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MongoDB

The document provides an overview of MongoDB, detailing its advantages such as agility from a schemaless data model, scalability for handling large datasets, and cost-effectiveness. It discusses MongoDB's architecture, including features like replication, sharding, and its compatibility with various programming languages. Additionally, it highlights use cases demonstrating significant performance and cost improvements over traditional relational databases.

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MongoDB
My name is Steve Francia @spf13
• 15+ years building the internet • BYU Alumnus • Father, husband, skateboarder • Chief Solutions Architect @ 10gen
Introduction to MongoDB
Why MongoDB?
Agility Easily model complex data Database speaks your languages (java, .net, PHP, etc) Schemaless data model enables faster development cycle
Scale Easy and automatic scale out
Cost Cost effectively manage abundant data (clickstreams, logs, etc.)
• Company behind MongoDB • (A)GPL license, own copyrights, engineering team • support, consulting, commercial license revenue • Management • Google/DoubleClick, Oracle, Apple, NetApp • Funding: Sequoia, Union Square, Flybridge • Offices in NYC and Redwood Shores, CA • 50+ employees
MongoDB Goals • OpenSource • Designed for today • Today’s hardware / environments • Today’s challenges • Easy development • Reliable • Scalable
A bit of history
1974 The relational database is created
1979
1979 1982‐1996
1979 1982‐1996 1995
Computers in 1995 • Pentium 100 mhz • 10base T • 16 MB ram • 200 MB HD
Cell Phones in 2011 • Dual core 1.5 Ghz • WiFi 802.11n (300+ Mbps) • 1 GB ram • 64GB Solid State
How about a DB designed for today?
It started with DoubleClick
Signs something needed • doubleclick - 400,000 ads/second • people writing their own stores • caching is de rigueur • complex ORM frameworks • computer architecture trends • cloud computing
Requirements • need a good degree of functionality to handle a large set of use cases • sometimes need strong consistency / atomicity • secondary indexes • ad hoc queries
Trim unneeded features • leave out a few things so we can scale • no choice but to leave out relational • distributed transactions are hard to scale
Needed a scalable data model • some options: • key/value • columnar / tabular • document oriented (JSON inspired) • opportunity to innovate -> agility
MongoDB philosphy • No longer one-size-fits all. but not 12 tools either. • Non-relational (no joins) makes scaling horizontally practical • Document data models are good • Keep functionality when we can (key/value stores are great, but we need more) • Database technology should run anywhere, being available both for running on your own servers or VMs, and also as a cloud pay-for-what-you-use service. • Ideally open source...
MongoDB • JSON Documents • Querying/Indexing/Updating similar to relational databases • Traditional Consistency • Auto-Sharding
Under the hood • Written in C++ • Available on most platforms • Data serialized to BSON • Extensive use of memory-mapped files
Database Landscape
MongoDB is: Application Document Oriented High { author: “steve”, date: new Date(), Performanc text: “About MongoDB...”, tags: [“tech”, “database”]} e Horizontally Scalable
This has led some to say “ MongoDB has the best features of key/ values stores, document databases and relational databases in one. John Nunemaker
Use Cases
Photo Meta- Problem: • Business needed more flexibility than Oracle could deliver Solution: • Used MongoDB instead of Oracle Results: • Developed application in one sprint cycle • 500% cost reduction compared to Oracle • 900% performance improvement compared to Oracle
