SlideShare a Scribd company logo
1 of 66
Download to read offline
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Advanced Design Patterns for
DynamoDB
Rick Houlihan
Principal Technologist, NoSQL
AWS
D A T 4 0 1
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Agenda
• Brief History of Data Processing (Why NoSQL?)
• Overview of DynamoDB
• NoSQL Data Modeling
• Normalized versus De-normalized schema
• Common NoSQL Design Patterns
• Composite Keys, Hierarchical Data, Relational Data
• Modeling Real Applications
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
History of Data Processing
“History repeats itself because nobody was
listening the first time”
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Timeline of Database TechnologyDataPressure
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Technology Adoption and the Hype Curve
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Why NoSQL?
Optimized for storage Optimized for compute
Normalized/relational Denormalized/hierarchical
Ad hoc queries Instantiated views
Scale vertically Scale horizontally
Good for OLAP Built for OLTP at scale
SQL NoSQL
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Amazon DynamoDB
Document or Key-Value Scales to Any WorkloadFully Managed NoSQL
Access Control Event Driven ProgrammingFast and Consistent
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Table
Table
Items
Attributes
Partition
Key
Sort
Key
Mandatory
Key-value access pattern
Determines data distribution
Optional
Model 1:N relationships
Enables rich query capabilities
All items for key
==, <, >, >=, <=
“begins with”
“between”
“contains”
“in”
sorted results
counts
top/bottom N values
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
00 55 A954 FFAA00 FF
Partition Keys
Partition Key uniquely identifies an item
Partition Key is used for building an unordered hash index
Allows table to be partitioned for scale
Id = 1
Name = Jim
Hash (1) = 7B
Id = 2
Name = Andy
Dept = Eng
Hash (2) = 48
Id = 3
Name = Kim
Dept = Ops
Hash (3) = CD
Key Space
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Partition 3
Partition:Sort Key
Partition:Sort Key uses two attributes together to uniquely identify an Item
Within unordered hash index, data is arranged by the sort key
No limit on the number of items (∞) per partition key
Except if you have local secondary indexes
00:0 FF:∞
Hash (2) = 48
Customer# = 2
Order# = 10
Item = Pen
Customer# = 2
Order# = 11
Item = Shoes
Customer# = 1
Order# = 10
Item = Toy
Customer# = 1
Order# = 11
Item = Boots
Hash (1) = 7B
Customer# = 3
Order# = 10
Item = Book
Customer# = 3
Order# = 11
Item = Paper
Hash (3) = CD
55 A9:∞54:∞ AAPartition 1 Partition 2
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Partitions are three-way replicated
Id = 2
Name = Andy
Dept = Engg
Id = 3
Name = Kim
Dept = Ops
Id = 1
Name = Jim
Id = 2
Name = Andy
Dept = Engg
Id = 3
Name = Kim
Dept = Ops
Id = 1
Name = Jim
Id = 2
Name = Andy
Dept = Engg
Id = 3
Name = Kim
Dept = Ops
Id = 1
Name = Jim
Replica 1
Replica 2
Replica 3
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Local Secondary Index (LSI)
Alternate sort key attribute
Index is local to a partition key
A1
(partition)
A3
(sort)
A2
(item key)
A1
(partition)
A2
(sort)
A3 A4 A5
LSIs A1
(partition)
A4
(sort)
A2
(item key)
A3
(projected)
Table
KEYS_ONLY
INCLUDE A3
A1
(partition)
A5
(sort)
A2
(item key)
A3
(projected)
A4
(projected)
ALL
10 GB max per partition key, i.e.
LSIs limit the # of range keys!
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Global Secondary Index (GSI)
Alternate partition and/or sort key
Index is across all partition keys
Use composite sort keys for compound indexes
A1
(partition)
A2 A3 A4 A5
A5
(partition)
A4
(sort)
A1
(item key)
A3
(projected)
INCLUDE A3
A4
(partition)
A5
(sort)
A1
(item key)
A2
(projected)
A3
(projected)
ALL
A2
(partition)
A1
(itemkey)
KEYS_ONLY
GSIs
Table
RCUs/WCUs provisioned
separately for GSIs
Online indexing
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
How Do GSI Updates Work?
Table
Primary
table
Primary
table
Primary
table
Primary
table
Global
Secondary
Index
Client
2. Asynchronous
update (in progress)
If GSIs don’t have enough write capacity, table writes will be throttled!
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Scaling NoSQL
“We are stuck with technology when what we
really want is just stuff that works.”
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
What bad NoSQL looks like …
Partition
Time
Heat
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Getting the most out of Amazon DynamoDB throughput
“To get the most out of DynamoDB throughput, create tables where the
partition key element has a large number of distinct values, and values
are requested fairly uniformly, as randomly as possible.”
—DynamoDB Developer Guide
Space: access is evenly spread over the key-space
Time: requests arrive evenly spaced in time
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Much better picture …
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Auto Scaling
Throughput automatically adapts to your actual traffic
With Auto ScalingWithout Auto Scaling
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
NoSQL Data Modeling
“A ship in port is safe, but that’s not what ships
were built for.”
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
It’s all about relationships…
Document management Process controlSocial network
Data treesIT monitoring
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
SQL vs. NoSQL design pattern
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Selecting a Partition Key
• Large number of distinct
values
• Items are uniformly requested
and randomly distributed
Selecting a Sort Key
• Model 1:n and n:n relationships
• Efficient/selective patterns
• Query multiple entities
• Leverage range queries
AMAZON DYNAMODB - KEY CONCEPTS
Examples:
• Bad: Status, Gender
• Good: CustomerId, DeviceId
Examples:
• Orders and OrderItems
• Hierarchical relationships
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
• Understand the use case
• Identify the access patterns
• Read/Write workloads
• Query dimensions and
aggregations
• Data-modeling
• Avoid relational design
patterns, use one table
• Review -> Repeat -> Review
TENETS OF NoSQL DATA MODELING
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
• Understand the use case
• Identify the access patterns
• Read/Write workloads
• Query dimensions and
aggregations
• Data-modeling
• Avoid relational design
patterns, use one table
• Review -> Repeat -> Review
• Nature of the application
• OLTP / OLAP / DSS
• Define the Entity-Relationship Model
• Identify Data Life Cycle
• TTL, Backup/Archival, etc.
TENETS OF NoSQL DATA MODELING
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
• Understand the use case
• Define the access patterns
• Read/Write workloads
• Data-modeling
• Avoid relational design
patterns, use one table
• Review -> Repeat -> Review
• Identify data sources
• Define query aggregations
• Document all workflows
TENETS OF NoSQL DATA MODELING
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
• Understand the use case
• Identify the access patterns
• Read/Write workloads
• Query dimensions and
aggregations
• Data-modeling
• Avoid relational design
patterns, use one table
• Review -> Repeat -> Review
TENETS OF NoSQL DATA MODELING
• 1 application service = 1 table
• Reduce round trips
• Simplify access patterns
• Identify Primary Keys
• How will items be inserted and
read?
• Overload items into partitions
• Define indexes for secondary access
patterns
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
• Understand the use case
• Identify the access patterns
• Read/Write workloads
• Query dimensions and
aggregations
• Data-modeling
• Avoid relational design
patterns, use one table
• Review -> Repeat-> Review
TENETS OF NoSQL DATA MODELING
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Complex Queries
“Computers are useless. They can only give
you answers.”
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
DynamoDB Streams and AWS Lambda
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Triggers
Lambda function
Notify change
Item/table level metrics
Amazon CloudSearch
Kinesis Firehose
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Composite Keys
“Hierarchies are celestial. In hell all are equal.”
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Secondary index
Opponent Date GameId Status Host
Alice 2014-10-02 d9bl3 DONE David
Carol 2014-10-08 o2pnb IN_PROGRESS Bob
Bob 2014-09-30 72f49 PENDING Alice
Bob 2014-10-03 b932s PENDING Carol
Bob 2014-10-03 ef9ca IN_PROGRESS David
BobPartition key Sort key
Multi-value Sorts and Filters
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Secondary Index
Approach 1: Query Filter
Bob
Opponent Date GameId Status Host
Alice 2014-10-02 d9bl3 DONE David
Carol 2014-10-08 o2pnb IN_PROGRESS Bob
Bob 2014-09-30 72f49 PENDING Alice
Bob 2014-10-03 b932s PENDING Carol
Bob 2014-10-03 ef9ca IN_PROGRESS David
