1,000,000,000,000,000 bytes. On demand. Online. Live. Big doesn't quite describe this data. Amazon Web Services makes it possible to construct highly elastic computing systems, and you can further increase cost efficiency by leveraging the Spot Pricing model for Amazon EC2. We showcase elasticity by demonstrating the creation and teardown of a petabyte-scale multiregion MongoDB NoSQL database cluster, using Amazon EC2 Spot Instances, for as little as $200 in total AWS costs. Oh and it offers up four million IOPS to storage via the power of PIOPS EBS. Christopher Biow, Principal Technologist at 10gen | MongoDB covers MongoDB best practices on AWS, so you can implement this NoSQL system (perhaps at a more pedestrian hundred-terabyte scale?) confidently in the cloud. You could build a massive enterprise warehouse, process a million human genomes, or collect a staggering number of cat GIFs. The possibilities are huMONGOus.

1. PetaMongo:
A Petabyte Database for as Little as $200
Chris Biow, MongoDB
Miles Ward, AWS
November 13, 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. Agenda
• MongoDB on AWS review
– Guidance, Storage, Architecture
• MongoDB at PetaScale on AWS

3. Tools to simplify your design
• Whitepaper
• AWS Marketplace
• AWS
CloudFormation
http://media.amazonwebservices.com/AWS_NoSQL_MongoDB.pdf

4. • Easy to start a
single node
• Correctly configured
PIOPS EBS Storage
• No extra cost
https://aws.amazon.com/marketplace/pp/B00COAAEH8/ref=srh_res_product_title?ie=UTF8&sr=0-6&qid=1383897659043

5. AWS CloudFormation
• Nested Templates
• Nodes and Storage
• Configurable Scale
• AWS CloudFormation: Your
Infrastructure belongs in your
source control
mongodb.org/display/DOCS/Automating+Deployment+with+CloudFormation

6. AWS Storage Options
EBS
PIOPS
SSD
• Amazon EBS – Provisioned IOPS volumes
•
Deliver predictable, high performance for I/O intensive workloads
•
Specify IOPS required upfront, and EBS provisions for lifetime of volume
– 4000 IOPS per volume, can stripe to get thousands of IOPS to an EC2 instance
• High IO Instances – hi1.4xlarge
•
•
For some applications that require tens of thousands of IOPS
Eliminates network latency/bandwidth as a performance constraint to storage

7. AWS Storage Options
Testing: random 4k reads
EBS
+
One Volume: ~200 MongoOPS with some variability, <1mb/s
Loaded instance: ~ 1000 MongoOPS with some variability <10mb/s
One Volume: 200
0 MongoOPS with <1% variability, 16mb/s
Loaded Instance: 16,000 MongoOPS with <1% variability, 64mb/s
PIOPS
Loaded Cluster Instance:
SSD
MongoOPS, 320mb/s
Hi1.4xlarge ephemeral: ~64,000 MongoOPS with low variability, ~245mb/s

8. Testing: random 4k reads
+
PIOPS
Stable
EBS
SSD

9. Stability Tips
• Ext4 or XFS, nodiratime, noatime
• Raise file descriptor limits
• Set disk read-ahead
• No large virtual memory pages
• SNAPSHOT SNAPSHOT SNAPSHOT

10. • Retain a PIOPS EBS
node for snapshot
backups
• Snapshots allow crossAZ and cross-region
recovery
• SSD hosts as primary
• Shard for scale

11. Another option…
244gb cr1.8xlarge

12. So, about that Petabyte
v.cheap
• Spot Market
• m1.small
• 1024 shards
• 1TB EBS from snapshot
• PowerBench reader
• Aggregation queries
v.fast
•
•
•
•
•
•
Auto Scaling On-Demand
m2.4xlarge
50 shards
20TB PIOPS indexed
PowerBench loader
Aggregation queries

13. The naming of parts
Amazon Terms
• Provisioned IOPS
• Elastic Compute Cloud
• EC2 Spot Instances
• Auto Scaling groups
Nicks
• PIOPS
• EC2
• Here, Spot!
• ASG

