back tobasicswebinar part6-rev

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