[GitHub] [flink] WeiZhong94 opened a new pull request #13905: [FLINK-19844][python][docs] Add documentation for Python UDAF.

[GitBox <gi...@apache.org>]

WeiZhong94 opened a new pull request #13905:
URL: https://github.com/apache/flink/pull/13905


   ## What is the purpose of the change
   
   *This pull request adds documentation for Python UDAF.*
   
   
   ## Brief change log
   
     - *Add documentation for Python UDAF.*
   
   ## Verifying this change
   
   This change is a trivial rework / code cleanup without any test coverage.
   
   ## Does this pull request potentially affect one of the following parts:
   
     - Dependencies (does it add or upgrade a dependency): (no)
     - The public API, i.e., is any changed class annotated with `@Public(Evolving)`: (no)
     - The serializers: (no)
     - The runtime per-record code paths (performance sensitive): (no)
     - Anything that affects deployment or recovery: JobManager (and its components), Checkpointing, Kubernetes/Yarn/Mesos, ZooKeeper: (no)
     - The S3 file system connector: (no)
   
   ## Documentation
   
     - Does this pull request introduce a new feature? (no)
     - If yes, how is the feature documented? (docs)
   


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[GitHub] [flink] flinkbot commented on pull request #13905: [FLINK-19844][python][docs] Add documentation for Python UDAF.

[GitBox <gi...@apache.org>]

flinkbot commented on pull request #13905:
URL: https://github.com/apache/flink/pull/13905#issuecomment-721225406


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[GitHub] [flink] dianfu commented on a change in pull request #13905: [FLINK-19844][python][docs] Add documentation for Python UDAF.

[GitBox <gi...@apache.org>]

dianfu commented on a change in pull request #13905:
URL: https://github.com/apache/flink/pull/13905#discussion_r516615229



##########
File path: docs/dev/python/table-api-users-guide/udfs/python_udfs.md
##########
@@ -227,3 +227,164 @@ def iterable_func(x):
       result = [1, 2, 3]
       return result
 {% endhighlight %}
+
+## Aggregation Functions
+
+A user-defined aggregate function (_UDAGG_) maps scalar values of multiple rows to a new scalar value.
+
+**NOTE:** Currently the general user-defined aggregate function is only supported in the group aggregate operation of the blink stream planner. For batch execution or windowed aggregate, it is recommended to use the [Vectorized Aggregate Functions]({% link dev/python/table-api-users-guide/udfs/vectorized_python_udfs.md %}#vectorized-aggregate-functions).
+
+The behavior of an aggregate function is centered around the concept of an accumulator. The _accumulator_
+is an intermediate data structure that stores the aggregated values until a final aggregation result
+is computed.
+
+For each set of rows that needs to be aggregated, the runtime will create an empty accumulator by calling

Review comment:
       ```suggestion
   For each set of rows that need to be aggregated, the runtime will create an empty accumulator by calling
   ```

##########
File path: docs/dev/python/table-api-users-guide/udfs/python_udfs.md
##########
@@ -227,3 +227,164 @@ def iterable_func(x):
       result = [1, 2, 3]
       return result
 {% endhighlight %}
+
+## Aggregation Functions
+
+A user-defined aggregate function (_UDAGG_) maps scalar values of multiple rows to a new scalar value.
+
+**NOTE:** Currently the general user-defined aggregate function is only supported in the group aggregate operation of the blink stream planner. For batch execution or windowed aggregate, it is recommended to use the [Vectorized Aggregate Functions]({% link dev/python/table-api-users-guide/udfs/vectorized_python_udfs.md %}#vectorized-aggregate-functions).

Review comment:
       ```suggestion
   **NOTE:** Currently the general user-defined aggregate function is only supported in the GroupBy aggregation of the blink planner in streaming mode. For batch mode or windowed aggregation, it's currently not supported and it is recommended to use the [Vectorized Aggregate Functions]({% link dev/python/table-api-users-guide/udfs/vectorized_python_udfs.md %}#vectorized-aggregate-functions).
   ```

##########
File path: docs/dev/python/table-api-users-guide/udfs/python_udfs.md
##########
@@ -227,3 +227,164 @@ def iterable_func(x):
       result = [1, 2, 3]
       return result
 {% endhighlight %}
+
+## Aggregation Functions
+
+A user-defined aggregate function (_UDAGG_) maps scalar values of multiple rows to a new scalar value.
+
+**NOTE:** Currently the general user-defined aggregate function is only supported in the group aggregate operation of the blink stream planner. For batch execution or windowed aggregate, it is recommended to use the [Vectorized Aggregate Functions]({% link dev/python/table-api-users-guide/udfs/vectorized_python_udfs.md %}#vectorized-aggregate-functions).
+
+The behavior of an aggregate function is centered around the concept of an accumulator. The _accumulator_
+is an intermediate data structure that stores the aggregated values until a final aggregation result
+is computed.
+
+For each set of rows that needs to be aggregated, the runtime will create an empty accumulator by calling
+`create_accumulator()`. Subsequently, the `accumulate(...)` method of the function is called for each input
+row to update the accumulator. Currently after each row has been processed, the `get_value(...)` method of the
+function is called to compute and return the immediate result. 
+
+The following example illustrates the aggregation process:
+
+<center>
+<img alt="UDAGG mechanism" src="{{ site.baseurl }}/fig/udagg-mechanism-python.png" width="80%">
+</center>
+
+In the example, we assume a table that contains data about beverages. The table consists of three columns (`id`, `name`,
+and `price`) and 5 rows. We would like to find the highest price of all beverages in the table, i.e., perform
+a `max()` aggregation. We need to consider each of the 5 rows. The result is a single numeric value.
+
+In order to define an aggregate function, one has to extend the base class `AggregateFunction` in
+`pyflink.table` and implement the evaluation method named `accumulate(...)`. 
+The result type and accumulator type of the aggregate function can be specified by such 2 approach:

Review comment:
       ```suggestion
   The result type and accumulator type of the aggregate function can be specified by one of the following two approaches:
   ```

##########
File path: docs/dev/python/table-api-users-guide/udfs/python_udfs.md
##########
@@ -227,3 +227,164 @@ def iterable_func(x):
       result = [1, 2, 3]
       return result
 {% endhighlight %}
+
+## Aggregation Functions
+
+A user-defined aggregate function (_UDAGG_) maps scalar values of multiple rows to a new scalar value.
+
+**NOTE:** Currently the general user-defined aggregate function is only supported in the group aggregate operation of the blink stream planner. For batch execution or windowed aggregate, it is recommended to use the [Vectorized Aggregate Functions]({% link dev/python/table-api-users-guide/udfs/vectorized_python_udfs.md %}#vectorized-aggregate-functions).
+
+The behavior of an aggregate function is centered around the concept of an accumulator. The _accumulator_
+is an intermediate data structure that stores the aggregated values until a final aggregation result
+is computed.
+
+For each set of rows that needs to be aggregated, the runtime will create an empty accumulator by calling
+`create_accumulator()`. Subsequently, the `accumulate(...)` method of the function is called for each input
+row to update the accumulator. Currently after each row has been processed, the `get_value(...)` method of the
+function is called to compute and return the immediate result. 
+
+The following example illustrates the aggregation process:
+
+<center>
+<img alt="UDAGG mechanism" src="{{ site.baseurl }}/fig/udagg-mechanism-python.png" width="80%">
+</center>
+
+In the example, we assume a table that contains data about beverages. The table consists of three columns (`id`, `name`,
+and `price`) and 5 rows. We would like to find the highest price of all beverages in the table, i.e., perform
+a `max()` aggregation. We need to consider each of the 5 rows. The result is a single numeric value.
+
+In order to define an aggregate function, one has to extend the base class `AggregateFunction` in
+`pyflink.table` and implement the evaluation method named `accumulate(...)`. 
+The result type and accumulator type of the aggregate function can be specified by such 2 approach:
+
+- implement the method named `get_result_type()` and `get_accumulator_type()`.
+- wrap the function instance with the decorator `udaf` in `pyflink.table.udf` and specify the parameter `result_type` and `accumulator_type`.
+
+The following example shows how to define your own aggregate function and call it in a query.
+
+{% highlight python %}
+from pyflink.common import Row
+from pyflink.datastream import StreamExecutionEnvironment
+from pyflink.table import AggregateFunction, DataTypes, StreamTableEnvironment
+from pyflink.table.expressions import call
+from pyflink.table.udf import udaf
+
+
+class WeightedAvg(AggregateFunction):
+
+    def create_accumulator(self):
+        # Row(sum, count)
+        return Row(0, 0)
+
+    def get_value(self, accumulator):
+        if accumulator[1] == 0:
+            return None
+        else:
+            return accumulator[0] / accumulator[1]
+
+    def accumulate(self, accumulator, value, weight):
+        accumulator[0] += value * weight
+        accumulator[1] += weight
+    
+    def retract(self, accumulator, value, weight):
+        accumulator[0] -= value * weight
+        accumulator[1] -= weight
+        
+    def get_result_type(self):
+        return DataTypes.BIGINT()
+        
+    def get_accumulator_type(self):
+        return DataTypes.ROW([
+            DataTypes.FIELD("f0", DataTypes.BIGINT()), 
+            DataTypes.FIELD("f1", DataTypes.BIGINT())])
+
+
+env = StreamExecutionEnvironment.get_execution_environment()
+table_env = StreamTableEnvironment.create(env)
+# the result type and accumulator type can also be specified in the udaf decorator:
+# weighted_avg = udaf(WeightedAvg(), result_type=DataTypes.BIGINT(), accumulator_type=...)
+weighted_avg = udaf(WeightedAvg())
+t = table_env.from_elements([(1, 2, "Lee"),
+                             (3, 4, "Jay"),
+                             (5, 6, "Jay"),
+                             (7, 8, "Lee")]).alias("value", "count", "name")
+
+# call function "inline" without registration in Table API
+result = t.group_by(t.name).select(weighted_avg(t.value, t.count).alias("avg")).to_pandas()
+print(result)
+
+# register function
+table_env.create_temporary_function("weighted_avg", WeightedAvg())
+
+# call registered function in Table API
+result = t.group_by(t.name).select(call("weighted_avg", t.value, t.count).alias("avg")).to_pandas()
+print(result)
+
+# register table
+table_env.create_temporary_view("source", t)
+
+# call registered function in SQL
+result = table_env.sql_query(
+    "SELECT weighted_avg(`value`, `count`) AS avg FROM source GROUP BY name").to_pandas()
+print(result)
+{% endhighlight %}
+
+The `accumulate(...)` method of our `WeightedAvg` class takes three inputs. The first one is the accumulator
+and the other two are user-defined inputs. In order to calculate a weighted average value, the accumulator
+needs to store the weighted sum and count of all the data that has been accumulated. In our example, we
+use a `Row` object to be the accumulator. Accumulators are automatically managed
+by Flink's checkpointing mechanism and are restored in case of a failure to ensure exactly-once semantics.
+
+### Mandatory and Optional Methods
+
+**The following methods are mandatory for each `AggregateFunction`:**
+
+- `create_accumulator()`
+- `accumulate(...)` 
+- `get_value(...)`
+
+**The following methods of `AggregateFunction` are required depending on the use case:**
+
+- `retract(...)` is required when there are other operations that generate retract messages before current UDAF call, e.g. group aggregate , outer join. \

Review comment:
       ```suggestion
   - `retract(...)` is required when there are operations that could generate retraction messages before the current aggregation operation, e.g. group aggregate , outer join. \
   ```

