2. Let's Pretend We Are DevOps
What my friends
think I do
What society
thinks I do
What my Mom
thinks I do
What my boss
thinks I do What I think I do What I really do
DevOps
4. Why do we need to shard?
• Reaching a limit on some resource
– RAM (working set)
– Disk space
– Disk IO
– Client network latency on writes (tag aware sharding)
– CPU
5. Do we need to shard right now?
• Two schools of thought:
1. Shard at the outset to avoid technical debt later
2. Shard later to avoid complexity and overhead today
• Either way, shard before you need to!
– 256GB data size threshold published in documentation
– Chunk migrations can cause memory contention and disk
IO
Working
Set
Free
RAM
Things seemed fine…
Working
Set
Chunk
Migration
… then I waited
too long to
shard
6. > db.mdbw.stats()
{
"ns" : "test.mdbw",
"count" : 16000, // one hour's worth of documents
"size" : 65280000, // size of user data, padding included
"avgObjSize" : 4080,
"storageSize" : 93356032, // size of data extents, unused space included
"numExtents" : 11,
"nindexes" : 1,
"lastExtentSize" : 31354880,
"paddingFactor" : 1,
"systemFlags" : 1,
"userFlags" : 1,
"totalIndexSize" : 801248,
"indexSizes" : { "_id_" : 801248 },
"ok" : 1
}
collection stats
7. Storage model spreadsheet
sensors 16,000
years to keep data 6
docs per day 384,000
docs per year 140,160,000
docs total across all years 840,960,000
indexes per day 801248 bytes
storage per hour 63 MB
storage per day 1.5 GB
storage per year 539 GB
storage across all years 3,235 GB
8. Why we need to shard now
539 GB in year one
alone
0
500
1,000
1,500
2,000
2,500
3,000
3,500
1 2 3 4 5 6
Year
Total storage
(GB)
16,000 sensors today…
… 47,000
tomorrow?
9. What will our sharded cluster look
like?
• We need to model the application to answer this
question
• Model should include:
– application write patterns (sensors)
– application read patterns (clients)
– analytic read patterns
– data storage requirements
• Two main collections
– summary data (fast query times)
– historical data (analysis of environmental conditions)
10. Option 1: Everything in one sharded
cluster
Primary Primary Primary
Secondary Secondary Secondary
Secondary Secondary Secondary
Shard 2 Shard 3 Shard N
…
Primary
Secondary
Secondary
Shard 1
Primary Shard
Primary
Secondary
Secondary
Shard 4
• Issue: prevent analytics jobs from affecting application
performance
• Summary data is small (16,000 * N bytes) and accessed
frequently
11. Option 2: Distinct replica set for
summaries
Primary Primary Primary
Secondary Secondary Secondary
Secondary Secondary Secondary
Shard 1 Shard 2 Shard N
…
Primary
Secondary
Secondary
Replica set
Primary
Secondary
Secondary
Shard 3
• Pros: Operational separation between business functions
• Cons: application must write to two different databases
12. Application read patterns
• Web browsers, mobile phones, and in-car
navigation devices
• Working set should be kept in RAM
• 5M subscribers * 1% active * 50
sensors/query *
1 device query/min = 41,667 reads/sec
• 41,667 reads/sec * 4080 bytes = 162 MB/sec
– and that's without any protocol overhead
• Gigabit Ethernet is ≈ 118 MB/sec
Primary
Secondary
Secondary
Replica set
1 Gbps
13. Application read patterns
(continued)
• Options
– provision more bandwidth ($$$)
– tune application read pattern
– add a caching layer
– secondary reads from the replica
set
Primary
Secondary
Secondary
Replica set
1 Gbps
1 Gbps
1 Gbps
14. Secondary Reads from the Replica
Set
• Stale data OK in this use case
• caution: read preference of
secondary could be disastrous in a
3-replica set if a secondary fails!
