This document discusses how to achieve scale with MongoDB. It covers optimization tips like schema design, indexing, and monitoring. Vertical scaling involves upgrading hardware like RAM and SSDs. Horizontal scaling involves adding shards to distribute load. The document also discusses how MongoDB scales for large customers through examples of deployments handling high throughput and large datasets.

2. Achieving Scale with MongoDB
Thomas Boyd
Manager, SolutionsArchitecture, MongoDB

3. MongoDB Doesn’t Scale!!
0
5,000
10,000
15,000
20,000
25,000
30,000
1 2 3 4 5 6 7 8
Operations/Second
Number of Nodes
MongoDB Cluster Throughput

4. Agenda
• Optimization Tips
– Schema Design
– Indexes
– Monitoring
• Vertical Scaling
• Horizontal Scaling
• Scaling your Operations Team
• Customer @ Scale on MongoDB

5. Optimization Tips: Schema Design

6. Document Model
• Matches Application
Objects
• Flexible
• High performance
{ "customer_id" : 123,
"first_name" : ”John",
"last_name" : "Smith",
"address" : {
"street": "123 Main Street",
"city": "Houston",
"state": "TX",
"zip_code": "77027"
}
policies: [ {
policy_number : 13,
description: “short term”,
deductible: 500
},
{ policy_number : 14,
description: “dental”,
visits: […]
} ]
}

7. The Importance of Schema Design
• Very different from RDBMS schema design
• MongoDB Schema:
– denormalize the data
– create a (potentially complex) schema with
prior knowledge of your actual (not just
predicted) query patterns
– write simple queries

8. Real World Example
Product catalog for retailer selling in 20 countries
{
_id: 375,
en_US: { name: …, description: …, <etc…> },
en_GB: { name: …, description: …, <etc…> },
fr_FR: { name: …, description: …, <etc…> },
fr_CA: { name: …, description: …, <etc…> },
de_DE: …,
<… and so on for other locales …>
}

9. Not a Good Match for Access Pattern
Actual application queries:
db.catalog.find( { _id: 375 }, { en_US: true } );
db.catalog.find( { _id: 375 }, { fr_FR: true } );
db.catalog.find( { _id: 375 }, { de_DE: true } );
… and so forth for other locales

10. Inefficient use of resources
Data in RED are being
used. Data in BLUE
take up memory but
are not in demand.
{
_id: 375,
en_US: { name: …, description: …, <etc…> },
en_GB: { name: …, description: …, <etc…> },
fr_FR: { name: …, description: …, <etc…> },
fr_CA: { name: …, description: …, <etc…> },
de_DE: …,
de_CH: …,
<… and so on for other locales …>
}
{
_id: 42,
en_US: { name: …, description: …, <etc…> },
en_GB: { name: …, description: …, <etc…> },
fr_FR: { name: …, description: …, <etc…> },
fr_CA: { name: …, description: …, <etc…> },
de_DE: …,
de_CH: …,
<… and so on for other locales …>
}

11. Consequences of Schema Redesign
• Queries induced minimal memory overhead
• 20x as many products fit in RAM at once
• Disk IO utilization reduced
• Application latency reduced
{
_id: "375-en_GB",
name: …,
description: …,
<… the rest of the document …>
}

12. Schema Design Patterns
• Pattern: pre-computing interesting
quantities, ideally with each write operation
• Pattern: putting unrelated items in different
collections to take advantage of indexing
• Anti-pattern: appending to arrays ad
infinitum
• Anti-pattern: importing relational schemas
directly into MongoDB

13. TAKE THE TIME TO
UNDERSTAND YOUR
APPLICATION

14. Schema Design Resources
• Blog series, "6 rules of thumb"
– Part 1: http://goo.gl/TFJ3dr
– Part 2: http://goo.gl/qTdGhP
– Part 3: http://goo.gl/JFO1pI
• Webinars, training, consulting,
etc…

15. Optimization Tips: Indexing

16. B-Tree Indexes
• Tree-structured references to your documents
• Single biggest tunable performance factor
• Indexing and schema design go hand in hand

17. Indexing Mistakes and Their Fixes
• Failing to build necessary indexes
– Run .explain(), examine slow query log, mtools,
system.profile collection
• Building unnecessary indexes
– Talk to your application developers about usage
• Running ad-hoc queries in production
– Use a staging environment, use secondaries

18. mongod log files
Sun Jun 29 06:35:37.646 [conn2]
query test.docs query: {
parent.company: "22794",
parent.employeeId: "83881" }
ntoreturn:1 ntoskip:0
nscanned:806381 keyUpdates:0
numYields: 5 locks(micros)
r:2145254 nreturned:0 reslen:20
1156ms

19. mtools
• http://github.com/rueckstiess/mtools
• log file analysis for poorly performing queries
– Show me queries that took more than 1000 ms
from 6 am to 6 pm:
– mlogfilter mongodb.log --from 06:00 --to
18:00 --slow 1000 > mongodb-filtered.log

20. Indexing Strategies
• Create indexes that support your queries!
• Create highly selective indexes
• Eliminate duplicate indexes with compound
indexes
– db.collection.ensureIndex({A:1, B:1, C:1})
– allows queries using leftmost prefix
• Order index columns to support scans & sorts
• Create indexes that support covered queries
• Prevent collection scans in pre-production
environments
db.getSiblingDB("admin").runCommand( {
setParameter: 1, notablescan: 1 } )

21. Optimization Tips: Monitoring

22. JUST DO IT
NOW
IN PRE-PROD

23. MongoDB Management Services (MMS)
Backup
Monitoring
Automation

24. MMS: Database and Hardware Metrics

25. MMS Monitoring Setup

26. Cloud Version of MMS
1. Go to http://mms.mongodb.com
2. Create an account
3. Install one agent in your datacenter
4. Add hosts from the web interface
5. Enjoy!

