diff --git a/docs/learn-flink/etl.md b/docs/learn-flink/etl.md
index dc42fa96c346d..78074ad70dc95 100644
--- a/docs/learn-flink/etl.md
+++ b/docs/learn-flink/etl.md
@@ -149,7 +149,7 @@ this would mean doing some sort of GROUP BY with the `startCell`, while in Flink
 {% highlight java %}
 rides
     .flatMap(new NYCEnrichment())
-    .keyBy(value -> value.startCell)
+    .keyBy(enrichedRide -> enrichedRide.startCell)
 {% endhighlight %}
 
 Every `keyBy` causes a network shuffle that repartitions the stream. In general this is pretty
@@ -157,32 +157,6 @@ expensive, since it involves network communication along with serialization and
 
 <img src="{% link /fig/keyBy.png %}" alt="keyBy and network shuffle" class="offset" width="45%" />
 
-In the example above, the key has been specified by a field name, "startCell". This style of key
-selection has the drawback that the compiler is unable to infer the type of the field being used for
-keying, and so Flink will pass around the key values as Tuples, which can be awkward. It is
-better to use a properly typed KeySelector, e.g.,
-
-{% highlight java %}
-rides
-    .flatMap(new NYCEnrichment())
-    .keyBy(
-        new KeySelector<EnrichedRide, int>() {
-
-            @Override
-            public int getKey(EnrichedRide enrichedRide) throws Exception {
-                return enrichedRide.startCell;
-            }
-        })
-{% endhighlight %}
-
-which can be more succinctly expressed with a lambda:
-
-{% highlight java %}
-rides
-    .flatMap(new NYCEnrichment())
-    .keyBy(enrichedRide -> enrichedRide.startCell)
-{% endhighlight %}
-
 ### Keys are computed
 
 KeySelectors aren't limited to extracting a key from your events. They can, instead, 
diff --git a/docs/learn-flink/etl.zh.md b/docs/learn-flink/etl.zh.md
index 190240b9e3b34..08ef552506a27 100644
--- a/docs/learn-flink/etl.zh.md
+++ b/docs/learn-flink/etl.zh.md
@@ -130,36 +130,13 @@ public static class NYCEnrichment implements FlatMapFunction<TaxiRide, EnrichedR
 {% highlight java %}
 rides
     .flatMap(new NYCEnrichment())
-    .keyBy(value -> value.startCell)
+    .keyBy(enrichedRide -> enrichedRide.startCell)
 {% endhighlight %}
 
 每个 `keyBy` 会通过 shuffle 来为数据流进行重新分区。总体来说这个开销是很大的，它涉及网络通信、序列化和反序列化。
 
 <img src="{% link /fig/keyBy.png %}" alt="keyBy and network shuffle" class="offset" width="45%" />
 
-在上面的例子中，将 "startCell" 这个字段定义为键。这种选择键的方式有个缺点，就是编译器无法推断用作键的字段的类型，所以 Flink 会将键值作为元组传递，这有时候会比较难处理。所以最好还是使用一个合适的 KeySelector，
-
-{% highlight java %}
-rides
-    .flatMap(new NYCEnrichment())
-    .keyBy(
-        new KeySelector<EnrichedRide, int>() {
-
-            @Override
-            public int getKey(EnrichedRide enrichedRide) throws Exception {
-                return enrichedRide.startCell;
-            }
-        })
-{% endhighlight %}
-
-也可以使用更简洁的 lambda 表达式：
-
-{% highlight java %}
-rides
-    .flatMap(new NYCEnrichment())
-    .keyBy(enrichedRide -> enrichedRide.startCell)
-{% endhighlight %}
-
 ### 通过计算得到键
 
 KeySelector 不仅限于从事件中抽取键。你也可以按想要的方式计算得到键值，只要最终结果是确定的，并且实现了 `hashCode()` 和 `equals()`。这些限制条件不包括产生随机数或者返回 Arrays 或 Enums 的 KeySelector，但你可以用元组和 POJO 来组成键，只要他们的元素遵循上述条件。