Explore Amazon DynamoDB capabilities and benefits in detail and learn how to get the most out of your DynamoDB database. We go over best practices for schema design with DynamoDB across multiple use cases, including gaming, AdTech, IoT, and others. We explore designing efficient indexes, scanning, and querying, and go into detail on a number of recently released features, including JSON document support, DynamoDB Streams, and more. We also provide lessons learned from operating DynamoDB at scale, including provisioning DynamoDB for IoT.

1. © 2016, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
December 1, 2016
DAT304
Deep Dive on Amazon DynamoDB
Rick Houlihan, Principal TPM, DBS NoSQL

2. What to Expect from the Session
• Brief history of data processing (Why NoSQL?)
• Overview of Amazon DynamoDB
• Scaling characteristics, hot keys, and design considerations
• NoSQL data modeling
• Normalized versus unstructured schema
• Common NoSQL design patterns
• Time Series, Write Sharding, MVCC, etc.
• Amazon DynamoDB in the serverless ecosystem

3. Timeline of Database Technology
DataPressure

4. Data volume since 2011
DataVolume
Historical Current
 90 percent of stored data
generated in last 2 years
 1 terabyte of data in 2011
equals 6.5 petabytes today
 Linear correlation between
data pressure and technical
innovation
 No reason these trends will
not continue over time

5. Technology adoption and the hype curve

6. 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

7. Amazon DynamoDB
Document or key-value Scales to any workloadFully managed NoSQL
Access control Event driven programmingFast and consistent

8. 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

9. 00 55 A954 FFAA00 FF
Partition Keys
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
Partition key uniquely identifies an item
Partition key is used for building an unordered hash index
Allows table to be partitioned for scale

10. Scaling

11. Scaling
Throughput
 Provision any amount of throughput to a table
Size
 Add any number of items to a table
- Max item size is 400 KB
- LSIs limit the number of range keys due to 10 GB limit
Scaling is achieved through partitioning

12. Throughput
Provisioned at the table level
 Write capacity units (WCUs) are measured in 1 KB per second
 Read capacity units (RCUs) are measured in 4 KB per second
- RCUs measure strictly consistent reads
- Eventually consistent reads cost 1/2 of consistent reads
Read and write throughput limits are independent
WCURCU

13. Partitioning Math
In the future, these details might change…
Number of Partitions
By Capacity (Total RCU / 3000) + (Total WCU / 1000)
By Size Total Size / 10 GB
Total Partitions CEILING(MAX (Capacity, Size))

14. Partitioning Example
Table size = 8 GB, RCUs = 5000, WCUs = 500
RCUs per partition = 5000/3 = 1666.67
WCUs per partition = 500/3 = 166.67
Data/partition = 10/3 = 3.33 GB
RCUs and WCUs are uniformly
spread across partitions
Number of Partitions
By Capacity (5000 / 3000) + (500 / 1000) = 2.17
By Size 8 / 10 = 0.8
Total Partitions CEILING(MAX (2.17, 0.8)) = 3

15. What causes throttling?
If sustained throughput goes beyond provisioned throughput per partition
Non-uniform workloads
 Hot keys/hot partitions
 Very large items
Mixing hot data with cold data
 Use a table per time period
From the example before:
 Table created with 5000 RCUs, 500 WCUs
 RCUs per partition = 1666.67
 WCUs per partition = 166.67
 If sustained throughput > (1666 RCUs or 166 WCUs) per key or partition, DynamoDB
may throttle requests
- Solution: Increase provisioned throughput