27. Vertical Scaling

28. Factors:
– RAM
– Disk
– CPU
– Network
We are Here to Pump you Up
Primary
Secondary
Secondary
Replica Set Primary
Secondary
Secondary
Replica Set

29. Working Set Exceeds Physical Memory

30. Real world Example
• Status changes for entities in the business
• State changes happen in batches and are
fully random
– sometimes 10% of entities get updated
– sometimes 100% get updated

31. Initial Architecture
Sharded Cluster, 4 shards backed by spinning disk
Application / mongos
mongod

32. Horizontal Scaling
Rapidly growing business means more
shards
Application / mongos
…16 more shards…
mongod

33. Vertical Scaling
Scaling random IOPS with SSDs
Application / mongos
mongod SSD

34. Before you add hardware....
• Make sure you are solving the right scaling problem
• Remedy schema and index problems first
– schema and index problems can look like hardware
problems
• Tune the Operating System
– ulimits, swap, NUMA, NOOP scheduler with hypervisors
• Tune the IO subsystem
– ext4 or XFS vs SAN, RAID10, readahead, noatime
• See MongoDB "production notes" page
• Heed logfile startup warnings

35. Horizontal Scaling

36. Why Shard?
• Space
• Throughput
• Latency
• (Manageability of individual nodes)
– restore, compaction, indexing, etc…

37. Sharding Overview
Primary
Secondary
Secondary
Shard 1
Primary
Secondary
Secondary
Shard 2
Primary
Secondary
Secondary
Shard 3
Primary
Secondary
Secondary
Shard N
…
Query
Router
Query
Router
Query
Router
……
Driver
Application

38. Range Sharding
mongod
Read/Write Scalability
Key Range
0..100

39. Range Sharding
Read/Write Scalability
mongod mongod
Key Range
0..50
Key Range
51..100

40. Sharding
mongod mongod mongod mongod
Key Range
0..25
Key Range
26..50
Key Range
51..75
Key Range
76.. 100
Read/Write Scalability

41. Shard Key Characteristics
• A good shard key has:
– sufficient cardinality
– distributed writes
– targeted reads ("query isolation")
• Shard key should be in every query if possible
– scatter gather otherwise
• Choosing a good shard key is important!
– affects performance and scalability
– changing it later is expensive

42. Beware of Ascending Shard Keys
• Monotonically increasing shard key values cause
"hot spots" on inserts
• Examples: timestamps, _id
Shard 1
mongos
Shard 2 Shard 3 Shard N
[ ISODate(…), $maxKey )

43. Beware of Scatter-Gather Queries
• Extra network traffic
• Extra work on each node
• Sorts in mongos
Primary
Secondary
Secondary
Primary
Secondary
Secondary
Primary
Secondary
Secondary
Primary
Secondary
Secondary
…
Query
Router
Query
Router
Query
Router
……
Driver
Application

44. Advanced Sharding Options
• Hash-based Sharding
• Tag-Aware Sharding
mongod mongod mongod mongod
Shard Tag Start End
Winter 23 Dec 21 Mar
Spring 22 Mar 21 Jun
Summer 21 Jun 23 Sep
Fall 24 Sep 22 Dec
Spring Summer FallWinter

45. I FORGOT THE SECTION
ON REPLICA SETS !?

46. Scaling your Operations Team

47. How MMS helps you
Scale EasilyMeet SLAs
Best Practices,
Automated
Cut Management
Overhead

48. Without MMS
Example Deployment – 12 Servers
Install, Configure
150+ steps
…Error handling, throttling, alerts
Scale out, move servers, resize oplog, etc.
10-180+ steps
Upgrades, downgrades
100+ steps

49. With MMS

50. Common Tasks, Performed in Minutes
• Deploy – any size, most topologies
• Upgrade/Downgrade – with no downtime
• Scale – add/remove shards or replicas, with no
downtime
• Resize Oplog – with no downtime
• Specify users, roles, custom roles
• Provision AWS instances and optimize for
MongoDB

51. Customers @ Scale on MongoDB

52. MonoDB at Scale
250M Ticks/Sec
300K+ Ops/Sec
500K+ Ops/SecFed Agency
Performance
1,400 Servers
1,000+ Servers
250+ Servers
Entertainment Co.
Cluster
Petabytes
10s of billions of objects
13B documents
Data
Asian Internet Co.

53. Foursquare Stats
• 50M users.
• 1.7M merchants using the platform for marketing
• Operations Per Second: 300,000
• Documents: 5.5B
• 11 MongoDB clusters
– 8 are sharded
• Largest cluster has 15 shards (check ins)
– Sharded on user id

54. MongoDB DOES Scale!!
0
5,000
10,000
15,000
20,000
25,000
30,000
1 2 3 4 5 6 7 8
Operations/Second
Number of Nodes
MongoDB Cluster Throughput