reduce

Some background information: I'm working with Dataiku DSS, HDFS, and partitioned datasets. I have a particular job running (Hive query) which has two input datasets - one a very large, partitioned dataset, the other a small (~250 rows, 2 columns), non-partitioned dataset. Let's call the partitioned table A, and the non-partitioned table B. Question: The query is of the following format, SELECT a.f1, f2, ..., fn FROM A as a LEFT JOIN B as b ON a.f1 = b.f1 WHERE {PARTITION_FILTER} Here is the current ouput from the MapReduce job (keep in mind this job is still running): [09:05:53] [INFO] [dku

I proceed java 8 learning. I have found interesting behaviour: lets see code sample: // identity value and accumulator and combiner Integer summaryAge = Person.getPersons().stream() //.parallel() //will return surprising result .reduce(1, (intermediateResult, p) -> intermediateResult + p.age, (ir1, ir2) -> ir1 + ir2); System.out.println(summaryAge); and model class: public class Person { String name; Integer age; ///... public static Collection<Person> getPersons() { List<Person> persons = new ArrayList<>(); persons.add(new Person("Vasya", 12)); persons.add(new Person("Petya", 32)); persons

Say I want to sum a.x for each element in arr . arr = [{x:1},{x:2},{x:4}] arr.reduce(function(a,b){return a.x + b.x}) >> NaN I have cause to believe that a.x is undefined at some point. The following works fine arr = [1,2,4] arr.reduce(function(a,b){return a + b}) >> 7 What am I doing wrong in the first example? After the first iteration your're returning a number and then trying to get property x of it to add to the next object which is undefined and maths involving undefined results in NaN . try returning an object contain an x property with the sum of the x properties of the parameters: var

According to Databricks best practices, Spark groupByKey should be avoided as Spark groupByKey processing works in a way that the information will be first shuffled across workers and then the processing will occur. Explanation So, my question is, what are the alternatives for groupByKey in a way that it will return the following in a distributed and fast way? // want this {"key1": "1", "key1": "2", "key1": "3", "key2": "55", "key2": "66"} // to become this {"key1": ["1","2","3"], "key2": ["55","66"]} Seems to me that maybe aggregateByKey or glom could do it first in the partition ( map ) and

How can I break the iteration on reduce method? for for (var i = Things.length - 1; i >= 0; i--) { if(Things[i] <= 0){ break; } }; reduce Things.reduce(function(memo, current){ if(current <= 0){ //break ??? //return; <-- this will return undefined to memo, which is not what I want } }, 0) UPDATE Some of the commentators make a good point that the original array is being mutated in order to break early inside the .reduce() logic. Therefore, I've modified the answer slightly by adding a .slice(0) before calling a follow-on .reduce() step. This is to preserve the original array by copying its