K8S：k8s is short for Kubernetes, which is an open-source container orchestration platform that provides automated deployment, scaling, and management of containerized applications. It can run on various cloud platforms, physical servers, and virtual machines, and supports multiple container runtimes, enabling high availability, load balancing, automatic scaling, and automatic repair, and other functions.
Graph Processing： Graph Processing is a computing model used to solve computational problems related to graph data structures. The graph computing model can be applied to solve many real-world problems, such as social network analysis, network traffic analysis, medical diagnosis, and more.
ISO-GQL：GQL is a standard query language for property graphs, which stands for "Graph Query Language", and is an ISO/IEC international standard database language. In addition to supporting the Gremlin query language, GeaFlow also supports GQL. This means that GeaFlow users can use the GQL language to query and analyze their graph data, thereby enhancing their graph data processing capabilities.
Cycle： The GeaFlow Scheduler is a core data structure in the scheduling model. A cycle is described as a basic unit that can be executed repeatedly, and it includes a description of input, intermediate calculations, data exchange, and output. It is generated by the vertex groups in the execution plan and supports nesting.
Event： The core data structure for the interaction between scheduling and computation at the Runtime layer is the Scheduler. The Scheduler constructs a state machine from a series of event sets and distributes it to workers for computation and execution. Some of these events are executable, meaning they have their own computational semantics, and the entire scheduling and computation process is executed asynchronously.
Graph Traversal : Graph Traversal refers to the process of traversing all nodes or some nodes in a graph data structure, visiting all nodes in a specific order, mainly using depth-first search (DFS) and breadth-first search (BFS). It is used to solve many problems, including finding the shortest path between two nodes, detecting cycles in a graph, and so on.
Graph State： GraphState is used to store the graph data or intermediate results of graph iteration calculations in Geaflow. It provides exactly-once semantics and the ability to reuse jobs at the job level. GraphState can be divided into two types: Static and Dynamic. Static GraphState views the entire graph as a complete entity, and all operations are performed on a complete graph. Dynamic GraphState assumes that the graph is dynamically changing and is composed of time slices, and all slices make up a complete graph, and all operations are performed on the slices.
Key State： KeyState is used to store intermediate results during the calculation process and is generally used for stream processing, such as recording intermediate aggregation results in KeyState when performing aggregation. Similar to GraphState, Geaflow regularly persists KeyState, so KeyState also provides exactly-once semantics. Depending on the data result, KeyState can be divided into KeyValueState, KeyListState, KeyMapState, and so on.