##########
File path: docs/dev/python/table-api-users-guide/udfs/python_udfs.md
##########
@@ -227,3 +227,164 @@ def iterable_func(x):
       result = [1, 2, 3]
       return result
 {% endhighlight %}
+
+## Aggregation Functions
+
+A user-defined aggregate function (_UDAGG_) maps scalar values of multiple rows to a new scalar value.
+
+**NOTE:** Currently the general user-defined aggregate function is only supported in the group aggregate operation of the blink stream planner. For batch execution or windowed aggregate, it is recommended to use the [Vectorized Aggregate Functions]({% link dev/python/table-api-users-guide/udfs/vectorized_python_udfs.md %}#vectorized-aggregate-functions).
+
+The behavior of an aggregate function is centered around the concept of an accumulator. The _accumulator_
+is an intermediate data structure that stores the aggregated values until a final aggregation result
+is computed.
+
+For each set of rows that needs to be aggregated, the runtime will create an empty accumulator by calling
+`create_accumulator()`. Subsequently, the `accumulate(...)` method of the function is called for each input
+row to update the accumulator. Currently after each row has been processed, the `get_value(...)` method of the
+function is called to compute and return the immediate result. 
+
+The following example illustrates the aggregation process:
+
+<center>
+<img alt="UDAGG mechanism" src="{{ site.baseurl }}/fig/udagg-mechanism-python.png" width="80%">
+</center>
+
+In the example, we assume a table that contains data about beverages. The table consists of three columns (`id`, `name`,
+and `price`) and 5 rows. We would like to find the highest price of all beverages in the table, i.e., perform
+a `max()` aggregation. We need to consider each of the 5 rows. The result is a single numeric value.
+
+In order to define an aggregate function, one has to extend the base class `AggregateFunction` in
+`pyflink.table` and implement the evaluation method named `accumulate(...)`. 
+The result type and accumulator type of the aggregate function can be specified by such 2 approach:
+
+- implement the method named `get_result_type()` and `get_accumulator_type()`.
+- wrap the function instance with the decorator `udaf` in `pyflink.table.udf` and specify the parameter `result_type` and `accumulator_type`.
+
+The following example shows how to define your own aggregate function and call it in a query.
+
+{% highlight python %}
+from pyflink.common import Row
+from pyflink.datastream import StreamExecutionEnvironment
+from pyflink.table import AggregateFunction, DataTypes, StreamTableEnvironment
+from pyflink.table.expressions import call
+from pyflink.table.udf import udaf
+
+
+class WeightedAvg(AggregateFunction):
+
+    def create_accumulator(self):
+        # Row(sum, count)
+        return Row(0, 0)
+
+    def get_value(self, accumulator):
+        if accumulator[1] == 0:
+            return None
+        else:
+            return accumulator[0] / accumulator[1]
+
+    def accumulate(self, accumulator, value, weight):
+        accumulator[0] += value * weight
+        accumulator[1] += weight
+    
+    def retract(self, accumulator, value, weight):
+        accumulator[0] -= value * weight
+        accumulator[1] -= weight
+        
+    def get_result_type(self):
+        return DataTypes.BIGINT()
+        
+    def get_accumulator_type(self):
+        return DataTypes.ROW([
+            DataTypes.FIELD("f0", DataTypes.BIGINT()), 
+            DataTypes.FIELD("f1", DataTypes.BIGINT())])
+
+
+env = StreamExecutionEnvironment.get_execution_environment()
+table_env = StreamTableEnvironment.create(env)
+# the result type and accumulator type can also be specified in the udaf decorator:
+# weighted_avg = udaf(WeightedAvg(), result_type=DataTypes.BIGINT(), accumulator_type=...)
+weighted_avg = udaf(WeightedAvg())
+t = table_env.from_elements([(1, 2, "Lee"),
+                             (3, 4, "Jay"),
+                             (5, 6, "Jay"),
+                             (7, 8, "Lee")]).alias("value", "count", "name")
+
+# call function "inline" without registration in Table API
+result = t.group_by(t.name).select(weighted_avg(t.value, t.count).alias("avg")).to_pandas()
+print(result)
+
+# register function
+table_env.create_temporary_function("weighted_avg", WeightedAvg())
+
+# call registered function in Table API
+result = t.group_by(t.name).select(call("weighted_avg", t.value, t.count).alias("avg")).to_pandas()
+print(result)
+
+# register table
+table_env.create_temporary_view("source", t)
+
+# call registered function in SQL
+result = table_env.sql_query(
+    "SELECT weighted_avg(`value`, `count`) AS avg FROM source GROUP BY name").to_pandas()
+print(result)
+{% endhighlight %}
+
+The `accumulate(...)` method of our `WeightedAvg` class takes three inputs. The first one is the accumulator
+and the other two are user-defined inputs. In order to calculate a weighted average value, the accumulator
+needs to store the weighted sum and count of all the data that has been accumulated. In our example, we
+use a `Row` object to be the accumulator. Accumulators are automatically managed
+by Flink's checkpointing mechanism and are restored in case of a failure to ensure exactly-once semantics.
+
+### Mandatory and Optional Methods
+
+**The following methods are mandatory for each `AggregateFunction`:**
+
+- `create_accumulator()`
+- `accumulate(...)` 
+- `get_value(...)`
+
+**The following methods of `AggregateFunction` are required depending on the use case:**
+
+- `retract(...)` is required when there are other operations that generate retract messages before current UDAF call, e.g. group aggregate , outer join. \
+This method is optional, but it is strongly recommended to be implemented to ensure the UDAF can be used in any use case.
+- `get_result_type()` and `get_accumulator_type()` is required if the result type and accumulator type would not be specified in the `udaf` decorator.
+
+### ListView and MapView
+
+If an accumulator needs to store large amounts of data, `pyflink.table.ListView` and `pyflink.table.MapView` 
+provide advanced features for leveraging Flink's state backends in unbounded data scenarios. 
+This feature can be enabled by declaring `DataTypes.LIST_VIEW(...)` and `DataTypes.MAP_VIEW(...)` in the accumulator type, e.g.:
+
+{% highlight python %}
+from pyflink.table import ListView
+
+class ListViewConcatAggregateFunction(AggregateFunction):
+
+    def get_value(self, accumulator):
+        # the ListView is iterable
+        return accumulator[1].join(accumulator[0])
+
+    def create_accumulator(self):
+        return Row(ListView(), '')
+
+    def accumulate(self, accumulator, *args):
+        accumulator[1] = args[1]
+        # the ListView support add, clear and iterate operations.
+        accumulator[0].add(args[0])
+
+    def get_accumulator_type(self):
+        return DataTypes.ROW([
+            # declare the first column of the accumulator as a string ListView.
+            DataTypes.FIELD("f0", DataTypes.LIST_VIEW(DataTypes.STRING())),
+            DataTypes.FIELD("f1", DataTypes.BIGINT())])
+
+    def get_result_type(self):
+        return DataTypes.STRING()
+{% endhighlight %}
+
+Currently there are 2 limitations to use the:
+
+1. The accumulator must be a `Row`.
+2. The `ListView` and `MapView` must be the first level children of the `Row` accumulator.
+
+Please see the docs of the corresponding classes for more information about this advanced feature.

