Zeebe vs kafka

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The Zeebe team just implemented a Kafka Connect Zeebe connector. Kafka Connect is the ecosystem of connectors into or out of Kafka. There are lots of existing connectors, e. Based on a POC, which I showed in a talk at Kafka Summit San Francisco recording available , the Zeebe team cleaned up the code which is pretty important if I wrote parts of it ;- and just released a version that is suitable for real-life usage. Send messages to a Kafka topic when a workflow instance reaches a specific activity.

Zeebe vs kafka

The Kafka Producer and Kafka Consumer connectors are found here. This project uses Kafka Connect from Confluence. This Kafka Connect connector for Zeebe can do two things:. Send messages to a Kafka topic when a workflow instance reached a specific activity. Please note that a message is more precisely a kafka record , which is also often named event. This is a source in the Kafka Connect speak. Consume messages from a Kafka topic and correlate them to a workflow. This is a Kafka Connect sink. It can work with Camunda Platform 8 SaaS or self-managed. See this blog post for some background on the implementation.

The record value is a JSON representation of the job itself, the record key is the job key.

Sign up. Sign in. In Zeebe. I showed how this allows you to leverage workflow automation in a lot more use cases , also in low latency, high-throughput scenarios. I revealed that Zeebe plays in the same league as e. Apache Kafka.

Sign up. Sign in. There are many use cases for workflow automation out there. Many people think that workflow automation is only used for slow and low frequency use cases like human task management. Despite the fact that this is not true see e. As traditional engines are based on relational databases they are naturally limited in scale to what that database can handle.

Zeebe vs kafka

Sign up. Sign in. In Zeebe.

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Therefore, these orchestration tools formed a layer to monitor and manage long-running business processes that span multiple microservices. Recently added articles. In order to elect a leader, or to successfully replicate data, you need a so called quorum , which means a certain number of acknowledgements of other Raft members. In this scenario Kafka solves the problem of communicating safely between microservices, and Zeebe solves the problem that you need stateful workflow patterns within certain microservices, like for example waiting for events for a longer period of time, having proper timeouts and escalations in place. Kafka Connect is the ecosystem of connectors into or out of Kafka. Main concepts include replication, fault tolerance, partitioning and elastic scaling. Notifications Fork 52 Star This also gives the possibility to start using commands to control the workflow. The append-only log consisting of events or records to be precise is a constant stream of events. Now the event log must be replicated to other nodes in the network. But we prefer to have the data in the memory of a couple of servers rather than on the disk of one of them. Learn how we entered the new era of cloud-scale workflow automation with the help of events. Unless you use the existing cloud service , you will need to invest a reasonable amount of effort to setup and operate Kafka. Apart from scalability, Zeebe is also built for high performance on a single node from ground up. If you are building an event-driven application you might end up having Kafka messages floating all over the place.

We often get questions about how Zeebe is the same as or different from other tools and frameworks that can be used to orchestrate workflows.

How we built a highly scalable distributed state machine. This can also be a first step into acting on potential failures, for example by adding timeouts and warnings when a process takes too long time to complete. Waehner points out that Kafka is about three concepts; messaging, storage and processing of data. Typical other properties are:. How integrating Zeebe and Apache Kafka together can help? Wondering how technical transactions work with Camunda and the Spring framework? Select your country Select a country I consent to InfoQ. Learn how we entered the new era of cloud-scale workflow automation with the help of events. He also points out that with one service responsible for a workflow, there is one point where you can check for the state of running orders, number of successful orders, and so on. All events related to one workflow instance must go onto the same partition, otherwise we would violate the single writer principle and also make it impossible to recreate the current state in a broker node locally. How do you make sense of it? Sitting in NYC again today, I wanted to take that opportunity to write a blog post about why and how Zeebe can play so well together with Kafka. Another great example is around messaging integration patterns. As soon as we have completely processed an event and applied it to the snapshot, we delete it right away.