introduction to azure documentdb
TRANSCRIPT
Introduction to Azure DocumentDB
Denny Lee,Principal Program Manager, Azure DocumentDB
Denny Lee• Principal Program Manager for Azure DocumentDB• 20+ years of experience in databases, distributed
systems, data sciences, and software development at Microsoft, Concur, and Databricks
• Noteable Projects:• Project Isotope: Incubation team for HDInsight• Yahoo! 24TB cube: Largest SSAS cube in production
@dennylee
A Brief Overview...
{ "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
Documentsschema-agnostic JSON store
for
hierarchical and de-normalized data at scale
Not these documents
{ "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
Documentsschema-agnostic JSON store
for
hierarchical and de-normalized data at scale
Elastically Scalable Throughput + Storage
Guaranteed low latency
Reads <10ms @ P99Writes <15ms @ P99
Globally Distributed
Speaks your language
DocumentDB Query Playground
Demo
Code: https://www.documentdb.com/sql/demo
A Primer on Scale...
The 4 Vs of Big DataExceeds physical limits of vertical scalabilityVolume
Many different formats making integration expensiveVariety
Small decision window compared to data change rateVelocity
Many options or variables confounding analysisVariability
The 4 Vs of Big DataVolume Variety Velocity Variability
Mobile Apps Retail Learning Telematics IoT Gaming
Let’s talk about scale.
Volume and Velocity
Ability to Scale from Day 1• Bursty • Unpredictable traffic
Gaming + Social Experience• Lag-free• Responsive experiences
Move fast without breaking things• Iterative development needs
More users, more problems
• Game scores, guilds and social membership
• Leaderboards by country and social• Guild management and messaging• #1 in Apple app store for free apps
<10ms
99P query latency
>1M game
downloads
~1B requests / day
The Walking Dead, results
Caches• Scores are continuously
updated• Write heavy without
locality
RDBMS• Scale-out requires partitioning• Schema and index
management
Other NoSQL Stores• Longer tail on latencies• Need to specify secondary
indexes for lookups
The right tool for the job ?
Fully managed NoSQL databaseHorizontal scaling for TB and RPSHigh performance, write optimizedSchema agnostic indexing
+Azure DocumentDB
The answer for low latency @ massive scale
Fact: Managing shards is really painful.
Managing shards or partitions
Good news: DocumentDB has done all the heavy lifting.
Elastic scale
Measuring Throughput (Request Units)
Replica gets a fixed budget of request units
Request Unit/sec (RU) is the normalized currency
% IOPS
% CPU
% Memory
READGET Document
Documents
INSERTPOST
REPLACEPUT Document
Operations consume request units (RUs)
QueryPOST Documents
…
Min RU/sec
Max RU/sec
Inco
min
g Re
ques
ts
Replica Quiescent
Ratelimit
Nothrottling
Requests get rate limited if they exceed the SLA Customers pay for reserved
request units by the hour
Configured @10,100 RUs
~940 writes / s~9800 RUs
Configured @250,000 RUs
~12,100 writes / s~128,800 RUsVM @ 99% CPU
A Global Distribution Primer…
Globally Distributed
Azure DocumentDB gives you the ability circumvent the speed of light!
High Availability and Disaster Recovery
Replicate to any Number of regions
Global low latency access
Dynamically configure write and read regions
… with well-defined consistency models!
