add example

Signed-off-by: Ruihang Xia <waynestxia@gmail.com>

docs/nightly/en/user-guide/continuous-aggregation/define-time-window.md

@@ -2,7 +2,7 @@
 
 Time window is an important attribute of your continuous aggregation query. It defines how the data is aggregated in the flow. GreptimeDB provides two types of time windows: `hop` and `tumble`, or "sliding window" and "fixed window" in other words. You can specify the time window in the `GROUP BY` clause using `hop()` function or `tumble()` function respectively. These two functions are only supported in continuous aggregate queries's `GROUP BY` position.
 
-Here illustrates the `hop()` and `tumble()` functions works:
+Here illustrates how the `hop()` and `tumble()` functions work:
 
 ![Time Window](/time-window.svg)
 

docs/nightly/en/user-guide/continuous-aggregation/overview.md

@@ -7,4 +7,82 @@ GreptimeDB provides a continuous aggregation feature that allows you to aggregat
 - [Query](./query.md) describes how to write a continuous aggregation query.
 - [Expression](./expression.md) is a reference of available expressions in the continuous aggregation query.
 
-![Continuous Aggregation](/flow-ani.svg)
+![Continuous Aggregation](/flow-ani.svg)
+
+## Example
+
+Here is a complete example of how a continuous aggregation query looks like.
+
+First, we create a source table `numbers_input` and a sink table `out_num_cnt` with following clauses:
+
+```sql
+CREATE TABLE numbers_input (
+    number INT,
+    ts TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+    PRIMARY KEY(number),
+    TIME INDEX(ts)
+);
+```
+
+```sql
+CREATE TABLE out_num_cnt (
+    sum_number BIGINT,
+    start_window TIMESTAMP TIME INDEX,
+    end_window TIMESTAMP,
+    update_at TIMESTAMP,
+);
+```
+
+Then create our flow `test_numbers` to aggregate the sum of `number` column in `numbers_input` table. The aggregation is calculated in 1-second fixed windows.
+
+```sql
+CREATE FLOW test_numbers 
+SINK TO out_num_cnt
+AS 
+SELECT sum(number) FROM numbers_input GROUP BY tumble(ts, '1 second');
+```
+
+Now we can insert some data into `numbers_input` table and see the result in `out_num_cnt` table.
+
+```sql
+INSERT INTO numbers_input 
+VALUES
+    (20,1625097600000),
+    (22,1625097600500);
+```
+
+The output table `out_num_cnt` should have the following data:
+
+```sql
+SELECT * FROM out_num_cnt;
+```
+
+```sql
+ sum_number |        start_window        |         end_window         |         update_at          
+------------+----------------------------+----------------------------+----------------------------
+         42 | 2021-07-01 00:00:00.000000 | 2021-07-01 00:00:01.000000 | 2024-05-17 08:32:56.026000
+(1 row)
+```
+
+Let's try to insert more data into `numbers_input` table.
+
+```sql
+public=> INSERT INTO numbers_input 
+VALUES
+    (23,1625097601000),
+    (24,1625097601500);
+```
+
+Now the output table `out_num_cnt` should have the following data:
+
+```sql
+SELECT * FROM out_num_cnt;
+```
+
+```sql
+ sum_number |        start_window        |         end_window         |         update_at          
+------------+----------------------------+----------------------------+----------------------------
+         42 | 2021-07-01 00:00:00.000000 | 2021-07-01 00:00:01.000000 | 2024-05-17 08:32:56.026000
+         47 | 2021-07-01 00:00:01.000000 | 2021-07-01 00:00:02.000000 | 2024-05-17 08:33:10.048000
+(2 rows)
+```