Customer Analytics Problem: • Deal with massive data volume across all customer sites Solution: • Used MongoDB to replace Google Analytics / Omniture options Results: • Less than one week to build prototype and prove business case • Rapid deployment of new features
Online Problem: • MySQL could not scale to handle their 5B+ documents Solution: • Switched from MySQL to MongoDB Results: • Massive simplification of code base • Eliminated need for external caching system • 20x performance improvement over MySQL
E-commerce Problem: • Multi-vertical E-commerce impossible to model (efficiently) in RDBMS Solution: • Switched from MySQL to MongoDB Results: • Massive simplification of code base • Rapidly build, halving time to market (and cost) • Eliminated need for external caching system • 50x+ improvement over MySQL
Tons more Pretty much if you can use a RDMBS or Key/ Value MongoDB is a great fit
In Good Company
Schema Design
Relational made normalized data look like this
Document databases make normalized data look like this
Terminology RDBMS Mongo Table, View ➜ Collection Row ➜ JSON Document Index ➜ Index Join ➜ Embedded Partition ➜ Document Shard Partition Key ➜ Shard Key
Tables to Documents
Tables to Documents { title: ‘MongoDB’, contributors: [ { name: ‘Eliot Horowitz’, email: ‘eh@10gen.com’ }, { name: ‘Dwight Merriman’, email: ‘dm@10gen.com’ } ], model: { relational: false, awesome: true }
DEMO TIME
Documents Blog Post Document > p = {author: “roger”, date: new Date(), text: “about mongoDB...”, tags: [“tech”, “databases”]} > db.posts.save(p)
Querying > db.posts.find() > { _id : ObjectId("4c4ba5c0672c685e5e8aabf3"), author : "roger", date : "Sat Jul 24 2010 19:47:11", text : "About MongoDB...", tags : [ "tech", "databases" ] } Note: _id is unique, but can be anything you’d like
Secondary Indexes Create index on any Field in Document
Secondary Indexes Create index on any Field in Document // 1 means ascending, -1 means descending > db.posts.ensureIndex({author: 1}) > db.posts.find({author: 'roger'}) > { _id : ObjectId("4c4ba5c0672c685e5e8aabf3"), author : "roger", ... }
Conditional Query Operators $all, $exists, $mod, $ne, $in, $nin, $nor, $or, $size, $type, $lt, $lte, $gt, $gte
Conditional Query Operators $all, $exists, $mod, $ne, $in, $nin, $nor, $or, $size, $type, $lt, $lte, $gt, $gte // find posts with any tags > db.posts.find( {tags: {$exists: true }} ) // find posts matching a regular expression > db.posts.find( {author: /^rog*/i } ) // count posts by author > db.posts.find( {author: ‘roger’} ).count()
Update Operations $set, $unset, $inc, $push, $pushAll, $pull, $pullAll, $bit
Update Operations $set, $unset, $inc, $push, $pushAll, $pull, $pullAll, $bit > comment = { author: “fred”, date: new Date(), text: “Best Movie Ever”} > db.posts.update( { _id: “...” }, $push: {comments: comment} );
Nested Documents { _id : ObjectId("4c4ba5c0672c685e5e8aabf3"), author : "roger", date : "Sat Apr 24 2011 19:47:11", text : "About MongoDB...", tags : [ "tech", "databases" ], comments : [ { author : "Fred", date : "Sat Apr 25 2010 20:51:03 GMT-0700", text : "Best Post Ever!" } ] }
Secondary Indexes // Index nested documents > db.posts.ensureIndex( “comments.author”: 1) > db.posts.find({‘comments.author’:’Fred’}) // Index on tags (multi-key index) > db.posts.ensureIndex( tags: 1) > db.posts.find( { tags: ‘tech’ } ) // geospatial index > db.posts.ensureIndex( “author.location”: “2d” ) > db.posts.find( “author.location”: { $near : [22,42] } )
Rich Documents { _id : ObjectId("4c4ba5c0672c685e5e8aabf3"), line_items : [ { sku: ‘tt-123’, name: ‘Coltrane: Impressions’ }, { sku: ‘tt-457’, name: ‘Davis: Kind of Blue’ } ], address : { name: ‘Banker’, street: ‘111 Main’, zip: 10010 }, payment: { cc: 4567, exp: Date(2011, 7, 7) }, subtotal: 2355 }