16. What bad NoSQL looks like…
Partition
Time
Heat

17. Getting the most out of 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

18. Much better picture…

19. Data Modeling in NoSQL

20. It’s all about aggregations…
Document management Process controlSocial network
Data treesIT monitoring

21. SQL vs. NoSQL Design Pattern

22. Hierarchical data
Tiered relational data structures

23. Hierarchical data structures as items
Use composite sort key to define a hierarchy
Highly selective result sets with sort queries
Index anything, scales to any size
Primary Key
Attributes
ProductID type
Items
1 bookID
title author genre publisher datePublished ISBN
Some Book John Smith Science Fiction Ballantine Oct-70 0-345-02046-4
2 albumID
title artist genre label studio released producer
Some Album Some Band Progressive Rock Harvest Abbey Road 3/1/73 Somebody
2 albumID:trackID
title length music vocals
Track 1 1:30 Mason Instrumental
2 albumID:trackID
title length music vocals
Track 2 2:43 Mason Mason
2 albumID:trackID
title length music vocals
Track 3 3:30 Smith Johnson
3 movieID
title genre writer producer
Some Movie Scifi Comedy Joe Smith 20th Century Fox
3 movieID:actorID
name character image
Some Actor Joe img2.jpg
3 movieID:actorID
name character image
Some Actress Rita img3.jpg
3 movieID:actorID
name character image
Some Actor Frito img1.jpg

24. … or as documents (JSON)
JSON data types (M, L, BOOL, NULL)
Document SDKs available
Indexing only by using DynamoDB Streams or AWS Lambda
400 KB maximum item size (limits hierarchical data structure)
Primary Key
Attributes
ProductID
Items
1
id title author genre publisher datePublished ISBN
bookID Some Book Some Guy Science Fiction Ballantine Oct-70 0-345-02046-4
2
id title artist genre Attributes
albumID Some Album Some Band Progressive Rock
{ label:"Harvest", studio: "Abbey Road", published: "3/1/73", producer: "Pink
Floyd", tracks: [{title: "Speak to Me", length: "1:30", music: "Mason", vocals:
"Instrumental"},{title: ”Breathe", length: ”2:43", music: ”Waters, Gilmour,
Wright", vocals: ”Gilmour"},{title: ”On the Run", length: “3:30", music: ”Gilmour,
Waters", vocals: "Instrumental"}]}
3
id title genre writer Attributes
movieID Some Movie Scifi Comedy Joe Smith
{ producer: "20th Century Fox", actors: [{ name: "Luke Wilson", dob: "9/21/71",
character: "Joe Bowers", image: "img2.jpg"},{ name: "Maya Rudolph", dob:
"7/27/72", character: "Rita", image: "img1.jpg"},{ name: "Dax Shepard", dob:
"1/2/75", character: "Frito Pendejo", image: "img3.jpg"}]

25. Design patterns and best practices

26. DynamoDB Streams and AWS Lambda

27. Triggers
Lambda function
Notify change
Derivative tables
Amazon CloudSearch
Amazon ElastiCache

28. Real-time voting
Write-heavy items

29. Partition 1
1000 WCUs
Partition K
1000 WCUs
Partition M
1000 WCUs
Partition N
1000 WCUs
Votes Table
Candidate A Candidate B
Scaling bottlenecks
Voters
Provision 200,000 WCUs

30. Write sharding
Candidate A_2
Candidate B_1
Candidate B_2
Candidate B_3
Candidate B_5
Candidate B_4
Candidate B_7
Candidate B_6
Candidate A_1
Candidate A_3
Candidate A_4
Candidate A_7 Candidate B_8
Candidate A_6 Candidate A_8
Candidate A_5
Voter
Votes Table

31. Write sharding
Candidate A_2
Candidate B_1
Candidate B_2
Candidate B_3
Candidate B_5
Candidate B_4
Candidate B_7
Candidate B_6
Candidate A_1
Candidate A_3
Candidate A_4
Candidate A_7 Candidate B_8
UpdateItem: “CandidateA_” + rand(0, 10)
ADD 1 to Votes
Candidate A_6 Candidate A_8
Candidate A_5
Voter
Votes Table

32. Votes Table
Shard aggregation
Candidate A_2
Candidate B_1
Candidate B_2
Candidate B_3
Candidate B_5
Candidate B_4
Candidate B_7
Candidate B_6
Candidate A_1
Candidate A_3
Candidate A_4
Candidate A_5
Candidate A_6 Candidate A_8
Candidate A_7 Candidate B_8
Periodic
process
Candidate A
Total: 2.5M
1. Sum
2. Store Voter

