Temporal Reviews

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.

There are some issues that we face with the versions of Temporal and the databases that we use for Temporal. That's the pain point for us. 

We have been using Astra DB for Temporal, and that has been creating issues. Versions lower than, I guess, v14 point something has some issues. We get some errors in the lower versions. So, sometimes, the workflow gets terminated because of that.

It's been around one and a half years.

Currently, I have not faced any major issues with stability. 

We face issues regarding the databases themselves and nothing apart from those.

60% of our employees use it. I prefer them to use Temporal unless it's for long-running workflows or something where you need to generalize a solution, then it's very useful. 

The other issue is that Temporal's debugging is a bit complex. I guess that's the other issue. I guess it's mostly sorted in the newer versions.

Like earlier, it does not support Elasticsearch. Actually, earlier, it wasn't supported. So, it supports it in the newer version.

I have met some core teams of Temporal. They have been to India and Bangalore, and there was an event for the same. I have been there.

From there, I realized that this issue that I get is due to the versions and all.

Positive

The initial setup is easy.  

The pricing is worth it. The price that they've set is, I guess, worth it.

It's completely up to your requirements. If those requirements meet the expectations of Temporal, I would suggest you go with Temporal rather than any other system. Because in Temporal, you can actually track the activities that have been performed, and it has a proper UI. So it would be very easy for you to debug things and the errors that you have rather than check all the logs.

It is easy for a beginner to learn to use Temporal for the first time. Overall, I would rate it a nine out of ten.

Our team's project is about a service that allows you to hit third-party APIs. Let's say we have multiple products at our company. They will want to hit some third-party APIs for document verification, for which we have some third-party vendors. Instead of them directly hitting their product, we act as a layer in between. They hit our APIs, and then we hit the vendors, ensuring that the response they get is generalized.

Different vendors might have different responses. We give a generic response so that it's easier for them to handle at their end. We have two different modes of operation, namely, sync and async APIs. Async APIs are where we mostly leverage Temporal because async workflows are prone to errors and are not that durable. That's where Temporal comes in. We use Temporal to ensure the durability and scalability of our product.

The solution's most valuable feature is its ability to retry from an interrupted state. I think this feature makes it more popular among developers, and we specifically use and leverage this feature the most in our project.

Temporal doesn't have built-in data storage to store the state of the ongoing execution. We have to configure separate RDS for state management and data storage. Temporal should consider having its own data store so that we don't have to integrate with a third-party data store app.

I have been using Temporal for a couple of months in my company's recent project.

We didn't face an incident where Temporal was down for any reason. The only issue we faced was that we had allocated less storage to the RDS, which Temporal was using to store all sorts of data. Since the storage was less and we had more data pushed, it broke down. However, that had nothing to do with the solution.

I rate the solution’s stability a nine out of ten.

Temporal is a scalable solution both in terms of integration and handling multiple requests. When I spin an activity in a workflow, Temporal can manage it pretty well and store the state of the ongoing code irrespective of how heavy that activity is. In case of a failure, it retries from the current state.

The major challenge we faced was durability. There are instances where an execution gets interrupted, or our service goes down for some reason. Since we mostly use Temporal to interact with third-party vendors, those vendors or endpoints would often be down.

We had to have our own custom retry mechanisms, which come with building Temporal. There was no way to ensure the execution could be durable. The best thing we could do was ask the product to hit that API again.

I would recommend the solution to other users. The main reason why Temporal was built was to have a defined state for microservices. For databases, if we do a transaction, we can be sure either it will succeed or fail. There won't be any state where it's somewhere in between that. Temporal does the same thing for microservices.

Many open-source applications have to implement their own custom logic to ensure things Temporal offers by using an API call to their service using gRPC. This custom logic is heavy, apart from the business logic itself.

Overall, I rate the solution a nine out of ten.

Temporal serves as an orchestration manager for our Mac platform. It allows us to view and manage all the workflows within the application, providing a framework for implementing these workflows efficiently. Temporal itself doesn't store data; our AWS setup handles storage and other functionalities. Temporal offers a platform for orchestration management and provides SDK tools.

Temporal serves as an Orchestration Manager for our microservices architecture. It automates the communication and actions between multiple microservices. With Temporal, we can track workflows' progress, identify failures, and retry actions as needed. It eliminates the need for each microservice. Previously, we had a dedicated microservice for orchestration, but Temporal replaced it seamlessly.

Temporal provides visibility into workflow progress and analytics andsupports scheduled tasks with customizable settings, making it very convenient.

There are areas where Temporal could improve. For instance, calling multiple microservices with Temporal introduces latency due to workflow registration and analytics overhead.

I have been using Temporal for the past year.

I haven't experienced any stability issues or bugs with Temporal. It has been stable and reliable throughout my use, up to version 1.5.

I once contacted Temporal's support team regarding a certificate issue, but it was our mistake. Overall, I was satisfied with the support.

The initial installation and setup of Temporal was straightforward. We needed to generate a certificate and private key, making the process very simple.

