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  1. Spark
  2. SPARK-10329

Cost RDD in k-means|| initialization is not storage-efficient

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    • Improvement
    • Status: Resolved
    • Major
    • Resolution: Later
    • 1.3.1, 1.4.1, 1.5.0
    • None
    • MLlib

    Description

      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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              hujiayin hujiayin
              mengxr Xiangrui Meng
              Xiangrui Meng Xiangrui Meng
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