Tips, tricks, and gotchas learned at Localytics for optimizing MongoDB installs. Includes information about document design, indexes, fragmentation, migration, AWS EC2/EBS, and more.

1. Optimizing MongoDB:
Lessons Learned at Localytics
Andrew Rollins
June 2011
MongoNYC

2. Me
• Email: my first name @ localytics.com
• twitter.com/andrew311
• andrewrollins.com
• Founder, Chief Software Architect at Localytics

3. Localytics
• Real time analytics for mobile applications
• Built on:
– Scala
– MongoDB
– Amazon Web Services
– Ruby on Rails
– and more…

4. Why I‟m here: brain dump!
• To share tips, tricks, and gotchas about:
– Documents
– Indexes
– Fragmentation
– Migrations
– Hardware
– MongoDB on AWS
• Basic to more advanced, a compliment to
MongoDB Perf Tuning at MongoSF 2011

5. MongoDB at Localytics
• Use cases:
– Anonymous loyalty information
– De-duplication of incoming data
• Requirements:
– High throughput
– Add capacity without long down-time
• Scale today:
– Over 1 billion events tracked in May
– Thousands of MongoDB operations a second

6. Why MongoDB?
• Stability
• Community
• Support
• Drivers
• Ease of use
• Feature rich
• Scale out

7. OPTIMIZE YOUR DATA
Documents and indexes

8. Shorten names
Bad:
{
super_happy_fun_awesome_name: “yay!”
}
Good:
{
s: “yay!”
}

9. Use BinData for UUIDs/hashes
Bad:
{
u: “21EC2020-3AEA-1069-A2DD-08002B30309D”,
// 36 bytes plus field overhead
}
Good:
{
u: BinData(0, “…”),
// 16 bytes plus field overhead
}

10. Override _id
Turn this
{
_id : ObjectId("47cc67093475061e3d95369d"),
u: BinData(0, “…”) // <- this is uniquely indexed
}
into
{
_id : BinData(0, “…”) // was the u field
}
Eliminated an extra index, but be careful about
locality... (more later, see Further Reading at end)

11. Pack „em in
• Look for cases where you can squish multiple
“records” into a single document.
• Why?
– Decreases number of index entries
– Brings documents closer to the size of a page,
alleviating potential fragmentation
• Example: comments for a blog post.

12. Prefix Indexes
Suppose you have an index on a large field, but that field doesn‟t have
many possible values. You can use a “prefix index” to greatly decrease
index size.
find({k: <kval>})
{
k: BinData(0, “…”), // 32 byte SHA256, indexed
}
into find({p: <prefix>, k: <kval>})
{
k: BinData(0, “…”), // 28 byte SHA256 suffix, not indexed
p: <32-bit integer> // first 4 bytes of k packed in integer, indexed
}
Example: git commits

13. FRAGMENTATION AND MIGRATION
Hidden evils

14. Fragmentation
• Data on disk is memory mapped into RAM.
• Mapped in pages (4KB usually).
• Deletes/updates will cause memory
fragmentation.
Disk RAM
doc1 doc1
find(doc1) Page
deleted deleted
… …

15. New writes mingle with old data
Data
doc1
Page
Write docX docX
doc3
doc4 Page
doc5
find(docX) also pulls in old doc1, wasting RAM

16. Dealing with fragmentation
• “mongod --repair” on a secondary, swap with
primary.
• 1.9 has in-place compaction, but this still holds a
write-lock.
• MongoDB will auto-pad records.
• Pad records yourself by including and then
removing extra bytes on first insert.
– Alternative offered in SERVER-1810.

17. The Dark Side of Migrations
• Chunks are a logical construct, not physical.
• Shard keys have serious implications.
• What could go wrong?
– Let‟s run through an example.

