Amazon DynamoDB is a fully managed NoSQL database service for applications that need consistent, single-digit millisecond latency at any scale. This talk explores DynamoDB capabilities and benefits in detail and discusses how to get the most out of your DynamoDB database. We go over schema design best practices with DynamoDB across multiple use cases, including gaming, AdTech, IoT, and others. We also explore designing efficient indexes, scanning, and querying, and go into detail on a number of recently released features, including JSON document support, Streams, and more.

1. © 2016, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Andreas Chatzakis, AWS Solutions Architecture
7th July 2016
Deep Dive on Amazon DynamoDB

2. Objectives
• Prepare for success
• Large tables & demanding use-cases
• High Performance
• Cost optimized
• New functionality

3. Technology adoption and the hype curve

4. Why NoSQL?
Optimized for storage Optimized for scalability
Normalized/relational Denormalized/hierarchical
Ad hoc queries Instantiated views
Scale vertically Scale horizontally
SQL NoSQL

5. Scaling efficiently

6. Size
(Gigabytes)
Throughput
(Requests per second)
Scaling

7. Partitioning

8. Partition count: Size
# 𝑜𝑓 𝑃𝑎𝑟𝑡𝑖𝑡𝑖𝑜𝑛𝑠 =
𝑇𝑎𝑏𝑙𝑒 𝑆𝑖𝑧𝑒 𝑖𝑛 𝑏𝑦𝑡𝑒𝑠
10 𝐺𝐵(𝑓𝑜𝑟 𝑠𝑖𝑧𝑒)
In the future, these details might change…

9. Throughput
• Write capacity units (WCUs): 1 KB
• Read capacity units (RCUs): 4 KB
• 1 RCU => 1 strongly consistent read
• 1 RCU => 2 eventually consistent reads

10. Partition count: Throughput
# 𝑜𝑓 𝑃𝑎𝑟𝑡𝑖𝑡𝑖𝑜𝑛𝑠
(𝑓𝑜𝑟 𝑡ℎ𝑟𝑜𝑢𝑔ℎ𝑝𝑢𝑡)
=
𝑅𝐶𝑈𝑓𝑜𝑟 𝑟𝑒𝑎𝑑𝑠
3000 𝑅𝐶𝑈
+
𝑊𝐶𝑈𝑓𝑜𝑟 𝑤𝑟𝑖𝑡𝑒𝑠
1000 𝑊𝐶𝑈
In the future, these details might change…

11. ProvisionedThroughputExceededException

12. Built-in flexibility for small spikes
0
400
800
1200
1600
CapacityUnits
Time
Provisioned Consumed
“save up” unused capacity
consume saved up capacity

13. Burst capacity
0
400
800
1200
1600
CapacityUnits
Time
Provisioned Consumed Attempted
Burst capacity: 300 seconds
(1200 × 300 = 3600 CU)
Throttled requests
Don’t completely depend on burst capacity… provision sufficient throughput

14. Throughput per partition
100,000 𝑅𝐶𝑈
50 𝑃𝑎𝑟𝑡𝑖𝑡𝑖𝑜𝑛𝑠
≈ 𝟐𝟎𝟎𝟎 𝑟𝑒𝑎𝑑 𝑐𝑎𝑝𝑎𝑐𝑖𝑡𝑦 𝑢𝑛𝑖𝑡𝑠 𝑝𝑒𝑟 𝑝𝑎𝑟𝑡𝑖𝑡𝑖𝑜𝑛
Partition 1
2000 RCU
Partition K
2000 RCU
Partition M
2000 RCU
Partition 50
2000 RCU
ProductCatalog Table

15. Space
(which partition keys)
Time
(consumed capacity
per second)
Aim for Uniformity

16. Examine your traffic pattern: Space
Partition
Time
Heat

17. Hot key issues manifest after you scale
Client
Client
Table
Partition
Table
Partition
Client
Client
Client
Client
Partition
Partition
Partition
Partition

18. A bad choice for a partition key
f(x)
Partition 1 Partition 2 Partition 3 Partition 4
Partition key: “07-07-2016”
Range key: “Session Attendee X”
Partition key: “07-07-2016”
Range key: “Session Attendee Y”
Table: SummitSessionAttendance

19. But I have random partition keys!
Keys/partition is important but also other outliers:
- Frequency (Hot keys)
- Size (Large objects or collections)
- Table history (partitions are not merged)
?

