blog-articles VS proposals

Thanks for answering! The bar is as I described. The events are the source of truth, only if you're using them in the write model as a basis for the state rehydration. If you're using materialised view, even though it's built based on events, then you outsourced the truth to other storage. If you're doing a pattern that you're just using events to build up the materialised view you use for the write model logic, that can lead to using Event Streaming tools like Kafka, Pulsar, etc. And, as you said, this is a dead-end. Read more on what I mean by getting the state in Event Sourcing system: https://event-driven.io/en/how_to_get_the_current_entity_sta...

Of course, rebuilding a state from events each time you're processing command may sound dubious. Still, event stores (even those with relational DBs as backing storage) are optimised to read events quickly. Typically, reading event 100 events are not an issue for them. Also, for a typical LoB application. The temptation to use Snapshots is strong, but it should be treated as an optimisation when there is huge performance requirements. They may get out of sync with events. They may be stale, etc. I wrote about that in: https://www.eventstore.com/blog/snapshots-in-event-sourcing. The critical part is to keep streams short. That's again a difference between Event Streaming and Event Sourcing. In Kafka, you may not care how long your topic is, as it's just a pipe. Event stores are databases, so the more events you have, the worse. It impacts not only performance but also makes schema versioning harder (more on that: https://event-driven.io/en/how_to_do_event_versioning/).

It's easy to fall into the trap, as using a streaming solution is tempting. They promise a lot, but eventually, you may end up having issues as you described.

Still, that can be said on any technology. Event Sourcing has some dark parts. Also, it's essential to highlight them, but it's dangerous to present other tools and patterns issues related to ES.

Event Sourcing by itself doesn't directly relate to eventual consistency, type of storage, messaging, etc. Those are implementation details and tradeoffs we're chosing. Each storage solution has it's patterns and anti-patterns. Relational databases have normalisation. Document databases are denormalised. Key-value stores have strategies for key definition. Event stores also have their specifics. The most important is (as I mentioned) to take into the account the temporal aspect of streams, so keeping them short. I agree that there is a huge gap missing in knowledge sharing. I'm working on the article recently about that aspect. Here's a draft: https://github.com/EventStore/blog-articles/blob/closing-the....

I think that you highlighted well what can happen if you use the Event Streaming solution as a basis for the Event Sourcing solution. But unfortunately, the title and description state that it's about Event Sourcing, which is unfortunate, as it's highly misleading for people who are not aware of what Event Sourcing is and may suggest that it's much more complicated than it's in reality.

See also my other comments below in the thread, where I commented also in more details on other parts.

Check out temporal.io open source project. It was built at Uber for large scale business-level processes. So any data pipelines are low-rate use cases by definition.

With KuFlow it is also possible to work with serverless workflows apart from Temporal.io, we explain it in this blog entry, but in summary, almost as a no-code tool, the correct use It would be a rather low-code tool; in just a matter of minutes with our drag-and-drop tool, you can have a workflow that interacts with one or more users of the organization.

Otherwise you may want to look into Kafka or Fluvio to ensure that task runs at least once. If you're doing something like batch operations as a background task, Temporal is another great option.

The runtime is developed using Temporal, which is one of the main tools that we are currently using at KuFlow. Thanks to, all the workflow executions are robust: your application will be durable, reliable, and scalable.

Hmmm I got confused by the name. I thought it's related to https://temporal.io/

Have you looked at temporal.io open source platform? It uses event sourcing as an implementation detail. But it greatly simplifies the user experience compared to "raw event sourcing."

Rewrite Airflow on top of temporal.io. This way, you get unlimited scalability and very high reliability out of the box and would be able to innovate on the features that matter for DE.

Hi all, founder @ Retool here. Over the past year, we’ve been working on Retool Workflows; a fast way for engineers to automate tasks with code. We started building the product because we ourselves (as developers) were looking for something in-between writing cron jobs (which involves a lot of boilerplate) and Zapier (which oftentimes isn’t customizable enough, since it doesn’t _really_ support writing code).

Workflows is a code-first automation tool: you’re _expected_ to write code, but we handle all the boilerplate for you. For example: out-of-the-box integration with 80+ resources (you probably don’t want to be trying to figure out OAuth 2.0 with Salesforce!), monitoring and observability (so you can see the output of every run in the past, and immediately be notified if something goes wrong), and permissions (e.g. some Okta groups can see the outputs of Workflows, but can’t change the code itself).

Right now, the product is cloud-only, but we’re hard at work at an on-prem, self-hosted version (in a Docker image). If you’re interested in that version, feel free to email us at [email protected]. We aim to get it out in the next few weeks. Self-hosted Retool is responsible for a large portion of our usage today, and we’re excited to be supporting Workflows too.

All Retool plans now include 1GB of Workflows throughput, which we think is quite generous (80% of active Workflows users are below 1GB). We don’t bill by run at all, so you’re welcome to run as many workflows as you want.

We use a bunch of interesting technology for Workflows; we are, for example, using Temporal (https://temporal.io/) under the hood. That’s something we’re going to be writing a blog post about later. (We’ve been hard at work on the launch, hah.)

In such a diverse world, it would be boring to have a single way of doing things. That's why at KuFlow we support different ways to implement the logic of our processes and tasks. And in this post, we will talk about one of them, the orchestration through Temporal, which gives us a powerful way to manage our workflows.