Frequently Asked Questions¶
1. What are the minimum resources and system requirements required to run GraphScope?
To use GraphScope Python interface, Python >= 3.6 and pip >= 19.0 is required. GraphScope engine can be deployed in standalone mode or distributed mode. For standalone deployment, a physical machine with at least 4 cores CPU and 8G memory is required. GraphScope is tested and supported on the following systems:
For distributed depolyment, a cluster managed by Kubernetes is required. GraphScope has tested on k8s version >= v1.12.0+.
2. Is Kubernetes an enssential to run GraphScope?
No. GraphScope supports build and run on local in a single machine. However, GraphScope depends on many third-party libraries and projects. To make our life easier, we suggest you build and run GraphScope on k8s with our provided dev-image and release-image. If you don’t have a kubernetes cluster, you may use tools like kind to setup a local cluster to use the images for trying graphscope.
3. How to debug or get detailed information when run GraphScope?
By default, GraphScope is usually running in a silent mode following the convention of Python applications. To enable verbose logging, turn on it by this:graphscope.set_option(show_log=True)
If you are running GraphScope in k8s, you can use kubectl describe/logs to check the log/status of the cluster. If the disk space is accessible(on local or via pods), you may also find logs in /tmp/graphscope/runtime/logs.
4. Why I find more pods than expected with command `kubectl get pod`?
For the failed pods, you may need to delete them manually. This case is observed when using GraphScope with helm. If users did not correctly set the role and rolebinding, the command helm uninstall GraphScope may not correctly recycle allocated resources. More details please refer to Helm Support.
5. Is GraphScope a graph database?
No, GraphScope is not a graph database. It cannot provide transactions on graph. Instead, it provides an efficient “immutable” in-memory store for fast queries and analysis, and a persistent store(service) for updates on graphs. Both are scale very well - you can launch a larger session from your python notebook to handle a bigger graph or run a complex algorithm.
6. What’s the compatibility of Gremlin in GraphScope?
GraphScope supports most querying operators in Gremlin. You may check the compatibility in this link.
7. The system seems get stuck, what are the possible reasons?
If GraphScope seems to get stuck, the possible cause might be:
In the session launching stage, the most cases are waiting for pods ready. The time consuming may be caused by a poor network connection during pulling image, or caused by the resources cannot meet the need to launch a session.
In the graph loading stage, it is time consuming to load and build a large graph.
When running a user-defined or built-in analytical algorithm, it takes time to compile/distribute the algorithm over the loaded graph.
I do have many other questions…