20. Partition key value Uniformity
User ID, where the application has many users and each
user has similar activity levels.
Status code, where there are only a few possible status
codes.
Device ID, where each device accesses data at relatively
similar intervals
Device ID, where one device generates a lot more traffic
than any other device

21. What a hot partition problem looks like
Read Capacity Throttled read requests
provisioned
consumed

22. Troubleshooting hot partitions
- CloudWatch
- AWS Support
- Access logs
- ReturnConsumedCapacity
- Sampling works well
- GSIs
- must also have enough write capacity
- uniformity requirement also applies

23. Examine your traffic pattern: Time
Partition
Time
Heat

24. Avoid Sudden Bursts of Read Activity
throttling

25. Query rather than scan
Query
- Specify partition key name
- Condition on sort key
- Cheap with high cardinality
keys
Scan
- Reads all data
- Conditions available
through filters
- Expensive for large tables
Partition Sort Atribute1 … Attribute N

26. When you have to scan a table
• Scans constrained by single
partition throughput
• Use parallel Scans if
table>20GB
• Avoid sudden bursts vs
provisioned capacity
• Offload to S3, HDFS,
Redshift, ElasticSearch or
second table

27. Design patterns & best practices

28. Product catalog
Popular items (read)

29. 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"

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

31. Real-time voting
Write-heavy items

32. 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

33. 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

34. 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

35. 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

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

37. Cost Optimization tips

38. Auto Scaling
• Cost saving technique
• Open Source solutions
• Set minimums and maximums
• Scale up proactively, scale down conservatively
• Scale up time can be from minutes to hours
• Implement a circuit-breaker

39. Event logging
Storing time series data

40. A mix of hot and cold data
Events_tableil
Event_id
(Partition)
Timestamp
(Sort)
Attribute1 …. Attribute N RCUs = 10000
WCUs = 10000Current table
Antipattern:
• Mix of hot and cold data
• Old data rarely accessed
• Unbounded data (partition) growth
• Partition dilution
• Scan costs increase with table size
• Deletes of old data not trivial or cheap

41. Time series tables
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

42. 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
Cheaper scans – free deletes
Dealing with time series data

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

44. GameId Date Host Opponent Status
d9bl3 2014-10-02 David Alice DONE
72f49 2014-09-30 Alice Bob PENDING
o2pnb 2014-10-08 Bob Carol IN_PROGRESS
b932s 2014-10-03 Carol Bob PENDING
ef9ca 2014-10-03 David Bob IN_PROGRESS
Games table
Hierarchical data structures

45. Query for incoming game requests
DynamoDB indexes provide partition and sort
What about queries for two equalities and a sort?
SELECT * FROM Game
WHERE Opponent='Bob‘
AND Status=‘PENDING'
ORDER BY Date DESC
(hash)
(range)
(?)

46. 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
Approach 1: Query filter
BobPartition key Sort key

47. 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)

48. Needle in a haystack
Bob

49. 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

50. 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
+ =

51. 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

52. 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'

53. Needle in a sorted haystack
Bob

54. Sparse indexes
CustomerId
(Partition)
OrderId
(Sort)
Total Date Open
1 234234 $100 2016-07-01
1 526346 $10 2016-07-02
2 746346 $200 2016-07-02
1 23462 $300 2016-07-05 X
3 635245 $150 2016-07-05
4 245362 $80 2016-07-07
Customer Orders
CustomerId
(Partition)
Open
(Sort)
Total OrderId Date
1 X $300 23462 2016-07-05
OpenOrders-GSI

55. 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

56. Messaging app
Large items, Varied Access Patterns
Filters vs. Indexes
M:N Modeling—inbox and outbox

57. 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

58. 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

59. Computing inbox query cost
Items evaluated by query
Average item size
Conversion ratio
Eventually consistent reads
50 * 256KB * (1 RCU / 4KB) * (1 / 2) = 1600 RCU
All those RCUs against one partition key

60. 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)

61. Inbox GSI
Define which attributes to copy into the index

62. Outbox Sender
Outbox GSI
SELECT *
FROM Messages
WHERE Sender ='David'
LIMIT 50
ORDER BY Date DESC

63. Messaging app
Messages
Table
David
Inbox
global secondary
index
Inbox
Outbox
global secondary
index
Outbox

64. 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

65. Event driven applications and
DynamoDB Streams

66. • Stream of updates
• Asynchronous
• Exactly once
• Strictly ordered (per item)
• Highly durable
• Scale with table
• 24-hour lifetime
• Sub-second latency
DynamoDB Streams

67. Stream
Table
Partition 1
Partition 2
Partition 3
Partition 4
Partition 5
Table
Shard 1
Shard 2
Shard 3
Shard 4
KCL
Worker
KCL
Worker
KCL
Worker
KCL
Worker
Amazon Kinesis Client
Library application
DynamoDB
client application
Updates
DynamoDB Streams and
Amazon Kinesis Client Library

68. DynamoDB Streams
Open Source Cross-
Region Replication Library
Asia Pacific (Sydney) EU (Ireland) Replica
US East (N. Virginia)
Cross-region replication

69. DynamoDB Streams and AWS Lambda

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

71. Search your DynamoDB tables

72. A polyglot data layer

73. Please remember to rate this
session under My Agenda on
awssummit.london