Temporal Reviews

I'm part of MoCS's platform engineering team. We initially searched for a workflow orchestration engine and discovered Temporal, which met all our development needs. We implemented Temporal in-house and built a self-service platform called Dash for developers, which allows them to use Temporal as needed.

I’m part of the API management team, focusing on deploying API proxies onto gateways. We use Temporal to orchestrate these proxies and manage the API management lifecycle, including deployment processes.

In addition to API management, our entire platform engineering team utilizes Temporal to orchestrate infrastructure tasks such as deployment, deletion, and management. On the business side, Temporal is also used to manage shipment bookings and invoicing processes.

Temporal focus on developers rather than business users. In contrast to older workflow orchestration engines like Camunda, which are more business-oriented and strongly emphasize UI and workflow authoring, Temporal is geared toward developers. It provides extensive capabilities for building complex workflows.

A standout feature of Temporal is its handling of long-running workflows, a significant advantage over many other solutions. Temporal excels in managing distributed transactions and application state durability, especially in microservice environments where transactions might fail due to network issues.

Temporal simplifies these challenges by managing retries, fail-safes, and circuit breakers. As a result, developers don't need to implement these features manually; Temporal handles them implicitly, though it also allows for tuning based on specific needs.

We normally use Temporal to create pipelines and to move data from FTP servers to some accounts.

When some jobs take a lot of time and fail midway, the solution’s retry feature automatically causes them to retry. We can put to sleep long-running jobs that take a lot of time and then run them again after some time.

I use it to handle messages from the WhatsApp API. There is a product in my company that uses that API, and Temporal helps us debug and handle retries about the messages we receive, persisting the delivery of the message to the final client.

I like the inner retry system that it has, as a developer. The availability it provides to our products is great. When a micro service crash, Temporal handles the retry by itself. It helps a lot in our company and is by far the best thing Temporal has created.

I think people need to have some experience, at least with event-driven design. When someone tries to understand the signals, queries, and updates, it becomes a little challenging if they haven’t worked with webhooks or event sockets. But if someone already knows about event-driven designs, it’s pretty simple.

In general, we use Temporal for workflow management. Some of the information is private because I work for a private company.

Temporal allows retryability for different workflows whenever they fail. It helps us to retry workflows without worrying about idempotence and ensures things get done.

My friend and I are building an online software as a service platform that uses Temporal extensively. One of my use cases is integrating Keycloak, a single sign-on solution that sends events for bootstrapping organizations and users. I've created an open-source plugin or module that anyone can download and use in their projects, published on GitHub. Another use case is bootstrapping cloud-native solutions. You can write code to initialize the whole solution, defining all steps needed in a serialized fashion. Temporal runs through all sequences, workflows, and activities that need to be processed.

What I like best about Temporal is its durable execution, which means you don't need to write many boilerplate code for critical pieces, especially for retries. It also has great observability and a nice dashboard to see issues without digging into logs. The interface for viewing activities is excellent, with good tracing that shows how long activities took and what ran, making it almost perfect for debugging.

We use the product in our environment to manage a complex data pipeline for our highly data-intensive virtual operating system. Our system processes continuous data flows from instruments worldwide that collect stratospheric weather data. This data undergoes multiple conversions through several layers to reach an L2 format. 

The platform has significantly improved our organization's handling of complex and long-running workflows. It has enhanced our distributed systems' resilience and fault tolerance, especially considering our extensive use of load balancers, API gateways, and complex server configurations. It ensures that if something goes wrong, it is handled in a way that minimizes harm.

We have some long-running workflows, like callbacks, that we get after a day or two. We use Temporal for that.

It enables us to use it for something like a continuous process that needs a callback or has multiple steps to be followed. 

It could also be something like having some set of activities that need to be created for a particular task but some other sets for different tasks. It's useful in those situations.

We use Temporal for several purposes. One is for scheduling purposes, where we have workflows that must run at regular intervals or over long durations. Another involves triggering specific events, like in a video processing pipeline, where processing begins as soon as a video becomes available. We also use it for ETL purposes, where once data is available in the data store or warehouse, Temporal triggers the necessary analytics processing. Different teams also define and use the tool based on their specific needs.

From my experience with the tool, I think its best feature is fault tolerance. If an issue occurs, it can be retried from the last successfully processed step without reprocessing the same message. This gives us confidence that completed events won't be retried unnecessarily, even in case of failure. Additionally, it offers a great dashboard and monitoring features, allowing us to see if and where any failures occur, and we can easily retry or replay events to identify issues. Another strong point is that Temporal stores all events in a database, handling everything independently. This feature makes it easier to debug and track logs as needed.

The tool is easy for a beginner to learn. The documentation covers activities, workflows, workers, servers, and more. While more examples could be beneficial, the existing resources are good enough to help you get started. There are also YouTube videos available that can provide additional context. The Slack community for Temporal is very active and helpful, similar to Stack Overflow, where you can find answers to a wide range of questions from basic to advanced levels. If you have a unique question, the community is responsive and provides knowledgeable support.