• app servers with mixed read
preferences of primary and
secondary are operationally
cumbersome
• Use nearest read preference to
access all nodes
Primary
Secondary
Secondary
Replica set
1 Gbps
1 Gbps
1 Gbps
db.collection.find().readPref
( { mode: 'nearest'} )
15. Replica Set Tags
• app servers in different data centers use
replica set tags plus read preferencenearest
• db.collection.find().readPref( { mode: 'nearest',
tags: [ {'datacenter': 'east'} ] } )
east
Secondary
Secondary
Primary
>rs.conf()
{"_id":"rs0",
"version":2,
"members":[
{"_id":0,
"host":"node0.example.net:27017",
"tags":{"datacenter":"east"}
},
{"_id":1,
"host":"node1.example.net:27017",
"tags":{"datacenter":"east"}
},
{"_id":2,
"host":"node2.example.net:27017",
"tags":{"datacenter":"east"}
},
}
17. eastcentralwest
Replica Set Tags
• Enables geographic distribution
• Allows scaling within each data center
Secondary
Secondary
Secondary
Secondary
Secondary
Secondary
Primary
Secondary
Secondary
18. Analytic read patterns
• How does an analyst look at the data on the
sharded cluster?
• 1 Year of data = 539 GB
3, 256
3, 192
5, 128
9, 64
17, 32
0
50
100
150
200
250
300
0 2 4 6 8 10 12 14 16 18
Server RAM
Number of machines
19. Application write patterns
• 16,000 sensors every minute = 267 writes/sec
• Could we handle 16,000 writes in one second?
– 16,000 writes * 4080 bytes = 62 MB
• Load test the app!
20. Modeling the Application - summary
• We modeled:
– application write patterns (sensors)
– application read patterns (clients)
– analytic read patterns
– data storage requirements
– the network, a little bit
22. Shard Key characteristics
• Agood 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
23. Hashed shard key
• Pros:
– Evenly distributed writes
• Cons:
– Random data (and index) updates can be IO intensive
– Range-based queries turn into scatter gather
Shard 1
mongos
Shard 2 Shard 3 Shard N
24. Low cardinality shard key
• Induces "jumbo chunks"
• Examples: sensor ID
Shard 1
mongos
Shard 2 Shard 3 Shard N
[ a, b )
27. What is our shard key?
• Let's choose: linkID, date
– example: { linkID: 9000006, date: 140312 }
– example: { _id: "900006:140312" }
– this application's _id is in this form already, yay!
28. Summary
• Model the read/write patterns and storage
• Choose an appropriate shard key
• DevOps influenced the application
– write recent summary data to separate database
– replica set tags for summary database
– avoid synchronous sensor checkins
– consider changing client polling frequency
– consider throttling RESTAPI access to app servers
31. $ mongo --nodb
> cluster = new ShardingTest({"shards": 1, "chunksize": 1})
$ mongo --nodb
> // now connect to mongos on 30999
> db = (new Mongo("localhost:30999")).getDB("test")
Sharding Experimentation
32. I decided to shard from the outset
• Sensor summary
documents can all fit in
RAM
– 16,000 sensors * N bytes
• Velocity of sensor events
is only 267 writes/sec
• Volume of sensor events
is what dictates sharding
{ _id:<linkID>,
update:ISODate(“2013-10-10T23:06:37.000Z”),
last10:{
avgSpeed:<int>,
avgTime:<int>
},
lastHour:{
avgSpeed:<int>,
avgTime:<int>
},
speeds:[52,49,45,51,...],
times:[237,224,246,233,...],
pavement:"WetSpots",
status:"WetConditions",
weather:"LightRain"
}
39. Tag Aware Sharding
• Control where data is written to, and read from
• Each member can have one or more tags
– tags: {dc: "ny"}
– tags: {dc: "ny", subnet: "192.168", rack:
"row3rk7"}
• Replica set defines rules for write concerns
• Rules can change without changing app code
Editor's Notes
Priority
Floating point number between 0..1000
Highest member that is up to date wins
Up to date == within 10 seconds of primary
If a higher priority member catches up, it will force election and win
Slave Delay
Lags behind master by configurable time delay
Automatically hidden from clients
Protects against operator errors
Fat fingering
Application corrupts data
Initialize -> Election
Primary + data replication from primary to secondary
Priority
Floating point number between 0..1000
Highest member that is up to date wins
Up to date == within 10 seconds of primary
If a higher priority member catches up, it will force election and win
Slave Delay
Lags behind master by configurable time delay
Automatically hidden from clients
Protects against operator errors
Fat fingering
Application corrupts data
