Comparing CoAP vs MQTT

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Overview of the architectures of MQTT vs CoAP, plus ways to make MQTT act like CoAP and vice versa. Also contains fun graphics to liven the mood

Internet Actualités & Politique Technologie

REST Architecture
Resource
GET
PUT
POST
DELETE
Client

REST Architecture
• A resource has a scalar value
• Last known value is important

Publish/Subscribe Architecture
• Decouple consumption from production
• Event stream
• Big data analytics

Pub/Sub On CoAP
• GET+Observe
• Loses data in high throughput scenarios
• Not decoupled

REST On MQTT
• Client
– Publisher, subscribes to response topic
• Server
– Subscriber

REST On MQTT
• Request
– $CTL/<domain>/<app>/<clientId>/POST/resource
• Response
– $CTL/<domain>/<app>/<clientId>/resource/<requ
estId>

REST On MQTT
• Not simple
– Future non-local subscribe could ease the pain
• Not composable or extensible
• Requires extra protocol above MQTT
• Response is not guaranteed
• Mutable state

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This is the beautiful story of MQTT and Apache Kafka with the villages of Tanzania. 2G in Africa is not going anywhere any time soon. We serve microfinance banks with limited server resources in remote areas. These banks are beloved in the villages of Africa because they provide cheap loans. In Tanzania and Africa by extension, Agency Banking is the best innovation that ever happened to address financial inclusion. In this model, commercial banks contract third party retail networks as bank agents across the villages. Upon successful application, vetting and approval, these agents are authorized to offer select banking services on behalf of the banks. Bank agents are issued with POS Terminals which have the agency banking application installed. Uchumi Commercial Bank has over 350+ agents all over Tanzania. The bank initially went live with the POS Terminals connecting to the bank servers (bare metal) using REST API. However, many issues emerged. Poor internet connectivity, poor electricity connectivity(agents had to travel far to charge their POS Terminals), limited server capacity that just could not allow scalability, expensive internet bundles. Fadhili Juma will showcase and give a deep analysis on how MQTT and Apache Kafka was the game changer in this resource constrained environment.

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Deploying Kafka to support multiple teams or even an entire company has many benefits. It reduces operational costs, simplifies onboarding of new applications as your adoption grows, and consolidates all your data in one place. However, this makes applications sharing the cluster vulnerable to any one or few of them taking all cluster resources. The combined cluster load also becomes less predictable, increasing the risk of overloading the cluster and data unavailability. In this talk, we will describe how to use quota framework in Apache Kafka to ensure that a misconfigured client or unexpected increase in client load does not monopolize broker resources. You will get a deeper understanding of bandwidth and request quotas, how they get enforced, and gain intuition for setting the limits for your use-cases. While quotas limit individual applications, there must be enough cluster capacity to support the combined application load. Onboarding new applications or scaling the usage of existing applications may require manual quota adjustments and upfront capacity planning to ensure high availability. We will describe the steps we took toward solving this problem in Confluent Cloud, where we must immediately support unpredictable load with high availability. We implemented a custom broker quota plugin (KIP-257) to replace static per broker quota allocation with dynamic and self-tuning quotas based on the available capacity (which we also detect dynamically). By learning our journey, you will have more insights into the relevant problems and techniques to address them.

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