Re: android + spark streaming?

any comment/feedback/advice on this is much appreciated! thanks. -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/android-spark-streaming-tp15661p15735.html Sent from the Apache Spark User List mailing list archive at Nabble.com. ---

scala Vector vs mllib Vector

what are the pros/cons of each? when should we use mllib Vector, and when to use standard scala Vector? thanks. -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/scala-Vector-vs-mllib-Vector-tp15736.html Sent from the Apache Spark User List mailing list arch

Re: scala Vector vs mllib Vector

thanks dean. thanks for the answer with great clarity! i'm working on an algorithm that has a weight vector W(w0, w1, .., wN). the elements of this weight vector are adjusted/updated frequently - every iteration of the algorithm. how would you recommend to implement this vector? what is the

mllib sparse vector/matrix vs. graphx graph

hi. i am working on an algorithm that has a graph data structure. it looks like there 2 ways to implement this with spark option 1: use graphx which already provide Vetices and Edges to build out the graph pretty nicely. option 2: use mllib sparse vector / matrix to build out the graph. th

graphx - mutable?

i understand that graphx is an immutable rdd. i'm working on an algorithm that requires a mutable graph. initially, the graph starts with just a few nodes and edges. then over time, it adds more and more nodes and edges. what would be the best way to implement this growing graph with graphx?

Re: graphx - mutable?

hi again. just want to check in again to see if anyone could advise on how to implement a "mutable, growing graph" with graphx? we're building a graph is growing over time. it adds more vertices and edges every iteration of our algorithm. it doesn't look like there is an obvious way to add a

mllib CoordinateMatrix

after creating a coordinate matrix from my rdd[matrixentry]... 1. how can i get/query the value at coordiate (i, j)? 2. how can i set/update the value at coordiate (i, j)? 3. how can i get all the values on a specific row i, ideally as a vector? thanks! -- View this message in context:

matrix operations?

hi there... is there any other matrix operations in addition to multiply()? like addition or dot product? -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/matrix-operations-tp16508.html Sent from the Apache Spark User List mailing list archive at Nabble.co

RowMatrix.multiply() ?

hi.. it looks like RowMatrix.multiply() takes a local Matrix as a parameter and returns the result as a distributed RowMatrix. how do you perform this series of multiplications if A, B, C, and D are all RowMatrix? ((A x B) x C) x D) thanks! -- View this message in context: http://apache-sp

reverse an rdd

hello... what is the best way to iterate through an rdd backward (last element first, first element last)? thanks! -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/reverse-an-rdd-tp16602.html Sent from the Apache Spark User List mailing list archive at Nabbl

scala: java.net.BindException?

hello... does anyone know how to resolve this issue? i'm running this locally on my computer. keep getting this BindException. much appreciated. 14/10/16 17:48:13 WARN component.AbstractLifeCycle: FAILED SelectChannelConnector@0.0.0.0:4040: java.net.BindException: Address already in use java.ne

object in an rdd: serializable?

i got an exception complaining about serializable. the sample code is below... class HelloWorld(val count: Int) { ... ... } object Test extends App { ... val data = sc.parallelize(List(new HelloWorld(1), new HelloWorld(2))) ... } what is the best way to serialize HelloWorld so that

complexity of each action / transformation

hello... is there a list that shows the complexity of each action/transformation? for example, what is the complexity of RDD.map()/filter() or RowMatrix.multiply() etc? that would be really helpful. thanks! -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/

spark best practices / guidelines?

i'm writing distributed algorithms for the first time and also using spark for the first time. my code looks extremely long and messy. the code runs, but it looks extremely inefficient. there gotta be a better way to write these distributed algorithms. is there a list of guidelines / best pra

mllib.linalg.Vectors vs Breeze?

hello... i'm looking at the source code for mllib.linalg.Vectors and it looks like it's a wrapper around Breeze with very small changes (mostly changing the names). i don't have any problem with using spark wrapper around Breeze or Breeze directly. i'm just curious to understand why this wrapper

real-time streaming

the spark tutorial shows that we can create a stream that reads "new files" from a directory. that seems to have some lag time, as we have to write the data to file first and then wait until spark stream picks it up. what is the best way to implement REAL 'REAL-TIME' streaming for analysis in r

