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+# Design Doc: XGBoost on SQLFlow
+
+## Introduction
+
+This design explains how SQLFlow calls [XGBoost](https://xgboost.ai/) for training models and prediciton.
+
+## Usage
+
+To explain the benefit of integrating XGBoost with SQLFlow, let us start with an example.  The following SQLFlow code snippet shows how users can train an XGBoost tree model named `my_xgb_model`.
+
+``` sql
+SELECT * FROM train_table
+TRAIN xgboost.multi.softmax
+WITH
+    train.num_round=2,
+    max_depth=2,
+    eta=1
+LABEL class
+INTO my_xgb_model;
+```
+
+The following example shows how to predict using the model `my_xgb_model`.
+
+``` sql
+SELECT * FROM test_table
+PREDICT pred_table.result
+USING my_xgb_model;
+```
+
+The the above examples,
+- `my_xgb_model` names the trained model.
+- `xgboost.multi.softmax` is the model spec, where
+    - the prefix `xgboost.` tells the model is a XGBoost one, but not a Tensorflow model, and
+    - `multi.softmax` names an [XGBoost learning task](https://xgboost.readthedocs.io/en/latest/parameter.html#learning-task-parameters).
+- In the `WITH` clause, 
+  - keys with the prefix `train.` identifies parameters of XGBoost API [`xgboost.train`](https://xgboost.readthedocs.io/en/latest/python/python_api.html#xgboost.train), and
+  - keys without any prefix identifies [XGBoost Parameters](https://xgboost.readthedocs.io/en/latest/parameter.html) except the `objective` parameter, which was specified by the identifier after the keyword `TRAIN`, as explained above.
+
+## The Code Generator
+
+The code generator `codegen_xgboost.go` outputs an XGBoost program in Python. It contains the following features:
+1. It tells the SQL engine to run the SELECT statement and retrieve the training/test data. It saves the data into a text file, which could be loaded by XGBoost using the DMatrix interface.
+1. Parse and resolve the WITH clause to fill the `xgboost.train` arguments and the XGBoost Parameters.
+1. Save the trained model on disk.
+1. For the PREDICT clause, it loads the trained model and test data and then outputs the prediction result to a SQL engine.