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High
Performance, Scala
ble MongoDB in a Bare
                Metal Cloud
Harold Hannon, Sr. Software Architect
100k                servers




24k          customers




23
 million domains
13 data centers

Reach   16 network POPs
        20Gb fiber interconnects
The 3 V’s
Physical Deployment
Public Cloud
The Dream
The Reality
Deployment Complexity
Bare Metal Cloud
Bare Metal
• Build to your specs
• Robust, quickly scaled environment
• Management of all aspects of environment
• Great for Big Data Solutions like MongoDB
Cloud Subscription
•   Preconfigured
•   Performance Tuned
•   Bare Metal Single Tenant
•   Complex Environment Configurations
Pre-configurations
• Set SSD Read Ahead Defaults to 16 Blocks – SSD drives have
  excellent seek times allowing for shrinking the Read Ahead to
  16 blocks. Spinning disks might require slight buffering so these
  have been set to 32 blocks.
• noatime – Adding the noatime option eliminates the need for
  the system to make writes to the file system for files which are
  simply being read — or in other words: Faster file access and
  less disk wear.
• Turn NUMA Off in BIOS – Linux, NUMA and MongoDB
  tend not to work well together. If you are running
  MongoDB on NUMA hardware, we recommend turning it
  off (running with an interleave memory policy). If you
  don’t, problems will manifest in strange ways like
  massive slow downs for periods of time or high system
  CPU time.

• Set ulimit – We have set the ulimit to 64000 for open files
  and 32000 for user processes to prevent failures due to a
  loss of available file handles or user processes.
Use ext4 – We have selected ext4 over ext3. We found ext3
to be very slow in allocating files (or removing them).
Additionally, access within large files is poor with ext3.
Deployment Serenity
  Shard 1
    mongos   mongos   mongos
    mongod   mongod   mongod
    config


  Shard 2
    mongos   mongos   mongos
    mongod   mongod   mongod
    config


  Shard 3
    mongos   mongos   mongos
    mongod   mongod   mongod
    config
The Proof is in the Pudding
Tests Performed
• Small MongoDB Cloud Subscription vs Shared
  Virtual Instance
• Medium MongoDB Cloud Subscription vs
  Shared Virtual Instance
   • SSD and 15K SAS
• Large MongoDB Cloud Subscription vs Shared
  Virtual Instance
   • SSD and 15K SAS
Small Test
Small (SM) Cloud Subscription MongoDB Server
Single 4-core Intel 1270 CPU
64-bit CentOS
8GB RAM
2 x 500GB SATAII – RAID1
1Gb Network

Virtual Provider Instance
4 Virtual Compute Units
64-bit CentOS
7.5GB RAM
2 x 500GB Network Storage – RAID1
1Gb Network
Small Test
Tests Performed
Small Data Set (8GB of .5mb documents)
200 iterations of 6:1 query-to-update operations
Concurrent client connections exponentially increased from 1 to 32
Test duration spanned 48 hours
Small Test
Average Read Operations per Second by Concurrent Client
Small Test
Peak Read Operations per Second by Concurrent Client
Small Test
Average Write Operations per Second by Concurrent Client
Small Test
Peak Write Operations per Second by Concurrent Client
Medium Test
Medium (MD) Cloud Subscription MongoDB Server
Dual 6-core Intel 5670 CPUs
64-bit CentOS
36GB RAM
2 x 64GB SSD – RAID1 (Journal Mount)
4 x 300GB 15K SAS – RAID10 (Data Mount)
1Gb Network – Bonded

Virtual Provider Instance
26 Virtual Compute Units
64-bit CentOS
30GB RAM
2 x 64GB Network Storage – RAID1 (Journal Mount)
4 x 300GB Network Storage – RAID10 (Data Mount)
1Gb Network
Medium Test
Tests Performed
Small Data Set (32GB of .5mb documents)
200 iterations of 6:1 query-to-update operations
Concurrent client connections exponentially increased from 1 to 128
Test duration spanned 48 hours
Medium Test 15k SAS
Average Read Operations per Second by Concurrent Client
Medium Test 15k SAS
Peak Read Operations per Second by Concurrent Client
Medium Test 15k SAS
Average Write Operations per Second by Concurrent Client
Medium Test 15k SAS
Peak Write Operations per Second by Concurrent Client
Medium Test SSD
Average Read Operations per Second by Concurrent Client
Medium Test SSD
Peak Read Operations per Second by Concurrent Client
Medium Test SSD
Average Write Operations per Second by Concurrent Client
Medium Test SSD
Peak Write Operations per Second by Concurrent Client
Large Test
Large (LG) Cloud Subscription MongoDB Server
Dual 8-core Intel E5-2620 CPUs
64-bit CentOS
128GB RAM
2 x 64GB SSD – RAID1 (Journal Mount)
6 x 600GB 15K SAS – RAID10 (Data Mount)
1Gb Network – Bonded