SELECT * FROM Game
WHERE Opponent='Bob'
ORDER BY Date DESC
FILTER ON Status='PENDING'
(filtered out)
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Approach 2: Composite Key
StatusDate
DONE_2014-10-02
IN_PROGRESS_2014-10-08
IN_PROGRESS_2014-10-03
PENDING_2014-09-30
PENDING_2014-10-03
Status
DONE
IN_PROGRESS
IN_PROGRESS
PENDING
PENDING
Date
2014-10-02
2014-10-08
2014-10-03
2014-10-03
2014-09-30
+ =
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Secondary Index
Approach 2: Composite Key
Opponent StatusDate GameId Host
Alice DONE_2014-10-02 d9bl3 David
Carol IN_PROGRESS_2014-10-08 o2pnb Bob
Bob IN_PROGRESS_2014-10-03 ef9ca David
Bob PENDING_2014-09-30 72f49 Alice
Bob PENDING_2014-10-03 b932s Carol
Partition key Sort key
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Opponent StatusDate GameId Host
Alice DONE_2014-10-02 d9bl3 David
Carol IN_PROGRESS_2014-10-08 o2pnb Bob
Bob IN_PROGRESS_2014-10-03 ef9ca David
Bob PENDING_2014-09-30 72f49 Alice
Bob PENDING_2014-10-03 b932s Carol
Secondary index
Approach 2: Composite Key
Bob
SELECT * FROM Game
WHERE Opponent='Bob'
AND StatusDate BEGINS_WITH 'PENDING'
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Advanced Data Modeling
“The great myth of our times is that technology
is communication.”
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
How OLTP Apps Use Data
 Mostly hierarchical structures
 Entity driven workflows
 Data spread across tables
 Requires complex queries
 Primary driver for ACID
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
ItemID
(PK)
Version
(SK)
CurVer Attrs
1
v0 2 …
v1 …
v2 …
v3 …
Maintaining Version History
(Many more item partitions)
Item versions
Overwrite v0 Item to
Commit changes
COPY Item.v0 -> Item1.v3 IF Item.v3 == NULL
UPDATE Item1.v3 SET Attr1 += 1
UPDATE Item1.v3 SET Attr2 = …
UPDATE Item1.v3 SET Attr3 = …
COPY Item1.v3 -> Item1.v0 SET CurVer = 3
Transaction
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Managing Relational Transactions
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
DynamoDB Transactions API
• TransactWriteItems
• Synchronous update, put, delete, and check
• Atomic
• Automated Rollbacks
• Up to 10 items within a transaction
• Supports multiple tables
• Complex conditional checks
• Good Use Cases
• Commit changes across items
• Conditional batch inserts/updates
• Bad Use Case
• Maintaining normalized data
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
DynamoDB Table Schema
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Reverse Lookup GSI
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Hierarchical Data
Joins? We don’t need no stinkin’ joins!
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Hierarchical Data Structures as Items
• Use composite sort key to define a hierarchy
• Highly selective queries with sort conditions
• Reduce query complexity
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Modeling Relational Data
Dude, where’s my Lookup Table?
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Modeling a Delivery Service – GetMeThat!
The Business Model:
Customers want things, we get them things.
How it works:
People are busy, they need stuff. GetMeThat! lets users create lists of stuff
they need and have it dropped where and when they need it.
• Download GetMeThat!
• Browse Stuff
• Get stuff
• Tell us where to put your stuff
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
The Entity Model
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
The Access Patterns
• Get Order(s)
• by date range
• by Customer
• by Vendor
• Get Order Details
• Status
• Items
• Get Deliveries
• by date range
• by Driver
• Get available drivers
• by Geo and Time
• Get Customer Data
• Get Vendor Data
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
The Relational Approach
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
The NoSQL Approach
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
The NoSQL Approach (Orders and Drivers GSI)
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
The NoSQL Approach (Vendor and Deliveries GSI)
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
A Real World Example
“Reality is that which, when you stop believing
in it, does not go away.”
Audible eBook Sync Service
• Allows users to save session
state for Audible eBooks
• Maintains mappings per user
for eBooks and audio products
• Spikey load patterns require
significant overprovisioning
• Large number of access
patterns
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Access Patterns
# USE CASE
1 CompanionMapping getCompanionMappingsByAsin
2 CompanionMapping getCompanionMappingsByEbookAndAudiobookContentId
3 CompanionMapping getCompanionMappingsFromCache
4 CompanionMapping getCompanionMappings
5 CompanionMapping getCompanionMappingsAvailable
6 AcrInfo getACRInfo
7 AcrInfo getACRs
8 AcrInfo getACRInfos
9 AcrInfo getACRInfosbySKU
10 AudioProduct getAudioProductsForACRs
11 AudioProduct getAudioProducts
12 AudioProduct deleteAudioProductsMatchingSkuVersions
13 AudioProduct getChildAudioProductsForSKU
14 Product getProductInfoByAsins
15 Product getParentChildDataByParentAsins
16 AudioFile getAudioFilesForACR
17 AudioFile getAudioFilesForChildACR
18 AudioFile getAudioFilesByParentAsinVersionFormat
19 AudioFile getAudioFiles
20 AudioFile getAudioFilesForChildAsin
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Primary Table
T
A
B
L
E
Primary Key
Attributes
PK SK (GSI 3)
ABOOKACR1
v0#ABOOKACR1
GSI-1 GSI-2
ABOOK-ASIN1 ABOOK-SKU1
EBOOKACR1
GSI-1 GSI-2
SyncFileAcr ABOOK-ASIN1
ABOOKACR1#TRACK#1
GSI-1 GSI-2
ABOOK-ASIN1 ABOOK-SKU1
ABOOKACR1#TRACK#2
GSI-1 GSI-2
ABOOK-ASIN1 ABOOK-SKU1
EBOOKACR1 EBOOKACR1
GSI-1 EBookAsin
EBOOK-SKU1 ASIN
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Indexes
G
S
I
1
Partition Key Projected Attributes
ABOOK-ASIN1 ABOOKACR1
ABOOKACR1-v1
ABOOKACR1#TRACK#1
ABOOKACR1#TRACK#2
SyncFileAcr ABOOKACR1 MAP-EBOOKACR1
EBOOK-SKU1 ABOOKACR1 EBOOKACR1
G
S
I
2
Partition Key Projected Attributes
ABOOK-ASIN1 ABOOKACR1 MAP-EBOOKACR1
ABOOK-SKU1 ABOOKACR1
ABOOKACR1-v1
ABOOKACR1#TRACK#1
ABOOKACR1#TRACK#2
G
S
I
3
GSI Partition Key Projected Attributes
V0#ABOOKACR1 ABOOKACR1 ABOOKACR1-v1
EBOOKACR1 ABOOKACR1 MAP-EBOOKACR1
ABOOKACR1#TRACK#1 ABOOKACR1 ABOOKACR1#TRACK#1
ABOOKACR1#TRACK#2 ABOOKACR1 ABOOKACR1#TRACK#2
EBOOKACR1 ABOOKACR1 EBOOKACR1
Query Conditions
# USE CASE Lookup parameters INDEX Key Conditions Filter Conditions
1 CompanionMapping getCompanionMappingsByAsin audiobookAsin/ebookSku GSI2 GSI-2=ABOOK-ASIN1 None
2 CompanionMapping
getCompanionMappingsByEbookAndA
udiobookContentId
ebookAcr/sku,version,format or
audiobookAcr/asin,version,format
GSI-3 on TargetACR attribute OR
PrimaryKey on Table
GSI-3=MAP-EBOOKACR1 version=v and format=f
3 CompanionMapping getCompanionMappingsFromCache
ebookAcr/sku,version,format or
audiobookAcr/asin,version,format
GSI-3 on TargetACR attribute OR
PrimaryKey on Table
GSI-3=MAP-EBOOKACR1 version=v and format=f
4 CompanionMapping getCompanionMappings
syncfileAcr, ebookAcr?,
audiobookAcr?
GSI1 GSI-1=SyncFileAcr None
5 CompanionMapping getCompanionMappingsAvailable ebookAcr, audiobookAcr Primary Key on Table
Acr=ABOOKACR1 and
TargetACR beginswith "MAP-"
6 AcrInfo getACRInfo acr Primary Key on Table
Acr=ABOOKACR1 and
TargetACR beginswith "ABOOKACR1-
v"
7 AcrInfo getACRs acr / asin,version,format Primary Key on Table Acr=ABOOKACR1 version=v and format=f
8 AcrInfo getACRInfos acr Primary Key on table
Acr=ABOOKACR1 and
TargetACR beginswith "ABOOKACR1"
9 AcrInfo getACRInfosbySKU sku GSI2 GSI-2=ABOOK-SKU1
10 AudioProduct getAudioProductsForACRs acr Primary Key on table
Acr=ABOOKACR1 and TargetACR
beginswith "ABOOKACR1"
11 AudioProduct getAudioProducts sku, version, format GSI2 GSI-2=ABOOK-SKU1 version=v and format=f
12 AudioProduct
deleteAudioProductsMatchingSkuVersi
ons
sku, version GSI2 GSI-2=ABOOK-SKU1 version=v
13 AudioProduct getChildAudioProductsForSKU sku GSI2 GSI-2=ABOOK-SKU1
14 Product getProductInfoByAsins asin GSI1 GSI-1=ABOOK-ASIN1
15 Product getParentChildDataByParentAsins asin GSI1 GSI-1=ABOOK-ASIN1
16 AudioFile getAudioFilesForACR acr Primary Key on table
Acr=ABOOKACR1 and
TargetACR beginswith "ABOOKACR1#"
17 AudioFile getAudioFilesForChildACR acr, parent_asin Primary Key on table Acr=ABOOKACR1 version=v and format=f
18 AudioFile
getAudioFilesByParentAsinVersionForm
at
parent_asin, version, format GSI1 GSI-1=ABOOK-ASIN1 version=v and format=f
19 AudioFile getAudioFiles sku, version, format GSI2 GSI-2=ABOOK-SKU1 version=v and format=f
20 AudioFile getAudioFilesForChildAsin asin, parent_asin, version, format GSI1 GSI-1=ABOOK-ASIN1 version=v and format=f
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
The Serverless Paradigm
“Fairly cheap home computing was what
changed my life.”
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Elastic Serverless Applications
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Conclusions
Thank you!
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.

