1. CMP.LY is a venture-funded startup that offers social media monitoring, measurement, and compliance solutions for Fortune 100 companies. Their tool, CommandPost, allows users to track, measure, analyze, and optimize social media engagement and address compliance needs. 2. The presentation discusses performance tuning of MongoDB deployments. Common issues addressed include concurrency, unindexed queries, increasing replication lag, and a plummeting oplog window. MongoDB Management Service (MMS) helps identify and diagnose these issues. 3. Solutions to the issues included schema changes, message queues, sharding, adding appropriate indexes, and making updates smaller to reduce replication lag and improve the oplog window. MMS alerts and backups

1. 1JUNE 2014
Performance Tuning
on the Fly at CMP.LY
Michael De Lorenzo
CTO, CMP.LY Inc.
michael@cmp.ly
@mikedelorenzo

2. 2JUNE 2014
Agenda
• CMP.LY and CommandPost
• What is MongoDB Management Service?
• Performance Tuning
• MongoDB Issues we’ve faced
• Slow response times and delayed writes
• Unindexed queries
• Increased Replication Lag and Plummeting oplog Window
• Keep your deployment healthy with MMS
• Using MMS Alerts
• Using MMS Backups

3. 3JUNE 2014
A venture-funded NYC startup that offers proprietary social media, monitoring,
measurement, insight and compliance solutions for Fortune 100
A Monitoring, Measurement & Insights (MMI) tool for managed social
communications.

4. 4JUNE 2014
Use CommandPost to:
• Track and measure cross-platform in real-time
• Identify and attribute high-value engagement
• Analyze and segment engaged audience
• Optimize content and engagement strategies
• Address compliance needs

5. 5JUNE 2014
What is MongoDB
Management Service?

6. 6JUNE 2014
MongoDB Management Service
• Free MongoDB Monitoring
• MongoDB Backup in the Cloud
• Free Cloud service or Available
to run On-Prem for Standard or
Enterprise Subscriptions
• Automation coming soon—FTW!
Ops
Makes MongoDB easier to use and
manage

7. 7JUNE 2014
Who Is MMS for?
• Developers
• Ops Team
• MongoDB Technical Service Team

8. 8JUNE 2014
Performance Tuning

9. 9JUNE 2014
How To Do Performance Tuning?
• Assess the problem and establish acceptable behavior.
• Measure the performance before modification.
• Identify the bottleneck.
• Remove the bottleneck.
• Measure performance after modification to confirm.
• Keep it or revert it and repeat.
Adapted from [http://en.wikipedia.org/wiki/Performance_tuning]

10. 10JUNE 2014
What We’ve Faced

11. 11JUNE 2014
Issues We’ve Faced
• Concurrency Issues
• Slow response times and delayed writes
• Querying without indexes
• Slow reads, timeouts
• Increasing Replication Lag + Plummeting oplog Window

12. 12JUNE 2014
Concurrency
Slow responses and delayed writes

13. 13JUNE 2014
Concurrency
• What is it?
• How did it affect us?
• How did MMS help identify it?
• How did we diagnose the issue in our app and fix it?
• Today

14. 14JUNE 2014
Concurrency in MongoDB
• MongoDB uses a readers-writer lock
• Many read operations can use a read lock
• If a write lock exists, a single write lock holds the lock exclusively
• No other read or write operations can share the lock
• Locks are “writer-greedy”

15. 15JUNE 2014
How Did This Affect Us?
• Slow API response times due to slow database operations
• Delayed writes
• Backed up queues

16. 16JUNE 2014
MMS: Identify Concurrency Issues

17. 17JUNE 2014
Lock % Greater than 100%?!?!?
• time spent in write lock state; sum of global lock + hottest database at that time,
can make value > 100%
• Global lock percentage is a derived metric:
% of time in global lock (small number)
+
% of time locked by hottest (“most locked”) database
• Data is sampled and combined, it is possible to see values over 100%.

