic
MapReduce
VPN Server
EC2: m1.small
Stats & Logging
EC2: 2x m1.medium
m1.small
Sharded MongoDB
Cluster
EC2: 9 Instances
CI & Chef
EC2: m1.medium
m1.small
Redis
EC2: m1.medium
Virtual Private Cloud

5. Agenda
• Why Scale MongoDB on AWS?
• Planning
• Deploying
• Maintaining

6. Why Scale MongoDB on AWS?

7. Why Scale MongoDB on AWS?
Supports
Diverse Set of
Scenarios
Rapidly Scale
On Demand
Simple To
Administer
Easy
Friendly Query
Syntax
Well
Documented
Flexible
Broad
Language
Support
Competitive
TCO
Cost
Effective
Fine Grain Control
Over Price &
Performance

8. Why Not Scale MongoDB on AWS?
Your Data is
Predominately
Relational in Nature
Don’t Want to Incur the
Administrative Costs
Consider RDS
Hosted Alternatives
Consider DynamoDB

9. 1. Planning

10. Planning Checklist
• Topologies
– MongoDB
– AWS
• Instance Selection
• Storage

11. MongoDB Topologies: Single Server
mongod

12. MongoDB Topologies: Single ReplicaSet w/ Arbiter
Automatic
Failover
mongod
(primary)
mongod
(secondary)
Contains Full Copy of
Data on the Primary –
Can be Used for Reads
mongod
(arbiter)
Arbiter Only Participates
in Voting to Elect a New
Primary
(Must Have Odd #)

13. MongoDB Topologies: Single ReplicaSet
Automatic
Failover
mongod
(primary)
mongod
(secondary)
Scale Across
Instance
Types
mongod
(secondary)
Data Replicated Within ReplicaSet

14. MongoDB Topologies: Sharded Cluster
App Server
mongos
App Server
…
mongos
mongod
process
config
config
config
Data Partitioned Across Shards
mongod
(primary)
mongod
(secondary)
mongod
(secondary)
Data Replicated Within Shard
…
mongod
(primary)
mongod
(secondary)
mongod
(secondary)

15. MongoDB Topologies: Picking One
• Single Server? Not For Production
• Don’t Shard Prematurely
– ReplicaSets can take you surprisingly far
• … But Don’t Wait Too Long to Shard
– Collections over 256GB may have issues migrating to shards
– Rebalancing consumes IO and can be very slow
• Pick the Right Instance Size for Your Topology…
– We’re going to get to this in a moment

16. AWS Topologies: AZs & Regions
• Obvious: Distribute Across Availability Zones in a
Region
– No Single Point of Failure
• Distributing Across Regions
– Shard per Region versus Shards Across Regions
– Considerations
•
•
•
•
Replication Latency
Data Transfer Costs
Administration Costs
Speedup from Geo-Based Tag Aware Sharding

17. Selecting an Instance: Considerations
Compute
Memory
EBS
Optimized?
Cost

18. Selecting an Instance: Compute
• Most Likely to Not Be A Significant Factor
– Exceptions: Heavy use of Map/Reduce, Aggregation Framework
– Mongo 2.4 added concurrency via V8
– Important! Only run 64-Bit ; 32-Bit is limited to ~2GB
• Real World Numbers on m1.large:

19. Selecting an Instance: Memory
• Estimate Necessary Working Set
–
db.runCommand( { serverStatus: 1, workingSet: 1 } )
Is pagesInMemory * 4k approaching total RAM? Is overSeconds decreasing / small?
– db.stats()
• Pick the Instance that Matches
• Monitor on MMS
– Page Faults (abstract)
– Queues (better)
– Response Times (best)

20. Selecting an Instance: EBS Optimization
• Run EBS Optimized When Available
– Especially with Provisioned IOPs
• Volume Config Impacts IO Perf Far More than
Instance Selection

21. Storage
• Instance Storage
– Non-Durable
– Fast But Inconsistent Performance
– Can’t Use Snapshots for Backups
• “Standard” EBS
– Slower
– Higher Variability Performance
• Provisioned IOPs EBS
– Consistent Performance
– Don’t Under Provision -- Watch Queue
Length

22. Storage
• RAID 10? Just use LVM on RAID 0
– More: http://blog.mongohq.com/debunking-myth-of-raid-10-asbest-practice-on-aws/
• Use XFS or Ext4
• Mount with noatime, noexec, nodiratime

23. Selecting an Instance: Summary
1. Lead with Working Set Requirements
2. Validate Compute is Sufficient
3. Enable EBS Optimized if Available
4. Use Provisioned IOPS EBS
5. (Confirm Cost is Acceptable)

24. 2. Deploying

25. It’s Easy. Let me show you.

26. Scaling Deployment
• DevOps: Go for ‘bilities:
– Reliability, Predictability, Repeatability, and Auditability
• The Result is Easy Replaceability and
Scalability
– Build your infrastructure so it can be treated like an appliance
– The impact of your decisions during planning will be significantly
mitigated

27. DevOps Tools
• AWS Marketplace AMIs
– Preconfigured with MongoDB best practices
– Do-it-yourself scaling to ReplicaSets / Shards
– Helpful, but not a DevOps Solution
• AWS CloudFormation
– Templates for Resource Setup & Initial Configuration
• Chef, Puppet, Ansible, SaltStack, & More
– AWS OpsWorks, but limited by chef-solo

28. Security
• Run in a VPC
– Complications: Cross Region, Multiple Source Ingress
• Use KeyFiles & Roles
– KeyFiles: Internal authentication for cluster members
– Roles allow for user-level fine grain access control
• Advanced:
– Keberos support in MongoDB 2.4
– SSL Support in Custom Builds & MongoDB Enterprise

29. 3. Maintaining

30. Monitoring: MongoDB Monitoring Service
• Very Good, Free Holistic Monitoring
–
–
Important: ReplLag, Page Faults, Lock %
Informative: OpCounters, Connections, Queue Lengths
• Includes Basic Alerting of Host Failures and Metric Thresholds
• Query Profiler Details Slow Queries
–
db.setProfilingLevel(1)

31. Monitoring: Amazon CloudWatch
• Detailed Resource Level Monitoring
– Important: Queue Length, Read/Write Latencies
• Versatile alerting based on Amazon Simple Notification
Service (SNS)

32. Backups
• Delayed Secondary
– Questionable as a primary backup strategy
• Dump/Restore
– Impractical for larger deployments
• MongoDB Service
– Managed, Secure, Point in Time. Unclear suitability for larger deployments
– Expensive
• Snapshots
– Fast, Easy, Scalable. Pay Attention to Consistency (RAID, Shards)

33. Easy Snapshot-Based Backups With Mongolly
• Automatic topology detection, snapshotting, and
snapshot management for EBS-backed MongoDB
Databases
• Easy as: $ mongolly backup
• https://github.com/msaffitz/mongolly

34. Conclusions
• MongoDB + AWS =
• Options For All Deployment / Workload Sizes
– I/O typically the focal point for optimization
• Investing in a DevOps Strategy + Solution
Makes It Near Effortless

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presentation
DAT209
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