Re: real-time streaming

thanks jay. do you think spark is a good fit for handling streaming & analyzing videos in real time? in this case, we're streaming 30 frames per second, and each frame is an image (size: roughly 500K - 1MB). we need to analyze every frame and return the analysis result back instantly in real ti

SparkContext.stop() ?

what is it for? when do we call it? thanks! -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/SparkContext-stop-tp17826.html Sent from the Apache Spark User List mailing list archive at Nabble.com. ---

is spark a good fit for sequential machine learning algorithms?

i'm struggling with implementing a few algorithms with spark. hope to get help from the community. most of the machine learning algorithms today are "sequential", while spark is all about "parallelism". it seems to me that using spark doesn't actually help much, because in most cases you can't r

sparse x sparse matrix multiplication

what is the best way to implement a sparse x sparse matrix multiplication with spark? -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/sparse-x-sparse-matrix-multiplication-tp18163.html Sent from the Apache Spark User List mailing list archive at Nabble.com.

Re: Matrix multiplication in spark

@sowen.. i am looking for distributed operations, especially very large sparse matrix x sparse matrix multiplication. what is the best way to implement this in spark? -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/Matrix-multiplication-in-spark-tp12562p181

word2vec: how to save an mllib model and reload it?

what is the best way to save an mllib model that you just trained and reload it in the future? specifically, i'm using the mllib word2vec model... thanks. -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/word2vec-how-to-save-an-mllib-model-and-reload-it-tp18

where is the org.apache.spark.util package?

i'm trying to compile some of the spark code directly from the source (https://github.com/apache/spark). it complains about the missing package org.apache.spark.util. it doesn't look like this package is part of the source code on github. where can i find this package? -- View this message i

Re: where is the org.apache.spark.util package?

i found util package under spark core package, but i now got this error "Sysmbol Utils is inaccessible from this place". what does this error mean? the org.apache.spark.util and org.apache.spark.spark.Utils are there now. thanks. -- View this message in context: http://apache-spark-user-li

Re: Fwd: Why is Spark not using all cores on a single machine?

hi. i did use local[8] as below, but it still ran on only 1 core. val sc = new SparkContext(new SparkConf().setMaster("local[8]").setAppName("abc")) any advice is much appreciated. -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/Fwd-Why-is-Spark-not-using

what is the best way to implement mini batches?

hi. what is the best way to pass through a large dataset in small, sequential mini batches? for example, with 1,000,000 data points and the mini batch size is 10, we would need to do some computation at these mini batches (0..9), (10..19), (20..29), ... (N-9, N) RDD.repartition(N/10).mapParti

RDD.aggregate?

can someone please explain how RDD.aggregate works? i looked at the average example done with aggregate() but i'm still confused about this function... much appreciated. -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/RDD-aggregate-tp20434.html Sent from th

broadcast: OutOfMemoryError

hi. i'm running into this OutOfMemory issue when i'm broadcasting a large array. what is the best way to handle this? should i split the array into smaller arrays before broadcasting, and then combining them locally at each node? thanks! -- View this message in context: http://apache-spark-

Re: RDD.aggregate?

any explaination on how aggregate works would be much appreciated. i already looked at the spark example and still am confused about the seqop and combop... thanks. -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/RDD-aggregate-tp20434p20634.html Sent from t

Re: what is the best way to implement mini batches?

any advice/comment on this would be much appreciated. -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/what-is-the-best-way-to-implement-mini-batches-tp20264p20635.html Sent from the Apache Spark User List mailing list archive at Nabble.com. --

why is spark + scala code so slow, compared to python?

hi.. i'm converting some of my machine learning python code into scala + spark. i haven't been able to run it on large dataset yet, but on small datasets (like http://yann.lecun.com/exdb/mnist/), my spark + scala code is much slower than my python code (5 to 10 times slower than python) i already