Resilient Kafka: How DNS Traffic Management and Client Wrappers Ensure Availability

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Kafka clients sometimes have problems producing messages because of an incident or maintenance. In most cases these issues are short lived, but clients get paged and while long incidents are rare, they are memorable. In this talk, we'll run through our Kafka infrastructure at Shopify and how clients connect to it. Next, we'll describe our solution for performing failovers using DNS. Afterwards, we'll look at some real world scenarios where this system saved us from major outages. In conclusion, Kafka clusters are more resilient to infrastructure hiccups with a DNS traffic manager and opinionated client wrappers.

Engineering

Vanessa Vuibert
Sta
ff
Production Engineer
Resilient Ka
f
ka: How DNS Tra
ff
ic Management
and Client Wrappers Ensure Availability
@V3_XD

862 14
Scale
Ka
f
ka brokers Ka
f
ka clusters
14M 9
Messages per sec GCP Regions
@V3_XD

• Maintenance
• Incidents
• Regionalize tra
ff
ic
Tra
ff
ic management use cases

Kubernetes (K8s) out of the box
🔓open source
Kafka broker

K8s out of the box
dig +short service.namespace.svc.cluster.local
IP0
IP1
IP2

K8s out of the box
bootstrap.servers=
service.namespace.svc.cluster.local:9092

K8s out of the box
dig +short pod2.service.namespace.svc.cluster.local
IP2

K8s out of the box
advertised.listeners=
pod2.service.namespace.svc.cluster.local:9092

• Readiness
• Startup
• Liveness
K8s StatefulSet: probes

dig +short service.namespace.svc.cluster.local
IP0
IP2
K8s readiness probe

dig +short service.namespace.svc.cluster.local
IP0
IP1
IP2
K8s readiness probe
not ready
publishNotReadyAddresses: true

External tra
ff
ic: load balancers
bootstrap.servers

External tra
ff
ic: load balancers
advertised.listeners

• Issues scaling
• Manual broker DNS
records
• Limited tra
ff
ic
control

Stateful buddy: load balancers
🔒closed source

Ka
f
ka access buddy: endpoints
🔒closed source

Ka
f
ka Access Buddy: producer failover
east

- Elasticsearch on call
“Let me failover real quick.”

Faster failovers with a
DNS tra
ff
ic manager.

DNS tra
ff
ic manager: normal
dig +short us-east1.somedomain.com
US-East1-IP

DNS tra
ff
ic manager: failover
dig +short us-east1.somedomain.com
US-Central1-IP

used to take
40
Minutes
now only takes
1
Minutes
Failover time savings
@V3_XD

Failover during
fl
ashsale
US Central1 -> US East1

• Failover reconnection
• Everything needed for connection
• Ruby, go and python
Client wrappers

K8s Deployment template: bootstrap.servers

Improved availability
with local consumers.

• More availability
• Reduced latency
• Reduced storage costs
• Reduced network costs
Local consumers

Aggregate
500
ms
Regional
20
ms
Latency 99th
@V3_XD

• More secure
• Reduced network costs
• Fetch from closest replica: KIP
-
392
Public to private tra
ff
ic

Reduction
-6%
bill
Network represents
29%
bill
Network cost reduction
@V3_XD

• GKE 1.24 -> 1.25
incident
• Apply
f
irewall rules
• LB more secure for
public tra
ff
ic

Single stop shop with Multi-
Cluster Services (MCS).

Regionalize tra
ff
ic: Ka
f
ka access buddy
east

40 18
MCS time savings
Minutes to regionalize tra
ff
ic Minutes to deploy
1 13
Minutes after migration Minutes after migration
@V3_XD

• Resiliency: DNS
tra
ff
ic management
• Toil: client wrappers
• Availability: local
consumption

