In this talk, we will introduce you to PGL, an efficient, flexible, and large-scale graph learning framework based on PaddlePaddle. One of the most important benefits of graph neural networks compared to other models is the ability to use node-to-node connectivity information, but coding the communication between nodes is very cumbersome. At PGL we adopt Message Passing Paradigm to make building a customize graph neural network convenient. We also provide several examples for industrial GNN deployment with a distributed trillion scale graph engine and parameter server. Furthermore, we will present our recent studies on GNNs which achieve several SOTAs or championship in recent graph challenges.