More Related Content

What's hot

Amazon DynamoDB Under the Hood: How We Built a Hyper-Scale Database (DAT321) ...
Amazon DynamoDB Under the Hood: How We Built a Hyper-Scale Database (DAT321) ...Amazon DynamoDB Under the Hood: How We Built a Hyper-Scale Database (DAT321) ...
Amazon DynamoDB Under the Hood: How We Built a Hyper-Scale Database (DAT321) ...Amazon Web Services
 
Amazon Redshift의 이해와 활용 (김용우) - AWS DB Day
Amazon Redshift의 이해와 활용 (김용우) - AWS DB DayAmazon Redshift의 이해와 활용 (김용우) - AWS DB Day
Amazon Redshift의 이해와 활용 (김용우) - AWS DB DayAmazon Web Services Korea
 
Getting Strated with Amazon Dynamo DB (Jim Scharf) - AWS DB Day
Getting Strated with Amazon Dynamo DB (Jim Scharf) - AWS DB DayGetting Strated with Amazon Dynamo DB (Jim Scharf) - AWS DB Day
Getting Strated with Amazon Dynamo DB (Jim Scharf) - AWS DB DayAmazon Web Services Korea
 
(BDT313) Amazon DynamoDB For Big Data
(BDT313) Amazon DynamoDB For Big Data(BDT313) Amazon DynamoDB For Big Data
(BDT313) Amazon DynamoDB For Big DataAmazon Web Services
 
AWS 101 Webinar: Journey to the AWS Cloud - Introduction to Cloud Computing w...
AWS 101 Webinar: Journey to the AWS Cloud - Introduction to Cloud Computing w...AWS 101 Webinar: Journey to the AWS Cloud - Introduction to Cloud Computing w...
AWS 101 Webinar: Journey to the AWS Cloud - Introduction to Cloud Computing w...Amazon Web Services
 
Amazon Relational Database Service (Amazon RDS)
Amazon Relational Database Service (Amazon RDS)Amazon Relational Database Service (Amazon RDS)
Amazon Relational Database Service (Amazon RDS)Amazon Web Services
 
워크로드 특성에 따른 안전하고 효율적인 Data Lake 운영 방안
워크로드 특성에 따른 안전하고 효율적인 Data Lake 운영 방안워크로드 특성에 따른 안전하고 효율적인 Data Lake 운영 방안
워크로드 특성에 따른 안전하고 효율적인 Data Lake 운영 방안Amazon Web Services Korea
 
Aws glue를 통한 손쉬운 데이터 전처리 작업하기
Aws glue를 통한 손쉬운 데이터 전처리 작업하기Aws glue를 통한 손쉬운 데이터 전처리 작업하기
Aws glue를 통한 손쉬운 데이터 전처리 작업하기Amazon Web Services Korea
 
Amazon Aurora Storage Demystified: How It All Works (DAT363) - AWS re:Invent ...
Amazon Aurora Storage Demystified: How It All Works (DAT363) - AWS re:Invent ...Amazon Aurora Storage Demystified: How It All Works (DAT363) - AWS re:Invent ...
Amazon Aurora Storage Demystified: How It All Works (DAT363) - AWS re:Invent ...Amazon Web Services
 
Deep Dive - Amazon Elastic MapReduce (EMR)
Deep Dive - Amazon Elastic MapReduce (EMR)Deep Dive - Amazon Elastic MapReduce (EMR)
Deep Dive - Amazon Elastic MapReduce (EMR)Amazon Web Services
 
Amazon DocumentDB vs MongoDB 의 내부 아키텍쳐 와 장단점 비교
Amazon DocumentDB vs MongoDB 의 내부 아키텍쳐 와 장단점 비교Amazon DocumentDB vs MongoDB 의 내부 아키텍쳐 와 장단점 비교
Amazon DocumentDB vs MongoDB 의 내부 아키텍쳐 와 장단점 비교Amazon Web Services Korea
 
Amazon EMR과 SageMaker를 이용하여 데이터를 준비하고 머신러닝 모델 개발 하기
Amazon EMR과 SageMaker를 이용하여 데이터를 준비하고 머신러닝 모델 개발 하기Amazon EMR과 SageMaker를 이용하여 데이터를 준비하고 머신러닝 모델 개발 하기
Amazon EMR과 SageMaker를 이용하여 데이터를 준비하고 머신러닝 모델 개발 하기Amazon Web Services Korea
 
Introduction to AWS Storage Services
Introduction to AWS Storage ServicesIntroduction to AWS Storage Services
Introduction to AWS Storage ServicesAmazon Web Services
 
AWS Route53 Fundamentals
AWS Route53 FundamentalsAWS Route53 Fundamentals
AWS Route53 FundamentalsPiyush Agrawal
 
Dynamodb Presentation
Dynamodb PresentationDynamodb Presentation
Dynamodb Presentationadvaitdeo
 

What's hot (20)

Amazon DynamoDB Under the Hood: How We Built a Hyper-Scale Database (DAT321) ...
Amazon DynamoDB Under the Hood: How We Built a Hyper-Scale Database (DAT321) ...Amazon DynamoDB Under the Hood: How We Built a Hyper-Scale Database (DAT321) ...
Amazon DynamoDB Under the Hood: How We Built a Hyper-Scale Database (DAT321) ...
 
Amazon Redshift의 이해와 활용 (김용우) - AWS DB Day
Amazon Redshift의 이해와 활용 (김용우) - AWS DB DayAmazon Redshift의 이해와 활용 (김용우) - AWS DB Day
Amazon Redshift의 이해와 활용 (김용우) - AWS DB Day
 
AWS DynamoDB and Schema Design
AWS DynamoDB and Schema DesignAWS DynamoDB and Schema Design
AWS DynamoDB and Schema Design
 
Getting Strated with Amazon Dynamo DB (Jim Scharf) - AWS DB Day
Getting Strated with Amazon Dynamo DB (Jim Scharf) - AWS DB DayGetting Strated with Amazon Dynamo DB (Jim Scharf) - AWS DB Day
Getting Strated with Amazon Dynamo DB (Jim Scharf) - AWS DB Day
 
(BDT313) Amazon DynamoDB For Big Data
(BDT313) Amazon DynamoDB For Big Data(BDT313) Amazon DynamoDB For Big Data
(BDT313) Amazon DynamoDB For Big Data
 
AWS 101 Webinar: Journey to the AWS Cloud - Introduction to Cloud Computing w...
AWS 101 Webinar: Journey to the AWS Cloud - Introduction to Cloud Computing w...AWS 101 Webinar: Journey to the AWS Cloud - Introduction to Cloud Computing w...
AWS 101 Webinar: Journey to the AWS Cloud - Introduction to Cloud Computing w...
 