33. Trade off read cost for write scalability
Consider throughput per partition key
Shard write-heavy partition keys
Your write workload is not horizontally
scalable

34. Event logging
Storing time series data

35. Time Series Tables (Static TTL Timestamps)
Events_table_2015_April
Event_id
(Partition)
Timestamp
(Sort)
Attribute1 …. Attribute N
Events_table_2015_March
Event_id
(Partition)
Timestamp
(Sort)
Attribute1 …. Attribute N
Events_table_2015_Feburary
Event_id
(Partition)
Timestamp
(Sort)
Attribute1 …. Attribute N
Events_table_2015_January
Event_id
(Partition)
Timestamp
(Sort)
Attribute1 …. Attribute N
RCUs = 1000
WCUs = 1
RCUs = 10000
WCUs = 10000
RCUs = 100
WCUs = 1
RCUs = 10
WCUs = 1
Current table
Older tables
HotdataColddata
Don’t mix hot and cold data; archive cold data to Amazon S3

36. Use a table per time period
Precreate daily, weekly, monthly tables
Provision required throughput for current table
Writes go to the current table
Turn off (or reduce) throughput for older tables
Dealing with static time series data

37. Time Series Tables (Dynamic TTL Timestamps)
Active Tickets_Table
Event_id
(Partition)
Timestamp
(Sort)
GSIKey
Rand(0-N)
… Attribute N
Expired Tickets GSI
GSIKey
(Partition)
Timestamp
(Sort)
Archive Table
Event_id
(Partition)
Timestamp
(Sort)
Attribute1 …. Attribute N
RCUs = 10000
WCUs = 10000
RCUs = 100
WCUs = 1
Current table
HotdataColddata
Scatter Query GSI for Expired Tickets and use Streams to archive

38. Use Lambda to purge expired items
Use a write sharded GSI to selectively query expired items
Create a Lambda “stored procedure” to delete items
Migrate data between tables with Streams/Lambda trigger
Dealing with dynamic time series data

39. Product catalog
Popular items (read)

40. Partition 1
2000 RCUs
Partition K
2000 RCUs
Partition M
2000 RCUs
Partition 50
2000 RCU
Scaling bottlenecks
Product A Product B
Shoppers
ProductCatalog Table
SELECT Id, Description, ...
FROM ProductCatalog
WHERE Id="POPULAR_PRODUCT"

42. Partition 1 Partition 2
ProductCatalog Table
User
DynamoDB
User
Cache
popular items
SELECT Id, Description, ...
FROM ProductCatalog
WHERE Id="POPULAR_PRODUCT"

44. Multiversion Concurrency
Transactions in NoSQL

45. How OLTP Apps Use Data
 Mostly hierarchical
structures
 Entity-driven workflows
 Data spread across tables
 Requires complex queries
 Primary driver for ACID

46. Reading item partitions
Get all items for a product
assembly
SELECT *
FROM Assemblies
WHERE ID=1
Order
Item
Picker
ID PartID Vendor Bin
1
1 ACME 27Z19
2 ACME 39M97
3 ACME 75B25
(Many more assemblies)
Assemblies table
Partition key Sort key

47. ID PartID Vendor Bin
1
1 ACME 27Z19
2 ACME 39M97
3 ACME 75B25
4 UAC 53G56
5 UAC 64B17
6 UAC 48J19
Updates are problematic
(Many more assemblies)
Assemblies table
Old items
SELECT *
FROM Assemblies
WHERE ID=1
Order Item Picker
New items

