Azure Cosmos DB is the industry's first globally distributed multi-model database service. Features of Cosmos DB include turn-key global distribution, elastic throughput and storage, multiple consistency models, and financially backed SLAs. As well, we are in preview for Table, Graph, and Spark Connector to Cosmos DB. Also includes healthcare scenarios!

1. Azure Cosmos DB
Globally distributed multi-model service
Denny Lee
Principal Program Manager, Azure Cosmos DB
Email: denny.lee@microsoft.com

3. Mission-critical applications for
a global userbase need …
Building globally distributed applications

4. What sets Azure Cosmos DB apart

5. Global distribution from the ground-up
Worldwide presence
Automatic multi-region replication
Multi-homing APIs
Manual and automatic failovers
Latency, throughput, consistency, and availability
guarantees

6. Guaranteed low latency
Globally distributed with reads and writes served
from local region
Write optimized, latch-free database engine designed
for SSDs
Synchronous and automatic indexing at sustained
ingestion rates
Single-digit millisecond latency at any scale
Read < 1 ms
Writes < 6 ms
Read < 10 ms
Writes < 15 ms
99%50%

7. Elastically scalable storage
Pay per GB used
No partition management, no limits
Automatically indexed SSD storage
Global distribution across Azure regions
Automatic expiration via TTL

8. Elastically scalable throughput
Pay by the hour, only what you need
No partition management, no limits
Change throughput at any time, effective in seconds
Scale from 100 to 100s of millions of requests/sec across
any number of regions
Now supports requests/min to handle spikes cost-
effectively
9 PM PST
Less throughput
More throughput
More throughput
Less throughput
11 PM PST
Provisioned request / sec
Time
12000000
10000000
8000000
6000000
4000000
2000000
Nov 2016 Dec 2016
Black Friday
Hourly throughput (request/sec)

9. Enterprise-grade SLAs
99.99% availability
Made possible with highly-redundant storage architecture
Low-latency, consistency, and throughput also covered by
financially backed SLAs
Durability – majority quorum committed, synchronous and
indexed writes
SLA

10. Well defined consistency models
Global distribution forces us to navigate the CAP theorem
Writing correct distributed applications is hard
Five well-defined consistency levels
Intuitive and practical with clear PACELC tradeoffs
Can be overridden on a per-request basis

11. Multi-model, multi-API
First party and popular third-party OSS APIs
Key-value, Document, Columnar, and Graph
DocumentDB (SQL and JavaScript), MongoDB, Table, and
Gremlin
Supported across number of programming languages
More APIs to be added

12. No schema or index migrations
Highly write-optimized database engine
Works across every data models
Automatic and synchronous indexing
Hash, range, and geospatial
Fully resource governed
Online and in-situ index transformations
Physical index
Schema

13. Compete and TCO
Industry’s first globally distributed multi-model
database service
Significantly cheaper than DynamoDB, Cassandra, Cloud
Spanner and MongoDB
Multi-tenant with throughput isolation. No dev/ops
expenses needed
Cost
$
$
10x
3x
Cosmos DB DynamoDB On-premises
MongoDB/
Cassandra

14. Recent updates & Roadmap

15. Table API (preview)
Premium experience for Azure Table Storage
Secondary indexes, dedicated throughput, low latency,
global distribution for Table storage customers
Backwards compatible with existing Table SDKs
Roadmap: update for standard Tables, optimized for
storage
Roadmap: seamless migration to Cosmos DB

16. Graph API (preview)
Social networks, recommendations, logistics, and IoT
Globally distributed, horizontally scalable graphs
Supports Apache TinkerPop Gremlin
Models real world entities and relationships
OSS friendly

17. Spark Connector for Cosmos DB
RDD and Dataset-based connectors available
Native integration with Spark SQL
Direct mapping to Cosmos DB partitions
Natively leverage Cosmos DB index
Predicate pushdown
GA in H2 CY2017

18. Security & Compliance
Always encrypted at rest and in motion
Highly scalable “row level” authorization
IP firewall rules
ISO, EUMC, HIPAA, PCI - certified
SOC1, SOC2, FedRAMP, IRS 1075, UK Official – audit
complete, Q2 2017

19. Who uses Cosmos DB?

20. Key benefits
• Cosmos DB supports fast ingestion
of message data from 1:1
communication, group chats
• Cosmos DB enables real-time query
over message and group
conversations, with custom filters on
when user enters/leaves thread
Business need
• Provide search capabilities over
TBs-PBs of Skype and Teams
conversations
• Fast ingestion with multiple
writes, overlay group
memberships
• Secure & compliant data storage
with high privacy requirements
Azure Cosmos DB
Azure Cosmos DB
Azure Cosmos DB
USERS
GROUPS
MESSAGES
Skype Ingestion
service
Skype Query
service
44TB
Message data
Skype powers 1M searches per
second over conversation data
6TB
User data
1TB
Group data

21. Key benefits
• Cosmos DB can scale elastically
without operational overhead of
MongoDB
• Perform fast queries over events to
deliver recommended services,
safety notices to vehicles
• Perform staged migration via
MongoDB APIs
Business need
• Need to ingest massive
volumes of diagnostic data
from vehicles and take real-
time actions as part of
connected car platform
• Management and operations
of database infrastructure to
handle exponential growth of
data
8TB
Vehicle Telemetry
250K
Lexus Cars
Toyota drives connected car push
forward with Azure Cosmos DB
Azure Cosmos DBAzure HDInsight
Storm
Azure Storage
(archival)