Amazon Relational Database Service (Amazon RDS)
Amazon Relational Database Service (Amazon RDS)Amazon Relational Database Service (Amazon RDS)
Amazon Relational Database Service (Amazon RDS)
 
워크로드 특성에 따른 안전하고 효율적인 Data Lake 운영 방안
워크로드 특성에 따른 안전하고 효율적인 Data Lake 운영 방안워크로드 특성에 따른 안전하고 효율적인 Data Lake 운영 방안
워크로드 특성에 따른 안전하고 효율적인 Data Lake 운영 방안
 
Aws glue를 통한 손쉬운 데이터 전처리 작업하기
Aws glue를 통한 손쉬운 데이터 전처리 작업하기Aws glue를 통한 손쉬운 데이터 전처리 작업하기
Aws glue를 통한 손쉬운 데이터 전처리 작업하기
 
Amazon Aurora Storage Demystified: How It All Works (DAT363) - AWS re:Invent ...
Amazon Aurora Storage Demystified: How It All Works (DAT363) - AWS re:Invent ...Amazon Aurora Storage Demystified: How It All Works (DAT363) - AWS re:Invent ...
Amazon Aurora Storage Demystified: How It All Works (DAT363) - AWS re:Invent ...
 
Deep Dive - Amazon Elastic MapReduce (EMR)
Deep Dive - Amazon Elastic MapReduce (EMR)Deep Dive - Amazon Elastic MapReduce (EMR)
Deep Dive - Amazon Elastic MapReduce (EMR)
 
AWS 101
AWS 101AWS 101
AWS 101
 
Amazon DocumentDB vs MongoDB 의 내부 아키텍쳐 와 장단점 비교
Amazon DocumentDB vs MongoDB 의 내부 아키텍쳐 와 장단점 비교Amazon DocumentDB vs MongoDB 의 내부 아키텍쳐 와 장단점 비교
Amazon DocumentDB vs MongoDB 의 내부 아키텍쳐 와 장단점 비교
 
Amazon EMR과 SageMaker를 이용하여 데이터를 준비하고 머신러닝 모델 개발 하기
Amazon EMR과 SageMaker를 이용하여 데이터를 준비하고 머신러닝 모델 개발 하기Amazon EMR과 SageMaker를 이용하여 데이터를 준비하고 머신러닝 모델 개발 하기
Amazon EMR과 SageMaker를 이용하여 데이터를 준비하고 머신러닝 모델 개발 하기
 
Introduction to AWS Storage Services
Introduction to AWS Storage ServicesIntroduction to AWS Storage Services
Introduction to AWS Storage Services
 
Aurora Deep Dive | AWS Floor28
Aurora Deep Dive | AWS Floor28Aurora Deep Dive | AWS Floor28
Aurora Deep Dive | AWS Floor28
 
Deep Dive on Amazon DynamoDB
Deep Dive on Amazon DynamoDBDeep Dive on Amazon DynamoDB
Deep Dive on Amazon DynamoDB
 
AWS Route53 Fundamentals
AWS Route53 FundamentalsAWS Route53 Fundamentals
AWS Route53 Fundamentals
 
ABD217_From Batch to Streaming
ABD217_From Batch to StreamingABD217_From Batch to Streaming
ABD217_From Batch to Streaming
 
Dynamodb Presentation
Dynamodb PresentationDynamodb Presentation
Dynamodb Presentation
 

Similar to Amazon DynamoDB Deep Dive Advanced Design Patterns for DynamoDB (DAT401) - AWS reInvent 2018.pdf

SRV307 Applying AWS Purpose-Built Database Strategy: Match Your Workload to ...
 SRV307 Applying AWS Purpose-Built Database Strategy: Match Your Workload to ... SRV307 Applying AWS Purpose-Built Database Strategy: Match Your Workload to ...
SRV307 Applying AWS Purpose-Built Database Strategy: Match Your Workload to ...Amazon Web Services
 
Applying AWS Purpose-Built Database Strategy - SRV307 - Toronto AWS Summit
Applying AWS Purpose-Built Database Strategy - SRV307 - Toronto AWS SummitApplying AWS Purpose-Built Database Strategy - SRV307 - Toronto AWS Summit
Applying AWS Purpose-Built Database Strategy - SRV307 - Toronto AWS SummitAmazon Web Services
 
Building with AWS Databases: Match Your Workload to the Right Database (DAT30...
Building with AWS Databases: Match Your Workload to the Right Database (DAT30...Building with AWS Databases: Match Your Workload to the Right Database (DAT30...
Building with AWS Databases: Match Your Workload to the Right Database (DAT30...Amazon Web Services
 
Applying AWS Purpose-Built Database Strategy - SRV307 - Anaheim AWS Summit
Applying AWS Purpose-Built Database Strategy - SRV307 - Anaheim AWS SummitApplying AWS Purpose-Built Database Strategy - SRV307 - Anaheim AWS Summit
Applying AWS Purpose-Built Database Strategy - SRV307 - Anaheim AWS SummitAmazon Web Services
 
Non-Relational Revolution: Database Week SF
Non-Relational Revolution: Database Week SFNon-Relational Revolution: Database Week SF
Non-Relational Revolution: Database Week SFAmazon Web Services
 
Workshop on Advanced Design Patterns for Amazon DynamoDB - DAT405 - re:Invent...
Workshop on Advanced Design Patterns for Amazon DynamoDB - DAT405 - re:Invent...Workshop on Advanced Design Patterns for Amazon DynamoDB - DAT405 - re:Invent...
Workshop on Advanced Design Patterns for Amazon DynamoDB - DAT405 - re:Invent...Amazon Web Services
 
DynamoDB and Dax - Miguel Cervantes
DynamoDB and Dax - Miguel CervantesDynamoDB and Dax - Miguel Cervantes
DynamoDB and Dax - Miguel CervantesAmazon Web Services
 
Implementing advanced design patterns for Amazon DynamoDB - ADB401 - Chicago ...
Implementing advanced design patterns for Amazon DynamoDB - ADB401 - Chicago ...Implementing advanced design patterns for Amazon DynamoDB - ADB401 - Chicago ...
Implementing advanced design patterns for Amazon DynamoDB - ADB401 - Chicago ...Amazon Web Services
 
Search Your DynamoDB Data with Amazon Elasticsearch Service (ANT302) - AWS re...
Search Your DynamoDB Data with Amazon Elasticsearch Service (ANT302) - AWS re...Search Your DynamoDB Data with Amazon Elasticsearch Service (ANT302) - AWS re...
Search Your DynamoDB Data with Amazon Elasticsearch Service (ANT302) - AWS re...Amazon Web Services
 
Data Transformation Patterns in AWS - AWS Online Tech Talks
Data Transformation Patterns in AWS - AWS Online Tech TalksData Transformation Patterns in AWS - AWS Online Tech Talks
Data Transformation Patterns in AWS - AWS Online Tech TalksAmazon Web Services
 
Choosing the Right Database for My Workload: Purpose-Built Databases
Choosing the Right Database for My Workload: Purpose-Built Databases Choosing the Right Database for My Workload: Purpose-Built Databases
Choosing the Right Database for My Workload: Purpose-Built Databases AWS Germany
 
Modernise your Data Warehouse with Amazon Redshift and Amazon Redshift Spectrum
Modernise your Data Warehouse with Amazon Redshift and Amazon Redshift SpectrumModernise your Data Warehouse with Amazon Redshift and Amazon Redshift Spectrum
Modernise your Data Warehouse with Amazon Redshift and Amazon Redshift SpectrumAmazon Web Services
 
AWS Data Lake: data analysis @ scale
AWS Data Lake: data analysis @ scaleAWS Data Lake: data analysis @ scale
AWS Data Lake: data analysis @ scaleAmazon Web Services
 
Workshop: Architecting a Serverless Data Lake
Workshop: Architecting a Serverless Data LakeWorkshop: Architecting a Serverless Data Lake
Workshop: Architecting a Serverless Data LakeAmazon Web Services
 

Similar to Amazon DynamoDB Deep Dive Advanced Design Patterns for DynamoDB (DAT401) - AWS reInvent 2018.pdf (20)

SRV307 Applying AWS Purpose-Built Database Strategy: Match Your Workload to ...
 SRV307 Applying AWS Purpose-Built Database Strategy: Match Your Workload to ... SRV307 Applying AWS Purpose-Built Database Strategy: Match Your Workload to ...
SRV307 Applying AWS Purpose-Built Database Strategy: Match Your Workload to ...
 