Consistency Level Strong Bounded Stateless 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 (within region and across regions outside of the staleness window)
Yes (for the given session)
No
Monotonic Writes Yes Yes Yes Yes
Read your writes Yes Yes (in the write region) Yes No
stronger consistency
faster performance
Global Distribution
Demo
Code: https://aka.ms/docdb-latency-script-nodejs
Common Scenarios
Common scenarios
Retail Gaming IoT Social
Product Catalog
Recommendations
Personalization
User Store
Recommendations
Personalization
Event Store
Device Registry
Telemetry Store
User Behavior
Telemetry
Personalization
Common scenarios
IoT
Event Store
Device Registry
Telemetry Store
IoT / Sensor Data Challenges:
• Hardware is relatively hard to update• Different generation of devices
=> different schemas (variety)• Many sensors emitting telemetry
=> high rate of ingestion (volume + variety)
Top 5 Automotive Manufacture in the World
Telematics services include:• Safety service• Diagnostic service• Remote service
Ingest and query 100+ TB of semi-structure data
IoT : Vehicle Telematics
IoT : Vehicle Telematics
Ingress API
Inbound Interface(Web API)
Raw Event Store (HOT)(DocumentDB)
Aggregated Event Store (Warm)(DocumentDB)
Aggregated Event Store (Cold)(Blob Storage)
Outbound Interface(Web API)
Message Queue(Event Hubs)
Stream Processor(Stream Analytics)
Common scenarios
Social + AdTech Challenges:
• Ingest + Analyze Third Party Data => Who dictates schema? (variety)=> How do you index?
• A lot of social and user data=> high rate of ingestion (volume +
variety)
Social
User Behavior
Telemetry
Personalization
• Startup - Advanced Marketing Intelligence Platform
• Utilizes deep learning to analyze billions of relational network connections to build a social fingerprint for each user
• Extracts knowledge and cultural insights by analyzing what people choose to follow
Social Analytics + Ad Technology
>1BSocial Media
Profiles
>50M
Tweets per Day
• Store tweets, geo-location data, and ML results in DocumentDB
• Data from each social media producer has its own schema that evolves independently
• Need to iterate rapidly… no time for managing VMs
Social Analytics + Ad Technology
>1BSocial Media
Profiles
>50M
Tweets per Day
Before moving to DocumentDB, my developers would need to come to me to confirm that our Elasticsearch deployment would support their data or if I would need to scale things to handle it. DocumentDB removed me as a bottleneck, which has been great for me and them.
Stephen Hankinson, CTO, Affinio
Quote
Geospatial Supportincluding polygons
Demo
Want to try? Go to DocumentDB Query Playgroundhttps://www.documentdb.com/sql/demo
Polygon Query Examplehttps://www.keene.edu/campus/maps/tool/
Polygon of coordinates-124.630000, 48.360000-123.870000, 46.140000-122.230000, 45.540000-119.170000, 45.950000-116.920000, 45.960000-116.990000, 49.000000-123.050000, 49.020000-123.150000, 48.310000-124.630000, 48.360000
Finding Volcanos with DocumentDB
https://www.documentdb.com/sql/demo
Data Sciences:Apache Spark + DocumentDB
Example: Graph Structures
Example: Graph Structures
Classic Graph Scenario: Flights
vertex = airports
edges = flights
Data Sciences:Apache Spark + DocumentDB
Demo
Notebook View: https://aka.ms/docdb-spark-graphpyView: https://aka.ms/pydocdb-spark-graphCode: https://aka.ms/docdb-spark-graph-code
Graph Calculations: Degrees, PageRank
What is the most important airport (most flights in / out)
tripGraph.inDegrees\
.sort(desc("inDegree"))\
.limit(10))
AdvantagesData Science Scenarios
• Blazing Fast IoT Scenarios
• Updateable columns
• Push-down predicate filtering
AdvantagesBlazing Fast IoT Scenarios
Flight information
global safetyalerts
weather
Data Science Scenarios
Device Notifications
Web / REST API
AdvantagesUpdateable Columns
Flight information
Data Science Scenarios
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}
AdvantagesPushdown Predicate Filtering Data Science Scenarios
{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, ...}, ...
References Get direct access to the engineering team -> [email protected]
Resources• Schema Agnostic Indexing with DocumentDB, VLDB 2015• Consistency Levels in DocumentDB• SQL Queries with DocumentDB• Language Integrated JavaScript queries and transactions with
DocumentDB• Distribute your data globally with DocumentDB
More Resources
AskDocDB@microsoft
Follow @DocumentDBUse #DocumentDB
documentdb.com
#azure-documentDB