DynamoDB is a fully managed NoSQL database service that provides fast and predictable performance with seamless scalability. It offers key-value and document data models, consistent and low-latency performance, continuous availability and automatic scaling of storage and throughput capacity. The document discusses DynamoDB's data model, operations, indexing capabilities and use cases like building a game scores and leaderboards application. It also shares a customer story of how market research firm IBOPE was able to develop a scalable system to monitor websites and collect advertising data using AWS services like DynamoDB for its flexibility and ability to quickly scale resources up and down as needed.

1. Under the Covers of DynamoDB
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3. What is
DynamoDB?
1

4. DynamoDB is a
managed NoSQL
database service.
Store and retrieve any amount of data.
Serve any level of request traffic.

5. Without the operational burden.

6. Consistent, predictable performance.
Single digit millisecond latency.
Backed on solid-state drives.

7. Key/attribute pairs. No schema required.
Easy to create. Easy to adjust.
Flexible data model.

8. No table size limits. Unlimited storage.
No downtime.
Seamless scalability.

9. Consistent, disk only writes.
Replication across data centers and availability zones.
Durable.

10. Focus on your app.

11. Reserve IOPS for reads and writes.
Scale up for down at any time.
Provisioned throughput.

12. Pay per capacity unit.
Priced per hour of provisioned throughput.

13. Size of item x writes per second
Write throughput.

14. Atomic increment and decrement.
Optimistic concurrency control: conditional writes.
Consistent writes.

15. Item level transactions only.
Puts, updates and deletes are ACID.
Transactions.

16. Read throughput.
Strong or eventual consistency

17. Read throughput.
Strong or eventual consistency
Provisioned units = size of item x reads per second

18. Read throughput.
Strong or eventual consistency
Provisioned units = size of item x reads per second
2

19. Read throughput.
Strong or eventual consistency
Same latency expectations.
Mix and match at ‘read time’.

20. Provisioned throughput is
managed by DynamoDB.

21. Data is partitioned and
managed by DynamoDB.

22. Tiered bandwidth pricing:
aws.amazon.com/dynamodb/pricing
Indexed data storage.

23. Up to 53% for 1 year reservation.
Up to 76% for 3 year reservation.
Reserved capacity.

24. Session based to minimize latency.
Uses the Amazon Security Token Service.
Handled by AWS SDKs.
Integrates with IAM.
Authentication.

25. CloudWatch metrics:
latency, consumed read and write throughput,
errors and throttling.
Monitoring.

26. Libraries, mappers and mocks.
ColdFusion, Django, Erlang, Java, .Net,
Node.js, Perl, PHP, Python, Ruby
http://j.mp/dynamodb-libs

27. NoSQL Data
Modeling
2

28. id = 100
date =
2012-05-16-12-00-10id = 101 total = 100.00
total = 25.00
id = 101 date =
2012-05-15-15-00-11 total = 35.00
date =
2012-05-16-09-00-10

29. id = 100 date =
2012-05-16-09-00-10 total = 25.00
id = 101 date =
2012-05-15-15-00-11 total = 35.00
Table
date =
2012-05-16-12-00-10id = 101 total = 100.00

30. id = 100 total = 25.00
id = 101 date =
2012-05-15-15-00-11 total = 35.00
Item
date =
2012-05-16-12-00-10id = 101 total = 100.00
date =
2012-05-16-09-00-10

31. id = 100
2012-05-15-15-00-11
total = 25.00
Attribute
date =
2012-05-16-12-00-10id = 101 total = 100.00
id = 101 date =
total = 35.00
date =
2012-05-16-09-00-10

32. Tables do not require a formal schema.
Items are an arbitrarily sized hash.
Where is the schema?

33. Items are indexed by primary and secondary keys.
Primary keys can be composite.
Secondary keys are local to the table.
Indexing.

34. ID Date Total

35. ID Date Total
Hash key

36. ID Date Total
Hash key Range key
Composite primary key

37. ID Date Total
Hash key Range key Secondary range key

38. Programming DynamoDB.
Small but perfectly formed API.

39. CreateTable
Scan
UpdateTable
DeleteTable
DescribeTable
ListTables
Query
PutItem
GetItem
UpdateItem
DeleteItem
BatchGetItem
BatchWriteItem

40. CreateTable
Scan
UpdateTable
DeleteTable
DescribeTable
ListTables
Query
PutItem
GetItem
UpdateItem
DeleteItem
BatchGetItem
BatchWriteItem

41. CreateTable
Scan
UpdateTable
DeleteTable
DescribeTable
ListTables
Query
PutItem
GetItem
UpdateItem
DeleteItem
BatchGetItem
BatchWriteItem

42. PutItem, UpdateItem, DeleteItem can take
optional conditions for operation.
UpdateItem performs atomic increments.
Conditional updates.

43. One API call, multiple items
BatchGet returns multiple items by key.
BatchWrite performs up to 25 put or delete operations.
Throughput is measured by IO, not API calls.

