2015年9月29日 · But note that this returns an Array and not an RDD. As for why the a.sample(false, 0.1) doesn't return the same sample size: it's because spark internally uses something called Bernoulli sampling for taking the sample. The fraction argument doesn't represent the fraction of the actual size of the RDD. It represent the probability of each ...

2014年7月17日 · Try using textFile.sample(false,fraction,seed) instead. takeSample will generally be very slow because it calls count() on the RDD. It needs to do this because otherwise it wouldn't take evenly from each partition, basically it uses the count along with the sample size you asked for to compute the fraction and calls sample internally.

2017年1月22日 · In short, if you are sampling with replacement, you can get the same element in sample twice, and w/o replacement you can only get it once. So if your RDD has [Bob, Alice and Carol] then your "with replacement" sample can be [Alice, Alice], but w/o replacement sample can't have duplicates like that.

2015年7月14日 · As Justin and Wang mentioned it is not straight forward to get the size of RDD. We can just do a estimate. We can sample a RDD and then use SizeEstimator to get the size of sample. As Wang and Justin mentioned, based on the size data sampled offline, say, X rows used Y GB offline, Z rows at runtime may take Z*Y/X GB

2017年6月4日 · I'd like to sample exactly one element from this RDD with probability proportional to value. In a naiive manner, this task can be accomplished as follows: pairs = myRDD.collect() #now pairs is a list of (key;value) tuples K, V = zip(*pairs) #separate keys and values V = numpy.array(V)/sum(V) #normalise probabilities extractedK = numpy.random ...

2015年12月1日 · I only see the method sample() which takes a fraction as parameter. Setting this fraction to 1/numberOfRows leads to random results, where sometimes I won't get any row. On RDD there is a method takeSample() that takes as a parameter the number of elements you want the sample to contain.

2017年1月24日 · I currently need to randomly sample items in a RDD in Spark for k elements. I noticed that there is the takeSample method. The method signature is as follows. takeSample(withReplacement: Boolean, num: Int, seed: Long = Utils.random.nextLong): Array[T] However, this does not return a RDD. There is another …

2014年9月18日 · // sample() does return an RDD so you may still want to collect() myHugeRDD.sample(true, 0.01).collect().foreach(a => println(a)) RDD.takeSample(): This is a hybrid: using random sampling that you can control, but both letting you specify the exact number of results and returning an Array.

2015年4月1日 · 2) You can use createDataFrame(rowRDD: RDD[Row], schema: StructType) as in the accepted answer, which is available in the SQLContext object. Example for converting an RDD of an old DataFrame: val rdd = oldDF.rdd val newDF = oldDF.sqlContext.createDataFrame(rdd, oldDF.schema) Note that there is no need to explicitly set any schema column.

rdd = sc.parallelize(numbers) rdd_sampled_1 = rdd.sample(False, 0.25) rdd_sampled_2 = rdd.sample(False, 0.25) rdd_sampled_3 = rdd.sample(False, 0.25) rdd_sampled_4 = rdd.sample(False, 0.25) The output should look like this: rdd_sample_1 = [3 2 7] rdd_sample_1 = [1 4 8] rdd_sample_3 = [9 5 10] rdd_sample_4 = [11 6 0]