Solution Architect | R&D Lead at a computer software company with 51-200 employees
Real User
Top 20
2024-11-06T08:05:52Z
Nov 6, 2024
There were instances of deployment pipelines getting stuck, and the dashboard not always accurately showing the application status, requiring manual intervention such as rerunning applications or refreshing the dashboard. Additionally, sometimes logs did not reflect actual data, requiring checking Skipper logs or making updates directly in Skipper databases, which could complicate maintenance.
I would improve the dashboard features as they are not very user-friendly. Another area for improvement is the documentation, as it is not very precise. There are limited resources available on Spring Cloud Dataflow, which makes it difficult for many users to find detailed information.
The solution's community support could be improved. I don't know why the Spring Cloud Data Flow community is not very strong. Community support is very limited whenever you face any problem or are stuck somewhere. I'm not sure whether it has improved in the last six months because this pipeline was set up almost two years ago. I struggled with that a lot. For example, there was limited support whenever I got an exception and sought help from Stack Overflow or different forums. Interacting with Kubernetes needs a few certificates. You need to define all the certificates within your application. With the help of those certificates, your Java application or Spring Cloud Data Flow can interact with Kubernetes. I faced a lot of hurdles while placing those certificates. Despite following the official documentation to define all the replicas, readiness, and liveliness probes within the Spring Cloud Data Flow application, it was not working. So, I had to troubleshoot while digging in and debugging the internals of Spring Cloud Data Flow at that time. It was just a configuration mismatch, and I was doing nothing weird. There was a small spelling difference between how Spring Cloud Data Flow was expecting it and how I passed it. I was just following the official documentation.
I am not an expert in the solution, so I cannot comment on what requires improvement in the product. Spring Cloud Data Flow is not an easy-to-use tool, so improvements are required.
Senior Software Engineer at QBE Regional Insurance
Real User
Top 20
2024-03-26T06:50:25Z
Mar 26, 2024
On the tool's online discussion forums, you may get stuck with an issue, making it an area where improvements are required. The online discussion forum for the tool should include possible questions that need to be covered in a broad manner, and if it is completely managed by Spring Cloud Data Flow, then it will be much easier for the users to find answers to their questions related to the product.
Spring Cloud Data Flow could improve the user interface. We can drag and drop in the application for the configuration and settings, and deploy it right from the UI, without having to run a CI/CD pipeline. However, that does not work with Kubernetes, it only works when we are working with jars as the Spring Cloud Data Flow applications. When we are using Docker images as the applications, the UI does not work, or it is not clear how it works. In a feature release, it would be beneficial if there could be more supported languages. They only support Spring, Node.js, and Python.
The configurations could be better. Some configurations are a little bit time-consuming in terms of trying to understand using the Spring Cloud documentation. The documentation on offer is not that good. Spring Cloud Data Flow documentation for the configurations is not exactly clear. Sometimes they provide some examples, which are not complete examples. Some parts are presented in the documentation, but not shown in an example code. When we try to implement multiple configurations, for example, when we integrated with PCF, Pivotal Cloud Foundry, with CDF, there were issues. It has workspace concept, however, in a CDF when we tried to implement the workspace some kind of boundary configuration was not integrating properly. Then we went to the documentation and tried to somehow customize it a little bit on the configuration level - not in the code level - to get the solution working. It is open source. Therefore, you need to work a little bit. You need to do some brainstorming on your own. There's no one to ask. We cannot call someone and ask what the problem is. It is an open-source project without technical support. It's up to us to figure out what the problem is.
Senior Platform Associate L2 at a tech services company with 10,001+ employees
Real User
2020-10-19T09:33:41Z
Oct 19, 2020
Some of the features, like the monitoring tools, are not very mature and are still evolving. With some of the products we used, they did not integrate well and were hanging a lot. One of the advantages of using open-source is that if you don't like a particular tool then you can use another one. If you want to use Kubernetes then you have to optimize a lot in terms of resources. I had a 15 GB MacBook Pro, but initially, it wouldn't work because it would hang. There were also some weird shutdowns. We weren't able to figure out exactly why it happened but it was clearly due to having not enough system resources. When then needed to optimize and increase our heap memory.