18. Suppose the following
Chunk 1 • K is the shard key
k: 1 to 5
• K is random
Chunk 2
k: 6 to 9
Shard 1
{k: 3, …} 1st write
{k: 9, …} 2nd write
{k: 1, …} and so on
{k: 7, …}
{k: 2, …}
{k: 8, …}

19. Migrate
Chunk 1 Chunk 1
k: 1 to 5 k: 1 to 5
Chunk 2
k: 6 to 9
Shard 1
Shard 2
{k: 3, …}
{k: 3, …}
{k: 9, …} Random IO
{k: 1, …}
{k: 1, …}
{k: 2, …}
{k: 7, …}
{k: 2, …}
{k: 8, …}

20. Shard 1 is now heavily fragmented
Chunk 1 Chunk 1
k: 1 to 5 k: 1 to 5
Chunk 2
k: 6 to 9
Shard 1
Shard 2
{k: 3, …}
{k: 3, …}
{k: 9, …}
{k: 1, …}
{k: 1, …} Fragmented
{k: 2, …}
{k: 7, …}
{k: 2, …}
{k: 8, …}

21. Why is this scenario bad?
• Random reads
• Massive fragmentation
• New writes mingle with old data

22. How can we avoid bad migrations?
• Pre-split, pre-chunk
• Better shard keys for better locality
– Ideally where data in the same chunk tends to be in
the same region of disk

23. Pre-split and move
• If you know your key distribution, then pre-create
your chunks and assign them.
• See this:
– http://blog.zawodny.com/2011/03/06/mongodb-pre-
splitting-for-faster-data-loading-and-importing/

24. Better shard keys
• Usually means including a time prefix in your
shard key (e.g., {day: 100, id: X})
• Beware of write hotspots
• How to Choose a Shard Key
– http://www.snailinaturtleneck.com/blog/2011/01/04/ho
w-to-choose-a-shard-key-the-card-game/

25. OPTIMIZING HARDWARE/CLOUD

26. Working Set in RAM
• EC2 m2.2xlarge, RAID0 setup with 16 EBS volumes.
• Workers hammering MongoDB with this loop, growing data:
– Loop { insert 500 byte record; find random record }
• Thousands of ops per second when in RAM
• Much less throughput when working set (in this case, all data
and index) grows beyond RAM.
Ops per second over time
In RAM
Not In RAM

27. Pre-fetch
• Updates hold a lock while they fetch the original
from disk.
• Instead do a read to warm the doc in RAM under
a shared read lock, then update.

28. Shard per core
• Instead of a shard per server, try a shard per
core.
• Use this strategy to overcome write locks when
writes per second matter.
• Why? Because MongoDB has one big write lock.

29. Amazon EC2
• High throughput / small working set
– RAM matters, go with high memory instances.
• Low throughput / large working set
– Ephemeral storage might be OK.
– Remember that EBS IO goes over Ethernet.
– Pay attention to IO wait time (iostat).
– Your only shot at consistent perf: use the biggest
instances in a family.
• Read this:
– http://perfcap.blogspot.com/2011/03/understanding-
and-using-amazon-ebs.html

30. Amazon EBS
• ~200 seeks per second per EBS on a good day
• EBS has *much* better random IO perf than
ephemeral, but adds a dependency
• Use RAID0
• Check out this benchmark:
– http://orion.heroku.com/past/2009/7/29/io_performanc
e_on_ebs/
• To understand how to monitor EBS:
– https://forums.aws.amazon.com/thread.jspa?messag
eID=124044

31. Further Reading
• MongoDB Performance Tuning
– http://www.scribd.com/doc/56271132/MongoDB-Performance-Tuning
• Monitoring Tips
– http://blog.boxedice.com/mongodb-monitoring/
• Markus‟ manual
– http://www.markus-gattol.name/ws/mongodb.html
• Helpful/interesting blog posts
– http://nosql.mypopescu.com/tagged/mongodb/
• MongoDB on EC2
– http://www.slideshare.net/jrosoff/mongodb-on-ec2-and-ebs
• EC2 and Ephemeral Storage
– http://www.gabrielweinberg.com/blog/2011/05/raid0-ephemeral-storage-on-aws-
ec2.html
• MongoDB Strategies for the Disk Averse
– http://engineering.foursquare.com/2011/02/09/mongodb-strategies-for-the-disk-averse/
• MongoDB Perf Tuning at MongoSF 2011
– http://www.scribd.com/doc/56271132/MongoDB-Performance-Tuning

32. Thank you.
• Check out Localytics for mobile analytics!
• Reach me at:
– Email: my first name @ localytics.com
– twitter.com/andrew311
– andrewrollins.com