back tobasicswebinar part6-rev
DESCRIPTION
TRANSCRIPT
Solution Architect, MongoDB
Sam Weaver
#MongoDBBasics
‘Build an Application’ Webinar Series
Deploying your application in production
Agenda
• Replica Sets Lifecycle
• Developing with Replica Sets
• Scaling your database
Q&A
• Virtual Genius Bar– Use chat to post
questions– EMEA Solution
Architecture / Support Team are on hand
– Make use of them during the sessions!!!
Recap
• Introduction to MongoDB
• Schema design
• Interacting with the database
• Indexing
• Analytics– Map Reduce– Aggregation Framework
Deployment Considerations
Working Set Exceeds Physical Memory
Why Replication?
• How many have faced node failures?
• How many have been woken up from sleep to do a fail-over(s)?
• How many have experienced issues due to network latency?
• Different uses for data– Normal processing– Simple analytics
Replica Set Lifestyle
Replica Set – Creation
Replica Set – Initialize
Replica Set – Failure
Replica Set – Failover
Replica Set – Recovery
Replica Set – Recovered
Developing with Replica Sets
Strong Consistency
Delayed Consistency
Write Concern
• Network acknowledgement
• Wait for error
• Wait for journal sync
• Wait for replication
Unacknowledged
MongoDB Acknowledged (wait for error)
Wait for Journal Sync
Wait for Replication
Tagging
• 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
{
_id : "mySet",
members : [
{_id : 0, host : "A", tags : {"dc": "ny"}},
{_id : 1, host : "B", tags : {"dc": "ny"}},
{_id : 2, host : "C", tags : {"dc": "sf"}},
{_id : 3, host : "D", tags : {"dc": "sf"}},
{_id : 4, host : "E", tags : {"dc": "cloud"}}],
settings : {
getLastErrorModes : {
allDCs : {"dc" : 3},
someDCs : {"dc" : 2}} }
}
> db.blogs.insert({...})
> db.runCommand({getLastError : 1, w : "someDCs"})
Tagging Example
Wait for Replication (Tagging)
Read Preference Modes
• 5 modes– primary (only) - Default– primaryPreferred– secondary– secondaryPreferred– Nearest
When more than one node is possible, closest node is used for reads (all modes but primary)
Tagged Read Preference
• Custom read preferences
• Control where you read from by (node) tags– E.g. { "disk": "ssd", "use": "reporting" }
• Use in conjunction with standard read preferences– Except primary
• SAFE writes acceptable for our use case
• Potential to use secondary reads for comments, but probably not needed
• Use tagged reads for analytics
Our application
Scaling
Working Set Exceeds Physical Memory
• When a specific resource becomes a bottle neck on a machine or replica set• RAM• Disk IO• Storage• Concurrency
When to consider Sharding?
Vertical Scalability (Scale Up)
Horizontal Scalability (Scale Out)
Partitioning
• User defines shard key
• Shard key defines range of data
• Key space is like points on a line
• Range is a segment of that line
Initially 1 chunk
Default max chunk size: 64mb
MongoDB automatically splits & migrates chunks when max reached
Data Distribution
Architecture
What is a Shard?
• Shard is a node of the cluster
• Shard can be a single mongod or a replica set
Meta Data Storage
• Config Server– Stores cluster chunk ranges and locations– Can have only 1 or 3 (production must have
3)– Not a replica set
Routing and Managing Data
• Mongos– Acts as a router / balancer– No local data (persists to config database)– Can have 1 or many
Sharding infrastructure
Cluster Request Routing
• Targeted Queries
• Scatter Gather Queries
• Scatter Gather Queries with Sort
Cluster Request Routing: Targeted Query
Routable request received
Request routed to appropriate shard
Shard returns results
Mongos returns results to client
Cluster Request Routing: Non-Targeted Query
Non-Targeted Request Received
Request sent to all shards
Shards return results to mongos
Mongos returns results to client
Cluster Request Routing: Non-Targeted Query with Sort
Non-Targeted request with sort received
Request sent to all shards
Query and sort performed locally
Shards return results to mongos
Mongos merges sorted results
Mongos returns results to client
Shard Key
Shard Key
• Shard key is immutable
• Shard key values are immutable
• Shard key must be indexed
• Shard key limited to 512 bytes in size
• Shard key used to route queries– Choose a field commonly used in queries
• Only shard key can be unique across shards– `_id` field is only unique within individual shard
A suitable shard key for our app…
• Occurs in most queries
• Routes to each shard
• Is granular enough to not exceed 64MB chunks
• Any candidates?– Author?– Date?– _id?– Title?– Author & Date?
Summary
Things to remember
• Size appropriately for your working set
• Shard when you need to, not before
• Pick a shard key wisely
Next Session – 17th April
• Backup and Disaster Recovery• Backup and restore options
Thank you