Continuous Delivery - Voxxed Days Bucharest 2017

•

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Workshop at the Voxxed Days Bucharest 2017 conference. Movies: 1. Install Spring Boot application: https://youtu.be/WN4gLvlKU0w 2. Create Continuous Integration pipeline: https://youtu.be/0kuIwjzBvss 3. Create Continuous Delivery pipeline: https://youtu.be/ObQ9AdK-j58

Technology

Rafal Leszko (@RafalLeszko, rafal.leszko@gmail.com)
Continuous Delivery: Jenkins,
Docker and Spring Boot

Continuous Delivery
The ability to get changes-features, configuration changes, bug
fixes, experiments - into production or into the hands of users
safely and quickly in a sustainable way

"Traditional" Delivery
Customer
"Agile" team
Centralized QA IT Operations
Analysis + Design
Development
Testing + Showcase
Integration + QA
Release and operation
Acceptance Tests

Continuous
Integration
Customer
"Agile" team
Centralized QA IT Operations
Analysis + Design
Development
Testing + Showcase
Integration + QA
Release and operation
Acceptance Tests

"last mile"
Continuous
Integration
Customer
"Agile" team
Centralized QA IT Operations
Analysis + Design
Development
Testing + Showcase
Integration + QA
Release and operation
Acceptance Tests

Automated Tests
"last mile"
Continuous
Integration
Customer
"Agile" team
Centralized QA IT Operations
Analysis + Design
Development
Testing + Showcase
Integration + QA
Release and operation
Acceptance Tests

Configuration
Management
+
MonitoringAutomated Tests
"last mile"
Continuous
Integration
Customer
"Agile" team
Centralized QA IT Operations
Analysis + Design
Development
Testing + Showcase
Integration + QA
Release and operation
Acceptance Tests

Checkout
Compile
Unit Tests
Deploy to Staging
Acceptance Tests
Release
Smoke Test

dev machine
github server
jenkins server
push
pull

issues?
● Each server has to be configured
● Different environment in staging and production
● Different Java version for different applications
● Applications using the same ports
● Versioning

server
application
app.jar
java -jar app.jar

server
application
app2.jar
java -jar app.jar
application
app.jar

treat your servers like cattle,
not pets

Dockerfile:
FROM frolvlad/alpineoraclejdk8:slim
ADD build/libs/vox0.0.1SNAPSHOT.jar app.jar
ENTRYPOINT ["java","jar","app.jar"]

FROM frolvlad/alpineoraclejdk8:slim
ADD build/libs/vox0.0.1SNAPSHOT.jar app.jar
ENTRYPOINT ["java","jar","app.jar"]
docker build t <image_name> .
Dockerfile:

$ docker run name <container_name> <image_name>

Docker registry
● registry for docker
images

<registry_address>/<image_name>
docker push <image_name>

<registry_address>/<image_name>
docker push <image_name>
docker pull <image_name>

Port Mapping
$ docker run p 8080:8080 name <container_name>
<image_name>

External Docker Host
$ docker run p 8080:8080 name <container_name>
<image_name>
$ docker run H 192.168.0.15:2375 p 8080:8080
name <container_name> <image_name>

Example
$ docker run H 192.168.0.15:2375 p 8080:8080 d
name exchangerate 192.168.0.20/exchangerate:15

dev machine
github server
jenkins server
push
pull
staging machine
production machine
deploy
acceptance test
deploy
smoke test
docker registry
push
pull
pull

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The presentation explores the development and application of artificial intelligence (AI) from its inception to its current status in the modern world. The term "artificial intelligence" was first coined by John McCarthy in 1956 to describe efforts to develop computer programs capable of performing tasks that typically require human intelligence. This concept was first introduced at a conference held at Dartmouth College, where programs demonstrated capabilities such as playing chess, proving theorems, and interpreting texts. In the early stages, Alan Turing contributed to the field by defining intelligence as the ability of a being to respond to certain questions intelligently, proposing what is now known as the Turing Test to evaluate the presence of intelligent behavior in machines. As the decades progressed, AI evolved significantly. The 1980s focused on machine learning, teaching computers to learn from data, leading to the development of models that could improve their performance based on their experiences. The 1990s and 2000s saw further advances in algorithms and computational power, which allowed for more sophisticated data analysis techniques, including data mining. By the 2010s, the proliferation of big data and the refinement of deep learning techniques enabled AI to become mainstream. Notable milestones included the success of Google's AlphaGo and advancements in autonomous vehicles by companies like Tesla and Waymo. A major theme of the presentation is the application of generative AI, which has been used for tasks such as natural language text generation, translation, and question answering. Generative AI uses large datasets to train models that can then produce new, coherent pieces of text or other media. The presentation also discusses the ethical implications and the need for regulation in AI, highlighting issues such as privacy, bias, and the potential for misuse. These concerns have prompted calls for comprehensive regulations to ensure the safe and equitable use of AI technologies. Artificial intelligence has also played a significant role in healthcare, particularly highlighted during the COVID-19 pandemic, where it was used in drug discovery, vaccine development, and analyzing the spread of the virus. The capabilities of AI in healthcare are vast, ranging from medical diagnostics to personalized medicine, demonstrating the technology's potential to revolutionize fields beyond just technical or consumer applications. In conclusion, AI continues to be a rapidly evolving field with significant implications for various aspects of society. The development from theoretical concepts to real-world applications illustrates both the potential benefits and the challenges that come with integrating advanced technologies into everyday life. The ongoing discussion about AI ethics and regulation underscores the importance of managing these technologies responsibly to maximize their their benefits while minimizing potential harms.

