We’re planning to launch a free web tool at kubernetesguru.getanteon.com, which leverages a large dataset of Kubernetes-related information that we've gathered from various sources on the internet. We used an RAG-based approach to create a tool that can provide quick, detailed responses to specific Kubernetes questions.
Our testing shows that it often gives more detailed and precise answers compared to general-purpose AI like ChatGPT. However, we haven’t fully tested the UI yet, so there might be some bugs – apologies in advance!
We aim to see if this concept is helpful to the community and to gather feedback on how we can improve it before the official launch. We’d appreciate any thoughts or suggestions you have.
To accurately assess the performance of your system, it is required to generate data that can help identify any bottlenecks. To achieve this, you can use third-party performance testing tools and correlate the resulting data with monitoring data. Alaz uses eBPF technology, similar to Pixie, to collect system data which it then sends to the Ddosify Platform - whether on the cloud or self-hosted. In Ddosify, Observability and Performance-testing are natively integrated, so you can view any performance bottlenecks in real-time without the need for correlating the performance-testing and observability data. It's all in one platform and there will be additional features soon based on community feedback.
Absolutely! Both Akita and Kubeshark have made significant contributions with their innovative approaches. However, Alaz - Ddosify stands out from the rest due to its effortless integration with the Ddosify Observability Platform (Cloud and Self-Hosted). This integration not only enables users to monitor their applications within the cluster but also to generate load tests for their applications that run in the cluster to spot glitches instantly. Moreover, the platform offers a unified dashboard that displays both the cluster and application performance metrics, service maps, and actionable insights (such as detecting zombie services), providing a comprehensive view of the system. Alaz is incredibly easy to set up by installing a DaemonSet into the cluster. We have many exciting developments planned for Kubernetes monitoring! With the help of our community, we are prioritizing the features on our roadmap. Thank you for your valuable input.
> Does it generate the Service Map from eBPF traces too?
Yes, Alaz generates the K8s service map from eBPF kernel trace points. If you wonder about the details and the internal of the system, check Alaz Architecture: https://github.com/ddosify/alaz/blob/master/Alaz-Architectur...
> Can you use the Service Map without k8s?
We have already planned to make Alaz available for direct installation on Linux or Docker without the need for K8s. We are developing this feature now and will be released soon. Stay tuned for the updates!
Currently, we don't collect traces from codes. However, we plan to make this feature available soon. Stay tuned for the updates! Currently, Alaz uses eBPF to collect network information from the kernel and correlate it with K8s services to generate a Service Map. In addition, Alaz utilizes Prometheus Node exporter to collect system resources such as CPU, memory, disk, and network from K8s nodes. This data is then sent to the Ddosify Platform for visualization. Our next plan is to collect code traces using eBPF. We understand that collecting code traces for scripted languages can be more challenging than for compiled languages, so we will utilize a hybrid approach using both eBPF and OTEL (Open Telemetry).
Pixie is great, but Alaz stands out due to its effortless integration with the Ddosify Observability Platform (Cloud and Self-Hosted), allowing native integration of K8s Observability and Performance Testing.
Thank you for your valuable feedback. We understand the importance of providing support for non-Kubernetes environments. We have already planned to make Alaz available for direct installation on Linux or Docker without the need for Kubernetes dependency. This feature is currently in development and will be released soon. Stay tuned for the updates!
Thank you for your kind words. We greatly appreciate your support, and we're excited for you to try out Alaz. If you have any feedback or suggestions, please don't hesitate to share them with us. Your input will help us improve our product. For support or to share your feedback, please join our community Discord channel at https://discord.com/invite/9KdnrSUZQg.
Thank you for providing us with your valuable feedback. At the moment, the metrics and eBPF sections are configurable but not yet documented. However, we are working on adding this information to our documentation as soon as possible. If you wish to disable the eBPF component and use only metrics instead, you can add the environment variable 'EBPF_ENABLED=false' to your alaz.yaml file.
Alaz utilizes minimal resources for both network and resource collection. CPU and Memory resources are limited in the K8s configuration file.
Alaz is still in its early stages of development and it's being improved based on developer feedback. Thanks again for providing us with your valuable input.
I'm using Zotero with Yandex WebDav and it's pretty amazing. But there is no official iPad/mobile application. Are you planning to release a mobile application?
We’re planning to launch a free web tool at kubernetesguru.getanteon.com, which leverages a large dataset of Kubernetes-related information that we've gathered from various sources on the internet. We used an RAG-based approach to create a tool that can provide quick, detailed responses to specific Kubernetes questions.
Our testing shows that it often gives more detailed and precise answers compared to general-purpose AI like ChatGPT. However, we haven’t fully tested the UI yet, so there might be some bugs – apologies in advance!
We aim to see if this concept is helpful to the community and to gather feedback on how we can improve it before the official launch. We’d appreciate any thoughts or suggestions you have.