High Availability
MongoDB Replication •MongoDB replication like MySQL replication (kinda) •Asynchronous master/slave •Variations •Master / slave •Replica Sets
Replica Set features • A cluster of N servers • Any (one) node can be primary • Consensus election of primary • Automatic failover • Automatic recovery • All writes to primary • Reads can be to primary (default) or a secondary
How MongoDB Replication works Member
1 Member
3 Member
2 Set is made up of 2 or more nodes
How MongoDB Replication works Member
1 Member
3 Member
2 PRIMARY Election establishes the PRIMARY Data replication from PRIMARY to SECONDARY
How MongoDB Replication works negotiate
 new
master Member
1 Member
3 Member
2 DOWN PRIMARY may fail Automatic election of new PRIMARY if majority exists
How MongoDB Replication works Member
3 Member
1 PRIMARY Member
2 DOWN New PRIMARY elected Replication Set re-established
How MongoDB Replication works Member
3 Member
1 PRIMARY Member
2 RECOVERING Automatic recovery
How MongoDB Replication works Member
3 Member
1 PRIMARY Member
2 Replication Set re-established
Creating a Replica Set > cfg = { _id : "acme_a", members : [ { _id : 0, host : "sf1.acme.com" }, { _id : 1, host : "sf2.acme.com" }, { _id : 2, host : "sf3.acme.com" } ] } > use admin > db.runCommand( { replSetInitiate : cfg } )
Replica Set Options • {arbiterOnly: True} • Can vote in an election • Does not hold any data • {hidden: True} • Not reported in isMaster() • Will not be sent slaveOk() reads • {priority: n} • {tags: }
Using Replicas for Reads • slaveOk() • - driver will send read requests to Secondaries • - driver will always send writes to Primary • Java examples • - DB.slaveOk() • - Collection.slaveOk() • find(q).addOption(Bytes.QUERYOPTION_SLAVEO K);
Safe Writes • db.runCommand({getLastError: 1, w : 1}) • - ensure write is synchronous • - command returns after primary has written to memory • w=n or w='majority' • n is the number of nodes data must be replicated to • driver will always send writes to Primary • w='myTag' [MongoDB 2.0] • Each member is "tagged" e.g. "US_EAST", "EMEA", "US_WEST" • Ensure that the write is executed in each tagged "region"
Safe Writes • fsync:true • Ensures changed disk blocks are flushed to disk • j:true • Ensures changes are flush to Journal
When are elections triggered? • When a given member see's that the Primary is not reachable • The member is not an Arbiter • Has a priority greater than other eligible members
Typical Use? Set
 size Deployments Data
Protection High
Availability Notes X One No No Must
use
‐‐journal
to
protect
against
crashes On
loss
of
one
member,
surviving
member
is
 Two Yes No read
only On
loss
of
one
member,
surviving
two
 Three Yes Yes
‐
1
failure members
can
elect
a
new
primary *
On
loss
of
two
members,
surviving
two
 X Four Yes Yes
‐
1
failure* members
are
read
only
 On
loss
of
two
members,
surviving
three
 Five Yes Yes
‐
2
failures members
can
elect
a
new
primary
Replication features • Reads from Primary are always consistent • Reads from Secondaries are eventually consistent • Automatic failover if a Primary fails • Automatic recovery when a node joins the set • Control of where writes occur
Scaling Sharding MongoDB
What is Sharding • Ad-hoc partitioning • Consistent hashing • Amazon Dynamo • Range based partitioning • Google BigTable • Yahoo! PNUTS • MongoDB
MongoDB Sharding • Automatic partitioning and management • Range based • Convert to sharded system with no downtime • Fully consistent
How MongoDB Sharding Works
How MongoDB Sharding works >
db.runCommand(
{
addshard
:
"shard1"
}
); >
db.runCommand(
 