18. 18JUNE 2014
Diagnosis
• Identified the write-heavy collections in our applications
• Used application logs to identify slow API responses
• Analyzed MongoDB logs to identify slow database queries

19. 19JUNE 2014
Our Remedies
• Schema changes
• Message queues
• Multiple databases
• Sharding

20. 20JUNE 2014
Schema Changes
• Denormalized our schema
• Allowed for atomic updates
• Customized documents’ _id attribute
• Leveraged existing index on _id attribute

21. 21JUNE 2014
Modeling for Atomic Operations
Document
{
_id: 123456789,
title: "MongoDB: The Definitive Guide",
author: [ "Kristina Chodorow", "Mike Dirolf"
],
published_date: ISODate("2010-09-24"),
pages: 216,
language: "English",
publisher_id: "oreilly",
available: 3,
checkout: [ { by: "joe", date:
ISODate("2012-10-15") } ]
}
Update Operation
db.books.update (
{ _id: 123456789, available: { $gt: 0 } },
{
$inc: { available: -1 },
$push: { checkout: { by: "abc", date: new
Date() } }
}
)
Result
WriteResult({ "nMatched" : 1, "nUpserted" : 0,
"nModified" : 1 })

22. 22JUNE 2014
Message Queues
• Controlled writes to specific collections using Pub/Sub
• We chose Amazon SQS
• Other options include Redis, Beanstalkd, IronMQ or any other message queue
• Created consistent flow of writes versus bursts
• Reduced length and frequency of write locks by controlling flow/speed of writes

23. 23JUNE 2014
Using Multiple Databases
• As of version 2.2, MongoDB implements locks at a per database granularity for
most read and write operations
• Planned to be at the document level in version 2.8
• Moved write-heavy collections to new (separate) databases

24. 24JUNE 2014
Using Sharding
• Improves concurrency by distributing databases across multiple mongod
instances
• Locks are per-mongod instance

25. 25JUNE 2014
Lock %: Today

26. 26JUNE 2014
Queries without Indexes
Slow responses and timeouts

27. 27JUNE 2014
Indexing
• What is it?
• How did it affect us?
• How did MMS help identify it?
• How did we diagnose the issue in our app and fix it?
• Today

28. 28JUNE 2014
Indexing with MongoDB
• Support for efficient execution of queries
• Without indexes, MongoDB must scan every document
• Example
Wed Jul 17 13:40:14 [conn28600] query x.y [snip] ntoreturn:16 ntoskip:0
nscanned:16779 scanAndOrder:1 keyUpdates:0 numYields: 906 locks(micros)
r:46877422 nreturned:16 reslen:6948 38172ms
38 seconds! Scanned 17k documents, returned 16
• Create indexes to cover all queries, especially support common and user-facing
• Collection scans can push entire working set out of RAM

29. 29JUNE 2014
How Did this Affect Us?
• Our web apps became slow
• Queries began to timeout
• Longer operations mean longer lock times

30. 30JUNE 2014
MMS: Identifying Indexing Issues
Page Faults
• The number of times that
MongoDB requires data
not located in physical
memory, and must read
from virtual memory.

31. 31JUNE 2014
Diagnosis
• Log Analysis
• Use mtools to analyze MongoDB logs
• mlogfilter
• filter logs for slow queries, collection scans, etc.
• mplotqueries
• graph query response times and volumes
• https://github.com/rueckstiess/mtools

32. 32JUNE 2014
Diagnosis
• Monitoring application logs
• Enabling ‘notablescan’ option in development and testing versions of apps
• MongoDB profiling

33. 33JUNE 2014
The MongoDB Profiler
• Collects fine grained data about MongoDB write operations, cursors, database
commands on a running mongod instance.
• Default slowOpThreshold value is 100ms, can be changed from the Mongo shell

34. 34JUNE 2014
Our Remedies
• Add indexes!
• Make sure queries are covered
• Utilize the projection specification to limit fields (data) returned

35. 35JUNE 2014
Adding Indexes
• Improved performance for common queries
• Alleviates the need to go to disk for many operations