Review comment:
       ```suggestion
   Please refer to the documentation of the corresponding classes for more information about this advanced feature.
   ```
   
   What about adding a link to the class documentation?

##########
File path: docs/dev/python/table-api-users-guide/udfs/python_udfs.md
##########
@@ -227,3 +227,164 @@ def iterable_func(x):
       result = [1, 2, 3]
       return result
 {% endhighlight %}
+
+## Aggregation Functions
+
+A user-defined aggregate function (_UDAGG_) maps scalar values of multiple rows to a new scalar value.
+
+**NOTE:** Currently the general user-defined aggregate function is only supported in the group aggregate operation of the blink stream planner. For batch execution or windowed aggregate, it is recommended to use the [Vectorized Aggregate Functions]({% link dev/python/table-api-users-guide/udfs/vectorized_python_udfs.md %}#vectorized-aggregate-functions).
+
+The behavior of an aggregate function is centered around the concept of an accumulator. The _accumulator_
+is an intermediate data structure that stores the aggregated values until a final aggregation result
+is computed.
+
+For each set of rows that needs to be aggregated, the runtime will create an empty accumulator by calling
+`create_accumulator()`. Subsequently, the `accumulate(...)` method of the function is called for each input
+row to update the accumulator. Currently after each row has been processed, the `get_value(...)` method of the
+function is called to compute and return the immediate result. 
+
+The following example illustrates the aggregation process:
+
+<center>
+<img alt="UDAGG mechanism" src="{{ site.baseurl }}/fig/udagg-mechanism-python.png" width="80%">
+</center>
+
+In the example, we assume a table that contains data about beverages. The table consists of three columns (`id`, `name`,

Review comment:
       ```suggestion
   In the above example, we assume a table that contains data about beverages. The table consists of three columns (`id`, `name`,
   ```

##########
File path: docs/dev/python/table-api-users-guide/udfs/python_udfs.md
##########
@@ -227,3 +227,164 @@ def iterable_func(x):
       result = [1, 2, 3]
       return result
 {% endhighlight %}
+
+## Aggregation Functions
+
+A user-defined aggregate function (_UDAGG_) maps scalar values of multiple rows to a new scalar value.
+
+**NOTE:** Currently the general user-defined aggregate function is only supported in the group aggregate operation of the blink stream planner. For batch execution or windowed aggregate, it is recommended to use the [Vectorized Aggregate Functions]({% link dev/python/table-api-users-guide/udfs/vectorized_python_udfs.md %}#vectorized-aggregate-functions).
+
+The behavior of an aggregate function is centered around the concept of an accumulator. The _accumulator_
+is an intermediate data structure that stores the aggregated values until a final aggregation result
+is computed.
+
+For each set of rows that needs to be aggregated, the runtime will create an empty accumulator by calling
+`create_accumulator()`. Subsequently, the `accumulate(...)` method of the function is called for each input
+row to update the accumulator. Currently after each row has been processed, the `get_value(...)` method of the
+function is called to compute and return the immediate result. 
+
+The following example illustrates the aggregation process:
+
+<center>
+<img alt="UDAGG mechanism" src="{{ site.baseurl }}/fig/udagg-mechanism-python.png" width="80%">
+</center>
+
+In the example, we assume a table that contains data about beverages. The table consists of three columns (`id`, `name`,
+and `price`) and 5 rows. We would like to find the highest price of all beverages in the table, i.e., perform
+a `max()` aggregation. We need to consider each of the 5 rows. The result is a single numeric value.
+
+In order to define an aggregate function, one has to extend the base class `AggregateFunction` in
+`pyflink.table` and implement the evaluation method named `accumulate(...)`. 
+The result type and accumulator type of the aggregate function can be specified by such 2 approach:
+
+- implement the method named `get_result_type()` and `get_accumulator_type()`.
+- wrap the function instance with the decorator `udaf` in `pyflink.table.udf` and specify the parameter `result_type` and `accumulator_type`.

Review comment:
       ```suggestion
   - Wrap the function instance with the decorator `udaf` in `pyflink.table.udf` and specify the parameters `result_type` and `accumulator_type`.
   ```

##########
File path: docs/dev/python/table-api-users-guide/udfs/python_udfs.md
##########
@@ -227,3 +227,164 @@ def iterable_func(x):
       result = [1, 2, 3]
       return result
 {% endhighlight %}
+
+## Aggregation Functions
+
+A user-defined aggregate function (_UDAGG_) maps scalar values of multiple rows to a new scalar value.
+
+**NOTE:** Currently the general user-defined aggregate function is only supported in the group aggregate operation of the blink stream planner. For batch execution or windowed aggregate, it is recommended to use the [Vectorized Aggregate Functions]({% link dev/python/table-api-users-guide/udfs/vectorized_python_udfs.md %}#vectorized-aggregate-functions).
+
+The behavior of an aggregate function is centered around the concept of an accumulator. The _accumulator_
+is an intermediate data structure that stores the aggregated values until a final aggregation result
+is computed.
+
+For each set of rows that needs to be aggregated, the runtime will create an empty accumulator by calling
+`create_accumulator()`. Subsequently, the `accumulate(...)` method of the function is called for each input
+row to update the accumulator. Currently after each row has been processed, the `get_value(...)` method of the
+function is called to compute and return the immediate result. 

Review comment:
       ```suggestion
   aggregate function will be called to compute the aggregated result. 
   ```