{
shardCollection
:
“mydb.blogs”,
 




key
:
{
age
:
1}
}
) -∞   +∞   •Range keys from -∞ to +∞   •Ranges are stored as “chunks”
How MongoDB Sharding works >
db.posts.save(
{age:40}
) -∞   +∞   -∞   40 41 +∞   •Data in inserted •Ranges are split into more “chunks”
How MongoDB Sharding works >
db.posts.save(
{age:40}
) >
db.posts.save(
{age:50}
) -∞   +∞   -∞   40 41 +∞   41 50 51 +∞   •More Data in inserted •Ranges are split into more“chunks”
How MongoDB Sharding works >
db.posts.save(
{age:40}
) >
db.posts.save(
{age:50}
) >
db.posts.save(
{age:60}
) -∞   +∞   -∞   40 41 +∞   41 50 51 +∞   51 60 61 +∞  
How MongoDB Sharding works >
db.posts.save(
{age:40}
) >
db.posts.save(
{age:50}
) >
db.posts.save(
{age:60}
) -∞   +∞   -∞   40 41 +∞   41 50 51 +∞   51 60 61 +∞  
How MongoDB Sharding works shard1 -∞   40 41 50 51 60 61 +∞  
How MongoDB Sharding works >
db.runCommand(
{
addshard
:
"shard2"
}
); -∞   40 41 50 51 60 61 +∞  
How MongoDB Sharding works >
db.runCommand(
{
addshard
:
"shard2"
}
); shard1 -∞   40 41 50 51 60 61 +∞  
How MongoDB Sharding works >
db.runCommand(
{
addshard
:
"shard2"
}
); shard1 shard2 -∞   40 41 50 51 60 61 +∞  
How MongoDB Sharding works >
db.runCommand(
{
addshard
:
"shard2"
}
); >
db.runCommand(
{
addshard
:
"shard3"
}
); shard1 shard2 shard3 -∞   40 41 50 51 60 61 +∞  
How MongoDB Sharding Works
Sharding Features • Shard data without no downtime • Automatic balancing as data is written • Commands routed (switched) to correct node • Inserts - must have the Shard Key • Updates - must have the Shard Key • Queries • With Shard Key - routed to nodes • Without Shard Key - scatter gather • Indexed Queries • With Shard Key - routed in order • Without Shard Key - distributed sort merge
Sharding Architecture
Architecture
Config Servers • 3 of them • changes are made with 2 phase commit • if any are down, meta data goes read only • system is online as long as 1/3 is up
Config Servers • 3 of them • changes are made with 2 phase commit • if any are down, meta data goes read only • system is online as long as 1/3 is up
Shards • Can be master, master/slave or replica sets • Replica sets gives sharding + full auto-failover • Regular mongod processes
Shards • Can be master, master/slave or replica sets • Replica sets gives sharding + full auto-failover • Regular mongod processes
Mongos • Sharding Router • Acts just like a mongod to clients • Can have 1 or as many as you want • Can run on appserver so no extra network traffic
Mongos • Sharding Router • Acts just like a mongod to clients • Can have 1 or as many as you want • Can run on appserver so no extra network traffic
Advanced Replication
Priorities • Prior to 2.0.0 • {priority:0} // Never can be elected Primary • {priority:1} // Can be elected Primary • New in 2.0.0 • Priority, floating point number between 0 and 1000 • During an election • Most up to date • Highest priority • Allows weighting of members during failover
Priorities - example • Assuming all members are up to date A D • Members A or B will be chosen first p:2 p:1 • Highest priority B E • Members C or D will be chosen next if p:2 p:0 • A and B are unavailable • A and B are not up to date C p:1 • Member E is never chosen • priority:0 means it cannot be elected
Tagging • New in 2.0.0 • Control over where data is written to • Each member can have one or more tags e.g. • tags: {dc: "ny"} • tags: {dc: "ny", ip: "192.168", rack: "row3rk7"} • Replica set defines rules for where data resides • Rules can change without change application code
Tagging - example { _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 : "allDCs"})
Use Cases - Multi Data Center • write to three data centers • allDCs : {"dc" : 3} • > db.runCommand({getLastError : 1, w : "allDCs"}) • write to two data centers and three availability zones • allDCsPlus : {"dc" : 2, "az": 3} • > db.runCommand({getLastError : 1, w : "allDCsPlus"}) US‐EAST‐1 US‐WEST‐1 LONDON‐1 tag
:
{dc:
"JFK", tag
:
{dc:
"SFO", tag
:
{dc:
"LHR", 






az:
"r1"} 






az
:
"r3"} 






az:
"r5"} US‐EAST‐2 US‐WEST‐2 tag
:
{dc:
"JFK" tag
:
{dc:
"SFO" 






az:
"r2"} 






az:
"r4"}
Use Cases - Data Protection & High Availability • A and B will take priority during a failover • C or D will become primary if A and B become unavailable • E cannot be primary • D and E cannot be read from with a slaveOk() • D can use be used for Backups, feed Solr index etc. • E provides a safe guard for operational or application error E A C priority:
0 priority:
2 priority:
1 hidden:
True slaveDelay:
3600 D B priority:
1 priority:
2 hidden:
True
Optimizing app performance
RAM Disk
RAM Disk
RAM Disk
RAM Disk
Goal Minimize memory turnover
What is your data access pattern?
10 days of data RAM Disk
http://spf13.com http://github.com/spf13 @spf13 Questions? download at mongodb.org PS: We’re hiring!! Contact us at jobs@10gen.com
MongoDB