36. 36JUNE 2014
Projection Specification
Controls the amount of data that needs to be (de-)serialized for use in your app
• We used it to limit data returned in embedded documents and arrays
db.inventory.find( { type: 'food' }, { item: 1, qty: 1 } )

37. 37JUNE 2014
Page Faults: Today

38. 38JUNE 2014
Increasing Replication Lag +
Plummeting oplog Window

39. 39JUNE 2014
Replication
• What is it?
• How did it affect us?
• How did MMS help identify it?
• How did we diagnose the issue in our app?
• How did we fix it?
• Today

40. 40JUNE 2014
What is Replication?
• A replica set is a group of mongod
processes that maintain the same data
set.
• Replica sets provide redundancy and
high availability, and are the basis for all
production deployments

41. 41JUNE 2014
What Is the Oplog?
• A special capped collection that keeps a rolling record of all operations that
modify the data stored in your databases.
• Operations are first applied on the primary and then recorded to its oplog.
• Secondary members then copy and apply these operations in an asynchronous
process.

42. 42JUNE 2014
What is Replication Lag?
• A delay between an operation on the primary and the application of that
operation from the oplog to the secondary.
• Effects of excessive lag
• “Lagged” members ineligible to quickly become primary
• Increases the possibility that distributed read operations will be inconsistent.

43. 43JUNE 2014
How did this affect us?
• Degraded overall health of our production deployment.
• Distributed reads are no longer eventually consistent.
• Unable to bring new secondary members online.
• Caused MMS Backups to do full re-syncs.

44. 44JUNE 2014
Identifying Replication Lag Issues
with MMS
The Replication Lag chart displays the lag for your deployment

45. 45JUNE 2014
Diagnosis
• Possible causes of replication lag include network latency, disk throughput,
concurrency and/or appropriate write concern
• Size of operations to be replicated
• Confirmed Non-Issues for us
• Network latency
• Disk throughput
• Possible Issues for us
• Concurrency/write concern
• Size of op is an issue because entire document is written to oplog

46. 46JUNE 2014
Concurrency/Write Concern
• Our applications apply many updates very quickly
• All operations need to be replicated to secondary members
• We use the default write concern—Acknowledge
• The mongod confirms receipt of the write operation
• Allows clients to catch network, duplicate key and other errors

47. 47JUNE 2014
Concurrency Wasn’t the Issue
Lock Percentage

48. 48JUNE 2014
Operation Size Was the Issue
Collection A (most active)
Total Updates: 3,373
Total Size of updates: 6.5 GB
Activity accounted for nearly 87% of total traffic
Collection B (next most active)
Total Updates: 85,423
Total Size of updates: 740 MB

49. 49JUNE 2014
Fast Growing oplog causes issues
Replication oplog Window – approximate hours available in the primary’s oplog

50. 50JUNE 2014
How We Fixed It
• Changed our schema
• Changed the types of updates that were made to documents
• Both allowed us to utilize atomic operations
• Led to smaller updates
• Smaller updates == less oplog space used

51. 51JUNE 2014
Replication Lag: Today

52. 52JUNE 2014
oplog Window: Today

53. 53JUNE 2014
Keeping Your Deployment
Healthy

54. 54JUNE 2014
MMS Alerts

55. 55JUNE 2014
Watch for Warnings
• Be warned if you are
• Running outdated versions
• Have startup warnings
• If a mongod is publicly visible
• Pay attention to these warnings

56. 56JUNE 2014
MMS Backups
• Engineered by MongoDB
• Continuous backup with point-in-time recovery
• Fully managed backups

57. 57JUNE 2014
Using MMS Backups
• Seeding new secondaries
• Repairing replica set members
• Development and testing databases
• Restores are free!

58. 58JUNE 2014
Summary
• Know what’s expected and “normal” in your systems
• Know when and what changes in your systems
• Utilize MMS alerts, visualizations and warnings to keep things running smoothly

59. 59JUNE 2014
Questions?
Michael De Lorenzo
CTO, CMP.LY Inc.
michael@cmp.ly
@mikedelorenzo