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CalArts毕业证学历书【微信95270640】CalArts毕业证’圣力嘉学院毕业证《Q微信95270640》办理CalArts毕业证√文凭学历制作{CalArts文凭}购买学历学位证书本科硕士,CalArts毕业证学历学位证【实体公司】办毕业证、成绩单、学历认证、学位证、文凭认证、办留信网认证、（网上可查，实体公司，专业可靠） (诚招代理)办理国外高校毕业证成绩单文凭学位证,真实使馆公证（留学回国人员证明）真实留信网认证国外学历学位认证雅思代考国外学校代申请名校保录开请假条改GPA改成绩ID卡 1.高仿业务:【本科硕士】毕业证,成绩单（GPA修改）,学历认证（教育部认证）,大学Offer,,ID,留信认证,使馆认证,雅思,语言证书等高仿类证书； 2.认证服务: 学历认证（教育部认证）,大使馆认证（回国人员证明）,留信认证（可查有编号证书）,大学保录取,雅思保分成绩单。 3.技术服务：钢印水印烫金激光防伪凹凸版设计印刷激凸温感光标底纹镭射速度快。办理加利福尼亚艺术学院加利福尼亚艺术学院毕业证文凭证书流程： 1客户提供办理信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：我们有专业老师帮你查询）； 2开始安排制作毕业证成绩单电子图； 3毕业证成绩单电子版做好以后发送给您确认； 4毕业证成绩单电子版您确认信息无误之后安排制作成品； 5成品做好拍照或者视频给您确认； 6快递给客户（国内顺丰国外DHLUPS等快读邮寄） -办理真实使馆公证（即留学回国人员证明） -办理各国各大学文凭（世界名校一对一专业服务,可全程监控跟踪进度） -全套服务：毕业证成绩单真实使馆公证真实教育部认证。让您回国发展信心十足！（详情请加一下文凭顾问+微信:95270640）欢迎咨询！子小伍玩小伍比山娃小一岁虎头虎脑的很霸气父亲让山娃跟小伍去夏令营听课山娃很高兴夏令营就设在附近一所小学山娃发现那所小学比自己的学校更大更美操场上还铺有塑胶跑道呢里面很多小朋友一班一班的快快乐乐原来城里娃都藏这儿来了怪不得平时见不到他们山娃恍然大悟起来吹拉弹唱琴棋书画山娃都不懂却什么都想学山娃怨自己太笨什么都不会斟酌再三山娃终于选定了学美术当听说每月要交元时父亲犹豫了山娃也说爸算了吧咱学校一学期才转

Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...

IJECEIAES

Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to precisely delineate tumor boundaries from magnetic resonance imaging (MRI) scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The model is rigorously trained and evaluated, exhibiting remarkable performance metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical image analysis and enhance healthcare outcomes. This research paves the way for future exploration and optimization of advanced CNN models in medical imaging, emphasizing addressing false positives and resource efficiency.

Manufacturing Process of molasses based distillery ppt.pptx

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Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...

shadow0702a

This document serves as a comprehensive step-by-step guide on how to effectively use PyCharm for remote debugging of the Windows Subsystem for Linux (WSL) on a local Windows machine. It meticulously outlines several critical steps in the process, starting with the crucial task of enabling permissions, followed by the installation and configuration of WSL. The guide then proceeds to explain how to set up the SSH service within the WSL environment, an integral part of the process. Alongside this, it also provides detailed instructions on how to modify the inbound rules of the Windows firewall to facilitate the process, ensuring that there are no connectivity issues that could potentially hinder the debugging process. The document further emphasizes on the importance of checking the connection between the Windows and WSL environments, providing instructions on how to ensure that the connection is optimal and ready for remote debugging. It also offers an in-depth guide on how to configure the WSL interpreter and files within the PyCharm environment. This is essential for ensuring that the debugging process is set up correctly and that the program can be run effectively within the WSL terminal. Additionally, the document provides guidance on how to set up breakpoints for debugging, a fundamental aspect of the debugging process which allows the developer to stop the execution of their code at certain points and inspect their program at those stages. Finally, the document concludes by providing a link to a reference blog. This blog offers additional information and guidance on configuring the remote Python interpreter in PyCharm, providing the reader with a well-rounded understanding of the process.

Electric vehicle and photovoltaic advanced roles in enhancing the financial p...

IJECEIAES

Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network

ML Based Model for NIDS MSc Updated Presentation.v2.pptx

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Embedded machine learning-based road conditions and driving behavior monitoring

IJECEIAES

Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.