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MongoDB

  • 1.
  • 2.
    My name is SteveFrancia @spf13
  • 3.
    • 15+ yearsbuilding the internet • BYU Alumnus • Father, husband, skateboarder • Chief Solutions Architect @ 10gen
  • 4.
  • 5.
  • 6.
    Agility Easily model complex data Database speaks your languages (java, .net, PHP, etc) Schemaless data model enables faster development cycle
  • 7.
  • 8.
    Cost Cost effectively manageabundant data (clickstreams, logs, etc.)
  • 9.
    • Company behindMongoDB • (A)GPL license, own copyrights, engineering team • support, consulting, commercial license revenue • Management • Google/DoubleClick, Oracle, Apple, NetApp • Funding: Sequoia, Union Square, Flybridge • Offices in NYC and Redwood Shores, CA • 50+ employees
  • 10.
    MongoDB Goals • OpenSource •Designed for today • Today’s hardware / environments • Today’s challenges • Easy development • Reliable • Scalable
  • 11.
    A bit ofhistory
  • 12.
  • 15.
  • 16.
    1979 1982‐1996
  • 17.
    1979 1982‐1996 1995
  • 18.
    Computers in 1995 •Pentium 100 mhz • 10base T • 16 MB ram • 200 MB HD
  • 19.
    Cell Phones in2011 • Dual core 1.5 Ghz • WiFi 802.11n (300+ Mbps) • 1 GB ram • 64GB Solid State
  • 20.
    How about aDB designed for today?
  • 21.
    It started with DoubleClick
  • 22.
    Signs something needed • doubleclick - 400,000 ads/second • people writing their own stores • caching is de rigueur • complex ORM frameworks • computer architecture trends • cloud computing
  • 23.
    Requirements • need agood degree of functionality to handle a large set of use cases • sometimes need strong consistency / atomicity • secondary indexes • ad hoc queries
  • 24.
    Trim unneeded features • leave out a few things so we can scale • no choice but to leave out relational • distributed transactions are hard to scale
  • 25.
    Needed a scalable data model • some options: • key/value • columnar / tabular • document oriented (JSON inspired) • opportunity to innovate -> agility
  • 26.
    MongoDB philosphy • No longer one-size-fits all. but not 12 tools either. • Non-relational (no joins) makes scaling horizontally practical • Document data models are good • Keep functionality when we can (key/value stores are great, but we need more) • Database technology should run anywhere, being available both for running on your own servers or VMs, and also as a cloud pay-for-what-you-use service. • Ideally open source...
  • 27.
    MongoDB • JSON Documents •Querying/Indexing/Updating similar to relational databases • Traditional Consistency • Auto-Sharding
  • 28.
    Under the hood •Written in C++ • Available on most platforms • Data serialized to BSON • Extensive use of memory-mapped files
  • 29.
  • 30.
    MongoDB is: Application Document Oriented High { author: “steve”, date: new Date(), Performanc text: “About MongoDB...”, tags: [“tech”, “database”]} e Horizontally Scalable
  • 31.
    This has led some to say “ MongoDB has the best features of key/ values stores, document databases and relational databases in one. John Nunemaker
  • 32.
  • 33.
    Photo Meta- Problem: • Businessneeded more flexibility than Oracle could deliver Solution: • Used MongoDB instead of Oracle Results: • Developed application in one sprint cycle • 500% cost reduction compared to Oracle • 900% performance improvement compared to Oracle
  • 34.
    Customer Analytics Problem: • Dealwith massive data volume across all customer sites Solution: • Used MongoDB to replace Google Analytics / Omniture options Results: • Less than one week to build prototype and prove business case • Rapid deployment of new features
  • 35.