Virtual Provider Instance
26 Virtual Compute Units
64-bit CentOS
64GB RAM (Maximum available on this provider)
2 x 64GB Network Storage – RAID1 (Journal Mount)
6 x 600GB Network Storage – RAID10 (Data Mount)
1Gb Network
Large Test
Tests Performed
Small Data Set (64GB of .5mb documents)
200 iterations of 6:1 query-to-update operations
Concurrent client connections exponentially increased from 1 to 128
Test duration spanned 48 hours
Large Test 15k SAS
Average Read Operations per Second by Concurrent Client
Large Test 15k SAS
Peak Read Operations per Second by Concurrent Client
Large Test 15k SAS
Average Write Operations per Second by Concurrent Client
Large Test 15k SAS
Peak Write Operations per Second by Concurrent Client
Large Test SSD
Average Read Operations per Second by Concurrent Client
Large Test SSD
Peak Read Operations per Second by Concurrent Client
Large Test SSD
Average Write Operations per Second by Concurrent Client
Large Test SSD
Peak Write Operations per Second by Concurrent Client
RSD

         Cloud Provider   MongoDB Cloud
                          Subscription
Small    6-36%            1-9%
Medium   8-43%            1-8%
Large    8-93%            1-9%
Avoid Storage I/O
   Altogether
If you must Virtualize
 Go with Local Disks
Journal and Data on
 Separate Mounts
Watch Sync Delay
More information:



      www.softlayer.com

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High Performance, Scalable MongoDB in a Bare Metal Cloud

