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Posted to issues@spark.apache.org by "Apache Spark (JIRA)" <ji...@apache.org> on 2015/09/01 05:04:46 UTC
[jira] [Commented] (SPARK-10329) Cost RDD in k-means||
initialization is not storage-efficient
[ https://issues.apache.org/jira/browse/SPARK-10329?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14724701#comment-14724701 ]
Apache Spark commented on SPARK-10329:
--------------------------------------
User 'HuJiayin' has created a pull request for this issue:
https://github.com/apache/spark/pull/8546
> Cost RDD in k-means|| initialization is not storage-efficient
> -------------------------------------------------------------
>
> Key: SPARK-10329
> URL: https://issues.apache.org/jira/browse/SPARK-10329
> Project: Spark
> Issue Type: Improvement
> Components: MLlib
> Affects Versions: 1.3.1, 1.4.1, 1.5.0
> Reporter: Xiangrui Meng
> Assignee: hujiayin
> 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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