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From "hujiayin (JIRA)" <>
Subject [jira] [Commented] (SPARK-10329) Cost RDD in k-means|| initialization is not storage-efficient
Date Fri, 28 Aug 2015 04:27:45 GMT


hujiayin commented on SPARK-10329:

Hi Xiangrui,

I'll try to fix it in 1.6.

> Cost RDD in k-means|| initialization is not storage-efficient
> -------------------------------------------------------------
>                 Key: SPARK-10329
>                 URL:
>             Project: Spark
>          Issue Type: Improvement
>          Components: MLlib
>    Affects Versions: 1.3.1, 1.4.1, 1.5.0
>            Reporter: Xiangrui Meng
>              Labels: clustering
> Currently we use `RDD[Vector]` to store point cost during k-means|| initialization, where
each `Vector` has size `runs`. This is not storage-efficient because `runs` is usually 1 and
then each record is a Vector of size 1. What we need is just the 8 bytes to store the cost,
but we introduce two objects (DenseVector and its values array), which could cost 16 bytes.
That is 200% overhead. Thanks [~Grace Huang] and Jiayin Hu from Intel for reporting this issue!
> There are several solutions:
> 1. Use `RDD[Array[Double]]` instead of `RDD[Vector]`, which saves 8 bytes per record.
> 2. Use `RDD[Array[Double]]` but batch the values for storage, e.g. each `Array[Double]`
object covers 1024 instances, which could remove most of the overhead.
> Besides, using MEMORY_AND_DISK instead of MEMORY_ONLY could prevent cost RDDs kicking
out the training dataset from memory.

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