##########
File path: docs/dev/python/table-api-users-guide/udfs/python_udfs.md
##########
@@ -227,3 +227,164 @@ def iterable_func(x):
       result = [1, 2, 3]
       return result
 {% endhighlight %}
+
+## Aggregation Functions
+
+A user-defined aggregate function (_UDAGG_) maps scalar values of multiple rows to a new scalar value.
+
+**NOTE:** Currently the general user-defined aggregate function is only supported in the group aggregate operation of the blink stream planner. For batch execution or windowed aggregate, it is recommended to use the [Vectorized Aggregate Functions]({% link dev/python/table-api-users-guide/udfs/vectorized_python_udfs.md %}#vectorized-aggregate-functions).
+
+The behavior of an aggregate function is centered around the concept of an accumulator. The _accumulator_
+is an intermediate data structure that stores the aggregated values until a final aggregation result
+is computed.
+
+For each set of rows that needs to be aggregated, the runtime will create an empty accumulator by calling
+`create_accumulator()`. Subsequently, the `accumulate(...)` method of the function is called for each input
+row to update the accumulator. Currently after each row has been processed, the `get_value(...)` method of the
+function is called to compute and return the immediate result. 
+
+The following example illustrates the aggregation process:
+
+<center>
+<img alt="UDAGG mechanism" src="{{ site.baseurl }}/fig/udagg-mechanism-python.png" width="80%">
+</center>
+
+In the example, we assume a table that contains data about beverages. The table consists of three columns (`id`, `name`,
+and `price`) and 5 rows. We would like to find the highest price of all beverages in the table, i.e., perform
+a `max()` aggregation. We need to consider each of the 5 rows. The result is a single numeric value.
+
+In order to define an aggregate function, one has to extend the base class `AggregateFunction` in
+`pyflink.table` and implement the evaluation method named `accumulate(...)`. 
+The result type and accumulator type of the aggregate function can be specified by such 2 approach:
+
+- implement the method named `get_result_type()` and `get_accumulator_type()`.
+- wrap the function instance with the decorator `udaf` in `pyflink.table.udf` and specify the parameter `result_type` and `accumulator_type`.
+
+The following example shows how to define your own aggregate function and call it in a query.
+
+{% highlight python %}
+from pyflink.common import Row
+from pyflink.datastream import StreamExecutionEnvironment
+from pyflink.table import AggregateFunction, DataTypes, StreamTableEnvironment
+from pyflink.table.expressions import call
+from pyflink.table.udf import udaf
+
+
+class WeightedAvg(AggregateFunction):
+
+    def create_accumulator(self):
+        # Row(sum, count)
+        return Row(0, 0)
+
+    def get_value(self, accumulator):
+        if accumulator[1] == 0:
+            return None
+        else:
+            return accumulator[0] / accumulator[1]
+
+    def accumulate(self, accumulator, value, weight):
+        accumulator[0] += value * weight
+        accumulator[1] += weight
+    
+    def retract(self, accumulator, value, weight):
+        accumulator[0] -= value * weight
+        accumulator[1] -= weight
+        
+    def get_result_type(self):
+        return DataTypes.BIGINT()
+        
+    def get_accumulator_type(self):
+        return DataTypes.ROW([
+            DataTypes.FIELD("f0", DataTypes.BIGINT()), 
+            DataTypes.FIELD("f1", DataTypes.BIGINT())])
+
+
+env = StreamExecutionEnvironment.get_execution_environment()
+table_env = StreamTableEnvironment.create(env)
+# the result type and accumulator type can also be specified in the udaf decorator:
+# weighted_avg = udaf(WeightedAvg(), result_type=DataTypes.BIGINT(), accumulator_type=...)
+weighted_avg = udaf(WeightedAvg())
+t = table_env.from_elements([(1, 2, "Lee"),
+                             (3, 4, "Jay"),
+                             (5, 6, "Jay"),
+                             (7, 8, "Lee")]).alias("value", "count", "name")
+
+# call function "inline" without registration in Table API
+result = t.group_by(t.name).select(weighted_avg(t.value, t.count).alias("avg")).to_pandas()
+print(result)
+
+# register function
+table_env.create_temporary_function("weighted_avg", WeightedAvg())
+
+# call registered function in Table API
+result = t.group_by(t.name).select(call("weighted_avg", t.value, t.count).alias("avg")).to_pandas()
+print(result)
+
+# register table
+table_env.create_temporary_view("source", t)
+
+# call registered function in SQL
+result = table_env.sql_query(
+    "SELECT weighted_avg(`value`, `count`) AS avg FROM source GROUP BY name").to_pandas()
+print(result)
+{% endhighlight %}
+
+The `accumulate(...)` method of our `WeightedAvg` class takes three inputs. The first one is the accumulator
+and the other two are user-defined inputs. In order to calculate a weighted average value, the accumulator
+needs to store the weighted sum and count of all the data that has been accumulated. In our example, we
+use a `Row` object to be the accumulator. Accumulators are automatically managed
+by Flink's checkpointing mechanism and are restored in case of a failure to ensure exactly-once semantics.

Review comment:
       ```suggestion
   by Flink's checkpointing mechanism and are restored in case of failover to ensure exactly-once semantics.
   ```

##########
File path: docs/dev/python/table-api-users-guide/udfs/python_udfs.md
##########
@@ -227,3 +227,164 @@ def iterable_func(x):
       result = [1, 2, 3]
       return result
 {% endhighlight %}
+
+## Aggregation Functions
+
+A user-defined aggregate function (_UDAGG_) maps scalar values of multiple rows to a new scalar value.
+
+**NOTE:** Currently the general user-defined aggregate function is only supported in the group aggregate operation of the blink stream planner. For batch execution or windowed aggregate, it is recommended to use the [Vectorized Aggregate Functions]({% link dev/python/table-api-users-guide/udfs/vectorized_python_udfs.md %}#vectorized-aggregate-functions).
+
+The behavior of an aggregate function is centered around the concept of an accumulator. The _accumulator_
+is an intermediate data structure that stores the aggregated values until a final aggregation result
+is computed.
+
+For each set of rows that needs to be aggregated, the runtime will create an empty accumulator by calling
+`create_accumulator()`. Subsequently, the `accumulate(...)` method of the function is called for each input
+row to update the accumulator. Currently after each row has been processed, the `get_value(...)` method of the
+function is called to compute and return the immediate result. 
+
+The following example illustrates the aggregation process:
+
+<center>
+<img alt="UDAGG mechanism" src="{{ site.baseurl }}/fig/udagg-mechanism-python.png" width="80%">
+</center>
+
+In the example, we assume a table that contains data about beverages. The table consists of three columns (`id`, `name`,
+and `price`) and 5 rows. We would like to find the highest price of all beverages in the table, i.e., perform
+a `max()` aggregation. We need to consider each of the 5 rows. The result is a single numeric value.
+
+In order to define an aggregate function, one has to extend the base class `AggregateFunction` in
+`pyflink.table` and implement the evaluation method named `accumulate(...)`. 
+The result type and accumulator type of the aggregate function can be specified by such 2 approach:
+
+- implement the method named `get_result_type()` and `get_accumulator_type()`.
+- wrap the function instance with the decorator `udaf` in `pyflink.table.udf` and specify the parameter `result_type` and `accumulator_type`.
+
+The following example shows how to define your own aggregate function and call it in a query.
+
+{% highlight python %}
+from pyflink.common import Row
+from pyflink.datastream import StreamExecutionEnvironment
+from pyflink.table import AggregateFunction, DataTypes, StreamTableEnvironment
+from pyflink.table.expressions import call
+from pyflink.table.udf import udaf
+
+
+class WeightedAvg(AggregateFunction):
+
+    def create_accumulator(self):
+        # Row(sum, count)
+        return Row(0, 0)
+
+    def get_value(self, accumulator):
+        if accumulator[1] == 0:
+            return None
+        else:
+            return accumulator[0] / accumulator[1]
+
+    def accumulate(self, accumulator, value, weight):
+        accumulator[0] += value * weight
+        accumulator[1] += weight
+    
+    def retract(self, accumulator, value, weight):
+        accumulator[0] -= value * weight
+        accumulator[1] -= weight
+        
+    def get_result_type(self):
+        return DataTypes.BIGINT()
+        
+    def get_accumulator_type(self):
+        return DataTypes.ROW([
+            DataTypes.FIELD("f0", DataTypes.BIGINT()), 
+            DataTypes.FIELD("f1", DataTypes.BIGINT())])
+
+
+env = StreamExecutionEnvironment.get_execution_environment()
+table_env = StreamTableEnvironment.create(env)
+# the result type and accumulator type can also be specified in the udaf decorator:
+# weighted_avg = udaf(WeightedAvg(), result_type=DataTypes.BIGINT(), accumulator_type=...)
+weighted_avg = udaf(WeightedAvg())
+t = table_env.from_elements([(1, 2, "Lee"),
+                             (3, 4, "Jay"),
+                             (5, 6, "Jay"),
+                             (7, 8, "Lee")]).alias("value", "count", "name")
+
+# call function "inline" without registration in Table API
+result = t.group_by(t.name).select(weighted_avg(t.value, t.count).alias("avg")).to_pandas()
+print(result)
+
+# register function
+table_env.create_temporary_function("weighted_avg", WeightedAvg())
+
+# call registered function in Table API
+result = t.group_by(t.name).select(call("weighted_avg", t.value, t.count).alias("avg")).to_pandas()
+print(result)
+
+# register table
+table_env.create_temporary_view("source", t)
+
+# call registered function in SQL
+result = table_env.sql_query(
+    "SELECT weighted_avg(`value`, `count`) AS avg FROM source GROUP BY name").to_pandas()
+print(result)
+{% endhighlight %}
+
+The `accumulate(...)` method of our `WeightedAvg` class takes three inputs. The first one is the accumulator

Review comment:
       ```suggestion
   The `accumulate(...)` method of our `WeightedAvg` class takes three input arguments. The first one is the accumulator
   ```