48. ID PartID Vendor Bin
1
1 ACME 27Z19
2 ACME 39M97
3 ACME 75B25
4 UAC 53G56
5 UAC 64B17
6 UAC 48J19
Creating partition “locks”
(Many more assemblies)
Assemblies table
• Use metadata item for versioning
SET #ItemList.i = list append(:newList, #ItemList.i ), #Lock =
1
• Obtain locks with conditional writes
--condition-expression “Lock = 0”
• Remove old items or add version info to Sort Key
Query composite keys for specific versions
• Readers can determine how to handle “fat” reads
• Add additional metadata to manage transactional
workflow as needed
Current State, Timeout, etc.
ID PartID ItemList Lock
1 0 {i:[[1,2,3],[4,5,6]]} 0

49. Use item partitions to manage transactional
workflows
Manage versioning across items with metadata
Tag attributes to maintain multiple versions
Code your app to recognize when updates are in progress
Implement app layer error handling and recovery logic
Transactional writes across items is
required

50. Messaging app
Large items
Filters vs. indexes
M:N Modeling—inbox and outbox

51. Messages
table
Messages app
David
SELECT *
FROM Messages
WHERE Recipient='David'
LIMIT 50
ORDER BY Date DESC
Inbox
SELECT *
FROM Messages
WHERE Sender ='David'
LIMIT 50
ORDER BY Date DESC
Outbox

52. Recipient Date Sender Message
David 2014-10-02 Bob …
… 48 more messages for David …
David 2014-10-03 Alice …
Alice 2014-09-28 Bob …
Alice 2014-10-01 Carol …
Large and small attributes mixed
(Many more messages)
David
Messages table
50 items × 256 KB each
Partition key Sort key
Large message bodies
Attachments
SELECT *
FROM Messages
WHERE Recipient='David'
LIMIT 50
ORDER BY Date DESC
Inbox

53. Computing inbox query cost
Items evaluated by query
Average item size
Conversion ratio
Eventually consistent reads
50 * 256KB * (1 RCU / 4 KB) * (1 / 2) = 1600 RCU

54. Recipient Date Sender Subject MsgId
David 2014-10-02 Bob Hi!… afed
David 2014-10-03 Alice RE: The… 3kf8
Alice 2014-09-28 Bob FW: Ok… 9d2b
Alice 2014-10-01 Carol Hi!... ct7r
Separate the bulk data
Inbox-GSI Messages table
MsgId Body
9d2b …
3kf8 …
ct7r …
afed …
David
1. Query inbox-GSI: 1 RCU
2. BatchGetItem messages: 1600 RCU
(50 separate items at 256 KB)
(50 sequential items at 128 bytes)
Uniformly distributes large item reads

55. Messaging app
Messages
table
David
Inbox
global secondary
index
Inbox
Outbox
global secondary
index
Outbox

56. Reduce one-to-many item sizes
Configure secondary index projections
Use GSIs to model M:N relationship
between sender and recipient
Distribute large items
Querying many large items at once
InboxMessagesOutbox

57. Multiplayer online gaming
Query filters vs.
composite key indexes

58. 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
Multivalue sorts and filters

59. 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)

60. Needle in a haystack
Bob

61. Send back less data “on the wire”
Simplify application code
Simple SQL-like expressions
• AND, OR, NOT, ()
Use query filter
Your index isn’t entirely selective

62. 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
+ =

63. 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

64. 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'

65. Needle in a sorted haystack
Bob

66. Sparse indexes
Id
(Partition)
User Game Score Date Award
1 Bob G1 1300 2012-12-23
2 Bob G1 1450 2012-12-23
3 Jay G1 1600 2012-12-24
4 Mary G1 2000 2012-10-24 Champ
5 Ryan G2 123 2012-03-10
6 Jones G2 345 2012-03-20
Game-scores-table
Award
(Partition)
Id User Score
Champ 4 Mary 2000
Award-GSI
Scan sparse GSIs

67. Concatenate attributes to form useful
secondary index keys
Take advantage of sparse indexes
Replace filter with indexes
You want to optimize a query as much
as possible
Status + Date

68. Reference architecture
Amazon
Kinesis
consumer
Amazon
EMR

69. Elastic Serverless Applications

70. Thank you!

71. Remember to complete
your evaluations!