Harnessing WebAssembly for Real-time Stateless Streaming Pipelines

Christina Lin

Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.

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Resilient Kafka: How DNS Traffic Management and Client Wrappers Ensure Availability

1. Vanessa Vuibert Sta ff Production Engineer Resilient Ka f ka: How DNS Tra ff ic Management and Client Wrappers Ensure Availability @V3_XD

2. 862 14 Scale Ka f ka brokers Ka f ka clusters 14M 9 Messages per sec GCP Regions @V3_XD

3. • Maintenance • Incidents • Regionalize tra ff ic Tra ff ic management use cases

4. Kubernetes (K8s) out of the box 🔓open source Kafka broker

5. K8s out of the box dig +short service.namespace.svc.cluster.local IP0 IP1 IP2

6. K8s out of the box bootstrap.servers= service.namespace.svc.cluster.local:9092

7. K8s out of the box dig +short pod2.service.namespace.svc.cluster.local IP2

8. K8s out of the box advertised.listeners= pod2.service.namespace.svc.cluster.local:9092

9. • Readiness • Startup • Liveness K8s StatefulSet: probes

10. dig +short service.namespace.svc.cluster.local IP0 IP2 K8s readiness probe

11. dig +short service.namespace.svc.cluster.local IP0 IP1 IP2 K8s readiness probe not ready publishNotReadyAddresses: true

12. Regional pairs

13. External tra ff ic: load balancers

14. External tra ff ic: load balancers bootstrap.servers

15. External tra ff ic: load balancers advertised.listeners

16. • Issues scaling • Manual broker DNS records • Limited tra ff ic control

17. Built automation with k8s controllers.

18. Stateful buddy: load balancers 🔒closed source

19. Name buddy: DNS records 🔒closed source

20. Ka f ka access buddy: endpoints 🔒closed source

21. Ka f ka Access Buddy: consumer

22. Ka f ka Access Buddy: producer failover east

23. - Elasticsearch on call “Let me failover real quick.”

24. Faster failovers with a DNS tra ff ic manager.

25. DNS tra ff ic manager 🔒closed source

26. DNS tra ff ic manager: normal dig +short us-east1.somedomain.com US-East1-IP

27. DNS tra ff ic manager: failover dig +short us-east1.somedomain.com US-Central1-IP

28. - A Ka f ka client “DNS trickery.”

29. used to take 40 Minutes now only takes 1 Minutes Failover time savings @V3_XD

30. Incident during fl ashsale

31. Failover during fl ashsale US Central1 -> US East1

32. Reduced toil with client wrappers.

33. • Failover reconnection • Everything needed for connection • Ruby, go and python Client wrappers

34. K8s Deployment template: bootstrap.servers

35. K8s Deployment template: client ID

36. K8s Deployment template

37. Improved availability with local consumers.

38. • More availability • Reduced latency • Reduced storage costs • Reduced network costs Local consumers

39. Aggregate consumer

40. Local consumers

41. Local consumers: DNS records

42. Aggregate 500 ms Regional 20 ms Latency 99th @V3_XD

43. Connect directly through private IPs.

44. • More secure • Reduced network costs • Fetch from closest replica: KIP - 392 Public to private tra ff ic

45. Tra ff ic manager: pod IPs

46. Reduction -6% bill Network represents 29% bill Network cost reduction @V3_XD

47. • GKE 1.24 -> 1.25 incident • Apply f irewall rules • LB more secure for public tra ff ic

48. Failover: pod IPs

49. Single stop shop with Multi- Cluster Services (MCS).

50. MCS endpoints 🔒closed source

51. Tra ff ic sources

52. Regional pairs: uneven distribution

53. Regionalize tra ff ic: Ka f ka access buddy east

54. Regionalize tra ff ic: MCS

55. 40 18 MCS time savings Minutes to regionalize tra ff ic Minutes to deploy 1 13 Minutes after migration Minutes after migration @V3_XD

56.

57. • Resiliency: DNS tra ff ic management • Toil: client wrappers • Availability: local consumption

58. Thanks! @V3_XD

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