1.
{
"name": "Andrew Liu",
"e-mail": "andrl@microsoft.com",
"twitter": "@aliuy8"
}

2.
• NoSQL is buzzword
• NoSQL is varied
• Key-value
• Wide-column
• Graph
• Document-oriented

3.
{
"name": "SmugMug",
"permalink": "smugmug",
"homepage_url": "http://www.smugmug.com",
"blog_url": "http://blogs.smugmug.com/",
"category_code": "photo_video",
"products": [
{
"name": "SmugMug",
"permalink": "smugmug"
}
],
"offices": [
{
"description": "",
"address1": "67 E. Evelyn Ave",
"address2": "",
"zip_code": "94041",
"city": "Mountain View",
"state_code": "CA",
"country_code": "USA",
"latitude": 37.390056,
"longitude": -122.067692
}
]
}
Perfect for these
Documents
schema-agnostic JSON store
for
hierarchical and de-normalized data at scale

4.
Not these
documents

5.
{
"name": "SmugMug",
"permalink": "smugmug",
"homepage_url": "http://www.smugmug.com",
"blog_url": "http://blogs.smugmug.com/",
"category_code": "photo_video",
"products": [
{
"name": "SmugMug",
"permalink": "smugmug"
}
],
"offices": [
{
"description": "",
"address1": "67 E. Evelyn Ave",
"address2": "",
"zip_code": "94041",
"city": "Mountain View",
"state_code": "CA",
"country_code": "USA",
"latitude": 37.390056,
"longitude": -122.067692
}
]
}
Perfect for these
Documents
schema-agnostic JSON store
for
hierarchical and de-normalized data at scale

6.
Azure DocumentDB
Millions of RPS
Many TBs of data
Transparent Partitioning
<10ms Reads
<15ms Writes
@P99
Low-latency access
around the globe!
Automatic Indexing
Easy-to-learn query
grammar
Multi-Record
Transactions
Blazing fast, planet scale NoSQL service
99.99% SLAs for availability, latency, and throughput

7.
How does this fit in the Azure family?

8.
Item Author Pages Language
Harry Potter and the Sorcerer’s
Stone
J.K. Rowling 309 English
Game of Thrones: A Song of Ice
and Fire
George R.R.
Martin
864 English

9.
Item Author Pages Language
Harry Potter and the Sorcerer’s
Stone
J.K. Rowling 309 English
Game of Thrones: A Song of Ice
and Fire
George R.R.
Martin
864 English
Lenovo Thinkpad X1 Carbon ??? ??? ???

10.
Item Author Pages Language Processor Memory Storage
Harry Potter
and the
Sorcerer’s
Stone
J.K.
Rowling
309 English ??? ??? ???
Game of
Thrones: A
Song of Ice
and Fire
George
R.R.
Martin
864 English ??? ??? ???
Lenovo
Thinkpad X1
Carbon
??? ??? ??? Core i7
3.3ghz
8 GB 256 GB
SSD

11.
Item Author Pages Language
Harry Potter and the Sorcerer’s
Stone
J.K. Rowling 309 English
Game of Thrones: A Song of Ice
and Fire
George R.R.
Martin
864 English
Item CPU Memory Storage
Lenovo Thinkpad X1 Carbon Core i7 3.3ghz 8 GB 256 GB
SSD

12.
ProductId Item
1 Harry Potter and the
Sorcerer’s Stone
2 Game of Thrones: A Song of
Ice and Fire
3 Lenovo Thinkpad X1 Carbon
ProductId Attribute Value
1 Author J.K. Rowling
1 Pages 309
…
2 Author George R.R. Martin
2 Pages 864
…
3 Processor Core i7 3.3ghz
3 Memory 8 GB
…

13.





14.
The Challenge
 Scale with expectation of
millions of users on Day 1
 Deliver real time responsiveness
for a lag-free, gaming experience
 Highly competitive – high scores
and global leaderboards critical
More Users, More Problems

15.
The Results
 #1 in Apple app store free apps
during launch week
 >1M downloads
 ~1B queries per day
 99p queries served under 10ms

16.
How?

17.
Just throw some data in a database!

18.
Just throw some data in a database!

19.
Not that easy…

20.
Why is this such a hard problem?
 Caches
 Scoreboard keeps updating…
 SQL database
 Need to shard
 Schema and Index Management
 Loss of relational benefits
 Azure Table Storage
 Secondary Indexes
 Latency
 Throughput

21.
Planet-Scale NoSQL
 Horizontal Scaling for storage and
throughput
 High performance with SSDs and
automatic indexing
 Operating on a global scale

22.
really painful

23.
Request Unit (RU) is the
normalized currency
% Memory
% IOPS
% CPU
Replica gets a fixed budget
of Request Units
Resource
Resource
set
Resource
Resource
DocumentsSQL
sprocs
args
Resource Resource
Predictable Performance

24.
Creating partitioned collections
//pre-defined collections
DocumentCollection collectionSpec = new DocumentCollection { Id = "Walkers" };
RequestOptions options = new RequestOptions { OfferType = "S3" };
DocumentCollection documentCollection = await client.CreateDocumentCollectionAsync("dbs/" +
database.Id, collectionSpec, options);
//partitioned collections
DocumentCollection collectionSpec = new DocumentCollection { Id = "Walkers" };
collectionSpec.PartitionKey.Paths.Add(“/walkerId”);
int collectionThroughput = 100000;
RequestOptions options = new RequestOptions { OfferThroughput = collectionThroughput };
DocumentCollection documentCollection = await client.CreateDocumentCollectionAsync("dbs/" +
database.Id, collectionSpec, options);

25.
Globally Distributed
• Not just for disaster recovery…. DocumentDB is unreasonably highly available
• Replicate data across any # of regions of your choice
• Low-latency access to your data around the globe
• Dynamically configure your write and read regions
Azure DocumentDB gives you the ability cheat the speed of light!