##########
File path: docs/dev/python/table-api-users-guide/udfs/python_udfs.md
##########
@@ -227,3 +227,164 @@ def iterable_func(x):
       result = [1, 2, 3]
       return result
 {% endhighlight %}
+
+## Aggregation Functions
+
+A user-defined aggregate function (_UDAGG_) maps scalar values of multiple rows to a new scalar value.
+
+**NOTE:** Currently the general user-defined aggregate function is only supported in the group aggregate operation of the blink stream planner. For batch execution or windowed aggregate, it is recommended to use the [Vectorized Aggregate Functions]({% link dev/python/table-api-users-guide/udfs/vectorized_python_udfs.md %}#vectorized-aggregate-functions).
+
+The behavior of an aggregate function is centered around the concept of an accumulator. The _accumulator_
+is an intermediate data structure that stores the aggregated values until a final aggregation result
+is computed.
+
+For each set of rows that needs to be aggregated, the runtime will create an empty accumulator by calling
+`create_accumulator()`. Subsequently, the `accumulate(...)` method of the function is called for each input
+row to update the accumulator. Currently after each row has been processed, the `get_value(...)` method of the
+function is called to compute and return the immediate result. 
+
+The following example illustrates the aggregation process:
+
+<center>
+<img alt="UDAGG mechanism" src="{{ site.baseurl }}/fig/udagg-mechanism-python.png" width="80%">
+</center>
+
+In the example, we assume a table that contains data about beverages. The table consists of three columns (`id`, `name`,
+and `price`) and 5 rows. We would like to find the highest price of all beverages in the table, i.e., perform
+a `max()` aggregation. We need to consider each of the 5 rows. The result is a single numeric value.
+
+In order to define an aggregate function, one has to extend the base class `AggregateFunction` in
+`pyflink.table` and implement the evaluation method named `accumulate(...)`. 
+The result type and accumulator type of the aggregate function can be specified by such 2 approach:
+
+- implement the method named `get_result_type()` and `get_accumulator_type()`.
+- wrap the function instance with the decorator `udaf` in `pyflink.table.udf` and specify the parameter `result_type` and `accumulator_type`.
+
+The following example shows how to define your own aggregate function and call it in a query.
+
+{% highlight python %}
+from pyflink.common import Row
+from pyflink.datastream import StreamExecutionEnvironment
+from pyflink.table import AggregateFunction, DataTypes, StreamTableEnvironment
+from pyflink.table.expressions import call
+from pyflink.table.udf import udaf
+
+
+class WeightedAvg(AggregateFunction):
+
+    def create_accumulator(self):
+        # Row(sum, count)
+        return Row(0, 0)
+
+    def get_value(self, accumulator):
+        if accumulator[1] == 0:
+            return None
+        else:
+            return accumulator[0] / accumulator[1]
+
+    def accumulate(self, accumulator, value, weight):
+        accumulator[0] += value * weight
+        accumulator[1] += weight
+    
+    def retract(self, accumulator, value, weight):
+        accumulator[0] -= value * weight
+        accumulator[1] -= weight
+        
+    def get_result_type(self):
+        return DataTypes.BIGINT()
+        
+    def get_accumulator_type(self):
+        return DataTypes.ROW([
+            DataTypes.FIELD("f0", DataTypes.BIGINT()), 
+            DataTypes.FIELD("f1", DataTypes.BIGINT())])
+
+
+env = StreamExecutionEnvironment.get_execution_environment()
+table_env = StreamTableEnvironment.create(env)
+# the result type and accumulator type can also be specified in the udaf decorator:
+# weighted_avg = udaf(WeightedAvg(), result_type=DataTypes.BIGINT(), accumulator_type=...)
+weighted_avg = udaf(WeightedAvg())
+t = table_env.from_elements([(1, 2, "Lee"),
+                             (3, 4, "Jay"),
+                             (5, 6, "Jay"),
+                             (7, 8, "Lee")]).alias("value", "count", "name")
+
+# call function "inline" without registration in Table API
+result = t.group_by(t.name).select(weighted_avg(t.value, t.count).alias("avg")).to_pandas()
+print(result)
+
+# register function
+table_env.create_temporary_function("weighted_avg", WeightedAvg())
+
+# call registered function in Table API
+result = t.group_by(t.name).select(call("weighted_avg", t.value, t.count).alias("avg")).to_pandas()
+print(result)
+
+# register table
+table_env.create_temporary_view("source", t)
+
+# call registered function in SQL
+result = table_env.sql_query(
+    "SELECT weighted_avg(`value`, `count`) AS avg FROM source GROUP BY name").to_pandas()
+print(result)
+{% endhighlight %}
+
+The `accumulate(...)` method of our `WeightedAvg` class takes three inputs. The first one is the accumulator
+and the other two are user-defined inputs. In order to calculate a weighted average value, the accumulator
+needs to store the weighted sum and count of all the data that has been accumulated. In our example, we

Review comment:
       ```suggestion
   needs to store the weighted sum and count of all the data that have already been accumulated. In our example, we
   ```

##########
File path: docs/dev/python/table-api-users-guide/udfs/python_udfs.md
##########
@@ -227,3 +227,164 @@ def iterable_func(x):
       result = [1, 2, 3]
       return result
 {% endhighlight %}
+
+## Aggregation Functions
+
+A user-defined aggregate function (_UDAGG_) maps scalar values of multiple rows to a new scalar value.
+
+**NOTE:** Currently the general user-defined aggregate function is only supported in the group aggregate operation of the blink stream planner. For batch execution or windowed aggregate, it is recommended to use the [Vectorized Aggregate Functions]({% link dev/python/table-api-users-guide/udfs/vectorized_python_udfs.md %}#vectorized-aggregate-functions).
+
+The behavior of an aggregate function is centered around the concept of an accumulator. The _accumulator_
+is an intermediate data structure that stores the aggregated values until a final aggregation result
+is computed.
+
+For each set of rows that needs to be aggregated, the runtime will create an empty accumulator by calling
+`create_accumulator()`. Subsequently, the `accumulate(...)` method of the function is called for each input
+row to update the accumulator. Currently after each row has been processed, the `get_value(...)` method of the
+function is called to compute and return the immediate result. 
+
+The following example illustrates the aggregation process:
+
+<center>
+<img alt="UDAGG mechanism" src="{{ site.baseurl }}/fig/udagg-mechanism-python.png" width="80%">
+</center>
+
+In the example, we assume a table that contains data about beverages. The table consists of three columns (`id`, `name`,
+and `price`) and 5 rows. We would like to find the highest price of all beverages in the table, i.e., perform
+a `max()` aggregation. We need to consider each of the 5 rows. The result is a single numeric value.
+
+In order to define an aggregate function, one has to extend the base class `AggregateFunction` in
+`pyflink.table` and implement the evaluation method named `accumulate(...)`. 
+The result type and accumulator type of the aggregate function can be specified by such 2 approach:
+
+- implement the method named `get_result_type()` and `get_accumulator_type()`.
+- wrap the function instance with the decorator `udaf` in `pyflink.table.udf` and specify the parameter `result_type` and `accumulator_type`.
+
+The following example shows how to define your own aggregate function and call it in a query.
+
+{% highlight python %}
+from pyflink.common import Row
+from pyflink.datastream import StreamExecutionEnvironment
+from pyflink.table import AggregateFunction, DataTypes, StreamTableEnvironment
+from pyflink.table.expressions import call
+from pyflink.table.udf import udaf
+
+
+class WeightedAvg(AggregateFunction):
+
+    def create_accumulator(self):
+        # Row(sum, count)
+        return Row(0, 0)
+
+    def get_value(self, accumulator):
+        if accumulator[1] == 0:
+            return None
+        else:
+            return accumulator[0] / accumulator[1]
+
+    def accumulate(self, accumulator, value, weight):
+        accumulator[0] += value * weight
+        accumulator[1] += weight
+    
+    def retract(self, accumulator, value, weight):
+        accumulator[0] -= value * weight
+        accumulator[1] -= weight
+        
+    def get_result_type(self):
+        return DataTypes.BIGINT()
+        
+    def get_accumulator_type(self):
+        return DataTypes.ROW([
+            DataTypes.FIELD("f0", DataTypes.BIGINT()), 
+            DataTypes.FIELD("f1", DataTypes.BIGINT())])
+
+
+env = StreamExecutionEnvironment.get_execution_environment()
+table_env = StreamTableEnvironment.create(env)
+# the result type and accumulator type can also be specified in the udaf decorator:
+# weighted_avg = udaf(WeightedAvg(), result_type=DataTypes.BIGINT(), accumulator_type=...)
+weighted_avg = udaf(WeightedAvg())
+t = table_env.from_elements([(1, 2, "Lee"),
+                             (3, 4, "Jay"),
+                             (5, 6, "Jay"),
+                             (7, 8, "Lee")]).alias("value", "count", "name")
+
+# call function "inline" without registration in Table API
+result = t.group_by(t.name).select(weighted_avg(t.value, t.count).alias("avg")).to_pandas()
+print(result)
+
+# register function
+table_env.create_temporary_function("weighted_avg", WeightedAvg())
+
+# call registered function in Table API
+result = t.group_by(t.name).select(call("weighted_avg", t.value, t.count).alias("avg")).to_pandas()
+print(result)
+
+# register table
+table_env.create_temporary_view("source", t)
+
+# call registered function in SQL
+result = table_env.sql_query(
+    "SELECT weighted_avg(`value`, `count`) AS avg FROM source GROUP BY name").to_pandas()
+print(result)
+{% endhighlight %}
+
+The `accumulate(...)` method of our `WeightedAvg` class takes three inputs. The first one is the accumulator
+and the other two are user-defined inputs. In order to calculate a weighted average value, the accumulator
+needs to store the weighted sum and count of all the data that has been accumulated. In our example, we
+use a `Row` object to be the accumulator. Accumulators are automatically managed

Review comment:
       ```suggestion
   use a `Row` object as the accumulator. Accumulators will be managed
   ```