44. CreateTable
UpdateTable
DeleteTable
DescribeTable
ListTables
Query
Scan
PutItem
GetItem
UpdateItem
DeleteItem
BatchGetItem
BatchWriteItem

45. Query returns items by key.
Scan reads the whole table sequentially.
Query vs Scan

46. Query patterns
Retrieve all items by hash key.
Range key conditions:
==, <, >, >=, <=, begins with, between.
Counts. Top and bottom n values.
Paged responses.

47. Example
3

48. Players
user_id =
mza
location =
Cambridge
joined =
2011-07-04
user_id =
jeffbarr
location =
Seattle
joined =
2012-01-20
user_id =
werner
location =
Worldwide
joined =
2011-05-15

49. Players
Scores
user_id =
mza
location =
Cambridge
joined =
2011-07-04
user_id =
jeffbarr
location =
Seattle
joined =
2012-01-20
user_id =
werner
location =
Worldwide
joined =
2011-05-15
user_id =
mza
game =
angry-birds
score =
11,000
user_id =
mza
game =
tetris
score =
1,223,000
user_id =
werner
location =
bejewelled
score =
55,000

50. Players
Scores Leader boards
user_id =
mza
location =
Cambridge
joined =
2011-07-04
user_id =
jeffbarr
location =
Seattle
joined =
2012-01-20
user_id =
werner
location =
Worldwide
joined =
2011-05-15
user_id =
mza
game =
angry-birds
score =
11,000
user_id =
mza
game =
tetris
score =
1,223,000
user_id =
werner
location =
bejewelled
score =
55,000
game =
angry-birds
score =
11,000
user_id =
mza
game =
tetris
score =
1,223,000
user_id =
mza
game =
tetris
score =
9,000,000
user_id =
jeffbarr

51. Players
Scores Leader boards
Query for scores
by user
user_id =
mza
location =
Cambridge
joined =
2011-07-04
user_id =
jeffbarr
location =
Seattle
joined =
2012-01-20
user_id =
werner
location =
Worldwide
joined =
2011-05-15
game =
angry-birds
score =
11,000
user_id =
mza
game =
tetris
score =
1,223,000
user_id =
mza
game =
tetris
score =
9,000,000
user_id =
jeffbarr
user_id =
mza
game =
angry-birds
score =
11,000
user_id =
mza
game =
tetris
score =
1,223,000
user_id =
werner
location =
bejewelled
score =
55,000

52. Players
Scores Leader boards
High scores by game
user_id =
mza
location =
Cambridge
joined =
2011-07-04
user_id =
jeffbarr
location =
Seattle
joined =
2012-01-20
user_id =
werner
location =
Worldwide
joined =
2011-05-15
user_id =
mza
game =
angry-birds
score =
11,000
user_id =
mza
game =
tetris
score =
1,223,000
user_id =
werner
location =
bejewelled
score =
55,000
game =
angry-birds
score =
11,000
user_id =
mza
game =
tetris
score =
1,223,000
user_id =
mza
game =
tetris
score =
9,000,000
user_id =
jeffbarr

53. Customer
Success
4

54. • O IBOPE é a maior empresa privada de
pesquisa da América Latina e a 12ª maior do
mundo;
• Amplamente presente no território brasileiro, a
empresa tem filiais nos Estados Unidos e em 13
países da América Latina;
• Por ser um provedor de informações e
conhecimento, a tecnologia é fundamental
para o suporte aos produtos oferecidos pelo
IBOPE.
“A utilização do AWS permitiu o desenvolvimento da
solução sem grande investimento inicial em CAPEX, além
de possibilitar flexibilidade na expansão da infraestrutura.”
“A AWS é ideal
quando os recursos
necessários são
incertos ou não
podem ser previstos
com precisão.”

55. O Desafio
• O desafio do IBOPE era desenvolver um
sistema para monitoramento dos principais
portais de internet para coletar as peças
publicitárias apresentadas e, com isso, prover
informações referentes ao investimento
publicitário em internet aos seus clientes;
• Permitir a coleta de grande quantidade de
dados de forma rápida e escalável;
• Permitir o crescimento para outras operações
do grupo em outros países.

56. Sobre a o Papel da AWS e Benefícios
alcançados
PARAGRAFO
RESUMO CASO _
KEY WORDS de
BENEFICIO,
DESAFIO VENCIDO
– RESUMO DO
CASO EM UM
PARAGRAFO
• Motivos para escolha do AWS:
• Incertezas com relação ao volume de dados a ser
capturado;
• Poder crescer e reduzir rapidamente a infraestrutura;
• Possibilidade de, no futuro, trabalhar com o autoscale;
• Alta disponibilidade dos serviços.
• Benefícios
• Administração simples e fácil;
• Maior disponibilidade e performance das aplicações;
• Escalabilidade;
• Desempenho rápido.

57. Thank you. Questions?
Learn More:
aws.amazon.com/dynamodb