Flink 的过去 offline Real-time Batch Processing Continuous Processing & Streaming Analytics Event-driven Applications ✔ 现在 Flink 1.9 的架构变化 Runtime Distributed Streaming Dataflow Query Processor Flink 的现在 offline Real-time Batch Processing Continuous Processing & Streaming Analytics Event-driven Applications ✔ ✔ 未来 Micro Services O_0 O_1 I_0 I_1 I_2 P_0 P_1 P_2 S_0 S_1 Order Flink 的未来 offline Real-time Batch Processing Continuous Processing & Streaming Analytics Event-driven Applications ✔ ✔ ✔ 扫码加入社群 与志同道合的码友一起 Code Up 阿里云开发者社区 Apache Flink China 2群 粘贴二维码 谢谢！

KEDA proposal Context KEDA allows for fine-grained autoscaling (including to/from zero) for event-driven Kubernetes workloads. KEDA serves as a Kubernetes Metrics Server and allows users to define autoscaling RocketMQ now. So we need to create PR in KEDA repo for new support for RocketMQ - KEDA has event-driven scaling which means it scales based on events instead of RAM or CPU usage, also can be combined

“frontend” App “backend” OpenCensus Distributed Calculator D E M O Functions with Dapr Event-driven Stateless Easy replication and sharing Input/Triggers App Output/Bindings Functions with Dapr

open source project which provides a set of components (Serving and Eventing) that introduce event-driven and serverless capabilities for Kubernetes clusters for deploying, running, and managing serverless

and incrementally adoptable equivalents that can run anywhere Introducing Dapr A portable, event-driven, serverless runtime for building distributed applications across cloud and edge Runs on multiple

miscellaneous issues from previous audit (Low) Conclusions Introduction “Dapr is a portable, event-driven runtime that makes it easy for developers to build resilient, microservice stateless and stateful

D 10709 Berlin cure53.de · mario@cure53.de Introduction “Dapr is a portable, event-driven runtime that makes it easy for developers to build resilient, microservice stateless and stateful

provides fine-grained control over state and timer, which allows for the implementation of advanced event-driven systems. You can run the latest version of these examples by yourself in ‘Live Notebook: DataStream’

provides fine-grained control over state and timer, which allows for the implementation of advanced event-driven systems. You can run the latest version of these examples by yourself in ‘Live Notebook: DataStream’

in this log file as collected data. 1.2.5. EventAdmin The Decanter EventAdmin Collector is an event-driven collector, listening for all internal events happening in the Apache Karaf Container. NOTE