26.
Bounded Staleness Session EventualStrong
LEFT TO RIGHT  Relaxed consistency => better performance and availability
Consistency Level Strong Bounded Staleness Session Eventual
Total global order Yes Yes, outside of the “staleness
window”
No, partial “session”
order
No
Consistent prefix
guarantee
Yes Yes Yes Yes
Monotonic reads Yes Yes, across regions outside of the
staleness window and within a
region all the time
Yes, for the given session No
Monotonic writes Yes Yes Yes Yes
Read your writes Yes Yes (in the write region) Yes No
Strong
consistency, High
latency
Eventual consistency,
Low latency
27%
3%
54%
16%
Observed Distribution
BoundedStalene
ss
Eventual
Session

27.
App defined regional preferences
ConnectionPolicy docClientConnectionPolicy = new ConnectionPolicy { ConnectionMode =
ConnectionMode.Direct, ConnectionProtocol = Protocol.Tcp };
docClientConnectionPolicy.PreferredLocations.Add(LocationNames.EastUS2);
docClientConnectionPolicy.PreferredLocations.Add(LocationNames.WestUS);
docClient = new DocumentClient(
new Uri("https://myglobaldb.documents.azure.com:443"),
"PARvqUuBw2QTO4rRXr6d1GnLCR7VinERcYrBQvDRh6EDTJLOHtZxgjTS4pv8nQv2Lg1QQLBLfO6TVziOZKvYow==",
docClientConnectionPolicy);

28.
Automatic Indexing
• Index is a union of all the document trees
Common
structure
Terms Postings List/Values
$/location/0/ 1, 2
location/0/country/ 1, 2
location/0/city/ 1, 2
0/country/Germany 1, 2
1/country/France 2
… …
0/city/Moscow 2
0/dealers/0 2
http://aka.ms/docdbvldb
No need to define secondary indices / schema hints!

29.
Index policies
customize index management including storage
overhead, throughput and query consistency
 range, hash and spatial indexes
 included and excluded paths
 indexing mode; consistent or lazy
 index precision
 online, in-place index transformations
{
"indexingMode": "consistent",
"automatic": true,
"includedPaths": [
{
"path": "/*",
"indexes": [
{
"kind": "Range",
"dataType": "Number",
"precision": -1
},
{
"kind": "Hash",
"dataType": "String",
"precision": 3
},
{
"kind": "Spatial",
"dataType": "Point"
}
]
}
],
"excludedPaths": []
}

30.
-- Nested lookup against index
SELECT Books.Author
FROM Books
WHERE Books.Author.Name = "Leo Tolstoy"
-- Transformation, Filters, Array access
SELECT { Name: Books.Title, Author: Books.Author.Name }
FROM Books
WHERE Books.Price > 10 AND Books.Languages[0] = "English"
-- Joins, User Defined Functions (UDF)
SELECT CalculateRegionalTax(Books.Price, "USA", "WA")
FROM Books
JOIN LanguagesArr IN Books.Languages
WHERE LanguagesArr.Language = "Russian"
SQL Query Grammar

31.
function(playerId1, playerId2) {
var playersToSwap = __.filter (function (document) {
return (document.id == playerId1 || document.id == playerId2);
});
var player1 = playersToSwap[0], player2 = playersToSwap[1];
var player1ItemTemp = player1.item;
player1.item = player2.item;
player2.item = player1ItemTemp;
__.replaceDocument(player1)
.then(function() { return __.replaceDocument(player2); })
.fail(function(error){ throw 'Unable to update players, abort'; });
}
client.executeStoredProcedureAsync
("procs/1234", ["MasterChief", "SolidSnake“])
.then(function (response) {
console.log(“success!");
}, function (err) {
console.log("Failed to swap!", error);
}
);
Client Database

32.
API and Toolchain Options
DocumentDB
REST over HTTPS/TCPJava .NET
PowerBI

33.
{
"id": "1",
"firstName": "Thomas",
"lastName": "Andersen",
"addresses": [
{
"line1": "100 Some Street",
"line2": "Unit 1",
"city": "Seattle",
"state": "WA",
"zip": 98012 }
],
"contactDetails": [
{"email: "thomas@andersen.com"},
{"phone": "+1 555 555-5555", "extension": 5555}
]
}
Try model your entity as a self-
contained document
Generally, use embedded data
models when:
contains
one-to-few
changes infrequently
won’t grow
integral
better read performance

34.
In general, use normalized data
models when:
Write performance
one-to-many
many-to-many
changes frequently
{
"id": "xyz",
"username: "user xyz"
}
{
"id": "address_xyz",
"userid": "xyz",
"address" : {
…
}
}
{
"id: "contact_xyz",
"userid": "xyz",
"email" : "user@user.com"
"phone" : "555 5555"
}
Normalizing typically provides better write performance

35.
No magic bullet
Think about how your data is
going to be written, read and
model accordingly
{
"id": "1",
"firstName": "Thomas",
"lastName": "Andersen",
"countOfBooks": 3,
"books": [1, 2, 3],
"images": [
{"thumbnail": "http://....png"}
{"profile": "http://....png"}
]
}
{
"id": 1,
"name": "DocumentDB 101",
"authors": [
{"id": 1, "name": "Thomas Andersen", "thumbnail": "http://....png"},
{"id": 2, "name": "William Wakefield", "thumbnail": "http://....png"}
]
}