    Online Problem: • MySQL couldnot scale to handle their 5B+ documents Solution: • Switched from MySQL to MongoDB Results: • Massive simplification of code base • Eliminated need for external caching system • 20x performance improvement over MySQL
  • 36.
    E-commerce Problem: • Multi-vertical E-commerceimpossible to model (efficiently) in RDBMS Solution: • Switched from MySQL to MongoDB Results: • Massive simplification of code base • Rapidly build, halving time to market (and cost) • Eliminated need for external caching system • 50x+ improvement over MySQL
  • 37.
    Tons more Pretty muchif you can use a RDMBS or Key/ Value MongoDB is a great fit
  • 38.
  • 39.
  • 40.
    Relational made normalized data look like this
  • 41.
  • 42.
    Terminology RDBMS Mongo Table, View ➜ Collection Row ➜ JSON Document Index ➜ Index Join ➜ Embedded Partition ➜ Document Shard Partition Key ➜ Shard Key
  • 43.
  • 44.
    Tables to Documents { title: ‘MongoDB’, contributors: [ { name: ‘Eliot Horowitz’, email: ‘eh@10gen.com’ }, { name: ‘Dwight Merriman’, email: ‘dm@10gen.com’ } ], model: { relational: false, awesome: true }
  • 45.
  • 46.
    Documents Blog Post Document >p = {author: “roger”, date: new Date(), text: “about mongoDB...”, tags: [“tech”, “databases”]} > db.posts.save(p)
  • 47.
    Querying > db.posts.find() > { _id : ObjectId("4c4ba5c0672c685e5e8aabf3"), author : "roger", date : "Sat Jul 24 2010 19:47:11", text : "About MongoDB...", tags : [ "tech", "databases" ] } Note: _id is unique, but can be anything you’d like
  • 48.
    Secondary Indexes Create indexon any Field in Document
  • 49.
    Secondary Indexes Create indexon any Field in Document // 1 means ascending, -1 means descending > db.posts.ensureIndex({author: 1}) > db.posts.find({author: 'roger'}) > { _id : ObjectId("4c4ba5c0672c685e5e8aabf3"), author : "roger", ... }
  • 50.
    Conditional Query Operators $all, $exists, $mod, $ne, $in, $nin, $nor, $or, $size, $type, $lt, $lte, $gt, $gte
  • 51.
    Conditional Query Operators $all, $exists, $mod, $ne, $in, $nin, $nor, $or, $size, $type, $lt, $lte, $gt, $gte // find posts with any tags > db.posts.find( {tags: {$exists: true }} ) // find posts matching a regular expression > db.posts.find( {author: /^rog*/i } ) // count posts by author > db.posts.find( {author: ‘roger’} ).count()
  • 52.
    Update Operations $set, $unset,$inc, $push, $pushAll, $pull, $pullAll, $bit
  • 53.
    Update Operations $set,$unset, $inc, $push, $pushAll, $pull, $pullAll, $bit > comment = { author: “fred”, date: new Date(), text: “Best Movie Ever”} > db.posts.update( { _id: “...” }, $push: {comments: comment} );
  • 54.
    Nested Documents { _id : ObjectId("4c4ba5c0672c685e5e8aabf3"), author : "roger", date : "Sat Apr 24 2011 19:47:11", text : "About MongoDB...", tags : [ "tech", "databases" ], comments : [ { author : "Fred", date : "Sat Apr 25 2010 20:51:03 GMT-0700", text : "Best Post Ever!" } ] }
  • 55.
    Secondary Indexes // Indexnested documents > db.posts.ensureIndex( “comments.author”: 1) > db.posts.find({‘comments.author’:’Fred’}) // Index on tags (multi-key index) > db.posts.ensureIndex( tags: 1) > db.posts.find( { tags: ‘tech’ } ) // geospatial index > db.posts.ensureIndex( “author.location”: “2d” ) > db.posts.find( “author.location”: { $near : [22,42] } )
  • 56.
    Rich Documents { _id : ObjectId("4c4ba5c0672c685e5e8aabf3"), line_items : [ { sku: ‘tt-123’, name: ‘Coltrane: Impressions’ }, { sku: ‘tt-457’, name: ‘Davis: Kind of Blue’ } ], address : { name: ‘Banker’, street: ‘111 Main’, zip: 10010 }, payment: { cc: 4567, exp: Date(2011, 7, 7) }, subtotal: 2355 }
  • 57.
  • 58.