14. Players

15. MongoDB
• Document-model,
NoSQL database
• Dev adoption is
STRONG
• MongoDB Inc.
trending toward
zero h/w
• Scale-up with commodity h/w
• Scale-out with sharding
• Scale-around with replication

16. AWS
•
•
•
•
PIOPS for an IO-hungry client
40% of MongoDB customer usage
90% of MongoDB internal usage
More ports :2701[79] than :[15]521

17. PB & Chocolate
Differentiators for mutual customers
•
•
•
•
•
Fast time-to-solution
Easy global distribution
Secondary index
Geo, text, security
Fast analytic aggregation

18. Challenge

19. Motivation: IWBCI…
•
•
•
•
•
Test scale-out of MongoDB beyond typical
Learn massive scale-out on AWS
Do it as cheaply as possible
Apply customer data
Break the petabarrier

20. m1.small us-east1 Spot Market

21. m1.small us-east1d Spot Market

22. Proposal
Item
Units
Time
Unit Cost
Net Cost
m1.small Spot 1050
3hr
$0.007/hr
$22.05
m1.large
3
48hrs
$0.056/hr
$8.07
S3
1TB
1wk
$95/TB/mo
23.75
EBS
1024 x 1TB
1hr
$100/TB/mo
142.22
S3  EBS
1PB
??
$0/TB
Total
0.00
$196.09
http://calculator.s3.amazonaws.com/G77798SS77SH72

23. Initial Directions
• Spot Instance requests
– m1.small market, mostly us-east-1 (my zone “d”)
– Net: $0.007 / hour = $7 / hr / K-shard
• Perl
– use Net::Amazon::EC2;
– gaps: parse EC2 command-line API
•
•
•
•
Defer Chef, Puppet, AWS CloudFormation
YCSB
userdata.sh
t1.micro / m1.small / cr1.8xlarge

24. MongoDB Architecture
• 3x Config Servers
– mongod --configsvr
• Routing
– mongos --configdb a,b,c
• Replica sets (not used)
• Shards
– mongod
• Client load
– java -cp [] com.yahoo.ycsb.Client

26. Range-based sharding

27. Hash-based sharding

28. Process Flow
Spot Instance Request (sir-)
• rejected
• Awaiting evaluation
• Awaiting fulfillment
– Partial
– Launch intervals
• Fulfilled
Instances (i-)
• Requested
• Initializing (i)
• Config running (C)
• MongoS starting (s)
• MongoS running (S)
• MongoD starting (D)
• Failed/slow response (X)

29. Config
sir-
Sharded
Shard
MongoD
MongoS
Spot Instance Lifecycle

30. Progress

31. Scaling Experience
•
•
•
•
•
•
4, 16, 64, 256, 1024
4: minimum magnitude for 3x Config
16: startup variation, process flow
64: full speed ahead!
256: chunk distribution time
1024: market dependence, client wire saturation

32. Lessons Learned
• Code defensively
• Monitor: MongoDB Mgt Svc, top, iftop,
mongostat
• Avoid sentimental attachment
• Prototype / refactor
• Make the instances do the work
• Mitigate chunk migration

33. Refactor
•
•
•
•
•
BenchPress YCSB
Auto Scaling groups request-spot-instances
use VM::EC2; Net::Amazon::EC2
gsh monolithic Perl
serf polling

34. Dee-Luxe

35. Docs Loaded, 512 shards
1.8E+09
1.6E+09
1.4E+09
1.2E+09
1E+09
800000000
600000000
^ 1X
RAM
400000000
200000000
0
5:16:48
5:45:36
6:14:24
6:43:12
7:12:00
7:40:48

36. Further Work
•
•
•
•
•
Replication
Self-healing
MongoDB-appropriate benchmarks
Customer data
Self-hosting cluster

37. Please give us your feedback on this
presentation
BDT307
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