Applying AWS Purpose-Built Database Strategy - SRV307 - Toronto AWS Summit
Applying AWS Purpose-Built Database Strategy - SRV307 - Toronto AWS SummitApplying AWS Purpose-Built Database Strategy - SRV307 - Toronto AWS Summit
Applying AWS Purpose-Built Database Strategy - SRV307 - Toronto AWS Summit
 
Building with AWS Databases: Match Your Workload to the Right Database (DAT30...
Building with AWS Databases: Match Your Workload to the Right Database (DAT30...Building with AWS Databases: Match Your Workload to the Right Database (DAT30...
Building with AWS Databases: Match Your Workload to the Right Database (DAT30...
 
Applying AWS Purpose-Built Database Strategy - SRV307 - Anaheim AWS Summit
Applying AWS Purpose-Built Database Strategy - SRV307 - Anaheim AWS SummitApplying AWS Purpose-Built Database Strategy - SRV307 - Anaheim AWS Summit
Applying AWS Purpose-Built Database Strategy - SRV307 - Anaheim AWS Summit
 
Non-Relational Revolution
Non-Relational RevolutionNon-Relational Revolution
Non-Relational Revolution
 
Non-Relational Revolution: Database Week SF
Non-Relational Revolution: Database Week SFNon-Relational Revolution: Database Week SF
Non-Relational Revolution: Database Week SF
 
DynamoDB & DAX
DynamoDB & DAXDynamoDB & DAX
DynamoDB & DAX
 
Data Warehouses and Data Lakes
Data Warehouses and Data LakesData Warehouses and Data Lakes
Data Warehouses and Data Lakes
 
Workshop on Advanced Design Patterns for Amazon DynamoDB - DAT405 - re:Invent...
Workshop on Advanced Design Patterns for Amazon DynamoDB - DAT405 - re:Invent...Workshop on Advanced Design Patterns for Amazon DynamoDB - DAT405 - re:Invent...
Workshop on Advanced Design Patterns for Amazon DynamoDB - DAT405 - re:Invent...
 
DynamoDB and Dax - Miguel Cervantes
DynamoDB and Dax - Miguel CervantesDynamoDB and Dax - Miguel Cervantes
DynamoDB and Dax - Miguel Cervantes
 
Data Warehouses and Data Lakes
Data Warehouses and Data LakesData Warehouses and Data Lakes
Data Warehouses and Data Lakes
 
Implementing advanced design patterns for Amazon DynamoDB - ADB401 - Chicago ...
Implementing advanced design patterns for Amazon DynamoDB - ADB401 - Chicago ...Implementing advanced design patterns for Amazon DynamoDB - ADB401 - Chicago ...
Implementing advanced design patterns for Amazon DynamoDB - ADB401 - Chicago ...
 
Search Your DynamoDB Data with Amazon Elasticsearch Service (ANT302) - AWS re...
Search Your DynamoDB Data with Amazon Elasticsearch Service (ANT302) - AWS re...Search Your DynamoDB Data with Amazon Elasticsearch Service (ANT302) - AWS re...
Search Your DynamoDB Data with Amazon Elasticsearch Service (ANT302) - AWS re...
 
Data Transformation Patterns in AWS - AWS Online Tech Talks
Data Transformation Patterns in AWS - AWS Online Tech TalksData Transformation Patterns in AWS - AWS Online Tech Talks
Data Transformation Patterns in AWS - AWS Online Tech Talks
 
Choosing the Right Database for My Workload: Purpose-Built Databases
Choosing the Right Database for My Workload: Purpose-Built Databases Choosing the Right Database for My Workload: Purpose-Built Databases
Choosing the Right Database for My Workload: Purpose-Built Databases
 
DynamoDB and DAX | AWS Floor28
DynamoDB and DAX | AWS Floor28DynamoDB and DAX | AWS Floor28
DynamoDB and DAX | AWS Floor28
 
Modernise your Data Warehouse with Amazon Redshift and Amazon Redshift Spectrum
Modernise your Data Warehouse with Amazon Redshift and Amazon Redshift SpectrumModernise your Data Warehouse with Amazon Redshift and Amazon Redshift Spectrum
Modernise your Data Warehouse with Amazon Redshift and Amazon Redshift Spectrum
 
遊戲數據解決方案
遊戲數據解決方案遊戲數據解決方案
遊戲數據解決方案
 
AWS Data Lake: data analysis @ scale
AWS Data Lake: data analysis @ scaleAWS Data Lake: data analysis @ scale
AWS Data Lake: data analysis @ scale
 
Workshop: Architecting a Serverless Data Lake
Workshop: Architecting a Serverless Data LakeWorkshop: Architecting a Serverless Data Lake
Workshop: Architecting a Serverless Data Lake
 

More from Amazon Web Services

Come costruire servizi di Forecasting sfruttando algoritmi di ML e deep learn...
Come costruire servizi di Forecasting sfruttando algoritmi di ML e deep learn...Come costruire servizi di Forecasting sfruttando algoritmi di ML e deep learn...
Come costruire servizi di Forecasting sfruttando algoritmi di ML e deep learn...Amazon Web Services
 
Big Data per le Startup: come creare applicazioni Big Data in modalità Server...
Big Data per le Startup: come creare applicazioni Big Data in modalità Server...Big Data per le Startup: come creare applicazioni Big Data in modalità Server...
Big Data per le Startup: come creare applicazioni Big Data in modalità Server...Amazon Web Services
 
Esegui pod serverless con Amazon EKS e AWS Fargate
Esegui pod serverless con Amazon EKS e AWS FargateEsegui pod serverless con Amazon EKS e AWS Fargate
Esegui pod serverless con Amazon EKS e AWS FargateAmazon Web Services
 
Costruire Applicazioni Moderne con AWS
Costruire Applicazioni Moderne con AWSCostruire Applicazioni Moderne con AWS
Costruire Applicazioni Moderne con AWSAmazon Web Services
 
Come spendere fino al 90% in meno con i container e le istanze spot
Come spendere fino al 90% in meno con i container e le istanze spot Come spendere fino al 90% in meno con i container e le istanze spot
Come spendere fino al 90% in meno con i container e le istanze spot Amazon Web Services
 
Rendi unica l’offerta della tua startup sul mercato con i servizi Machine Lea...
Rendi unica l’offerta della tua startup sul mercato con i servizi Machine Lea...Rendi unica l’offerta della tua startup sul mercato con i servizi Machine Lea...
Rendi unica l’offerta della tua startup sul mercato con i servizi Machine Lea...Amazon Web Services
 
OpsWorks Configuration Management: automatizza la gestione e i deployment del...
OpsWorks Configuration Management: automatizza la gestione e i deployment del...OpsWorks Configuration Management: automatizza la gestione e i deployment del...
OpsWorks Configuration Management: automatizza la gestione e i deployment del...Amazon Web Services
 
Microsoft Active Directory su AWS per supportare i tuoi Windows Workloads
Microsoft Active Directory su AWS per supportare i tuoi Windows WorkloadsMicrosoft Active Directory su AWS per supportare i tuoi Windows Workloads
Microsoft Active Directory su AWS per supportare i tuoi Windows WorkloadsAmazon Web Services
 
Database Oracle e VMware Cloud on AWS i miti da sfatare
Database Oracle e VMware Cloud on AWS i miti da sfatareDatabase Oracle e VMware Cloud on AWS i miti da sfatare
Database Oracle e VMware Cloud on AWS i miti da sfatareAmazon Web Services
 
Crea la tua prima serverless ledger-based app con QLDB e NodeJS
Crea la tua prima serverless ledger-based app con QLDB e NodeJSCrea la tua prima serverless ledger-based app con QLDB e NodeJS
Crea la tua prima serverless ledger-based app con QLDB e NodeJSAmazon Web Services
 
