[FLINK-19749][docs] Improve the documentation in 'Table API' page, e.…

…g. typo, sync between the English and Chinese doc, etc

This closes apache#13791.

docs/dev/table/tableApi.md

@@ -24,7 +24,7 @@ under the License.
 
 The Table API is a unified, relational API for stream and batch processing. Table API queries can be run on batch or streaming input without modifications. The Table API is a super set of the SQL language and is specially designed for working with Apache Flink. The Table API is a language-integrated API for Scala, Java and Python. Instead of specifying queries as String values as common with SQL, Table API queries are defined in a language-embedded style in Java, Scala or Python with IDE support like autocompletion and syntax validation. 
 
-The Table API shares many concepts and parts of its API with Flink's SQL integration. Have a look at the [Common Concepts & API]({{ site.baseurl }}/dev/table/common.html) to learn how to register tables or to create a `Table` object. The [Streaming Concepts](./streaming) pages discuss streaming specific concepts such as dynamic tables and time attributes.
+The Table API shares many concepts and parts of its API with Flink's SQL integration. Have a look at the [Common Concepts & API]({% link dev/table/common.md %}) to learn how to register tables or to create a `Table` object. The [Streaming Concepts](./streaming) pages discuss streaming specific concepts such as dynamic tables and time attributes.
 
 The following examples assume a registered table called `Orders` with attributes `(a, b, c, rowtime)`. The `rowtime` field is either a logical [time attribute](./streaming/time_attributes.html) in streaming or a regular timestamp field in batch.
 
@@ -621,7 +621,7 @@ Table result = orders.addColumns(concat($("c"), "sunny"));
   <span class="label label-primary">Batch</span> <span class="label label-primary">Streaming</span>
   </td>
   <td>
-  <p>Performs a field add operation. Existing fields will be replaced if add columns name is the same as the existing column name. Moreover, if the added fields have duplicate field name, then the last one is used. </p>
+  <p>Performs a field add operation. Existing fields will be replaced if the added column name is the same as the existing column name. Moreover, if the added fields have duplicate field name, then the last one is used. </p>
 {% highlight java %}
 Table orders = tableEnv.from("Orders");
 Table result = orders.addOrReplaceColumns(concat($("c"), "sunny").as("desc"));
@@ -687,7 +687,7 @@ val result = orders.addColumns(concat($"c", "Sunny"))
  <span class="label label-primary">Batch</span> <span class="label label-primary">Streaming</span>
  </td>
  <td>
- <p>Performs a field add operation. Existing fields will be replaced if add columns name is the same as the existing column name. Moreover, if the added fields have duplicate field name, then the last one is used. </p>
+ <p>Performs a field add operation. Existing fields will be replaced if the added column name is the same as the existing column name. Moreover, if the added fields have duplicate field name, then the last one is used. </p>
 {% highlight scala %}
 val orders = tableEnv.from("Orders");
 val result = orders.addOrReplaceColumns(concat($"c", "Sunny") as "desc")
@@ -755,7 +755,7 @@ result = orders.add_columns(concat(orders.c, 'sunny'))
   <span class="label label-primary">Batch</span> <span class="label label-primary">Streaming</span>
   </td>
   <td>
-  <p>Performs a field add operation. Existing fields will be replaced if add columns name is the same as the existing column name. Moreover, if the added fields have duplicate field name, then the last one is used. </p>
+  <p>Performs a field add operation. Existing fields will be replaced if the added column name is the same as the existing column name. Moreover, if the added fields have duplicate field name, then the last one is used. </p>
 {% highlight python %}
 from pyflink.table.expressions import concat
 
@@ -836,7 +836,7 @@ Table result = orders.groupBy($("a")).select($("a"), $("b").sum().as("d"));
 {% highlight java %}
 Table orders = tableEnv.from("Orders");
 Table result = orders
- .window(Tumble.over(lit(5).minutes())).on($("rowtime")).as("w")) // define window
+ .window(Tumble.over(lit(5).minutes()).on($("rowtime")).as("w")) // define window
  .groupBy($("a"), $("w")) // group by key and window
  // access window properties and aggregate
  .select(
@@ -895,7 +895,7 @@ Table groupByDistinctResult = orders
 // Distinct aggregation on time window group by
 Table groupByWindowDistinctResult = orders
  .window(Tumble
- .over(lit(5).minutes()))
+ .over(lit(5).minutes())
  .on($("rowtime"))
  .as("w")
  )
@@ -1264,7 +1264,7 @@ Table result = left.join(right)
  .where(
  and(
  $("a").isEqual($("d")),
- $("ltime").isGreaterEqual($("rtime").minus(lit(5).minutes())),
+ $("ltime").isGreaterOrEqual($("rtime").minus(lit(5).minutes())),
  $("ltime").isLess($("rtime").plus(lit(10).minutes()))
  ))
  .select($("a"), $("b"), $("e"), $("ltime"));
@@ -2740,7 +2740,7 @@ A session window is defined by using the `Session` class as follows:
 
 ### Over Windows
 
-Over window aggregates are known from standard SQL (`OVER` clause) and defined in the `SELECT` clause of a query. Unlike group windows, which are specified in the `GROUP BY` clause, over windows do not collapse rows. Instead over window aggregates compute an aggregate for each input row over a range of its neighboring rows. 
+Over window aggregates are known from standard SQL (`OVER` clause) and defined in the `SELECT` clause of a query. Unlike group windows, which are specified in the `GROUP BY` clause, over windows do not collapse rows. Instead over window aggregates compute an aggregate for each input row over a range of its neighboring rows.
 
