2021年7月25日 (日) 08:04的版本

Future相关操作的设计

AsyncResult

AsyncResult表示一个异步操作的结果，这个结果要么是成功的，要么是失败的。

interface AsyncResult<T> {
    T getResult();
    Throwable cause();
    boolean succeeded();
    boolean failed();
    AsyncResult<V> map(Function<T, V> mapper);
    AsyncResult<V> map(V value);
    AsyncResult<V> mapEmpty();
    AsyncResult<T> otherwise(T value);
    AsyncResult<T> otherwise(Function<Throwable, T> mapper);
    AsyncResult<T> otherwiseEmpty();
}

@@ 第1行： / 第1行： @@
-RedisBloom provides Redis with support for additional probabilistic data structures. These structures allow for constant memory space and extremely fast processing while still maintaining a low error rate. It supports scalable Bloom and Cuckoo filters to determine whether an item is present or absent from a collection with a given degree of certainty, Count-min sketch to count the frequency of the different items in sub-linear space, and Top-K to count top k events in a near deterministic manner.
+=Future相关操作的设计=
+== AsyncResult ==
-A good use case for a Bloom filter is to check for an already used username. On a small scale, this is no problem, but as a service grows, this can be very taxing on a database. It is very simple to implement this with a ReBloom.
+AsyncResult表示一个异步操作的结果，这个结果要么是成功的，要么是失败的。
+<syntaxhighlight lang="java">
+interface AsyncResult<T> {
-<syntaxhighlight lang="lisp">
+    T getResult();
-> BF.ADD usernames funnyfred
+    Throwable cause();
-(integer) 1
+    boolean succeeded();
-> BF.ADD usernames fredisfunny
+    boolean failed();
-(integer) 1
+    AsyncResult<V> map(Function<T, V> mapper);
-> BF.ADD usernames fred
+    AsyncResult<V> map(V value);
-(integer) 1
+    AsyncResult<V> mapEmpty();
-> BF.ADD usernames funfred
+    AsyncResult<T> otherwise(T value);
-(integer) 1
+    AsyncResult<T> otherwise(Function<Throwable, T> mapper);
-</syntaxhighlight>
+    AsyncResult<T> otherwiseEmpty();
+}
-<syntaxhighlight lang="lisp">
-> BF.EXISTS usernames fred
-(integer) 1
-> BF.EXISTS usernames fred_is_funny
-(integer) 0
 </syntaxhighlight>
-As expected, fred_is_funny yields a 0. A response of zero means we can be sure that this username has not been used. A response of 1 means it might have been used. We can’t say for certain as it might a case of overlapping bits between multiple items.
-Generally, the chances of false positives are low, but non-zero. As the Bloom filter “fills up” the chances increase. You can tweak the error rate and size with the BF.RESERVE command.
+[[Category:Vert.x]]
-[[Category:Database]]
-[[Category:Redis]]