##########
File path: docs/dev/python/table-api-users-guide/udfs/python_udfs.md
##########
@@ -227,3 +227,164 @@ def iterable_func(x):
       result = [1, 2, 3]
       return result
 {% endhighlight %}
+
+## Aggregation Functions
+
+A user-defined aggregate function (_UDAGG_) maps scalar values of multiple rows to a new scalar value.
+
+**NOTE:** Currently the general user-defined aggregate function is only supported in the group aggregate operation of the blink stream planner. For batch execution or windowed aggregate, it is recommended to use the [Vectorized Aggregate Functions]({% link dev/python/table-api-users-guide/udfs/vectorized_python_udfs.md %}#vectorized-aggregate-functions).
+
+The behavior of an aggregate function is centered around the concept of an accumulator. The _accumulator_
+is an intermediate data structure that stores the aggregated values until a final aggregation result
+is computed.
+
+For each set of rows that needs to be aggregated, the runtime will create an empty accumulator by calling
+`create_accumulator()`. Subsequently, the `accumulate(...)` method of the function is called for each input

Review comment:
       ```suggestion
   `create_accumulator()`. Subsequently, the `accumulate(...)` method of the aggregate function will be called for each input
   ```

##########
File path: docs/dev/python/table-api-users-guide/udfs/python_udfs.md
##########
@@ -227,3 +227,164 @@ def iterable_func(x):
       result = [1, 2, 3]
       return result
 {% endhighlight %}
+
+## Aggregation Functions
+
+A user-defined aggregate function (_UDAGG_) maps scalar values of multiple rows to a new scalar value.
+
+**NOTE:** Currently the general user-defined aggregate function is only supported in the group aggregate operation of the blink stream planner. For batch execution or windowed aggregate, it is recommended to use the [Vectorized Aggregate Functions]({% link dev/python/table-api-users-guide/udfs/vectorized_python_udfs.md %}#vectorized-aggregate-functions).
+
+The behavior of an aggregate function is centered around the concept of an accumulator. The _accumulator_
+is an intermediate data structure that stores the aggregated values until a final aggregation result
+is computed.
+
+For each set of rows that needs to be aggregated, the runtime will create an empty accumulator by calling
+`create_accumulator()`. Subsequently, the `accumulate(...)` method of the function is called for each input
+row to update the accumulator. Currently after each row has been processed, the `get_value(...)` method of the
+function is called to compute and return the immediate result. 
+
+The following example illustrates the aggregation process:
+
+<center>
+<img alt="UDAGG mechanism" src="{{ site.baseurl }}/fig/udagg-mechanism-python.png" width="80%">
+</center>
+
+In the example, we assume a table that contains data about beverages. The table consists of three columns (`id`, `name`,
+and `price`) and 5 rows. We would like to find the highest price of all beverages in the table, i.e., perform
+a `max()` aggregation. We need to consider each of the 5 rows. The result is a single numeric value.
+
+In order to define an aggregate function, one has to extend the base class `AggregateFunction` in
+`pyflink.table` and implement the evaluation method named `accumulate(...)`. 
+The result type and accumulator type of the aggregate function can be specified by such 2 approach:
+
+- implement the method named `get_result_type()` and `get_accumulator_type()`.

Review comment:
       ```suggestion
   - Implement the method named `get_result_type()` and `get_accumulator_type()`.
   ```

##########
File path: docs/dev/python/table-api-users-guide/udfs/python_udfs.md
##########
@@ -227,3 +227,164 @@ def iterable_func(x):
       result = [1, 2, 3]
       return result
 {% endhighlight %}
+
+## Aggregation Functions
+
+A user-defined aggregate function (_UDAGG_) maps scalar values of multiple rows to a new scalar value.
+
+**NOTE:** Currently the general user-defined aggregate function is only supported in the group aggregate operation of the blink stream planner. For batch execution or windowed aggregate, it is recommended to use the [Vectorized Aggregate Functions]({% link dev/python/table-api-users-guide/udfs/vectorized_python_udfs.md %}#vectorized-aggregate-functions).
+
+The behavior of an aggregate function is centered around the concept of an accumulator. The _accumulator_
+is an intermediate data structure that stores the aggregated values until a final aggregation result
+is computed.
+
+For each set of rows that needs to be aggregated, the runtime will create an empty accumulator by calling
+`create_accumulator()`. Subsequently, the `accumulate(...)` method of the function is called for each input
+row to update the accumulator. Currently after each row has been processed, the `get_value(...)` method of the
+function is called to compute and return the immediate result. 
+
+The following example illustrates the aggregation process:
+
+<center>
+<img alt="UDAGG mechanism" src="{{ site.baseurl }}/fig/udagg-mechanism-python.png" width="80%">
+</center>
+
+In the example, we assume a table that contains data about beverages. The table consists of three columns (`id`, `name`,
+and `price`) and 5 rows. We would like to find the highest price of all beverages in the table, i.e., perform
+a `max()` aggregation. We need to consider each of the 5 rows. The result is a single numeric value.

Review comment:
       ```suggestion
   a `max()` aggregation.
   ```

##########
File path: docs/dev/python/table-api-users-guide/udfs/python_udfs.md
##########
@@ -227,3 +227,164 @@ def iterable_func(x):
       result = [1, 2, 3]
       return result
 {% endhighlight %}
+
+## Aggregation Functions
+
+A user-defined aggregate function (_UDAGG_) maps scalar values of multiple rows to a new scalar value.
+
+**NOTE:** Currently the general user-defined aggregate function is only supported in the group aggregate operation of the blink stream planner. For batch execution or windowed aggregate, it is recommended to use the [Vectorized Aggregate Functions]({% link dev/python/table-api-users-guide/udfs/vectorized_python_udfs.md %}#vectorized-aggregate-functions).
+
+The behavior of an aggregate function is centered around the concept of an accumulator. The _accumulator_
+is an intermediate data structure that stores the aggregated values until a final aggregation result
+is computed.
+
+For each set of rows that needs to be aggregated, the runtime will create an empty accumulator by calling
+`create_accumulator()`. Subsequently, the `accumulate(...)` method of the function is called for each input
+row to update the accumulator. Currently after each row has been processed, the `get_value(...)` method of the
+function is called to compute and return the immediate result. 
+
+The following example illustrates the aggregation process:
+
+<center>
+<img alt="UDAGG mechanism" src="{{ site.baseurl }}/fig/udagg-mechanism-python.png" width="80%">
+</center>
+
+In the example, we assume a table that contains data about beverages. The table consists of three columns (`id`, `name`,
+and `price`) and 5 rows. We would like to find the highest price of all beverages in the table, i.e., perform
+a `max()` aggregation. We need to consider each of the 5 rows. The result is a single numeric value.
+
+In order to define an aggregate function, one has to extend the base class `AggregateFunction` in
+`pyflink.table` and implement the evaluation method named `accumulate(...)`. 
+The result type and accumulator type of the aggregate function can be specified by such 2 approach:
+
+- implement the method named `get_result_type()` and `get_accumulator_type()`.
+- wrap the function instance with the decorator `udaf` in `pyflink.table.udf` and specify the parameter `result_type` and `accumulator_type`.
+
+The following example shows how to define your own aggregate function and call it in a query.
+
+{% highlight python %}
+from pyflink.common import Row
+from pyflink.datastream import StreamExecutionEnvironment
+from pyflink.table import AggregateFunction, DataTypes, StreamTableEnvironment
+from pyflink.table.expressions import call
+from pyflink.table.udf import udaf
+
+
+class WeightedAvg(AggregateFunction):
+
+    def create_accumulator(self):
+        # Row(sum, count)
+        return Row(0, 0)
+
+    def get_value(self, accumulator):
+        if accumulator[1] == 0:
+            return None
+        else:
+            return accumulator[0] / accumulator[1]
+
+    def accumulate(self, accumulator, value, weight):
+        accumulator[0] += value * weight
+        accumulator[1] += weight
+    
+    def retract(self, accumulator, value, weight):
+        accumulator[0] -= value * weight
+        accumulator[1] -= weight
+        
+    def get_result_type(self):
+        return DataTypes.BIGINT()
+        
+    def get_accumulator_type(self):
+        return DataTypes.ROW([
+            DataTypes.FIELD("f0", DataTypes.BIGINT()), 
+            DataTypes.FIELD("f1", DataTypes.BIGINT())])
+
+
+env = StreamExecutionEnvironment.get_execution_environment()
+table_env = StreamTableEnvironment.create(env)
+# the result type and accumulator type can also be specified in the udaf decorator:
+# weighted_avg = udaf(WeightedAvg(), result_type=DataTypes.BIGINT(), accumulator_type=...)
+weighted_avg = udaf(WeightedAvg())
+t = table_env.from_elements([(1, 2, "Lee"),
+                             (3, 4, "Jay"),
+                             (5, 6, "Jay"),
+                             (7, 8, "Lee")]).alias("value", "count", "name")
+
+# call function "inline" without registration in Table API
+result = t.group_by(t.name).select(weighted_avg(t.value, t.count).alias("avg")).to_pandas()
+print(result)
+
+# register function
+table_env.create_temporary_function("weighted_avg", WeightedAvg())
+
+# call registered function in Table API
+result = t.group_by(t.name).select(call("weighted_avg", t.value, t.count).alias("avg")).to_pandas()
+print(result)
+
+# register table
+table_env.create_temporary_view("source", t)
+
+# call registered function in SQL
+result = table_env.sql_query(
+    "SELECT weighted_avg(`value`, `count`) AS avg FROM source GROUP BY name").to_pandas()
+print(result)
+{% endhighlight %}
+
+The `accumulate(...)` method of our `WeightedAvg` class takes three inputs. The first one is the accumulator
+and the other two are user-defined inputs. In order to calculate a weighted average value, the accumulator
+needs to store the weighted sum and count of all the data that has been accumulated. In our example, we
+use a `Row` object to be the accumulator. Accumulators are automatically managed
+by Flink's checkpointing mechanism and are restored in case of a failure to ensure exactly-once semantics.
+
+### Mandatory and Optional Methods
+
+**The following methods are mandatory for each `AggregateFunction`:**
+
+- `create_accumulator()`
+- `accumulate(...)` 
+- `get_value(...)`
+
+**The following methods of `AggregateFunction` are required depending on the use case:**
+
+- `retract(...)` is required when there are other operations that generate retract messages before current UDAF call, e.g. group aggregate , outer join. \
+This method is optional, but it is strongly recommended to be implemented to ensure the UDAF can be used in any use case.
+- `get_result_type()` and `get_accumulator_type()` is required if the result type and accumulator type would not be specified in the `udaf` decorator.
+
+### ListView and MapView
+
+If an accumulator needs to store large amounts of data, `pyflink.table.ListView` and `pyflink.table.MapView` 
+provide advanced features for leveraging Flink's state backends in unbounded data scenarios. 

Review comment:
       ```suggestion
   could be used instead of list and map. These two data structures provide the similar functionalities as list and map, however usually having better performance by leveraging Flink's state backend to eliminate unnecessary state access.
   ```