If someone asked me whether they should use Temporal for the first time, I'd say it depends on their customer base. If they have few customers, RabbitMQ might be a better choice. However, if they have many customers or an event-driven system, Temporal would be easier to manage and track. For a beginner joining our team, learning Temporal might initially have a moderatelearning curve, but once you get the hang of it, it becomes pretty easy to understand.

I would rate Temporal nine out of ten because it's the best workflow manager I've used.The one-point deduction is mainly for potential improvements and the latency issue that can sometimes occur.

We use Temporal to manage workflows for a client project involving interactions with MongoDB. We needed a framework to manage workflows and set the correct order and timing. We chose Temporal over Azure Functions because it worked better for our needs.

What I like best about the tool is that it's easy to install, especially since it uses JavaScript. It's also easy to set up with Docker, and the documentation is easy to understand.

Configuring workflows can be improved —the solution could offer more options, but it's not a must-have.

I have been using the product for a year. 

I haven't experienced any stability issues or bugs with Temporal. Any problems I encountered were more related to our specific project than the tool itself.

I think Temporal's scalability is very high. While our current project hasn't required much scaling yet, I can see the benefits for future use. I'd rate its scalability as an eight out of ten, mainly because it was easy to implement with Docker. 

In my organization, at least three people used Temporal when I was involved in the project.

I haven't contacted the support. I can manage with the documentation. 

Before implementing Temporal, we struggled with Azure Functions, which was hard to understand and manage. Temporal made it clearer how the workflow would function from start to finish.

Temporal is open-source and free to use, which is great. We didn't have to pay for any premium features.

You need to know Node.js, Express, and Docker to use the tool effectively. Docker is particularly important for easy setup and image mounting.

Overall, I'd give the tool a solid an eight out of ten. It's easy to use and start up, making it simple to begin a project.

I would recommend Temporal to others. My advice would be to clearly understand Docker, as it goes hand-in-hand with using Temporal for setup and implementation. I'd also recommend reading the documentation about creating plugins for Temporal to understand how to build workflows for any project.

We use this solution to power an internal workflow engine we've developed. Currently, it's being used within our company to manage workloads running on the orchestration platform.

Temporal fault tolerance has been beneficial for our critical business operations. It handles scheduled tasks and failures effectively, automatically retrying processes in case of issues. Additionally, our workflows are managed smoothly, with our internal engine providing inputs toTemporal for task execution. As for AI-driven projects, we haven't utilized Temporal in this domain yet.

The most valuable feature is its ability to manage and automate workflows without manual intervention efficiently. This includes handling functions and activities and orchestrating various tasks seamlessly. Temporal's reliability and performance have been exceptional for us, even with thousands of workflows running daily on-premises.

Temporal could be improved by making it more user-friendly for beginners and non-technical staff, ensuring easier integration and usability across different use cases.

I have been using Temporal for the past year and a half.

In my experience, I haven't encountered any issues or bugs. Everything has been working smoothly.

It is very scalable. We can run thousands of workflows simultaneously without any issues, so scalability-wise, it's perfect.

The initial setup of Temporal is straightforward. It's similar to deploying other servers; you must adjust the resources if necessary. Overall, no special knowledge is required.

I would rate it an eight because while it's robust and scalable, once you're familiar with it, there's a learning curve for newcomers due to the complexities involved. Additionally, managing specific errors and concepts can be initially challenging. However, its scalability and open-source nature are significant advantages.

We needed to implement different workflows for various processes, each involving distinct steps. We used these workflows to handle large data flows or complex operations within our system. We employed them to limit rates, as it was the simplest solution. Furthermore, we implemented some cron jobs, not because they were required but because we wanted to avoid excessive zooming.

It is fairly easy, though it has some undefined aspects if you're unfamiliar with it. For instance, you need to properly define your functions and handle various small issues that can arise. It's easy to get started and user-friendly. There are some internal challenges. For example, I initially missed some error handling and connectivity issues, which led to problems because I implemented things incorrectly. 

I don't like the limitations on data flow, particularly the difficulty of passing large amounts of data between different activities. I encountered issues with this and explored various approaches. I decided to store the data in files, but there were other methods. This was redundant because it added complexity to the implementation, making it more challenging to manage.

I have been using Temporal for about a year.

Due to an overload on our cluster, we encountered some problems. We started too many tasks simultaneously. Initially, we attempted to run many tasks at once, but this approach caused issues.

I rate the solution’s stability a five out of ten.

The system has good scalability, though it does have some limitations. For instance, Postgres handles scalability well up to a certain point, typically around eight to ten instances. Using a NoSQL database might address some scaling issues more effectively. Postgres was sufficient, and we could work within its constraints.

I rate the solution’s scalability an eight out of ten.

You can use it for free if you want.

In most cases, you don't need extensive prior knowledge to use this technology; the documentation is usually sufficient. It was likely my mistake for not understanding the problem correctly, even though it was logically straightforward. Fortunately, we had an experienced developer on our team who helped me identify some best practices.

I recommend this technology even to large tech companies. It’s pretty substantial and impactful. I suggest reading the documentation more carefully, as it affected my experience.

Overall, I rate the solution an eight out of ten.