reduce

再读《深入浅出 rxjs》的小收获

由 巧了我就是萌 提交于 2020-03-18 18:29:29
3 月，跳不动了？>>> Observbale 对象可以看作一个数据集合，但这个数据集合可以不是一次产生，而是在一个时间段上逐个产生每个数据，因为这，一个 observable 对象即使产生超庞大的数据，依然不会消耗很多内存，因为每一次只产生一个，吐出来之后再产生另一个，不会挤压; 每一个操作符都需要考虑: 返回一个全新的 observable 对象、对上游和下游的订阅及退订处理、处理异常情况，及时释放资源； 为什么使用 pipe 来组合操作数？好处：1. 它通过删除操作符来清除 Observable.prototype 2. 让 rxjs 的库更加容易被摇树优化 3. 更易写和使用第三方操作数，因为不需要给OBservable.prototype打补丁； Throttle 和 debounce, 节流和去抖动； throttleTime 的作用是限制在 duration 时间范围内，从上游传递给下游数据的 个数 ，debounceTime 的作用是让传递给下游的时间间隔不能小于给定 时间 dueTime; combineLatest: 取得各个 observable 最后送出的值，再合并在一起输出一个observable；zip 会取每个 observable 相同顺位的元素组合成一个 observable；（ 平时没事不要用 zip， 除非真的需要，因为zip必须cache

How to group by json using reduce?

由 怎甘沉沦 提交于 2020-01-25 06:12:08
问题 Please take a look at my current json. I want to recursively group by this one. [{ "mode": "AR", "fname": "ta", "lname": "da", "w_lng": "1.23", "w_lat": "2.23","other":"a"}, { "mode": "AR", "fname": "ta", "lname": "Dash", "w_lng": "1.23", "w_lat": "2.23","other":"b" }, { "mode": "AR1", "fname": "ka", "lname": "ja", "w_lng": "3.23", "w_lat": "4.23","other":"c" }, { "mode": "AR", "fname": "Kiran", "lname": "Dash", "w_lng": "5.23", "w_lat": "6.23","other":"d" }, { "mode": "AR", "fname": "Milan",

Order guarantees using streams and reducing chain of consumers

由 ＊爱你＆永不变心＊ 提交于 2020-01-24 12:01:05
问题 So as it goes in the current scenario, we have a set of APIs as listed below: Consumer<T> start(); Consumer<T> performDailyAggregates(); Consumer<T> performLastNDaysAggregates(); Consumer<T> repopulateScores(); Consumer<T> updateDataStore(); Over these, one of our schedulers performs the tasks e.g. private void performAllTasks(T data) { start().andThen(performDailyAggregates()) .andThen(performLastNDaysAggregates()) .andThen(repopulateScores()) .andThen(updateDataStore()) .accept(data); }

Spark: difference of semantics between reduce and reduceByKey

由 谁说我不能喝 提交于 2020-01-22 11:06:08
问题 In Spark's documentation, it says that RDDs method reduce requires a associative AND commutative binary function. However, the method reduceByKey ONLY requires an associative binary function. sc.textFile("file4kB", 4) I did some tests, and apparently it's the behavior I get. Why this difference? Why does reduceByKey ensure the binary function is always applied in certain order (to accommodate for the lack of commutativity) when reduce does not? Example, if a load some (small) text with 4

Spark: difference of semantics between reduce and reduceByKey

由 泄露秘密 提交于 2020-01-22 11:06:01
问题 In Spark's documentation, it says that RDDs method reduce requires a associative AND commutative binary function. However, the method reduceByKey ONLY requires an associative binary function. sc.textFile("file4kB", 4) I did some tests, and apparently it's the behavior I get. Why this difference? Why does reduceByKey ensure the binary function is always applied in certain order (to accommodate for the lack of commutativity) when reduce does not? Example, if a load some (small) text with 4

JavaScript reduce returns object on Array of objects

由 白昼怎懂夜的黑 提交于 2020-01-14 09:18:10
问题 I have an array of objects, let's say [{x:2, y:3}, {x:5, y:4}] and i call reduce((c, n) => c.y + n.y); on it. It obviouslly returns 7 . However, if the array contains a single object, let's say [{x:2, y:4}] the same reduce call will return the object itself {x:2, y:4} . Is this normal behaviour? Am I obliged to check if the result is an object and not an number afterwards? 回答1: Yes, this is the normal behaviour of reduce when you don't pass an initial value for the accumulator (which you

Ramda to loop over array

由 人盡茶涼 提交于 2020-01-11 13:10:51
问题 Loop may be the wrong term, but it kind of describes what I am attempting. I want to give structure to flat data, but I also need to keep track of the array it came from. Basically my rules are (per array): If level 1 exists- give it the name of the item, and a typechild array. EACH time a level 1 appears (even in the same array) it should create a new entry. Inside typechild , put the any items with level >1 If NO level 1 exists- give it the name of the item, and a typechild array. My code

Clojure: summing values in a collection of maps

由 大兔子大兔子 提交于 2020-01-11 09:18:12
问题 I am trying to sum up values of a collection of maps by their common keys. I have this snippet: (def data [{:a 1 :b 2 :c 3} {:a 1 :b 2 :c 3}] (for [xs data] (map xs [:a :b])) ((1 2) (1 2)) Final result should be ==> (2 4) Basically, I have a list of maps. Then I perform a list of comprehension to take only the keys I need. My question now is how can I now sum up those values? I tried to use "reduce" but it works only over sequences, not over collections. Thanks. ===EDIT==== Using the

Java mapToInt vs Reduce with map

由 一个人想着一个人 提交于 2020-01-11 09:06:10
问题 I've been reading up on reduce and have just found out that there is a 3 argument version that can essentially perform a map reduce like this: String[] strarr = {"abc", "defg", "vwxyz"}; System.out.println(Arrays.stream(strarr).reduce(0, (l, s) -> l + s.length(), (s1, s2) -> s1 + s2)); However I can't see the advantage of this over a mapToInt with a reduce. System.out.println(Arrays.stream(strarr).mapToInt(s -> s.length()).reduce(0, (s1, s2) -> s1 + s2)); Both produce the correct answer of 12

Java mapToInt vs Reduce with map

由 淺唱寂寞╮ 提交于 2020-01-11 09:06:00
问题 I've been reading up on reduce and have just found out that there is a 3 argument version that can essentially perform a map reduce like this: String[] strarr = {"abc", "defg", "vwxyz"}; System.out.println(Arrays.stream(strarr).reduce(0, (l, s) -> l + s.length(), (s1, s2) -> s1 + s2)); However I can't see the advantage of this over a mapToInt with a reduce. System.out.println(Arrays.stream(strarr).mapToInt(s -> s.length()).reduce(0, (s1, s2) -> s1 + s2)); Both produce the correct answer of 12
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