    MongoDB Replication •MongoDB replication like MySQL replication (kinda) •Asynchronous master/slave •Variations •Master / slave •Replica Sets
  • 59.
    Replica Set features • A cluster of N servers • Any (one) node can be primary • Consensus election of primary • Automatic failover • Automatic recovery • All writes to primary • Reads can be to primary (default) or a secondary
  • 60.
    How MongoDB Replication works Member
1 Member
3 Member
2 Set is made up of 2 or more nodes
  • 61.
    How MongoDB Replication works Member
1 Member
3 Member
2 PRIMARY Election establishes the PRIMARY Data replication from PRIMARY to SECONDARY
  • 62.
    How MongoDB Replication works negotiate
 new
master Member
1 Member
3 Member
2 DOWN PRIMARY may fail Automatic election of new PRIMARY if majority exists
  • 63.
    How MongoDB Replication works Member
3 Member
1 PRIMARY Member
2 DOWN New PRIMARY elected Replication Set re-established
  • 64.
    How MongoDB Replication works Member
3 Member
1 PRIMARY Member
2 RECOVERING Automatic recovery
  • 65.
    How MongoDB Replication works Member
3 Member
1 PRIMARY Member
2 Replication Set re-established
  • 66.
    Creating a Replica Set > cfg = { _id : "acme_a", members : [ { _id : 0, host : "sf1.acme.com" }, { _id : 1, host : "sf2.acme.com" }, { _id : 2, host : "sf3.acme.com" } ] } > use admin > db.runCommand( { replSetInitiate : cfg } )
  • 67.
    Replica Set Options • {arbiterOnly: True} • Can vote in an election • Does not hold any data • {hidden: True} • Not reported in isMaster() • Will not be sent slaveOk() reads • {priority: n} • {tags: }
  • 68.
    Using Replicas for Reads • slaveOk() • - driver will send read requests to Secondaries • - driver will always send writes to Primary • Java examples • - DB.slaveOk() • - Collection.slaveOk() • find(q).addOption(Bytes.QUERYOPTION_SLAVEO K);
  • 69.
    Safe Writes • db.runCommand({getLastError: 1, w : 1}) • - ensure write is synchronous • - command returns after primary has written to memory • w=n or w='majority' • n is the number of nodes data must be replicated to • driver will always send writes to Primary • w='myTag' [MongoDB 2.0] • Each member is "tagged" e.g. "US_EAST", "EMEA", "US_WEST" • Ensure that the write is executed in each tagged "region"
  • 70.
    Safe Writes • fsync:true • Ensures changed disk blocks are flushed to disk • j:true • Ensures changes are flush to Journal
  • 71.
    When are elections triggered? • When a given member see's that the Primary is not reachable • The member is not an Arbiter • Has a priority greater than other eligible members
  • 72.
    Typical Use? Set
 size Deployments Data
Protection High
Availability Notes X One No No Must
use
‐‐journal
to
protect
against
crashes On
loss
of
one
member,
surviving
member
is
 Two Yes No read
only On
loss
of
one
member,
surviving
two
 Three Yes Yes
‐
1
failure members
can
elect
a
new
primary *
On
loss
of
two
members,
surviving
two
 X Four Yes Yes
‐
1
failure* members
are
read
only
 On
loss
of
two
members,
surviving
three
 Five Yes Yes
‐
2
failures members
can
elect
a
new
primary
  • 73.
    Replication features • Reads from Primary are always consistent • Reads from Secondaries are eventually consistent • Automatic failover if a Primary fails • Automatic recovery when a node joins the set • Control of where writes occur
  • 74.
  • 75.
    What is Sharding •Ad-hoc partitioning • Consistent hashing • Amazon Dynamo • Range based partitioning • Google BigTable • Yahoo! PNUTS • MongoDB
  • 76.
    MongoDB Sharding • Automaticpartitioning and management • Range based • Convert to sharded system with no downtime • Fully consistent
  • 77.
  • 78.
    How MongoDB Shardingworks >
db.runCommand(
{
addshard
:
"shard1"
}
); >
db.runCommand(
 