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1. Rafal Leszko (@RafalLeszko, rafal.leszko@gmail.com) Continuous Delivery: Jenkins, Docker and Spring Boot

2. Jez Humble

5. Continuous Delivery The ability to get changes-features, configuration changes, bug fixes, experiments - into production or into the hands of users safely and quickly in a sustainable way

7. What is Continuous Delivery?

10.

11.

12.

13. "Traditional" Delivery Customer "Agile" team Centralized QA IT Operations Analysis + Design Development Testing + Showcase Integration + QA Release and operation Acceptance Tests

14. Continuous Integration Customer "Agile" team Centralized QA IT Operations Analysis + Design Development Testing + Showcase Integration + QA Release and operation Acceptance Tests

15. Continuous Integration

16. https://github.com/Dynatrace/ufo

17.

18. Continuous Integration Customer "Agile" team Centralized QA IT Operations Analysis + Design Development Testing + Showcase Integration + QA Release and operation Acceptance Tests

19. "last mile" Continuous Integration Customer "Agile" team Centralized QA IT Operations Analysis + Design Development Testing + Showcase Integration + QA Release and operation Acceptance Tests

20. Automated Tests "last mile" Continuous Integration Customer "Agile" team Centralized QA IT Operations Analysis + Design Development Testing + Showcase Integration + QA Release and operation Acceptance Tests

21. Automated Testing

22.

23.

24. Automated Tests "last mile" Continuous Integration Customer "Agile" team Centralized QA IT Operations Analysis + Design Development Testing + Showcase Integration + QA Release and operation Acceptance Tests

25. Configuration Management + MonitoringAutomated Tests "last mile" Continuous Integration Customer "Agile" team Centralized QA IT Operations Analysis + Design Development Testing + Showcase Integration + QA Release and operation Acceptance Tests

26. Continuous Delivery Pipeline

27. Checkout Compile Unit Tests Deploy to Staging Acceptance Tests Release Smoke Test

28.

29.

30. dev machine github server jenkins server push pull

31. Checkout Compile Unit Tests Deploy to Staging Acceptance Tests Release Smoke Test

32. issues? ● Each server has to be configured ● Different environment in staging and production ● Different Java version for different applications ● Applications using the same ports ● Versioning

33.

34. server application app.jar

35. server application app.jar java -jar app.jar

36. server application app2.jar java -jar app.jar application app.jar

37.

38. treat your servers like cattle, not pets

39.

40.

41.

42. Dockerfile: FROM frolvlad/alpineoraclejdk8:slim ADD build/libs/vox0.0.1SNAPSHOT.jar app.jar ENTRYPOINT ["java","jar","app.jar"]

43. FROM frolvlad/alpineoraclejdk8:slim ADD build/libs/vox0.0.1SNAPSHOT.jar app.jar ENTRYPOINT ["java","jar","app.jar"] docker build t <image_name> . Dockerfile:

44. $ docker run name <container_name> <image_name>

45. $ docker run name <container_name> <image_name>

46. Docker registry ● registry for docker images

47. <registry_address>/<image_name>

48. <registry_address>/<image_name> docker push <image_name>

49. <registry_address>/<image_name> docker push <image_name> docker pull <image_name>

50. Port Mapping $ docker run p 8080:8080 name <container_name> <image_name>

51. External Docker Host $ docker run p 8080:8080 name <container_name> <image_name> $ docker run H 192.168.0.15:2375 p 8080:8080 name <container_name> <image_name>

52. Example $ docker run H 192.168.0.15:2375 p 8080:8080 d name exchangerate 192.168.0.20/exchangerate:15

53. dev machine github server jenkins server push pull staging machine production machine deploy acceptance test deploy smoke test docker registry push pull pull

54. Manual Testing

55. Legacy Projects

56.

57. Performance Tests

58. Blue-Green Deployment

59. Canary Release

60. Code coverage

61. Static code analysis

62. “continuous”

63. Feature Toggle

64.

65. Easy?