22. Business need
• Process Ms of retail transactions
per second in milliseconds in
inventory pipeline during peak
(“Black Friday”)
• Fast development cycles and
loosely coupled micro-services to
keep up with a competitive
marketplace
Key benefits
• Cosmos DB provides elastic
scalability from 1-10M requests per
second
• Improved reliability, and faster order
processing times than previous OSS
solution
• Reduced development time and
operational overhead
Azure Cosmos DB
Azure Service
Fabric
Pricing Service
Azure Cosmos DB
Azure Service
Fabric
Inventory Service
E-commerce challenger eyes the top spot,
runs on Azure Cosmos DB
12TB
Provisioned
10M
Peak RPS
64
Databases

23. Business need
• Handle millions of players on
Day 1 due to popularity of the
TV series
• Match-making of players for
competitive and lag-free
experience
• Provide new content weekly,
and iterate on social
functionality
•
Key benefits
• Cosmos DB provides elastic
scalability for millions of users and
flexible schema to support social
features and gameplay
• Global distribution allows for low
latency for players spread
worldwide
• Automatic indexing used to build
real-time leaderboards
Performance at massive scale allows
millions to play mobile game
Azure Traffic
Manager
Azure API Aps
(game backend)
Azure CDN
Azure Cosmos DB
Azure Functions
Azure Notification
Hubs (push
notifications)
Azure Storage
(game files)
1M
Peak Active
#1
iOS App Store
1B
Daily Queries

24. Cosmos DB: In Summary

25. Global distribution Elastic scale out Guaranteed low latency Comprehensive SLAs
Azure Cosmos DB
Key-Value Column-family GraphDocuments
A globally-distributed, multi-model database service
… more coming soon
Five consistency models
SQL

26. Thank you!
Status updates – Follow us @AzureCosmosDB or #CosmosDB
Support – Email AskCosmosDB@Microsoft.com

27. Demos
Elastic Scale | Global Replication | Spark to Cosmos DB + Graph

28. Elastic Scale
Code: https://aka.ms/docdb-benchmark

29. Configured
@10,100 RUs
~940 writes / s
~9800 RUs

30. Configured
@250,000 RUs
~12,100 writes / s
~128,800 RUs
VM @ 99% CPU

31. Global
Replication
Code: https://aka.ms/docdb-latency-script-nodejs

35. Apache Spark to
Azure Cosmos DB

36. Advantages: Blazing Fast IoT Scenarios
Flight
information
global safety
alerts
weather
Device
Notifications
Web / REST API

37. Advantages: Updateable Columns
Flight
information
Device
Notifications
Web / REST API
{
tripid: “100100”,
delay: -5,
time: “01:00:01”
}
{
tripid: “100100”,
delay: -30,
time: “01:00:01”
}
{delay:-30}
{delay:-30}
{delay:-30}

38. Advantages: Pushdown Predicate Filtering
{city:SEA}
locations headquarter exports
0 1
country
Germany
city
Seattle
country
France
city
Paris
city
Moscow
city
Athens
Belgium 0 1
{city:SEA, dst: POR, ...},
{city:SEA, dst: JFK, ...},
{city:SEA, dst: SFO, ...},
{city:SEA, dst: YVR, ...},
{city:SEA, dst: YUL, ...},
...

39. Connecting via the Gateway

40. Connecting via the Data Nodes

41. Multi-model: Graph and Spark Together

42. Bed Sensors | OR Scheduling | Precision Medicine
Healthcare Scenarios

43. Bed Sensors Scenario
Classic Internet of Things (IoT) Scenario – large
stream of data from disparate sources (e.g.
connected car scenario).
Cosmos DB is well suited as it is a write-optimized
database engine with SLAs for latency, consistency,
throughput, and availability
Build models to interact with data at change feed or
build Spark Streaming continuous application in front
Models able to decipher what information is important
and when to alert

44. Bed Sensor Scenario
respiratory rate,
bed movement,
heart rate, etc.
Use Spark Streaming to filter
and extract important data
Store FHIR / json data
Change feed contains all
the changes so
downstream alerting and
ML algorithms can quickly
act on patient vitals
Actions: e.g. turn
patient to prevent
pressure ulcers due
to lack of body
movement for x
hours.
Patient Care
Cosmos DB
Data Sciences

45. Operating Room Scheduling
Efficiency dependent on staffing and allocation, not
workload (cases)
Lack of OR availability means loss of patients
Better utilization = better profit margins, reduced
support, and maintenance costs
Better medical staff efficiencies = better outcomes
Shorter waiting times and less cancellations

46. Image Source: Anesthesia & Analgesia,
December 2006, Volume 103, Issue 6
“You are not going to get the elephant to shrink or change its size. You
need to face the fact that the elephant is 8 OR tall and 11hr wide”
Steven Shafer, MD

47. Events | Alerts Cosmos DB Change Feed
ML algorithms to predict
scheduling available
Auto-rescheduling based
on triggers or ML algo
Notify / alert for resources
Actions via ML and Triggers
Cosmos DB: Change Feed, Data Sciences

48. Precision Medicine: FHIR + Genomics
HL7 FHIR is JSON data, well suited for Cosmos DB
Notifying patients of HL7 FHIR health care record
changes using Logic Apps and Azure Cosmos DB
Combine FHIR data with genomics with Data
Sciences (Machine Learning, Deep Learning, AI, etc.)
Developer Personalized Medicine solutions
+

49. EHR
Lab Data
Treatments
Responses
VCF
BAM / SAM
FASTQ
Reference Source: Ember Architecture