  • 1. High Performance, Scala ble MongoDB in a Bare Metal Cloud Harold Hannon, Sr. Software Architect
  • 2. 100k servers 24k customers 23 million domains
  • 3. 13 data centers Reach 16 network POPs 20Gb fiber interconnects
  • 4.
  • 5.
  • 13. Bare Metal • Build to your specs • Robust, quickly scaled environment • Management of all aspects of environment • Great for Big Data Solutions like MongoDB
  • 14. Cloud Subscription • Preconfigured • Performance Tuned • Bare Metal Single Tenant • Complex Environment Configurations
  • 15. Pre-configurations • Set SSD Read Ahead Defaults to 16 Blocks – SSD drives have excellent seek times allowing for shrinking the Read Ahead to 16 blocks. Spinning disks might require slight buffering so these have been set to 32 blocks. • noatime – Adding the noatime option eliminates the need for the system to make writes to the file system for files which are simply being read — or in other words: Faster file access and less disk wear.
  • 16. • Turn NUMA Off in BIOS – Linux, NUMA and MongoDB tend not to work well together. If you are running MongoDB on NUMA hardware, we recommend turning it off (running with an interleave memory policy). If you don’t, problems will manifest in strange ways like massive slow downs for periods of time or high system CPU time. • Set ulimit – We have set the ulimit to 64000 for open files and 32000 for user processes to prevent failures due to a loss of available file handles or user processes.
  • 17. Use ext4 – We have selected ext4 over ext3. We found ext3 to be very slow in allocating files (or removing them). Additionally, access within large files is poor with ext3.
  • 18. Deployment Serenity Shard 1 mongos mongos mongos mongod mongod mongod config Shard 2 mongos mongos mongos mongod mongod mongod config Shard 3 mongos mongos mongos mongod mongod mongod config
  • 19. The Proof is in the Pudding
  • 20. Tests Performed • Small MongoDB Cloud Subscription vs Shared Virtual Instance • Medium MongoDB Cloud Subscription vs Shared Virtual Instance • SSD and 15K SAS • Large MongoDB Cloud Subscription vs Shared Virtual Instance • SSD and 15K SAS
  • 21. Small Test Small (SM) Cloud Subscription MongoDB Server Single 4-core Intel 1270 CPU 64-bit CentOS 8GB RAM 2 x 500GB SATAII – RAID1 1Gb Network Virtual Provider Instance 4 Virtual Compute Units 64-bit CentOS 7.5GB RAM 2 x 500GB Network Storage – RAID1 1Gb Network
  • 22. Small Test Tests Performed Small Data Set (8GB of .5mb documents) 200 iterations of 6:1 query-to-update operations Concurrent client connections exponentially increased from 1 to 32 Test duration spanned 48 hours
  • 23. Small Test Average Read Operations per Second by Concurrent Client
  • 24. Small Test Peak Read Operations per Second by Concurrent Client
  • 25. Small Test Average Write Operations per Second by Concurrent Client
  • 26. Small Test Peak Write Operations per Second by Concurrent Client
  • 27. Medium Test Medium (MD) Cloud Subscription MongoDB Server Dual 6-core Intel 5670 CPUs 64-bit CentOS 36GB RAM 2 x 64GB SSD – RAID1 (Journal Mount) 4 x 300GB 15K SAS – RAID10 (Data Mount) 1Gb Network – Bonded Virtual Provider Instance 26 Virtual Compute Units 64-bit CentOS 30GB RAM 2 x 64GB Network Storage – RAID1 (Journal Mount) 4 x 300GB Network Storage – RAID10 (Data Mount) 1Gb Network
  • 28. Medium Test Tests Performed Small Data Set (32GB of .5mb documents) 200 iterations of 6:1 query-to-update operations Concurrent client connections exponentially increased from 1 to 128 Test duration spanned 48 hours
  • 29. Medium Test 15k SAS Average Read Operations per Second by Concurrent Client
  • 30. Medium Test 15k SAS Peak Read Operations per Second by Concurrent Client
  • 31. Medium Test 15k SAS Average Write Operations per Second by Concurrent Client
  • 32. Medium Test 15k SAS Peak Write Operations per Second by Concurrent Client
  • 33. Medium Test SSD Average Read Operations per Second by Concurrent Client
  • 34. Medium Test SSD Peak Read Operations per Second by Concurrent Client
  • 35. Medium Test SSD Average Write Operations per Second by Concurrent Client
  • 36. Medium Test SSD Peak Write Operations per Second by Concurrent Client
  • 37. Large Test Large (LG) Cloud Subscription MongoDB Server Dual 8-core Intel E5-2620 CPUs 64-bit CentOS 128GB RAM 2 x 64GB SSD – RAID1 (Journal Mount) 6 x 600GB 15K SAS – RAID10 (Data Mount) 1Gb Network – Bonded Virtual Provider Instance 26 Virtual Compute Units 64-bit CentOS 64GB RAM (Maximum available on this provider) 2 x 64GB Network Storage – RAID1 (Journal Mount) 6 x 600GB Network Storage – RAID10 (Data Mount) 1Gb Network
  • 38. Large Test Tests Performed Small Data Set (64GB of .5mb documents) 200 iterations of 6:1 query-to-update operations Concurrent client connections exponentially increased from 1 to 128 Test duration spanned 48 hours
  • 39. Large Test 15k SAS Average Read Operations per Second by Concurrent Client
  • 40. Large Test 15k SAS Peak Read Operations per Second by Concurrent Client
  • 41. Large Test 15k SAS Average Write Operations per Second by Concurrent Client
  • 42. Large Test 15k SAS Peak Write Operations per Second by Concurrent Client
  • 43. Large Test SSD Average Read Operations per Second by Concurrent Client
  • 44. Large Test SSD Peak Read Operations per Second by Concurrent Client
  • 45. Large Test SSD Average Write Operations per Second by Concurrent Client
  • 46. Large Test SSD Peak Write Operations per Second by Concurrent Client
  • 47. RSD Cloud Provider MongoDB Cloud Subscription Small 6-36% 1-9% Medium 8-43% 1-8% Large 8-93% 1-9%
  • 48.
  • 49. Avoid Storage I/O Altogether
  • 50. If you must Virtualize Go with Local Disks
  • 51. Journal and Data on Separate Mounts
  • 53. More information: www.softlayer.com