API moderne real-time per applicazioni mobili e web
API moderne real-time per applicazioni mobili e webAPI moderne real-time per applicazioni mobili e web
API moderne real-time per applicazioni mobili e webAmazon Web Services
 
Database Oracle e VMware Cloud™ on AWS: i miti da sfatare
Database Oracle e VMware Cloud™ on AWS: i miti da sfatareDatabase Oracle e VMware Cloud™ on AWS: i miti da sfatare
Database Oracle e VMware Cloud™ on AWS: i miti da sfatareAmazon Web Services
 
Tools for building your MVP on AWS
Tools for building your MVP on AWSTools for building your MVP on AWS
Tools for building your MVP on AWSAmazon Web Services
 
How to Build a Winning Pitch Deck
How to Build a Winning Pitch DeckHow to Build a Winning Pitch Deck
How to Build a Winning Pitch DeckAmazon Web Services
 
Building a web application without servers
Building a web application without serversBuilding a web application without servers
Building a web application without serversAmazon Web Services
 
AWS_HK_StartupDay_Building Interactive websites while automating for efficien...
AWS_HK_StartupDay_Building Interactive websites while automating for efficien...AWS_HK_StartupDay_Building Interactive websites while automating for efficien...
AWS_HK_StartupDay_Building Interactive websites while automating for efficien...Amazon Web Services
 
Introduzione a Amazon Elastic Container Service
Introduzione a Amazon Elastic Container ServiceIntroduzione a Amazon Elastic Container Service
Introduzione a Amazon Elastic Container ServiceAmazon Web Services
 

More from Amazon Web Services (20)

Come costruire servizi di Forecasting sfruttando algoritmi di ML e deep learn...
Come costruire servizi di Forecasting sfruttando algoritmi di ML e deep learn...Come costruire servizi di Forecasting sfruttando algoritmi di ML e deep learn...
Come costruire servizi di Forecasting sfruttando algoritmi di ML e deep learn...
 
Big Data per le Startup: come creare applicazioni Big Data in modalità Server...
Big Data per le Startup: come creare applicazioni Big Data in modalità Server...Big Data per le Startup: come creare applicazioni Big Data in modalità Server...
Big Data per le Startup: come creare applicazioni Big Data in modalità Server...
 
Esegui pod serverless con Amazon EKS e AWS Fargate
Esegui pod serverless con Amazon EKS e AWS FargateEsegui pod serverless con Amazon EKS e AWS Fargate
Esegui pod serverless con Amazon EKS e AWS Fargate
 
Costruire Applicazioni Moderne con AWS
Costruire Applicazioni Moderne con AWSCostruire Applicazioni Moderne con AWS
Costruire Applicazioni Moderne con AWS
 
Come spendere fino al 90% in meno con i container e le istanze spot
Come spendere fino al 90% in meno con i container e le istanze spot Come spendere fino al 90% in meno con i container e le istanze spot
Come spendere fino al 90% in meno con i container e le istanze spot
 
Open banking as a service
Open banking as a serviceOpen banking as a service
Open banking as a service
 
Rendi unica l’offerta della tua startup sul mercato con i servizi Machine Lea...
Rendi unica l’offerta della tua startup sul mercato con i servizi Machine Lea...Rendi unica l’offerta della tua startup sul mercato con i servizi Machine Lea...
Rendi unica l’offerta della tua startup sul mercato con i servizi Machine Lea...
 
OpsWorks Configuration Management: automatizza la gestione e i deployment del...
OpsWorks Configuration Management: automatizza la gestione e i deployment del...OpsWorks Configuration Management: automatizza la gestione e i deployment del...
OpsWorks Configuration Management: automatizza la gestione e i deployment del...
 
Microsoft Active Directory su AWS per supportare i tuoi Windows Workloads
Microsoft Active Directory su AWS per supportare i tuoi Windows WorkloadsMicrosoft Active Directory su AWS per supportare i tuoi Windows Workloads
Microsoft Active Directory su AWS per supportare i tuoi Windows Workloads
 
Computer Vision con AWS
Computer Vision con AWSComputer Vision con AWS
Computer Vision con AWS
 
Database Oracle e VMware Cloud on AWS i miti da sfatare
Database Oracle e VMware Cloud on AWS i miti da sfatareDatabase Oracle e VMware Cloud on AWS i miti da sfatare
Database Oracle e VMware Cloud on AWS i miti da sfatare
 
Crea la tua prima serverless ledger-based app con QLDB e NodeJS
Crea la tua prima serverless ledger-based app con QLDB e NodeJSCrea la tua prima serverless ledger-based app con QLDB e NodeJS
Crea la tua prima serverless ledger-based app con QLDB e NodeJS
 
API moderne real-time per applicazioni mobili e web
API moderne real-time per applicazioni mobili e webAPI moderne real-time per applicazioni mobili e web
API moderne real-time per applicazioni mobili e web
 
Database Oracle e VMware Cloud™ on AWS: i miti da sfatare
Database Oracle e VMware Cloud™ on AWS: i miti da sfatareDatabase Oracle e VMware Cloud™ on AWS: i miti da sfatare
Database Oracle e VMware Cloud™ on AWS: i miti da sfatare
 
Tools for building your MVP on AWS
Tools for building your MVP on AWSTools for building your MVP on AWS
Tools for building your MVP on AWS
 
How to Build a Winning Pitch Deck
How to Build a Winning Pitch DeckHow to Build a Winning Pitch Deck
How to Build a Winning Pitch Deck
 
Building a web application without servers
Building a web application without serversBuilding a web application without servers
Building a web application without servers
 
Fundraising Essentials
Fundraising EssentialsFundraising Essentials
Fundraising Essentials
 
AWS_HK_StartupDay_Building Interactive websites while automating for efficien...
AWS_HK_StartupDay_Building Interactive websites while automating for efficien...AWS_HK_StartupDay_Building Interactive websites while automating for efficien...
AWS_HK_StartupDay_Building Interactive websites while automating for efficien...
 
Introduzione a Amazon Elastic Container Service
Introduzione a Amazon Elastic Container ServiceIntroduzione a Amazon Elastic Container Service
Introduzione a Amazon Elastic Container Service
 

Amazon DynamoDB Deep Dive Advanced Design Patterns for DynamoDB (DAT401) - AWS reInvent 2018.pdf