##########
File path: docs/dev/python/table-api-users-guide/udfs/python_udfs.md
##########
@@ -227,3 +227,164 @@ def iterable_func(x):
       result = [1, 2, 3]
       return result
 {% endhighlight %}
+
+## Aggregation Functions
+
+A user-defined aggregate function (_UDAGG_) maps scalar values of multiple rows to a new scalar value.
+
+**NOTE:** Currently the general user-defined aggregate function is only supported in the group aggregate operation of the blink stream planner. For batch execution or windowed aggregate, it is recommended to use the [Vectorized Aggregate Functions]({% link dev/python/table-api-users-guide/udfs/vectorized_python_udfs.md %}#vectorized-aggregate-functions).
+
+The behavior of an aggregate function is centered around the concept of an accumulator. The _accumulator_
+is an intermediate data structure that stores the aggregated values until a final aggregation result
+is computed.
+
+For each set of rows that needs to be aggregated, the runtime will create an empty accumulator by calling
+`create_accumulator()`. Subsequently, the `accumulate(...)` method of the function is called for each input
+row to update the accumulator. Currently after each row has been processed, the `get_value(...)` method of the
+function is called to compute and return the immediate result. 
+
+The following example illustrates the aggregation process:
+
+<center>
+<img alt="UDAGG mechanism" src="{{ site.baseurl }}/fig/udagg-mechanism-python.png" width="80%">
+</center>
+
+In the example, we assume a table that contains data about beverages. The table consists of three columns (`id`, `name`,
+and `price`) and 5 rows. We would like to find the highest price of all beverages in the table, i.e., perform
+a `max()` aggregation. We need to consider each of the 5 rows. The result is a single numeric value.
+
+In order to define an aggregate function, one has to extend the base class `AggregateFunction` in
+`pyflink.table` and implement the evaluation method named `accumulate(...)`. 
+The result type and accumulator type of the aggregate function can be specified by such 2 approach:
+
+- implement the method named `get_result_type()` and `get_accumulator_type()`.
+- wrap the function instance with the decorator `udaf` in `pyflink.table.udf` and specify the parameter `result_type` and `accumulator_type`.
+
+The following example shows how to define your own aggregate function and call it in a query.
+
+{% highlight python %}
+from pyflink.common import Row
+from pyflink.datastream import StreamExecutionEnvironment
+from pyflink.table import AggregateFunction, DataTypes, StreamTableEnvironment
+from pyflink.table.expressions import call
+from pyflink.table.udf import udaf
+
+
+class WeightedAvg(AggregateFunction):
+
+    def create_accumulator(self):
+        # Row(sum, count)
+        return Row(0, 0)
+
+    def get_value(self, accumulator):
+        if accumulator[1] == 0:
+            return None
+        else:
+            return accumulator[0] / accumulator[1]
+
+    def accumulate(self, accumulator, value, weight):
+        accumulator[0] += value * weight
+        accumulator[1] += weight
+    
+    def retract(self, accumulator, value, weight):
+        accumulator[0] -= value * weight
+        accumulator[1] -= weight
+        
+    def get_result_type(self):
+        return DataTypes.BIGINT()
+        
+    def get_accumulator_type(self):
+        return DataTypes.ROW([
+            DataTypes.FIELD("f0", DataTypes.BIGINT()), 
+            DataTypes.FIELD("f1", DataTypes.BIGINT())])
+
+
+env = StreamExecutionEnvironment.get_execution_environment()
+table_env = StreamTableEnvironment.create(env)
+# the result type and accumulator type can also be specified in the udaf decorator:
+# weighted_avg = udaf(WeightedAvg(), result_type=DataTypes.BIGINT(), accumulator_type=...)
+weighted_avg = udaf(WeightedAvg())
+t = table_env.from_elements([(1, 2, "Lee"),
+                             (3, 4, "Jay"),
+                             (5, 6, "Jay"),
+                             (7, 8, "Lee")]).alias("value", "count", "name")
+
+# call function "inline" without registration in Table API
+result = t.group_by(t.name).select(weighted_avg(t.value, t.count).alias("avg")).to_pandas()
+print(result)
+
+# register function
+table_env.create_temporary_function("weighted_avg", WeightedAvg())
+
+# call registered function in Table API
+result = t.group_by(t.name).select(call("weighted_avg", t.value, t.count).alias("avg")).to_pandas()
+print(result)
+
+# register table
+table_env.create_temporary_view("source", t)
+
+# call registered function in SQL
+result = table_env.sql_query(
+    "SELECT weighted_avg(`value`, `count`) AS avg FROM source GROUP BY name").to_pandas()
+print(result)
+{% endhighlight %}
+
+The `accumulate(...)` method of our `WeightedAvg` class takes three inputs. The first one is the accumulator
+and the other two are user-defined inputs. In order to calculate a weighted average value, the accumulator
+needs to store the weighted sum and count of all the data that has been accumulated. In our example, we
+use a `Row` object to be the accumulator. Accumulators are automatically managed
+by Flink's checkpointing mechanism and are restored in case of a failure to ensure exactly-once semantics.
+
+### Mandatory and Optional Methods
+
+**The following methods are mandatory for each `AggregateFunction`:**
+
+- `create_accumulator()`
+- `accumulate(...)` 
+- `get_value(...)`
+
+**The following methods of `AggregateFunction` are required depending on the use case:**
+
+- `retract(...)` is required when there are other operations that generate retract messages before current UDAF call, e.g. group aggregate , outer join. \
+This method is optional, but it is strongly recommended to be implemented to ensure the UDAF can be used in any use case.
+- `get_result_type()` and `get_accumulator_type()` is required if the result type and accumulator type would not be specified in the `udaf` decorator.
+
+### ListView and MapView
+
+If an accumulator needs to store large amounts of data, `pyflink.table.ListView` and `pyflink.table.MapView` 
+provide advanced features for leveraging Flink's state backends in unbounded data scenarios. 
+This feature can be enabled by declaring `DataTypes.LIST_VIEW(...)` and `DataTypes.MAP_VIEW(...)` in the accumulator type, e.g.:
+
+{% highlight python %}
+from pyflink.table import ListView
+
+class ListViewConcatAggregateFunction(AggregateFunction):
+
+    def get_value(self, accumulator):
+        # the ListView is iterable
+        return accumulator[1].join(accumulator[0])
+
+    def create_accumulator(self):
+        return Row(ListView(), '')
+
+    def accumulate(self, accumulator, *args):
+        accumulator[1] = args[1]
+        # the ListView support add, clear and iterate operations.
+        accumulator[0].add(args[0])
+
+    def get_accumulator_type(self):
+        return DataTypes.ROW([
+            # declare the first column of the accumulator as a string ListView.
+            DataTypes.FIELD("f0", DataTypes.LIST_VIEW(DataTypes.STRING())),
+            DataTypes.FIELD("f1", DataTypes.BIGINT())])
+
+    def get_result_type(self):
+        return DataTypes.STRING()
+{% endhighlight %}
+
+Currently there are 2 limitations to use the:

Review comment:
       ```suggestion
   Currently there are 2 limitations to use the ListView and MapView:
   ```

##########
File path: docs/dev/python/table-api-users-guide/udfs/python_udfs.md
##########
@@ -227,3 +227,164 @@ def iterable_func(x):
       result = [1, 2, 3]
       return result
 {% endhighlight %}
+
+## Aggregation Functions
+
+A user-defined aggregate function (_UDAGG_) maps scalar values of multiple rows to a new scalar value.
+
+**NOTE:** Currently the general user-defined aggregate function is only supported in the group aggregate operation of the blink stream planner. For batch execution or windowed aggregate, it is recommended to use the [Vectorized Aggregate Functions]({% link dev/python/table-api-users-guide/udfs/vectorized_python_udfs.md %}#vectorized-aggregate-functions).
+
+The behavior of an aggregate function is centered around the concept of an accumulator. The _accumulator_
+is an intermediate data structure that stores the aggregated values until a final aggregation result
+is computed.
+
+For each set of rows that needs to be aggregated, the runtime will create an empty accumulator by calling
+`create_accumulator()`. Subsequently, the `accumulate(...)` method of the function is called for each input
+row to update the accumulator. Currently after each row has been processed, the `get_value(...)` method of the
+function is called to compute and return the immediate result. 
+
+The following example illustrates the aggregation process:
+
+<center>
+<img alt="UDAGG mechanism" src="{{ site.baseurl }}/fig/udagg-mechanism-python.png" width="80%">
+</center>
+
+In the example, we assume a table that contains data about beverages. The table consists of three columns (`id`, `name`,
+and `price`) and 5 rows. We would like to find the highest price of all beverages in the table, i.e., perform
+a `max()` aggregation. We need to consider each of the 5 rows. The result is a single numeric value.
+
+In order to define an aggregate function, one has to extend the base class `AggregateFunction` in
+`pyflink.table` and implement the evaluation method named `accumulate(...)`. 
+The result type and accumulator type of the aggregate function can be specified by such 2 approach:
+
+- implement the method named `get_result_type()` and `get_accumulator_type()`.
+- wrap the function instance with the decorator `udaf` in `pyflink.table.udf` and specify the parameter `result_type` and `accumulator_type`.
+
+The following example shows how to define your own aggregate function and call it in a query.
+
+{% highlight python %}
+from pyflink.common import Row
+from pyflink.datastream import StreamExecutionEnvironment
+from pyflink.table import AggregateFunction, DataTypes, StreamTableEnvironment
+from pyflink.table.expressions import call
+from pyflink.table.udf import udaf
+
+
+class WeightedAvg(AggregateFunction):
+
+    def create_accumulator(self):
+        # Row(sum, count)
+        return Row(0, 0)
+
+    def get_value(self, accumulator):
+        if accumulator[1] == 0:
+            return None
+        else:
+            return accumulator[0] / accumulator[1]
+
+    def accumulate(self, accumulator, value, weight):
+        accumulator[0] += value * weight
+        accumulator[1] += weight
+    
+    def retract(self, accumulator, value, weight):
+        accumulator[0] -= value * weight
+        accumulator[1] -= weight
+        
+    def get_result_type(self):
+        return DataTypes.BIGINT()
+        
+    def get_accumulator_type(self):
+        return DataTypes.ROW([
+            DataTypes.FIELD("f0", DataTypes.BIGINT()), 
+            DataTypes.FIELD("f1", DataTypes.BIGINT())])
+
+
+env = StreamExecutionEnvironment.get_execution_environment()
+table_env = StreamTableEnvironment.create(env)
+# the result type and accumulator type can also be specified in the udaf decorator:
+# weighted_avg = udaf(WeightedAvg(), result_type=DataTypes.BIGINT(), accumulator_type=...)
+weighted_avg = udaf(WeightedAvg())
+t = table_env.from_elements([(1, 2, "Lee"),
+                             (3, 4, "Jay"),
+                             (5, 6, "Jay"),
+                             (7, 8, "Lee")]).alias("value", "count", "name")
+
+# call function "inline" without registration in Table API
+result = t.group_by(t.name).select(weighted_avg(t.value, t.count).alias("avg")).to_pandas()
+print(result)
+
+# register function
+table_env.create_temporary_function("weighted_avg", WeightedAvg())
+
+# call registered function in Table API
+result = t.group_by(t.name).select(call("weighted_avg", t.value, t.count).alias("avg")).to_pandas()
+print(result)
+
+# register table
+table_env.create_temporary_view("source", t)
+
+# call registered function in SQL
+result = table_env.sql_query(
+    "SELECT weighted_avg(`value`, `count`) AS avg FROM source GROUP BY name").to_pandas()
+print(result)
+{% endhighlight %}
+
+The `accumulate(...)` method of our `WeightedAvg` class takes three inputs. The first one is the accumulator
+and the other two are user-defined inputs. In order to calculate a weighted average value, the accumulator
+needs to store the weighted sum and count of all the data that has been accumulated. In our example, we
+use a `Row` object to be the accumulator. Accumulators are automatically managed
+by Flink's checkpointing mechanism and are restored in case of a failure to ensure exactly-once semantics.
+
+### Mandatory and Optional Methods
+
+**The following methods are mandatory for each `AggregateFunction`:**
+
+- `create_accumulator()`
+- `accumulate(...)` 
+- `get_value(...)`
+
+**The following methods of `AggregateFunction` are required depending on the use case:**
+
+- `retract(...)` is required when there are other operations that generate retract messages before current UDAF call, e.g. group aggregate , outer join. \
+This method is optional, but it is strongly recommended to be implemented to ensure the UDAF can be used in any use case.
+- `get_result_type()` and `get_accumulator_type()` is required if the result type and accumulator type would not be specified in the `udaf` decorator.
+
+### ListView and MapView
+
+If an accumulator needs to store large amounts of data, `pyflink.table.ListView` and `pyflink.table.MapView` 
+provide advanced features for leveraging Flink's state backends in unbounded data scenarios. 
+This feature can be enabled by declaring `DataTypes.LIST_VIEW(...)` and `DataTypes.MAP_VIEW(...)` in the accumulator type, e.g.:

Review comment:
       ```suggestion
   You can use them by declaring `DataTypes.LIST_VIEW(...)` and `DataTypes.MAP_VIEW(...)` in the accumulator type, e.g.:
   ```




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[GitHub] [flink] flinkbot commented on pull request #13905: [FLINK-19844][python][docs] Add documentation for Python UDAF.

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flinkbot edited a comment on pull request #13905:
URL: https://github.com/apache/flink/pull/13905#issuecomment-721225406


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[GitHub] [flink] flinkbot edited a comment on pull request #13905: [FLINK-19844][python][docs] Add documentation for Python UDAF.

[GitBox <gi...@apache.org>]

flinkbot edited a comment on pull request #13905:
URL: https://github.com/apache/flink/pull/13905#issuecomment-721225406


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[GitHub] [flink] WeiZhong94 commented on pull request #13905: [FLINK-19844][python][docs] Add documentation for Python UDAF.

[GitBox <gi...@apache.org>]

WeiZhong94 commented on pull request #13905:
URL: https://github.com/apache/flink/pull/13905#issuecomment-721584083


   @dianfu Thanks for your review! I have updated this PR.


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[GitHub] [flink] dianfu closed pull request #13905: [FLINK-19844][python][docs] Add documentation for Python UDAF.

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dianfu closed pull request #13905:
URL: https://github.com/apache/flink/pull/13905


   


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