Editor's Notes

  1. I am HH work for Softlayer for about 6-7 years now, Work in product innovation as a Sr Software ArchitectPart of what we do is R&D for new product solutions for Softlayer which gives me the opportunity to get exposure to a lot of exciting new technologies and solutionsOne thing I’ve been working with lately has been Big Data solutions specifically MongoDBToday we are talking about MongoDB Cloud SubscriptionSome of how we put it togetherSome considerations for deployment and how we arrived at the model we didSome metrics/info on performanceSome helpful hints
  2. Softlayer?
  3. In celebration of Valentines Weekend…….
  4. When I talk about ANY big data solution I love to put this slide upIt’s a great illustration of why we are doing thisHelps to get you thinking about the challenges of deploying a solution like MongoDB
  5. So here is our one and only obligatory analyst slide I promiseThink in terms of the 3 V’s Gartner definedThere are lots of 4th V’s (Value, Veracity, etc.) But really these apply to all data right? These 3 are at the coreAlso for our discussion today we are mostly going to be focused on Volume and Velocity (Variety is a given for us)These are important to consider when we start talking about how we want to deploy our solutionHow much and How fast is our data going to come at us?
  6. So when we talk about Physical deployment we have 2 options obviously:Multi Tenant and Single TenantBoth have their strengths and weaknesses
  7. Typically fast to setup up frontGreat for entry level, POC, Testing, Small applications where maybe things like Velocity aren’t as importantAt first these deployments look very affordableBut we are usually talking about shared network attached resourcesWith shared I/O comes widely varied performance that I am convinced is based upon the direction of the wind in some casesPersonal tests have shown that standard deviation swings are as large (30% or higher)You are going to here me talk a lot today about RSD relative standard deviation when we get to some actual performance testing numbersMost platforms use network attached storageI DO NOT USE NETWORK ATTACHED STORAGE BACKED VIRTUAL INSTANCES for disk intensive applications MongoDBFor everyone that hit the snooze button on my presentation this is probably the most important take away I can give youSo I will repeat that because it is very importantWe found that most customers wanting I/O intensive applications like MongoDB that have an absolute requirement for virtual instances do better with local disk for obvious reasonsNo network hop to data = better performance so we push our customers implementing heavy disk I/O solutions MongoDB to our Local Disc Virtual Instances when they have a hard requirement Multi Tenant Public CloudThat’s not our best solution but when they just can’t leave a virtual instance at least local disk helps alleviate some of the shared resource pain for these sorts of applications
  8. When we talk about a public cloud deployment everyone has this dream of just right clicking and “adding new” and everything is perfect
  9. Although at first things seem simple scaling on multi tenant (especially with NAS) gets trickyIn this case this is a SINGLE instance of a Mongo Node (This is one node, most deployments are going to have 3 or more of these)In order to achieve desired performance you have to raid network volumes and attach them to virtual instancesThis still doesn’t solve shared I/O deviation issues it just smears them so they may not spike as drastically
  10. It gets even crazier when you do highly available deploymentsStriped volumes (sometimes up to 10) attachedSo you can see that as you scale on a NAS Virtual environment You start to see when you look at this picture that your simple Virtualized environment has suddenly started to get very complexIf you are an engineer that believes in keeping things simple to avoid issues, this sort of thing keeps you up at nightBoth complexity and cost can start to spiral beyond what you may have anticipated
  11. So lets look at a different strategy for deployingWe have seen a growing number of customers coming to us wanting single tenant solution for high disk I/O data storage solutions like Big Data applicationsWe consider our platform to be a complete portfolio of Cloud Offerings including Single Tenant Options beyond our multi tenant public cloud offeringsWe do have multi tenant with local disk, but we believe our Bare Metal Cloud offering is far better suited for Big Data solutions than any otherAll the advantages of the Cloud without the pain pointsEasy automated provisioningConsistent high performanceBecause you have noShared I/ONetwork DiskWildly Varied deviated performanceYou get Consistent Solid performance every time because our single tenant offerings are backed by BARE METALStress consistent
  12. So lets go back to that deployment insanity and inject a little serenity into the pictureAll of the stacking of raided NAS volumes and configuration is simplified to a “node” deployment concept with some optionsPhysical Volumes are Already raided behind a controller to appear as a single volumeConsistent dependable performanceThis is the strategy we are taking for MongoDB that we feel best serves our customersAnd I think it is the best platform for any application wanting to use a high disk I/O like MongoDBYou just can’t beat physical volumes with single tenancy when it becomes to I/O performance for the dollarIt brings simplicity, performance, and consistent dependability that you need to back your MongoDB
  13. This is caramel mango macadamia nut pudding by the way and it is deliciousSo I can talk all I want about how theoretically sharing resources and network hops impact high storage I/O deploymentsBut let’s look at some numbers
  14. Thank you for your time, I hope you found this helpful I guess we have some time to take some questions