  • 1.
  • 2. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Advanced Design Patterns for DynamoDB Rick Houlihan Principal Technologist, NoSQL AWS D A T 4 0 1
  • 3. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Agenda • Brief History of Data Processing (Why NoSQL?) • Overview of DynamoDB • NoSQL Data Modeling • Normalized versus De-normalized schema • Common NoSQL Design Patterns • Composite Keys, Hierarchical Data, Relational Data • Modeling Real Applications
  • 4. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. History of Data Processing “History repeats itself because nobody was listening the first time”
  • 5. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Timeline of Database TechnologyDataPressure
  • 6. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Technology Adoption and the Hype Curve
  • 7. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Why NoSQL? Optimized for storage Optimized for compute Normalized/relational Denormalized/hierarchical Ad hoc queries Instantiated views Scale vertically Scale horizontally Good for OLAP Built for OLTP at scale SQL NoSQL
  • 8. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Amazon DynamoDB Document or Key-Value Scales to Any WorkloadFully Managed NoSQL Access Control Event Driven ProgrammingFast and Consistent
  • 9. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Table Table Items Attributes Partition Key Sort Key Mandatory Key-value access pattern Determines data distribution Optional Model 1:N relationships Enables rich query capabilities All items for key ==, <, >, >=, <= “begins with” “between” “contains” “in” sorted results counts top/bottom N values
  • 10. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. 00 55 A954 FFAA00 FF Partition Keys Partition Key uniquely identifies an item Partition Key is used for building an unordered hash index Allows table to be partitioned for scale Id = 1 Name = Jim Hash (1) = 7B Id = 2 Name = Andy Dept = Eng Hash (2) = 48 Id = 3 Name = Kim Dept = Ops Hash (3) = CD Key Space
  • 11. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Partition 3 Partition:Sort Key Partition:Sort Key uses two attributes together to uniquely identify an Item Within unordered hash index, data is arranged by the sort key No limit on the number of items (∞) per partition key Except if you have local secondary indexes 00:0 FF:∞ Hash (2) = 48 Customer# = 2 Order# = 10 Item = Pen Customer# = 2 Order# = 11 Item = Shoes Customer# = 1 Order# = 10 Item = Toy Customer# = 1 Order# = 11 Item = Boots Hash (1) = 7B Customer# = 3 Order# = 10 Item = Book Customer# = 3 Order# = 11 Item = Paper Hash (3) = CD 55 A9:∞54:∞ AAPartition 1 Partition 2
  • 12. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Partitions are three-way replicated Id = 2 Name = Andy Dept = Engg Id = 3 Name = Kim Dept = Ops Id = 1 Name = Jim Id = 2 Name = Andy Dept = Engg Id = 3 Name = Kim Dept = Ops Id = 1 Name = Jim Id = 2 Name = Andy Dept = Engg Id = 3 Name = Kim Dept = Ops Id = 1 Name = Jim Replica 1 Replica 2 Replica 3
  • 13. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Local Secondary Index (LSI) Alternate sort key attribute Index is local to a partition key A1 (partition) A3 (sort) A2 (item key) A1 (partition) A2 (sort) A3 A4 A5 LSIs A1 (partition) A4 (sort) A2 (item key) A3 (projected) Table KEYS_ONLY INCLUDE A3 A1 (partition) A5 (sort) A2 (item key) A3 (projected) A4 (projected) ALL 10 GB max per partition key, i.e. LSIs limit the # of range keys!
  • 14. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Global Secondary Index (GSI) Alternate partition and/or sort key Index is across all partition keys Use composite sort keys for compound indexes A1 (partition) A2 A3 A4 A5 A5 (partition) A4 (sort) A1 (item key) A3 (projected) INCLUDE A3 A4 (partition) A5 (sort) A1 (item key) A2 (projected) A3 (projected) ALL A2 (partition) A1 (itemkey) KEYS_ONLY GSIs Table RCUs/WCUs provisioned separately for GSIs Online indexing
  • 15. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. How Do GSI Updates Work? Table Primary table Primary table Primary table Primary table Global Secondary Index Client 2. Asynchronous update (in progress) If GSIs don’t have enough write capacity, table writes will be throttled!
  • 16. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Scaling NoSQL “We are stuck with technology when what we really want is just stuff that works.”
  • 17. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. What bad NoSQL looks like … Partition Time Heat
  • 18. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Getting the most out of Amazon DynamoDB throughput “To get the most out of DynamoDB throughput, create tables where the partition key element has a large number of distinct values, and values are requested fairly uniformly, as randomly as possible.” —DynamoDB Developer Guide Space: access is evenly spread over the key-space Time: requests arrive evenly spaced in time
  • 19. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Much better picture …
  • 20. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Auto Scaling Throughput automatically adapts to your actual traffic With Auto ScalingWithout Auto Scaling
  • 21. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. NoSQL Data Modeling “A ship in port is safe, but that’s not what ships were built for.”
  • 22. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. It’s all about relationships… Document management Process controlSocial network Data treesIT monitoring
  • 23. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. SQL vs. NoSQL design pattern
  • 24. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Selecting a Partition Key • Large number of distinct values • Items are uniformly requested and randomly distributed Selecting a Sort Key • Model 1:n and n:n relationships • Efficient/selective patterns • Query multiple entities • Leverage range queries AMAZON DYNAMODB - KEY CONCEPTS Examples: • Bad: Status, Gender • Good: CustomerId, DeviceId Examples: • Orders and OrderItems • Hierarchical relationships
  • 25. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. • Understand the use case • Identify the access patterns • Read/Write workloads • Query dimensions and aggregations • Data-modeling • Avoid relational design patterns, use one table • Review -> Repeat -> Review TENETS OF NoSQL DATA MODELING
  • 26. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. • Understand the use case • Identify the access patterns • Read/Write workloads • Query dimensions and aggregations • Data-modeling • Avoid relational design patterns, use one table • Review -> Repeat -> Review • Nature of the application • OLTP / OLAP / DSS • Define the Entity-Relationship Model • Identify Data Life Cycle • TTL, Backup/Archival, etc. TENETS OF NoSQL DATA MODELING
  • 27. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. • Understand the use case • Define the access patterns • Read/Write workloads • Data-modeling • Avoid relational design patterns, use one table • Review -> Repeat -> Review • Identify data sources • Define query aggregations • Document all workflows TENETS OF NoSQL DATA MODELING
  • 28. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. • Understand the use case • Identify the access patterns • Read/Write workloads • Query dimensions and aggregations • Data-modeling • Avoid relational design patterns, use one table • Review -> Repeat -> Review TENETS OF NoSQL DATA MODELING • 1 application service = 1 table • Reduce round trips • Simplify access patterns • Identify Primary Keys • How will items be inserted and read? • Overload items into partitions • Define indexes for secondary access patterns
  • 29. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. • Understand the use case • Identify the access patterns • Read/Write workloads • Query dimensions and aggregations • Data-modeling • Avoid relational design patterns, use one table • Review -> Repeat-> Review TENETS OF NoSQL DATA MODELING
  • 30. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Complex Queries “Computers are useless. They can only give you answers.”
  • 31. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. DynamoDB Streams and AWS Lambda
  • 32. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Triggers Lambda function Notify change Item/table level metrics Amazon CloudSearch Kinesis Firehose
  • 33. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Composite Keys “Hierarchies are celestial. In hell all are equal.”
  • 34. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Secondary index Opponent Date GameId Status Host Alice 2014-10-02 d9bl3 DONE David Carol 2014-10-08 o2pnb IN_PROGRESS Bob Bob 2014-09-30 72f49 PENDING Alice Bob 2014-10-03 b932s PENDING Carol Bob 2014-10-03 ef9ca IN_PROGRESS David BobPartition key Sort key Multi-value Sorts and Filters
  • 35. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Secondary Index Approach 1: Query Filter Bob Opponent Date GameId Status Host Alice 2014-10-02 d9bl3 DONE David Carol 2014-10-08 o2pnb IN_PROGRESS Bob Bob 2014-09-30 72f49 PENDING Alice Bob 2014-10-03 b932s PENDING Carol Bob 2014-10-03 ef9ca IN_PROGRESS David SELECT * FROM Game WHERE Opponent='Bob' ORDER BY Date DESC FILTER ON Status='PENDING' (filtered out)
