Using MongoDB, you’re able to develop and deploy applications quickly. But how do you maintain high performance when you have a small team and are developing at a breakneck pace? At CMP.LY, we tune our applications on the fly using key performance metrics from MongoDB Management Service. Identifying bottlenecks before they become production issues let’s us keep our focus on our application. You’ll walk away from this talk with a clear understanding of how to leverage MMS and key performance metrics to keep your application, and team, humming as your MongoDB usage grows.
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
• Rising Replication Lag + Falling 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
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
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]
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
17. 17JUNE 2014
Lock % Greater than 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
24. 24JUNE 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
25. 25JUNE 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
26. 26JUNE 2014
Using Sharding
• Improves concurrency by distributing databases across multiple mongod
instances
• Locks are per-mongod instance
29. 29JUNE 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
30. 30JUNE 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
31. 31JUNE 2014
How Did this Affect Us?
• Our web apps became slow
• Queries began to timeout
• Longer operations mean longer lock times
32. 32JUNE 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.
33. 33JUNE 2014
Diagnosis
• Log Analysis
• Use mtools
A collection of scripts to parse and visualize MongoDB log files developed by
MongoDB Engineer Thomas Rueckstiess.
• mlogfilter
• filter logs for slow queries, collection scans, etc.
• mplotqueries
• graph query response times and volumes
• https://github.com/rueckstiess/mtools
34. 34JUNE 2014
Diagnosis
• Monitoring application logs
• Enabling ‘notablescan’ option in development and testing versions of apps
• MongoDB profiling
35. 35JUNE 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
• When enabled, profiling has a minor effect on performance
36. 36JUNE 2014
Our Remedies
• Add indexes!
• Make sure queries are covered
• Utilize the projection specification to limit fields (data) returned
37. 37JUNE 2014
Adding Indexes
• Improved performance for common queries
• Alleviates the need to go to disk for many operations
38. 38JUNE 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.content.find(
{
tweet_id: ’12345678'
},
{
text: 1, screen_name: 1
});
41. 41JUNE 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
42. 42JUNE 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
43. 43JUNE 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.
44. 44JUNE 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.
45. 45JUNE 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.
47. 47JUNE 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
48. 48JUNE 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 (w:1)
• The mongod confirms receipt of the write operation
• Allows clients to catch network, duplicate key and other errors
50. 50JUNE 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
51. 51JUNE 2014
Fast Growing oplog causes issues
Replication oplog Window – approximate hours available in the primary’s oplog
52. 52JUNE 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
57. 57JUNE 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
58. 58JUNE 2014
MMS Backups
• Engineered by MongoDB
• Continuous backup with point-in-time recovery
• Fully managed backups
59. 59JUNE 2014
Using MMS Backups
• Seeding new secondaries
• Repairing replica set members
• Development and testing databases
• Restores are free!
60. 60JUNE 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
Developers, what we’re focused on today – track bottlenecks
Ops team :: great for small teams where your developers are also part of your ops team (DevOps) – monitor health of clusters, backup dbs, automate updates and add capacity
MongoDB technical service team :: helps them help you
Important for us because we maintain a small tech team
PRO-TIP: Know what is “normal” for you system.
Know what changed when something happens, what do you expect to be normal behavior, what are you normal MMS metrics
readers-writer lock allows concurrent read access to the db,
but exclusive access to a single write
“Writer-greedy” - When both a read and write are waiting for a
lock, MongoDB grants the lock to the write.
The exclusivity of write locks is one of the keys to why getting
our lock % under control is so important.
Lock %
time spent in write lock state; sum of global lock + hottest database at that time, can make value > 100%
Our Issue: Primary database maintaining a write lock of 150-175% of the time
Global lock percentage has remained about the same
Primary client-facing database has seen lock % drop
Developed by a MongoDB engineer
- Purple bar indicates downtime
- Alerts for down hosts, down agents and more
- According to Technical Services, In many cases, fixing warnings will fix issues