Spring Cloud Data Flow is a toolkit for building data integration and real-time data processing pipelines.Pipelines consist of Spring Boot apps, built using the Spring Cloud Stream or Spring Cloud Task microservice frameworks. This makes Spring Cloud Data Flow suitable for a range of data processing use cases, from import/export to event streaming and predictive analytics. Use Spring Cloud Data Flow to connect your Enterprise to the Internet of Anything—mobile devices, sensors, wearables,...
There were instances of deployment pipelines getting stuck, and the dashboard not always accurately showing the application status, requiring manual intervention such as rerunning applications or refreshing the dashboard. Additionally, sometimes logs did not reflect actual data, requiring checking Skipper logs or making updates directly in Skipper databases, which could complicate maintenance.
I would improve the dashboard features as they are not very user-friendly. Another area for improvement is the documentation, as it is not very precise. There are limited resources available on Spring Cloud Dataflow, which makes it difficult for many users to find detailed information.
The solution's community support could be improved. I don't know why the Spring Cloud Data Flow community is not very strong. Community support is very limited whenever you face any problem or are stuck somewhere. I'm not sure whether it has improved in the last six months because this pipeline was set up almost two years ago. I struggled with that a lot. For example, there was limited support whenever I got an exception and sought help from Stack Overflow or different forums. Interacting with Kubernetes needs a few certificates. You need to define all the certificates within your application. With the help of those certificates, your Java application or Spring Cloud Data Flow can interact with Kubernetes. I faced a lot of hurdles while placing those certificates. Despite following the official documentation to define all the replicas, readiness, and liveliness probes within the Spring Cloud Data Flow application, it was not working. So, I had to troubleshoot while digging in and debugging the internals of Spring Cloud Data Flow at that time. It was just a configuration mismatch, and I was doing nothing weird. There was a small spelling difference between how Spring Cloud Data Flow was expecting it and how I passed it. I was just following the official documentation.
I am not an expert in the solution, so I cannot comment on what requires improvement in the product. Spring Cloud Data Flow is not an easy-to-use tool, so improvements are required.
On the tool's online discussion forums, you may get stuck with an issue, making it an area where improvements are required. The online discussion forum for the tool should include possible questions that need to be covered in a broad manner, and if it is completely managed by Spring Cloud Data Flow, then it will be much easier for the users to find answers to their questions related to the product.
Spring Cloud Data Flow could improve the user interface. We can drag and drop in the application for the configuration and settings, and deploy it right from the UI, without having to run a CI/CD pipeline. However, that does not work with Kubernetes, it only works when we are working with jars as the Spring Cloud Data Flow applications. When we are using Docker images as the applications, the UI does not work, or it is not clear how it works. In a feature release, it would be beneficial if there could be more supported languages. They only support Spring, Node.js, and Python.
The configurations could be better. Some configurations are a little bit time-consuming in terms of trying to understand using the Spring Cloud documentation. The documentation on offer is not that good. Spring Cloud Data Flow documentation for the configurations is not exactly clear. Sometimes they provide some examples, which are not complete examples. Some parts are presented in the documentation, but not shown in an example code. When we try to implement multiple configurations, for example, when we integrated with PCF, Pivotal Cloud Foundry, with CDF, there were issues. It has workspace concept, however, in a CDF when we tried to implement the workspace some kind of boundary configuration was not integrating properly. Then we went to the documentation and tried to somehow customize it a little bit on the configuration level - not in the code level - to get the solution working. It is open source. Therefore, you need to work a little bit. You need to do some brainstorming on your own. There's no one to ask. We cannot call someone and ask what the problem is. It is an open-source project without technical support. It's up to us to figure out what the problem is.
Some of the features, like the monitoring tools, are not very mature and are still evolving. With some of the products we used, they did not integrate well and were hanging a lot. One of the advantages of using open-source is that if you don't like a particular tool then you can use another one. If you want to use Kubernetes then you have to optimize a lot in terms of resources. I had a 15 GB MacBook Pro, but initially, it wouldn't work because it would hang. There were also some weird shutdowns. We weren't able to figure out exactly why it happened but it was clearly due to having not enough system resources. When then needed to optimize and increase our heap memory.
The visual user interface could use some help; it needs improvement. The setup and integration could also be better.