  • 36. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Approach 2: Composite Key StatusDate DONE_2014-10-02 IN_PROGRESS_2014-10-08 IN_PROGRESS_2014-10-03 PENDING_2014-09-30 PENDING_2014-10-03 Status DONE IN_PROGRESS IN_PROGRESS PENDING PENDING Date 2014-10-02 2014-10-08 2014-10-03 2014-10-03 2014-09-30 + =
  • 37. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Secondary Index Approach 2: Composite Key Opponent StatusDate GameId Host Alice DONE_2014-10-02 d9bl3 David Carol IN_PROGRESS_2014-10-08 o2pnb Bob Bob IN_PROGRESS_2014-10-03 ef9ca David Bob PENDING_2014-09-30 72f49 Alice Bob PENDING_2014-10-03 b932s Carol Partition key Sort key
  • 38. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Opponent StatusDate GameId Host Alice DONE_2014-10-02 d9bl3 David Carol IN_PROGRESS_2014-10-08 o2pnb Bob Bob IN_PROGRESS_2014-10-03 ef9ca David Bob PENDING_2014-09-30 72f49 Alice Bob PENDING_2014-10-03 b932s Carol Secondary index Approach 2: Composite Key Bob SELECT * FROM Game WHERE Opponent='Bob' AND StatusDate BEGINS_WITH 'PENDING'
  • 39. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Advanced Data Modeling “The great myth of our times is that technology is communication.”
  • 40. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. How OLTP Apps Use Data  Mostly hierarchical structures  Entity driven workflows  Data spread across tables  Requires complex queries  Primary driver for ACID
  • 41. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. ItemID (PK) Version (SK) CurVer Attrs 1 v0 2 … v1 … v2 … v3 … Maintaining Version History (Many more item partitions) Item versions Overwrite v0 Item to Commit changes COPY Item.v0 -> Item1.v3 IF Item.v3 == NULL UPDATE Item1.v3 SET Attr1 += 1 UPDATE Item1.v3 SET Attr2 = … UPDATE Item1.v3 SET Attr3 = … COPY Item1.v3 -> Item1.v0 SET CurVer = 3 Transaction
  • 42. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Managing Relational Transactions
  • 43. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. DynamoDB Transactions API • TransactWriteItems • Synchronous update, put, delete, and check • Atomic • Automated Rollbacks • Up to 10 items within a transaction • Supports multiple tables • Complex conditional checks • Good Use Cases • Commit changes across items • Conditional batch inserts/updates • Bad Use Case • Maintaining normalized data
  • 44. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. DynamoDB Table Schema
  • 45. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Reverse Lookup GSI
  • 46. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Hierarchical Data Joins? We don’t need no stinkin’ joins!
  • 47. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Hierarchical Data Structures as Items • Use composite sort key to define a hierarchy • Highly selective queries with sort conditions • Reduce query complexity © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
  • 48. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Modeling Relational Data Dude, where’s my Lookup Table?
  • 49. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Modeling a Delivery Service – GetMeThat! The Business Model: Customers want things, we get them things. How it works: People are busy, they need stuff. GetMeThat! lets users create lists of stuff they need and have it dropped where and when they need it. • Download GetMeThat! • Browse Stuff • Get stuff • Tell us where to put your stuff
  • 50. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. The Entity Model
  • 51. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. The Access Patterns • Get Order(s) • by date range • by Customer • by Vendor • Get Order Details • Status • Items • Get Deliveries • by date range • by Driver • Get available drivers • by Geo and Time • Get Customer Data • Get Vendor Data
  • 52. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. The Relational Approach
  • 53. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. The NoSQL Approach
  • 54. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. The NoSQL Approach (Orders and Drivers GSI)
  • 55. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. The NoSQL Approach (Vendor and Deliveries GSI)
  • 56. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. A Real World Example “Reality is that which, when you stop believing in it, does not go away.”
  • 57. Audible eBook Sync Service • Allows users to save session state for Audible eBooks • Maintains mappings per user for eBooks and audio products • Spikey load patterns require significant overprovisioning • Large number of access patterns © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
  • 58. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Access Patterns # USE CASE 1 CompanionMapping getCompanionMappingsByAsin 2 CompanionMapping getCompanionMappingsByEbookAndAudiobookContentId 3 CompanionMapping getCompanionMappingsFromCache 4 CompanionMapping getCompanionMappings 5 CompanionMapping getCompanionMappingsAvailable 6 AcrInfo getACRInfo 7 AcrInfo getACRs 8 AcrInfo getACRInfos 9 AcrInfo getACRInfosbySKU 10 AudioProduct getAudioProductsForACRs 11 AudioProduct getAudioProducts 12 AudioProduct deleteAudioProductsMatchingSkuVersions 13 AudioProduct getChildAudioProductsForSKU 14 Product getProductInfoByAsins 15 Product getParentChildDataByParentAsins 16 AudioFile getAudioFilesForACR 17 AudioFile getAudioFilesForChildACR 18 AudioFile getAudioFilesByParentAsinVersionFormat 19 AudioFile getAudioFiles 20 AudioFile getAudioFilesForChildAsin
  • 59. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Primary Table T A B L E Primary Key Attributes PK SK (GSI 3) ABOOKACR1 v0#ABOOKACR1 GSI-1 GSI-2 ABOOK-ASIN1 ABOOK-SKU1 EBOOKACR1 GSI-1 GSI-2 SyncFileAcr ABOOK-ASIN1 ABOOKACR1#TRACK#1 GSI-1 GSI-2 ABOOK-ASIN1 ABOOK-SKU1 ABOOKACR1#TRACK#2 GSI-1 GSI-2 ABOOK-ASIN1 ABOOK-SKU1 EBOOKACR1 EBOOKACR1 GSI-1 EBookAsin EBOOK-SKU1 ASIN
  • 60. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Indexes G S I 1 Partition Key Projected Attributes ABOOK-ASIN1 ABOOKACR1 ABOOKACR1-v1 ABOOKACR1#TRACK#1 ABOOKACR1#TRACK#2 SyncFileAcr ABOOKACR1 MAP-EBOOKACR1 EBOOK-SKU1 ABOOKACR1 EBOOKACR1 G S I 2 Partition Key Projected Attributes ABOOK-ASIN1 ABOOKACR1 MAP-EBOOKACR1 ABOOK-SKU1 ABOOKACR1 ABOOKACR1-v1 ABOOKACR1#TRACK#1 ABOOKACR1#TRACK#2 G S I 3 GSI Partition Key Projected Attributes V0#ABOOKACR1 ABOOKACR1 ABOOKACR1-v1 EBOOKACR1 ABOOKACR1 MAP-EBOOKACR1 ABOOKACR1#TRACK#1 ABOOKACR1 ABOOKACR1#TRACK#1 ABOOKACR1#TRACK#2 ABOOKACR1 ABOOKACR1#TRACK#2 EBOOKACR1 ABOOKACR1 EBOOKACR1
  • 61. Query Conditions # USE CASE Lookup parameters INDEX Key Conditions Filter Conditions 1 CompanionMapping getCompanionMappingsByAsin audiobookAsin/ebookSku GSI2 GSI-2=ABOOK-ASIN1 None 2 CompanionMapping getCompanionMappingsByEbookAndA udiobookContentId ebookAcr/sku,version,format or audiobookAcr/asin,version,format GSI-3 on TargetACR attribute OR PrimaryKey on Table GSI-3=MAP-EBOOKACR1 version=v and format=f 3 CompanionMapping getCompanionMappingsFromCache ebookAcr/sku,version,format or audiobookAcr/asin,version,format GSI-3 on TargetACR attribute OR PrimaryKey on Table GSI-3=MAP-EBOOKACR1 version=v and format=f 4 CompanionMapping getCompanionMappings syncfileAcr, ebookAcr?, audiobookAcr? GSI1 GSI-1=SyncFileAcr None 5 CompanionMapping getCompanionMappingsAvailable ebookAcr, audiobookAcr Primary Key on Table Acr=ABOOKACR1 and TargetACR beginswith "MAP-" 6 AcrInfo getACRInfo acr Primary Key on Table Acr=ABOOKACR1 and TargetACR beginswith "ABOOKACR1- v" 7 AcrInfo getACRs acr / asin,version,format Primary Key on Table Acr=ABOOKACR1 version=v and format=f 8 AcrInfo getACRInfos acr Primary Key on table Acr=ABOOKACR1 and TargetACR beginswith "ABOOKACR1" 9 AcrInfo getACRInfosbySKU sku GSI2 GSI-2=ABOOK-SKU1 10 AudioProduct getAudioProductsForACRs acr Primary Key on table Acr=ABOOKACR1 and TargetACR beginswith "ABOOKACR1" 11 AudioProduct getAudioProducts sku, version, format GSI2 GSI-2=ABOOK-SKU1 version=v and format=f 12 AudioProduct deleteAudioProductsMatchingSkuVersi ons sku, version GSI2 GSI-2=ABOOK-SKU1 version=v 13 AudioProduct getChildAudioProductsForSKU sku GSI2 GSI-2=ABOOK-SKU1 14 Product getProductInfoByAsins asin GSI1 GSI-1=ABOOK-ASIN1 15 Product getParentChildDataByParentAsins asin GSI1 GSI-1=ABOOK-ASIN1 16 AudioFile getAudioFilesForACR acr Primary Key on table Acr=ABOOKACR1 and TargetACR beginswith "ABOOKACR1#" 17 AudioFile getAudioFilesForChildACR acr, parent_asin Primary Key on table Acr=ABOOKACR1 version=v and format=f 18 AudioFile getAudioFilesByParentAsinVersionForm at parent_asin, version, format GSI1 GSI-1=ABOOK-ASIN1 version=v and format=f 19 AudioFile getAudioFiles sku, version, format GSI2 GSI-2=ABOOK-SKU1 version=v and format=f 20 AudioFile getAudioFilesForChildAsin asin, parent_asin, version, format GSI1 GSI-1=ABOOK-ASIN1 version=v and format=f
  • 62. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. The Serverless Paradigm “Fairly cheap home computing was what changed my life.”
  • 63. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Elastic Serverless Applications
  • 64. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Conclusions
  • 65. Thank you! © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  • 66. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.