 Over windows are defined using the `window(w: OverWindow*)` clause (using `over_window(*OverWindow)` in Python API) and referenced via an alias in the `select()` method. The following example shows how to define an over window aggregation on a table.
 
@@ -3177,7 +3177,7 @@ Table table = input
   </td>
   <td>
   <p>Similar to a <b>GroupBy Aggregation</b>. Groups the rows on the grouping keys with the following running table aggregation operator to aggregate rows group-wise. The difference from an AggregateFunction is that TableAggregateFunction may return 0 or more records for a group. You have to close the "flatAggregate" with a select statement. And the select statement does not support aggregate functions.</p>
-  <p>Instead of using <code>emitValue</code> to output results, you can also use the <code>emitUpdateWithRetract</code> method. Different from <code>emitValue</code>, <code>emitUpdateWithRetract</code> is used to emit values that have been updated. This method outputs data incrementally in retract mode, i.e., once there is an update, we have to retract old records before sending new updated ones. The <code>emitUpdateWithRetract</code> method will be used in preference to the <code>emitValue</code> method if both methods are defined in the table aggregate function, because the method is treated to be more efficient than <code>emitValue</code> as it can output values incrementally. See <a href="{{ site.baseurl }}/dev/table/functions/udfs.html#table-aggregation-functions">Table Aggregation Functions</a> for details.</p>
+  <p>Instead of using <code>emitValue</code> to output results, you can also use the <code>emitUpdateWithRetract</code> method. Different from <code>emitValue</code>, <code>emitUpdateWithRetract</code> is used to emit values that have been updated. This method outputs data incrementally in retract mode, i.e., once there is an update, we have to retract old records before sending new updated ones. The <code>emitUpdateWithRetract</code> method will be used in preference to the <code>emitValue</code> method if both methods are defined in the table aggregate function, because the method is treated to be more efficient than <code>emitValue</code> as it can output values incrementally. See <a href="{% link dev/table/functions/udfs.md %}#table-aggregation-functions">Table Aggregation Functions</a> for details.</p>
 {% highlight java %}
 /**
  * Accumulator for Top2.
@@ -3403,7 +3403,7 @@ val table = input
   </td>
   <td>
   <p>Similar to a <b>GroupBy Aggregation</b>. Groups the rows on the grouping keys with the following running table aggregation operator to aggregate rows group-wise. The difference from an AggregateFunction is that TableAggregateFunction may return 0 or more records for a group. You have to close the "flatAggregate" with a select statement. And the select statement does not support aggregate functions.</p>
-  <p>Instead of using <code>emitValue</code> to output results, you can also use the <code>emitUpdateWithRetract</code> method. Different from <code>emitValue</code>, <code>emitUpdateWithRetract</code> is used to emit values that have been updated. This method outputs data incrementally in retract mode, i.e., once there is an update, we have to retract old records before sending new updated ones. The <code>emitUpdateWithRetract</code> method will be used in preference to the <code>emitValue</code> method if both methods are defined in the table aggregate function, because the method is treated to be more efficient than <code>emitValue</code> as it can output values incrementally. See <a href="{{ site.baseurl }}/dev/table/functions/udfs.html#table-aggregation-functions">Table Aggregation Functions</a> for details.</p>
+  <p>Instead of using <code>emitValue</code> to output results, you can also use the <code>emitUpdateWithRetract</code> method. Different from <code>emitValue</code>, <code>emitUpdateWithRetract</code> is used to emit values that have been updated. This method outputs data incrementally in retract mode, i.e., once there is an update, we have to retract old records before sending new updated ones. The <code>emitUpdateWithRetract</code> method will be used in preference to the <code>emitValue</code> method if both methods are defined in the table aggregate function, because the method is treated to be more efficient than <code>emitValue</code> as it can output values incrementally. See <a href="{% link dev/table/functions/udfs.md %}#table-aggregation-functions">Table Aggregation Functions</a> for details.</p>
 {% highlight scala %}
 import java.lang.{Integer => JInteger}
 import org.apache.flink.table.api.Types
@@ -3576,9 +3576,9 @@ Please see the dedicated page about [data types](types.html).
 
 Generic types and (nested) composite types (e.g., POJOs, tuples, rows, Scala case classes) can be fields of a row as well.
 
-Fields of composite types with arbitrary nesting can be accessed with [value access functions]({{ site.baseurl }}/dev/table/functions/systemFunctions.html#value-access-functions).
+Fields of composite types with arbitrary nesting can be accessed with [value access functions]({% link dev/table/functions/systemFunctions.md %}#value-access-functions).
 
-Generic types are treated as a black box and can be passed on or processed by [user-defined functions]({{ site.baseurl }}/dev/table/functions/udfs.html).
+Generic types are treated as a black box and can be passed on or processed by [user-defined functions]({% link dev/table/functions/udfs.md %}).
 
 {% top %}