How to scale MongoDB

June-22-2018
How to Scale MongoDB

● Location: Skopje, Republic of Macedonia
● Education: MSc, Software Engineering
● Experience:
○ Lead Database Consultant (since 2016)
○ Database Consultant (2012 - 2016)
○ Web Developer, DBA (2007 - 2012)
● Certifications: C100DBA - MongoDB certified DBA (since 2016)
https://mk.linkedin.com/in/igorle @igorle
About me
© 2018 Pythian. Confidential

Overview
• What is HA, how MongoDB achieves HA
• Replica set, components, deployment topologies
• Scaling (Vertical vs Horizontal)
• What is a sharded cluster in MongoDB
• Cluster components - shards, config servers, mongos
• Shard keys and chunks
• Hashed vs range based sharding
• Choosing shard key
• QA

HA

What is HA?
• High availability refers to systems that are durable and likely to operate
continuously without failure for a long time
• single point of failure (SPOF) is a part of a system that, if it fails, will stop
the entire system from working
Application Database
SPOF? SPOF?

Database
Replica
What is HA?
• HA on the Application vs Database layer
• Splitting Writes and Read operations
Application Primary
Application
…………...
Replica
LoadBalancer
LoadBalancer?
Writes
Reads

Database
Replica
What is HA?
• Application node failure
• Database node failure
• Failover (elect new Primary node)
Application Primary
Application
…………...
Replica
LoadBalancer
LoadBalancer?
Writes
Reads

• Group of mongod processes that maintain
the same data set
• Redundancy and high availability
• Increased read capacity (scaling reads)
• Automatic failover
HA with MongoDB - replica sets

Replication concept
1. Write operations go to the Primary node
2. All changes are recorded into operations log
3. Asynchronous replication to Secondary
4. Secondaries copy the Primary oplog
5. Secondary can use sync source Secondary
1.

Replication concept
2. oplog
1.

Replication concept
2. oplog
1.
3. 3.

Replication concept
2. oplog
1.
3. 3.
4. 4.

Replication concept
5. Secondary can use sync source Secondary*
*settings.chainingAllowed (true by default)
2. oplog
1.
3. 3.
4. 4.
5.

Configuration options
• 50 members per replica set (7 voting members)
• Arbiter node
• Priority 0 node
• Hidden node
• Delayed node

• Does not hold copy of data
• Votes in elections
Arbiter node
hidden : true
Arbiter

Priority 0 node
Priority - floating point (i.e. decimal) number between 0 and 1000
• Cannot become primary, cannot trigger election
• Visible to application
Secondary
priority : 0

Hidden node
• Not visible to application
• Never becomes primary
• Use cases
○ reporting
○ backups
hidden : true
hidden: true priority:0
Secondary
hidden : true priority : 0

Delayed node
• Must be priority 0 member
• Should be hidden member (not mandatory)
• Mainly used for backups
• Recovery in case of human error
Secondary
slaveDelay : 3600
priority : 0
hidden : true

Scaling

• Working set - amount of memory that a process requires in a given time
interval
• Time for scaling ?
Scaling

● Vertical scaling
○ Adding more power (CPU, RAM, DISK) to an existing machine
○ Might require downtime while scaling up
● Horizontal scaling
○ Adding more machines into your pool of resources
○ Partitioning the data on multiple machines
○ Parallelizing the processing
○ More complex to implement and maintain
Scaling

Scaling (vertical)
1TB
1TB

Scaling (horizontal) - Sharding
1TB
256GB 256GB 256GB 256GB
1TB
Shard 1 Shard 2 Shard 3 Shard 4

* https://www.amazon.com/dp/0596102356
Scaling costs

Scaling with MongoDB

Scaling (sharding) with MongoDB
Shard

Sharding with MongoDB
• Shard/Replica set
(subset of the sharded data)
• Config servers
(metadata and config settings)
• mongos
(query router, cluster interface)
mongos> sh.addShard("shardN")
1 2 N-1 N

Shards
• Contains subset of sharded data
• Replica set for redundancy and HA
• Primary shard
• Non sharded collections
• --shardsvr in config file (port 27018)

Config servers
• Stores the metadata for sharded cluster in
config database
• Authentication configuration information in
admin database
• Config servers as replica set only (>= 3.4)
• Holds balancer on Primary node (>= 3.4)
• --configsvr in config file (port 27019)

mongos
• Caching metadata from config servers
• Routes queries to shards
• No persistent state
• Updates cache on metadata changes
• Holds balancer (mongodb <= 3.2)
• mongos version 3.4 cannot connect to
earlier mongod version

Sharding collection
• Enable sharding on database "users"
sh.enableSharding("users")
• Shard collection history
sh.shardCollection("users.history", { user_id : 1 } )
• Shard key - indexed key that exists in every document in the collection
○ range based
sh.shardCollection("users.history", { user_id : 1 } )
○ hashed based
sh.shardCollection( "users.history", { user_id : "hashed" } )

Ranged sharding
• Dividing data into contiguous ranges determined by the shard key values
• Documents with “close” shard key values are likely to be in the same
chunk or shard
• Query Isolation - more likely to target single shard for range queries

Hashed sharding
• Uses a hashed index of a single field as the shard key to partition data
• More even data distribution at the cost of reducing Query Isolation
• Applications do not need to compute hashes

Shard keys and chunks
Choosing Shard key
• Large Shard Key Cardinality
• Low Shard Key Frequency
• Non-Monotonically changing shard keys (ranged or hashed sharding)
Chunks
• A contiguous range of shard key values within a particular shard
• Inclusive lower and exclusive upper range based on shard key
• Default size of 64MB ([1 .. 1024] MB)

Shard key cardinality

Shard key frequency

Monotonically changing shard key

Choosing Shard key
Analyse your query patterns before making any decision (mtools - mloginfo)
● users
○ Querying data by username (unique key?). Consider {username} as
shard key
● user history
○ Querying data by user_id. Consider {user_id} as shard key
○ Querying data by user_id, date_created. Consider {user_id,
date_created} as shard key
● views
○ Write intensive workload. created_on (timestamp). Consider hashed
sharding on {timestamp}

Shard keys limitations
• Must be ascending indexed key or indexed compound keys that exists in
every document in the collection
• Cannot be multikey index, a text index or a geospatial index
• Cannot exceed 512 bytes
• Immutable key - can not be updated/changed
• Update operations that affect a single document must include the shard
key or the _id field
• No option for sharding if unique indexes on other fields exist
• No option for second unique index if the shard key is unique index

Summary
• Replica set with odd number of voting members
• Hidden or Delayed member for dedicated functions (reporting, backups …)
• Dataset fits into single server, keep unsharded deployment
• Horizontal scaling - shards running as replica sets
• Shard keys are immutable with max size of 512 bytes
• Shard keys must exists in every document in the collection
• Ranged sharding may not distribute the data evenly
• Hashed sharding distributes the data randomly
• Sharding adds complexity for maintenance (backups and upgrades)

Questions?

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