{
shardCollection
:
“mydb.blogs”,
 




key
:
{
age
:
1}
}
) -∞   +∞   •Range keys from -∞ to +∞   •Ranges are stored as “chunks”
  • 79.
    How MongoDB Shardingworks >
db.posts.save(
{age:40}
) -∞   +∞   -∞   40 41 +∞   •Data in inserted •Ranges are split into more “chunks”
  • 80.
    How MongoDB Shardingworks >
db.posts.save(
{age:40}
) >
db.posts.save(
{age:50}
) -∞   +∞   -∞   40 41 +∞   41 50 51 +∞   •More Data in inserted •Ranges are split into more“chunks”
  • 81.
    How MongoDB Shardingworks >
db.posts.save(
{age:40}
) >
db.posts.save(
{age:50}
) >
db.posts.save(
{age:60}
) -∞   +∞   -∞   40 41 +∞   41 50 51 +∞   51 60 61 +∞  
  • 82.
    How MongoDB Shardingworks >
db.posts.save(
{age:40}
) >
db.posts.save(
{age:50}
) >
db.posts.save(
{age:60}
) -∞   +∞   -∞   40 41 +∞   41 50 51 +∞   51 60 61 +∞  
  • 83.
    How MongoDB Shardingworks shard1 -∞   40 41 50 51 60 61 +∞  
  • 84.
    How MongoDB Shardingworks >
db.runCommand(
{
addshard
:
"shard2"
}
); -∞   40 41 50 51 60 61 +∞  
  • 85.
    How MongoDB Shardingworks >
db.runCommand(
{
addshard
:
"shard2"
}
); shard1 -∞   40 41 50 51 60 61 +∞  
  • 86.
    How MongoDB Shardingworks >
db.runCommand(
{
addshard
:
"shard2"
}
); shard1 shard2 -∞   40 41 50 51 60 61 +∞  
  • 87.
    How MongoDB Shardingworks >
db.runCommand(
{
addshard
:
"shard2"
}
); >
db.runCommand(
{
addshard
:
"shard3"
}
); shard1 shard2 shard3 -∞   40 41 50 51 60 61 +∞  
  • 88.
  • 89.
    Sharding Features • Shard data without no downtime • Automatic balancing as data is written • Commands routed (switched) to correct node • Inserts - must have the Shard Key • Updates - must have the Shard Key • Queries • With Shard Key - routed to nodes • Without Shard Key - scatter gather • Indexed Queries • With Shard Key - routed in order • Without Shard Key - distributed sort merge
  • 90.
  • 91.
  • 92.
    Config Servers • 3of them • changes are made with 2 phase commit • if any are down, meta data goes read only • system is online as long as 1/3 is up
  • 93.
    Config Servers • 3of them • changes are made with 2 phase commit • if any are down, meta data goes read only • system is online as long as 1/3 is up
  • 94.
    Shards • Can bemaster, master/slave or replica sets • Replica sets gives sharding + full auto-failover • Regular mongod processes
  • 95.
    Shards • Can bemaster, master/slave or replica sets • Replica sets gives sharding + full auto-failover • Regular mongod processes
  • 96.
    Mongos • Sharding Router •Acts just like a mongod to clients • Can have 1 or as many as you want • Can run on appserver so no extra network traffic
  • 97.
    Mongos • Sharding Router •Acts just like a mongod to clients • Can have 1 or as many as you want • Can run on appserver so no extra network traffic
  • 98.
  • 99.
    Priorities • Prior to 2.0.0 • {priority:0} // Never can be elected Primary • {priority:1} // Can be elected Primary • New in 2.0.0 • Priority, floating point number between 0 and 1000 • During an election • Most up to date • Highest priority • Allows weighting of members during failover
  • 100.
    Priorities - example • Assuming all members are up to date A D • Members A or B will be chosen first p:2 p:1 • Highest priority B E • Members C or D will be chosen next if p:2 p:0 • A and B are unavailable • A and B are not up to date C p:1 • Member E is never chosen • priority:0 means it cannot be elected
  • 101.
    Tagging • New in 2.0.0 • Control over where data is written to • Each member can have one or more tags e.g. • tags: {dc: "ny"} • tags: {dc: "ny", ip: "192.168", rack: "row3rk7"} • Replica set defines rules for where data resides • Rules can change without change application code
  • 102.
    Tagging - example { _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 : "allDCs"})
  • 103.
    Use Cases -Multi Data Center • write to three data centers • allDCs : {"dc" : 3} • > db.runCommand({getLastError : 1, w : "allDCs"}) • write to two data centers and three availability zones • allDCsPlus : {"dc" : 2, "az": 3} • > db.runCommand({getLastError : 1, w : "allDCsPlus"}) US‐EAST‐1 US‐WEST‐1 LONDON‐1 tag
:
{dc:
"JFK", tag
:
{dc:
"SFO", tag
:
{dc:
"LHR", 






az:
"r1"} 






az
:
"r3"} 






az:
"r5"} US‐EAST‐2 US‐WEST‐2 tag
:
{dc:
"JFK" tag
:
{dc:
"SFO" 






az:
"r2"} 






az:
"r4"}
  • 104.
    Use Cases -Data Protection & High Availability • A and B will take priority during a failover • C or D will become primary if A and B become unavailable • E cannot be primary • D and E cannot be read from with a slaveOk() • D can use be used for Backups, feed Solr index etc. • E provides a safe guard for operational or application error E A C priority:
0 priority:
2 priority:
1 hidden:
True slaveDelay:
3600 D B priority:
1 priority:
2 hidden:
True
  • 105.
  • 107.
  • 108.
  • 109.
  • 110.
  • 111.
  • 112.
    What is yourdata access pattern?
  • 113.
    10 days ofdata RAM Disk
  • 114.
    http://spf13.com http://github.com/spf13 @spf13 Questions? download at mongodb.org PS: We’re hiring!! Contact us at jobs@10gen.com

Editor's Notes